FLOOD SAFETY: WHAT IS AN ACCEPTABLE LEVEL OF … · Civil Engineering & Management/Water Engineering ... of uncertainty in the safety assessment of a dike on piping. To answer this

MSc Thesis

FLOOD SAFETY: WHAT IS AN ACCEPTABLE LEVEL OF UNCERTAINTY IN THE SAFETY ASSESSMENT OF PIPING

J.J.C. Bink

6 November, 2017

Flood safety: What is an acceptable level of uncertainty in the safety assessment for piping J.J.C. Bink

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Title: Flood safety: What is an acceptable level of uncertainty in the safety

assessment of piping Date: 6 November, 2017 Location: Deventer Author: J.J.C. Bink [email protected] University: University of Twente

Civil Engineering & Management/Water Engineering & Management Drienerlolaan 5, Horst PO‐box 217 7522 NB, Enschede 7400 AE, Enschede Company: BZ Ingenieurs en managers Zutphenseweg 51 PO‐box 445 7418 AH, Deventer 7400 AK, Deventer Graduation committee: dr. K.M. Wijnberg, University of Twente

dr. J.J. Warmink, University of Twente ing. W.S. Zomer MSc, BZ Ingenieurs en Managers


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Preface In front of you is the final report of my master thesis, as the last part of my study Water Engineering

and Management at the University of Twente. This marks the end of a six‐year period as a student

and the start of a new period.

During the process of this research I have had the help of some people I will not forget to thank. First

of all, Wouter Zomer, my daily supervisor. Thank you for helping me at times that I had some struggles

and for providing me with feedback when I needed it. Secondly, I want to thank Jord Warmink and

Kathelijne Wijnberg for their feedback on the report and for helping me with the research itself. I also

want to thank the colleagues of BZIM for their presence at the office. Sander Bakkenist, Caspar ter

Brake, Jan‐Gert Rinsema and Julius van Stokkum, thank you for all the fun we had.

Last, I want to thank my parents, brother and sister for their support during my life as a student and

especially during my graduation period.

Enjoy reading this thesis!

Jasper Bink

Deventer, October 2017


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Abstract The Netherlands are vulnerable to flooding, due to its many low‐lying areas. To protect the

Netherlands from flooding many kilometres of dikes and other water retaining structures are built

along the Dutch rivers. Piping is one of the main failure mechanisms to which the Dutch dikes are

prone. Due to the heterogeneity of the subsoil, uncertainties come into play when assessing a dike on

piping. Besides uncertainty in the subsoil, there is also uncertainty due to models and statistical

uncertainty due to extrapolation of extreme events.

The main goal of this research is to define acceptable and to determine a maximum acceptable level

of uncertainty in the safety assessment of a dike on piping. To answer this question several steps have

been followed. First, the philosophy behind the development of the WBI2017 is investigated. The

second step consists of the identification of the use of the WBI 2017. This was done by searching the

documents of the WBI2017 and by interviewing experts/assessors from Water Boards. The last step

was to define acceptable and to determine this acceptable level of uncertainty according to the

interviewed experts is achievable and reachable with the current methods and models.

The philosophy behind the development of the WBI2017 consists of three main factors. The first one

is that uncertainties are included in a more explicit way than it was done before. This is done to make

sure that the choices assessors make are more objective and rational. Secondly, a signaling value is

introduced. This signaling value makes sure that weak dikes are identified in time, amply before the

lower limit is reached, so a decent reinforcement plan can be made. As a last factor, the calculated

failure probability is presented as the ‘real’ failure probability. This means that the ‘real’ failure

probability will differ between assessments if done by another assessor. This is caused by the fact that

another assessor is likely to use different assumptions.

In the use of the WBI2017, the incorporation of uncertainties can be seen in the use of the WTI‐SOS.

By using this WTI‐SOS, uncertainties about the subsoil are included in scenarios. Each of these

scenarios gets a certain probability of occurring. Uncertainties about weak links are included using the

length‐effect. The interviewed experts indicated that, since the methodology of the WBI2017 is new,

they are still finding out how it has to be used and what the influence is on the safety assessment.

Since these unclarities exist among the experts from Water Boards, there is a lack of experience with,

and insight in the WBI2017 and therefore in the uncertainties in the assessment. To be able to give a

decent assessment of the safety of a dike it is important to fully understand the methods as well as

the uncertainties and their influence. If this experience and insight lacks, a wrong interpretation of the

calculation results can be made and therefore the result of the assessment may be wrong. Therefore,

it is important to have a check on the understanding of the methods and procedures by the assessors.

By doing this, it can be made sure that the results are ‘reliable’ and ‘traceable’ as indicated in the

philosophy.

Besides that, it was indicated that not all relevant and known uncertainties are yet included in the

safety assessment. Time dependency is not yet included, but a very important factor along the coastal

areas and, though less influential, for the lake areas. This time dependency will, however, be included

in 2019.

Each of the interviewed experts has a different definition of what is to be an acceptable uncertainty.

Some indicate that the uncertainty is acceptable if it does not have a significant influence on the

assessment result. Others indicate that the uncertainty is acceptable if the methods and procedures

prescribed in the WBI2017 are followed and used. In the detailed assessment this means, for example,

that the calculation rule of Sellmeijer has to be used for piping. For the assessment result and its

uncertainty to be acceptable the result has to be ‘reliable’ and ‘traceable’. The interviewees also


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indicated that they want as much certainty as possible in the assessment result. From previous

research by Van Stokkum (2016) however, it has become clear that such a level of uncertainty cannot

be achieved with the current methods and models. Sellmeijer et al. (2011) also indicates that the

formula of Sellmeijer is calibrated with small‐scale laboratory tests on homogeneous samples, while

in the real world the subsoil is not homogeneous and the scale is larger. In 2011 the formula has been

improved with larger scale experiments, such as the IJkdijk. The formula, however, gets a good result

when the subsoil consists of fine sand, but when the subsoil is coarser, the formula does not perform

that well. Therefore, it is important that more research is done in alternative methods and models for

the assessment of piping in order to be able to reduce the uncertainty towards an acceptable level.


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Content

PREFACE ......................................................................................................................................... 2

ABSTRACT ....................................................................................................................................... 3

CONTENT ........................................................................................................................................ 5

1. INTRODUCTION ....................................................................................................................... 6

1.1. UNCERTAINTY .................................................................................................................................... 6 1.2. PROBLEM DESCRIPTION........................................................................................................................ 8 1.3. RESEARCH GOAL ................................................................................................................................. 9 1.4. RESEARCH QUESTIONS ......................................................................................................................... 9

2. METHODS .............................................................................................................................. 10

2.1. PHILOSOPHY BEHIND THE DEVELOPMENT OF THE WBI2017 ..................................................................... 10 2.2. USE OF THE WBI2017 ....................................................................................................................... 10 2.3. VISUALIZATION OF UNCERTAINTY IN THE SAFETY ASSESSMENT ................................................................. 10 2.4. INTERVIEWS ..................................................................................................................................... 12

3. PHILOSOPHY BEHIND WBI2017 ............................................................................................... 14

3.1. STARTING POINTS OF WBI2017 ........................................................................................................... 14 3.2. SIGNALING VALUE AND LOWER LIMIT .................................................................................................... 15 3.3. UNCERTAINTIES IN THE SAFETY ASSESSMENT ......................................................................................... 16 3.4. ACCEPTANCE CRITERIA ....................................................................................................................... 20 3.5. FAILURE PROBABILITY ........................................................................................................................ 20

4. USE OF WBI2017 ..................................................................................................................... 22

4.1. GLOBAL STOCHASTIC SUBSOIL SCHEMATIZATION (WTI‐SOS) ................................................................. 22 4.2. CALCULATION AND MODELS ............................................................................................................... 23 4.3. LENGTH EFFECT ................................................................................................................................ 23 4.4. RELEVANT UNCERTAINTIES ................................................................................................................. 24 4.5. USE BY EXPERTS ............................................................................................................................... 24

5. ACCEPTABILITY OF UNCERTAINTY ......................................................................................... 26

5.1. DEFINITION OF ACCEPTABLE ............................................................................................................... 26 5.2. ACCEPTABILITY OF UNCERTAINTY ........................................................................................................ 26 5.3. ANALYSIS ........................................................................................................................................ 29

6. DISCUSSION .......................................................................................................................... 32

6.1. METHODS ........................................................................................................................................ 32 6.2. RESULTS .......................................................................................................................................... 33

7. CONCLUSION AND RECOMMENDATIONS ............................................................................... 35

7.1. CONCLUSION ................................................................................................................................... 35 7.2. RECOMMENDATIONS ......................................................................................................................... 36

REFERENCES ................................................................................................................................. 38

APPENDICES.................................................................................................................................. 40

A. STEPS OF THE SAFETY ASSESSMENT ......................................................................................................... 41 B. ASSEMBLING ........................................................................................................................................ 45 C. QUESTIONS INTERVIEWS ........................................................................................................................ 47 D. CASES INTERVIEWS ................................................................................................................................ 48 E. SELLMEIJER ........................................................................................................................................... 57


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1. Introduction Since the 1st of January 2017 the norms for dikes in the Netherlands are changed. The norms are no

longer based on the exceedance probability of a flood event, but on the probability of failing of a dike.

The way the assessment is done has changed as well. The assessment is done in several steps, which

starts at a very coarse and general level and becomes more detailed along the way.

The schematization manuals (Rijkswaterstaat Water, Verkeer en Leefomgeving, 2017a) describe for

every failure mechanism which steps have to be followed in order to get to a proper schematization

of a dike section. These steps are shown in Figure 1. The first step is to collect the necessary data, such

as the composition of the subsoil and the shape and characteristics of the dike. These data are then

used to schematize the dike. This schematization can then be used to make calculations. After the

calculations, the results have to be interpreted. Based on this information the dike can be assessed.

Figure 1 ‐ Schematization steps of the safety assessment (Adapted after: Rijkswaterstaat Water, Verkeer en Leefomgeving, 2017).

The amount and quality of the gathered data has a major influence on the result of an assessment

(Jones et al., 2008). If little data is available, the schematization will be rough and the uncertainty will

be high. This means that the result of the safety assessment will be highly uncertain as well, which

means the actual strength is somewhere in a wide bandwidth around the calculated strength of the

dike. As more data becomes available in the detailed assessment, the schematization will become

more precise and the uncertainty will decrease. Often this process is an iterative process, which will

be explained in chapter 5. Especially if the schematization is very rough at the start, some iterations

will have to be performed (Rijkswaterstaat Water, Verkeer en Leefomgeving, 2017a).

1.1. Uncertainty In the new approach to the safety assessment of dikes (WBI2017), uncertainty and how it should be

included in the safety assessment has gotten a more prominent position. Uncertainties will be

included in an explicit way, instead of the implicit way they were considered before. There are two

different types of uncertainty which influence the failure probability (Diermanse, 2016a):

1. Inherent uncertainty or natural variability

2. Knowledge uncertainty (statistical and model uncertainties)

1.1.1. Inherent uncertainty Inherent or aleatory uncertainty includes all unpredictable fluctuations that are observable in nature.

There can be made distinction between fluctuations in time and fluctuations in space (Vrouwenvelder

& Vrijling, 2000). This aleatory uncertainty is irreducible, as it is existing in nature and natural to the

process (Kiureghian & Ditlevsen, 2009). An example of inherent uncertainty is the throw of a dice, as

it contains inherent randomness. When performing an infinite number of repeated trials, the


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probabilities can be determined with certainty, but the probabilities themselves cannot be changed

(Porter, 2017). For piping, the composition of the subsoil can be seen as an inherent uncertainty.

1.1.2. Knowledge uncertainty Knowledge uncertainty can be divided into two types of uncertainty, statistical uncertainty and model

uncertainty (Vrouwenvelder & Vrijling, 2000). In contrast to inherent uncertainty, knowledge

uncertainty or epistemic uncertainty can be reduced (Porter, 2017). Statistical uncertainty is caused

by the limited number of observations. A dike, for example, is designed with a failure probability of

1/1,000 year, while there are only observations of water levels and discharges for about 100 years

(Vrouwenvelder & Vrijling, 2000). In this case statistical uncertainty appears when extrapolating the

data of discharges and water levels, as extremes are needed for the design of a dike (Diermanse,

2016a). Another form of statistical uncertainty appears when the probability density distribution of

strength parameters is estimated with limited observations (Van Stokkum, 2016).

Model uncertainty is caused by the use of models. These models are used to simplify reality in order

to predict the load on a dike and the strength of a dike (Diermanse, 2016a). In the calculation of the

failure probability of a dike, models are used to determine at what combination of load and strength

a dike is expected to fail. These models, however, are never an exact representation of reality. It will

always be a simplification (Vrouwenvelder & Vrijling, 2000). This uncertainty exists as attribute to the

mathematical model and it is not inherent in the real world, as it does not exist in nature (Porter, 2017).

By comparing predictions from a model with measurements from the real world, an estimate can be

made of the model error (Vrouwenvelder & Vrijling, 2000).

Statistical uncertainty and model uncertainty are caused by a lack of knowledge. Given an assumption

of the shape of the probability density distribution, statistical uncertainty can be estimated in an

objective way based on the available amount of data. Model uncertainty is highly influenced by

intuition and engineering judgement, which means it is not objective (Vrouwenvelder & Vrijling,

2000). The different uncertainties and their influence on the failure probability are shown in Figure 2.

Figure 2 ‐ Uncertainties that influence the failure probability (Adapted after Diermanse, 2016)

The way the new safety assessment (WBI2017) has to be done has some changes with respect to the

Wettelijk ToetsInstrumentarium 2006 (WTI 2006). The WTI2006 is the old legislation with the old

norms. These changes can be categorized in four classes:

1. The input of statistics and schematizations

2. The way uncertainties are included. A consequence is that for the Upper Rivers

(‘Bovenrivieren’) a complete probabilistic approach will be used

3. New norms, both the type and numerically

4. Other schematizations of the subsoil, in which the Room for the River projects are included


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These changes do not necessarily mean that the loads will get higher. Around the IJssel‐lake the

needed crest height will increase due to the new norm and the inclusion of uncertainties, while in the

‘Upper Rivers’ the norm will often get less strict (Helpdesk Water, 2017a).

1.2. Problem description The safety assessment of piping is influenced by two main sources of uncertainty: inherent

uncertainty and knowledge uncertainty. Knowledge uncertainty can be subdivided in model

uncertainty and statistical uncertainty. For piping, for example, the composition of the subsoil and

the schematization of the dike are of high importance. Inherent uncertainty and model uncertainty

are of influence on the calculation of strength of the dike. Statistical uncertainty is mainly applicable

on the calculation of the load on the dike.

For piping, the inherent uncertainty can mainly be found in the composition of the subsoil. In the

WBI2017, scenarios are used in the form of the WTI‐SOS to take this uncertainty into account (Hijma

& Lam, 2015). The final result of the safety assessment for piping is one failure probability, which is

later combined for all failure mechanisms into one failure probability for a dike trajectory

(Rijkswaterstaat Water, Verkeer en Leefomgeving, 2017c). This is presented as the ‘real’ failure

probability, while it actually is a calculable failure probability based on limited available knowledge

and models. The failure probability changes if other data, other models, or other assumptions in the

schematization are used (Vrouwenvelder & Vrijling, 2000).

The inherent uncertainty and knowledge uncertainty influence the final result. Van Stokkum (2016)

determined that at some point gathering more data will have almost no influence for the estimation

of the probability density function. At that point, the costs of doing more research exceed the

benefits. It is, however, useful to do more research to find the ‘real’ value for the parameter at a certain

location. In the WBI2017 a failure probability budget (’faalkansbegroting’) has to be used in accordance

with a semi‐probabilistic approach per dike section (‘dijkvak’). In 2019 a probabilistic approach for the

dike trajectories (‘dijktraject’) will become available, in which the failure probability budget does not

have to be used. In the failure probability budget 24% of the total allowed failure probability can be

allocated to piping (Rijkswaterstaat Water, Verkeer en Leefomgeving, 2017c). It is however possible

to assign the failure probability in an alternative way. If one failure mechanism meets its

requirements, while another failure mechanism does not, it is allowed to assign a bigger portion of

the failure probability to the failure mechanism that does not meet the requirements and a smaller

part to the failure mechanism that does meet the requirements (Diermanse, 2016b).

Even if a complete analysis of the failure probability is performed, without using intuition and expert

judgement, different values for the failure probability are calculated (Vrouwenvelder & Vrijling, 2000).

According to Vrouwenvelder & Vrijling (2000), the ‘real intrinsic value, which is constant and invariable

is a fiction’. Even though the failure probability is presented as an objective value, it is influenced by

subjective estimations and assumptions. This means the failure probability is ‘ambiguous’ and

‘subjective’. The failure probability is dependent on the experience and level of knowledge of the

assessor (Vrouwenvelder & Vrijling, 2000). The economical optimal strength is used in the

determination of the optimal failure probability. This means that uncertainties increase the failure

probability (Vrouwenvelder & Vrijling, 2000). Based on the current methods, uncertainty can only be

reduced to a limited amount. In the WBI, more uncertainties are explicitly included than before.

However, there still remain uncertainties in the assessment result. These uncertainties can not only

be reduced by doing more measurements. Model and statistical uncertainty are still influencing the

assessment result. Therefore, it is important to determine what level of uncertainty is considered

acceptable. When that is known it becomes possible to determine what improvements to the

methods have to be done in order to reduce uncertainty in the assessment result. As mentioned by


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Van Stokkum (2016), a lot of uncertainty remains in the assessment for piping, even if many

measurements are used. Therefore, piping is used as the main focus of this study.

1.3. Research goal The goal of this research is to determine the acceptable level of uncertainty in the assessment for

piping and to define the term acceptable using interviews with experts from Water Boards and

developers of the WBI.

1.4. Research questions As a guide towards the goal of this research, the following research questions have been formulated.

1. What is the philosophy behind the development of the WBI2017?

2. How is the WBI2017 used by experts from Water Boards?

3. How can acceptable regarding uncertainty be defined and can it be achieved as such?

4. What level of uncertainty is acceptable in the assessment of a dike on piping?


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2. Methods In this chapter, the methods and assumptions used will be described per research question.

2.1. Philosophy behind the development of the WBI2017 To answer the first research question, several background documents of the WBI2017 have been

investigated. Based on those background documents, an insight in the philosophy behind the norms

is created. The following documents and websites have been investigated.

‐ ‘Uitgangspunten WTI 2017’ by H. de Waal and H. Knoeff (2014)

‐ ‘Globale stochastische ondergrondschematisatie (WTI‐SOS) voor de primaire waterkeringen’

by M. Hijma and K.S. Lam (2015)

‐ ‘WTI 2017: Handleiding lokaal schematiseren met WTI‐SOS’ by G. Kruse and M. Hijma (2015)

‐ ‘Basisrapport WBI 2017’ by J.P. de Waal (2016)

‐ ‘Grondslagen voor hoogwaterbescherming’ by Expertise Netwerk Waterveiligheid (2016)

‐ ‘WTI – Onzekerheden’ by F. Diermanse (2016)

‐ ‘Over Wettelijk Beoordelingsinstrumentarium’ by Helpdesk Water (2017)

‐ ‘Regeling veiligheid primaire waterkeringen 2017; Bijlage I Procedure’ by Rijkswaterstaat

Water, Verkeer en Leefomgeving (2017)

‐ ‘Regeling veiligheid primaire waterkeringen 2017; Bijlage III Sterkte en veiligheid’ by

Rijkswaterstaat Water, Verkeer en Leefomgeving (2017)

‐ ‘Schematiseringshandleiding piping; WBI 2017’ by Rijkswaterstaat Water, Verkeer en

Leefomgeving (2017)

These documents are all used for the development of the WBI or are part of the WBI. To find these

documents, the summaries of all documents related to the WBI and piping have been read. Based on

that information it was determined which documents would be relevant for the answering of the first

research question. The relevant documents are listed above. Based on these documents the

philosophy behind the development of the WBI2017 has been identified.

2.2. Use of the WBI2017 For the second research question, two documents have been used, as well as the answers from the

experts gathered in interviews (see paragraph 2.4). The documents used are:

‐ ‘Gebruikershandleiding D‐Soil Model’ by Deltares (2016)

‐ ‘Gebruikershandleiding Ringtoets’ by Deltares (2017)

Based on these documents and the answers from the experts it is identified if the philosophy behind

the development of the WBI2017 and the way it is used are in accordance.

2.3. Visualization of uncertainty in the safety assessment Uncertainty can be shown in several different ways. The first example is by using the principle of a

‘traffic light’, in which green is certain, red is uncertain and orange/yellow is in between. This can be

translated into a continuous scale as well in order to express the uncertainty (Tak & Toet, 2014).

Another way of indicating the uncertainty is by showing a bandwidth in the graph. Another way of

showing the bandwidth is by using random lines or a gradient. The downside of using a gradient is

that it might look different if printed or if shown on a different screen (Tak et al., 2014).

Tak et al. (2015) have done another study into the best way to visualize uncertainty. Instead of looking

at a completely unknown case, such as was used in their research of 2014, a known case was used

now, in the form of temperature forecasts. Instead of a point prediction, a range prediction was used.


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With these temperature forecasts a certain point on the graph was indicated and the question was

asked what the probability was of a temperature above that point. On the basis of those results it is

determined what visualization of the uncertainty is best in their case. The results were similar to the

results of their previous research, which means that indicating a bandwidth is best (Tak et al., 2015).

In the research of Warmink & Goedhart (2010) nine different ways of expressing uncertainty were

used to determine what would be best for decision‐makers. To determine this they interviewed ten

decision makers and asked them to rank the nine expressions of uncertainty. According to Warmink

& Goedhart (2010) the use of a bandwidth to express uncertainty is the best solution if the changes in

time are of importance as well. If this is not important, a histogram or box‐plot is useful as well

(Warmink & Goedhart, 2010).

Based on this investigation of the different possibilities for the visualization, cases for interviews have

been made. An example of such a case is shown in Figure 3. To do so, several assumptions have been

made. First of all, it was assumed that load and strength are normally distributed. According to

Vrouwenvelder & Vrijling (2000) an exponential distribution for the load would be more realistic.

However, for the sake of simplicity, a normal distribution was selected.

This normal distribution was created using a fictive dataset, which creates a perfect normal

distribution. To create the medium curve, a convenient standard deviation was chosen. For the small

uncertainty curves, this standard deviation was halved, while for the large uncertainty curves the

standard deviation was doubled. In Figure 3, the medium uncertainty load curve is shown together

with the three possible types of curves for the strength. By halving the standard deviation (for the

small uncertainty curve) the curve becomes narrower, while by doubling the standard deviation (for

the large uncertainty curve) the curve becomes wider.

Figure 3 ‐ The medium uncertainty load is shown in green, with the three possible types of uncertainty for the strength

Now the curves are made, the band around the strength has to be added. This band is based on the

research of Van Stokkum (2016). In his research he indicates that the strength can differentiate a lot

from the calculated strength, up to 100%. As an example, the differentiation is assumed to be 50% of


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the standard deviation in these cases. This bandwidth was added to indicate the uncertainty that will

remain due to model uncertainty. An example of the addition of this bandwidth is shown in Figure 4.

Figure 4 ‐ Scenario with a medium uncertainty in load and strength

The combinations of load and strength, which form the nine scenarios, are shown in Table 1. For all

these scenarios the failure probability was the same, so all scenarios could be compared.

Table 1 ‐ Scenarios for the interviews

Level of Uncertainty in Load

Level of Uncertainty in Strength

Small Small

Small Medium

Small Large

Medium Small

Medium Medium

Medium Large

Large Small

Large Medium

Large Large

The result of the visualization of the nine scenarios that were used in the interviews are displayed in

Appendix D. On the y‐axis, the probability of occurring is displayed, while on the x‐axis the (critical)

head is shown. The graphs are capped at the left and right end to make them as clear as possible. In

theory, the graphs should continue infinitely. Besides that, the bandwidth of the strength is capped

at zero, since a probability of occurring smaller than zero is not possible.

2.4. Interviews For this research seven interviews have been conducted. Five interviews at different Water Boards,

one at Rijkswaterstaat and one at Deltares. According to Scholl & Olivier (2014), only four to eight

interviews are needed to gather 80% of the available information. According to Feijt (2017), five to

twenty‐five interviews are needed to cover all information. The saturation in the answers is an

important indicator if enough interviews have been conducted. If nothing new comes up in the

interviews, enough interviews have been conducted (Feijt, 2017). Therefore, after seven interviews it

was evaluated if more interviews would be needed. The answers to the interview questions had often

similarities, and therefore it was decided to conduct no more interviews. Of these seven interviews,

four could be used for the quantitative part (questions 8 through 11). According to literature, more


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results would be needed for the quantification. With the results of the four interviews, however, a

clear pattern could be distinguished and therefore it was decided to gather no more results on this

part.

The experts that were interviewed are employees at the following companies. Of each company one

expert was interviewed:

‐ Deltares

‐ Hoogheemraadschap (Water Board) De Stichtse Rijnlanden

‐ Rijkswaterstaat

‐ Waterschap (Water Board) Drents Overijsselse Delta

‐ Waterschap (Water Board) Rivierenland

‐ Waterschap (Water Board) Scheldestromen

‐ Waterschap (Water Board) Vallei & Veluwe

These seven experts cover all areas of the development and use of the WBI. These experts all have

experience with the WBI methods and procedures.

The questions of the interview can be found in Appendix C1. The first two questions of the interviews

are used to assess the level of expertise of the interviewees. First, it was asked how familiar they are

with the WBI (question 1) and its uncertainties (question 2) on a scale of 1 (junior) to 5 (expert involved

with the development of the WBI from the beginning). After the assessment of the level of expertise,

it was assessed how the different experts cope with the uncertainties in the safety assessment of a

dike for piping (question 3). This question was followed by several questions on what they think of the

way the uncertainties are included in the WBI and if more uncertainties should be included (questions

4, 5 and 6). Next, the term ‘acceptability’ was defined and it was clarified what the experts consider to

be acceptable (question 7). As a last part 9 cases were presented, which the experts had to score from

1 (not acceptable) to 5 (very acceptable) in question 8 and 9. The last part was to rank the cases from

1 (most acceptable) to 9 (least acceptable) in question 10 and 11. Question 8 and 9, however, have

been dropped after three interviews, since the interviewees already started to, unconsciously, rank

the scenarios at this question. They did so to be able to compare the different scenarios. This caused

the answers of questions 8 & 9 and 10 & 11 to be identical.

As mentioned before, the first two questions of the interviews were used to assess the level of

expertise of the interviewees. During the interviews, it became clear that all but one interviewees

think they have quite some knowledge of the WBI and its uncertainties, since they gave themselves 4

out of 5 points on these questions. There was only one person who thought to have less knowledge

of the WBI and the uncertainties, which gave 2 out of 5 points on these questions. The answers of this

expert have been given a lower importance than the answers of the other experts.

1 The transcripts of the interviews can be requested by contacting the author


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3. Philosophy behind WBI2017 In the Delta program of 2014 it was already concluded that the central theme in the Netherlands was

prevention of flooding, but a shift is made towards anticipation. Spatial design and crisis management

however, are still needed to limit the risks of flooding. To be able to keep the flood safety in the

Netherlands at an acceptable level, norms are defined in the Dutch Water Act. Those norms are

translated towards use in practice by models and uniform calculation methods.

In practice, there will always be a risk of flooding, which makes it difficult to determine how safe is

safe enough. Such a question cannot just be answered by simple calculations, as there are social and

political factors to be included as well. Reducing the risk of flooding towards zero is not feasible. That

is why a consideration has to be made on what level of safety is wanted, based on the costs and

benefits of more or less safety. The protection levels as they were set until 2017 were established

based on a consideration between the costs of additional safety and the benefit of the reduction is

risk. As the consequences of flooding in the west of the Netherlands were the most severe, the safety

levels were the highest over there as well. The norms as they were asserted until 2017 were

established in 1953. Since 2017 new norms have been developed in the Wettelijk

BeoordelingsInstrumentarium (WBI2017), which are based on three types of risk: (1) expected yearly

costs of damage, (2) individual risk and (3) group risk. The Dutch government has decided that the

individual risk for every person has to be the same in the Netherlands, namely 1/100,000 per year.

3.1. Starting points of WBI2017 The maintenance of the appropriate level of water safety can be distinguished in three levels (de Waal

& Knoeff, 2014):

1. Maintaining the current legal safety levels in the context of the norm, which is part of the ‘duty

of care’ (‘zorgplicht’)

2. Assessment of the dike when the norms change or when there are changed insights in the

load or strength of the dike, which is done at least once every twelve years. The results of this

assessment are reported to the Dutch House of Representatives

3. Periodical review of the goal and the norms and the way those are derived and allocated

The first two levels together have to guarantee that the dikes are actually coping with the norm and

continue to do so, based on the norms that are determined in the third level and which are based on

socially accepted flood risks. How the activities of level 1 and 2 maintain the norm is shown in Figure

5 (H. de Waal & Knoeff, 2014).


15

Figure 5 ‐ Interconnection between activities and information of level 1 and 2 (information in blue, activities in yellow) (Adapted after: Waal & Knoeff, 2014)

In 2014, Deltares, commissioned by Rijkswaterstaat WVL, has formulated 160 starting points for the

new WBI. Those starting points are divided over 7 categories:

1. Redevelopment of water safety

2. System requirements WTI2017

3. Implementation of the ‘probability of flooding approach’ (‘overstromingskansbenadering’)

4. Functional design WTI2017

5. Hydraulic loads

6. Starting points failure mechanisms (‘toetssporen’)

7. Starting points software development

Those principles vary between requirements on how to save data, so it can be used generally, the

description of how the WTI2017 has to be structured and what has to be described in it and the

language that has to be used for all the documents (H. de Waal & Knoeff, 2014).

3.2. Signaling value and lower limit In the WBI2017, two types of norms are used. The first one is the lower limit, which is the absolute

maximum probability of flooding a dike is allowed to have. If a dike copes with this lower limit the

basic protection level is guaranteed. The dike will still have to be registered for the HWBP

(‘Hoogwaterbeschermingsprogramma’ or ‘Flood Protection Program’), if it does not cope with the

signaling value. The signaling value is the second type of norm. This is the value on which the dikes

are first assessed in the WBI2017 (Rijkswaterstaat Water, Verkeer en Leefomgeving, 2017b). The

signaling value is approximately 3 times smaller than the lower limit, illustrated in Figure 6. There are

six classes of norms, which are shown in Table 2 (STOWA & Rijkswaterstaat Infrastructuur en Milieu,

2016):


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Table 2 ‐ Classes of norms (Adapted after STOWA & Rijkswaterstaat Infrastructuur en Milieu, 2016)

Signaling value (per year) Lower limit (per year)

1/300 1/100

1/1,000 1/300

1/3,000 1/1,000

1/10,000 1/3,000

1/30,000 1/10,000

1/100,000 1/30,000

There is one exception to these classes. For the nuclear powerplant of Borssele a signaling value of

1/1,000,000 per year has been set.

If a dike does not cope with the signaling value it will have to be assessed on the lower limit as well. If

the signalling value is exceeded a dike is registered for the HWBP. It is not possible to register a dike

for the WHBP is the signalling value is not exceeded. Registration for the HWBP means that the dike

will become one of the reinforcement projects (Rijkswaterstaat Infrastructuur en Milieu, 2017). In 2050

all dikes have to meet the lower limits, so the basic protection level will be guaranteed (STOWA &

Rijkswaterstaat Infrastructuur en Milieu, 2016).

Figure 6 ‐ Signaling value and lower limit. The blue line represents the safety level of the dike over time (Adapted after: STOWA & Rijkswaterstaat Infrastructuur en Milieu, 2016)

The goal of the signaling value is early detection of dike sections that will not comply with the norm

in the near future. By detecting such a dike in an early stage, it becomes possible to investigate what

the best way is to make the dike future proof. It can also be investigated whether the reinforcement

of the dike can be combined with the development of the surrounding area.

3.3. Uncertainties in the safety assessment The safety assessment of a dike is influenced by uncertainty for an important part. For piping, the

models use a schematization of reality, the subsoil varies over the width of the dike and the loads on

the dike can only be estimated. In the WBI2017, these uncertainties are included in an explicit manner,

instead of the implicit manner that was used in the past. The main differences between the WTI2006

and the WBI2017 can be found in the detailed assessment. These changes have not been introduced


17

to all failure mechanisms and for all variables yet, but only for the failure mechanisms that are

considered to be most relevant, so the WBI could be completed in time (Diermanse, 2016a). For

piping, however, these changes have been implemented. The main differences between the WTI2006

and the WBI2017 are:

1. Knowledge uncertainty of hydraulic loads is now accounted for explicitly

2. Instead of characteristic ‘calculation values’ probability distributions are used

3. Complete probabilistic assessment prescriptions are developed for some failure mechanisms

to be able to include uncertainties in a consistent and complete manner.

4. Safety factors are based on the results of probabilistic calculations

For the detailed assessment, there is a difference between the detailed assessment per dike section

and the detailed assessment per dike trajectory. In the detailed assessment per dike section the

choice can be made to do the assessment using a semi‐probabilistic or a probabilistic method. In the

detailed assessment per dike trajectory the assessment has to be done using a probabilistic method.

Since the probabilistic methods will become available in 2019, it is not yet possible to do an

assessment on a dike trajectory level (Diermanse, 2016b).

3.3.1. Knowledge uncertainty of hydraulic loads The hydraulic load is composed of a combination of water level and wave characteristics for a

representative location for the considered dike section. To predict the conditions for this dike section

models are used for the calculation of the hydraulic load. This causes two kinds of uncertainties to

occur: (1) uncertainty in the basic stochastic variables (‘basisstochasten’) and (2) uncertainty in the

simulation of the hydraulic models. The probability distributions of the basic stochastic variables can

be seen as a best guess of reality, as the real probability distribution is unknown (De Waal, 2016).

For the prediction of the hydraulic loads, seven different hydraulic load models are used:

1. Upper Rivers (Rhine and Meuse)

2. Lower Rivers (Rhine, Meuse and Europoort)

3. Vecht en IJssel‐delta

4. Coastal areas (dikes)

5. Lake area (IJssel lake and Marker lake)

6. Easter Scheldt (dikes)

7. Dunes

For each of the models, different stochastic variables are defined. Which variables are used is stated

in Table A.1 of (Diermanse, 2016a). In general, it can be said that:

‐ for the rivers, the discharges and wind conditions are used

‐ for the deltas/lower rivers, the discharges, wind conditions and water levels of the water

bodies (lakes or see) they debouch into are used

‐ for the sea parts, the water levels and wind conditions are of importance

Discharge statistics are determined with the GRADE‐model for the Rhine at Lobith and for the Meuse

at Borgharen. For the Meuse, the influence of dike breaches in Belgium is not included, since this

would only have a marginal influence (Diermanse, 2016a). For the IJssel at Olst the WAQUA‐Rhine

branches model is used and for the Meuse at Lith the WAQUA model of the Meuse is used. For the

Vecht at Dalfsen the discharges and confidence intervals are determined directly from the

measurements. Discharges of more than 800 m3/s are physically impossible for the Vecht, so if the

confidence interval exceeds this discharge it is capped at 800 m3/s. For the prediction of water levels


18

of the Rhine, the influence of emergency measures in Germany is included. If this would not be done,

the water levels at Lobith would be underestimated. Based on the historical data, discharge waves

are calculated for each of the locations (Lobith, Olst, Borgharen, Lith and Dalfsen). For each return

period, a mean discharge and a 95% confidence interval (following a normal distribution) is

determined. As an example, the results for the calculations for the Rhine at Lobith are shown in Table

3 (Diermanse, 2016a). The statistical uncertainty is made explicit by this method.

Table 3 ‐ Discharges of the Rhine at Lobith with the 95% confidence limits (Adapted after: Diermanse, 2016a)

Repetition time Discharge [m3/s] 95% Confidence interval Lower limit Upper limit

2 5,940 5,280 6,600 5 7,970 7,110 8,840

10 9,130 8,160 10,100 30 10,910 9,730 12,080

100 12,770 11,400 14,150 300 14,000 12,910 15,100

1,000 14,840 13,620 16,050 1,250 14,970 13,720 16,230 3,000 15,520 14,060 16,980

10,000 16,270 14,450 18,100 30,000 16,960 14,750 19,160

100,000 17,710 15,060 20,350

3.3.2. Probability distributions of strength parameters For all failure mechanisms (‘toetssporen’) combined, there are around 400 parameters that are used.

Some of these parameters are treated as deterministic parameters in the software corresponding to

the failure mechanism. This means their uncertainty is considered to be negligible and therefore not

significant enough to include in the calculations of the (semi)‐probabilistic approach. All other

parameters are included as a stochastic variable, for which is assumed that they are uncorrelated

between each other. For each stochastic variable, the following information is needed for the

calculations:

1. Type of probability distribution

2. Average value (μ)

3. Standard deviation (σ)

4. Correlation length of the autocorrelation function

5. Residual correlation of the autocorrelation function

The type of probability distribution is in the WBI‐models included as a “default”, and it cannot be

changed. The correlation length and the residual length of the autocorrelation function are also

included as “default” and are determined during sessions with experts or are adapted from the VNK2‐

study. The average value and standard deviation of some stochastic variables is included as a “default”

as well, which cannot be changed. For all other stochastic variables, the assessor has to determine

their values based on measurements, other data and sometimes expert judgement.

3.3.3. Semi‐probabilistic and probabilistic approach In the WBI2017 two types of assessments can be applied: the semi‐probabilistic approach and the

probabilistic approach. In the probabilistic approach, all possible failure events are used with their

probability of occurring, while in the semi‐probabilistic approach the assessment is done based on

characteristic events. The semi‐probabilistic approach uses probabilistic calculations for testcases


19

that are chosen strategically (Diermanse, 2016b). Both approaches use the same strength model for

the failure mechanism, the same probability distributions of the strength‐characteristics and the

same norm. The input however is different for both approaches. In the semi‐probabilistic approach

characteristic values are the input, while in the probabilistic approach probability distributions are the

input. The input variables for the semi‐probabilistic calculation are usually chosen conservative, so the

result of the assessment will give sufficient confidence that the dike is as strong or stronger than

calculated (Diermanse, 2016a). This means the result of the assessment with the semi‐probabilistic

method is conservative in most cases, but it is not possible to determine how conservative (Expertise

Netwerk Waterveiligheid, 2016). There is, however, still a possibility of overestimating the strength if

a weak spot is missed (Van Stokkum, 2016). If a stochastic variable is considered to be relatively

unimportant, often the average value is used for the calculation. As can be seen in Figure 7, the

characteristic values for strength and the load are always chosen conservative. The strength is chosen

lower than the mean expected value, while the load is chosen higher than the mean expected value.

Above that, safety factors are included in the semi‐probabilistic test, to make sure that the dike is

really safe when approved in the safety assessment (Diermanse, 2016a).

Figure 7 ‐ Characteristic values for strength (Rk) and load (Sk) (Adapted after: Diermanse, 2016a)

The output of semi‐probabilistic or probabilistic approaches are different as well. In the semi‐

probabilistic approach, a verdict is given, which says whether the dike complies with the norm or not.

In the probabilistic approach, the result of the calculation is a failure probability. Based on this failure

probability it can be determined if the dike complies with the norm or not, and how well it complies

with this norm (Diermanse, 2016b). This means it becomes possible to give a ranking to the dikes that

need reinforcement. By doing so, the dikes that have the most severe problems can be reinforced first

and the dikes with the least severe problems come last (Vrijling, 2001).

Because a more precise assessment is possible, the probabilistic approach is more desirable than the

semi‐probabilistic approach. There are however some drawbacks to a probabilistic approach as well:

(1) it can only be performed by a small group of experts, (2) the methods used are hard to explain and

are usually experienced as a “black‐box” and (3) sometimes probabilistic analysis are very computing‐

intensive, which limits the applicability (Diermanse, 2016a).

3.3.4. Safety factors As mentioned before, safety factors are included in the semi‐probabilistic calculations to make sure

the result is certain enough. It is however also important to choose the safety factors not too strict.

Otherwise a lot of dikes would be decried unnecessarily. Therefore, the safety factors for the semi‐

probabilistic approach are determined based on probabilistic calculations of strategically chosen test

cases. The starting point for the determination of the safety factors is that there has to be as little

inconsistency between the semi‐probabilistic and probabilistic approach. This means that the cases

where the probabilistic approach approves the dike, while the semi‐probabilistic approach decries it,

have to be minimized. Of course, complete consistency is not realistic, as the semi‐probabilistic

approach is a simplification.


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If there is any inconsistency between both approaches, the semi‐probabilistic approach has to be

more conservative than the probabilistic approach. This is based on the fact that cases in which the

semi‐probabilistic approach decries the dike are less problematic than vice versa, as the probabilistic

approach is a back‐up in case there are doubts about the result of the semi‐probabilistic approach. If

the semi‐probabilistic approach approves the dike, there is concluded that the dike is strong enough

and that the probabilistic calculations are not needed. Therefore, it is undesirable to have the semi‐

probabilistic approach approve the dike, while the probabilistic approach decries it. In practice, the

semi‐probabilistic approach will be calibrated in such a way that, the failure probability is equal or

smaller than in the probabilistic approach. This means there will be (almost) no cases where the semi‐

probabilistic approach results in a better failure probability than the probabilistic approach.

3.4. Acceptance criteria Authorities often impose restrictions, in the form of so‐called acceptance criteria, on the engineering

decisions and operation practice in the regulations of larger projects or operations. Those acceptance

criteria are meant to keep the occurrence rate of different categories of some adverse events below a

certain limit. In general, the authorities formulating the acceptance criteria are more concerned about

getting more severe public reaction when there is a large number of casualties by one single rare

incident than to the same number of casualties accumulated over several incidents. Governments

often set those limits very strict. This is a type of risk aversion, which is usually motivated by the fear

of authorities of being claimed responsible if a large adverse event occurs. This risk aversion

phenomenon should, however, be taken into consideration by the responsible owner of the project or

operation, instead of the authorities formulating the acceptance criteria. This is especially the case if

the adverse events have such a rare occurrence that the event will probably happen if the politician

that set the acceptance criteria does no longer have the authority (Ditlevsen, 2003; Friis‐Hansen &

Ditlevsen, 2003).

The total expected loss can be calculated by (Ditlevsen, 2003; Friis‐Hansen & Ditlevsen, 2003):

In which:

. .

In this formula, the total expected loss and the expected costs of intervention are included. The

interest rate is a combination of the monetary capital and the human capital, which will both vary

between regions and countries (Ditlevsen, 2003; Friis‐Hansen & Ditlevsen, 2003). When determining

the optimal investment, the equation 0 has to be solved. This result is then used for the

determination of the norms.

3.5. Failure probability The failure probability that is calculated in the assessment is considered to be the ‘real’ failure

probability. This is also called a Bayesian approach, in which the probability is interpreted as the

expectation representing the current state of knowledge (Bernardo & Smith, 1994). This means the

failure probability as it is calculated is the best estimate of the truth, based on the current state of

knowledge.


21

The calculated failure probability is therefore considered to be the ‘true’ failure probability. The

calculated failure probability, however, will be different if someone else would perform the

assessment, as this other person will use other assumptions (Vrouwenvelder & Vrijling, 2000). The

calculated failure probability is an estimation of the ‘real’ failure probability, which cannot be known

(Diermanse, 2016a). The calculated failure probability is a combination of the strength of a dike and

the uncertainty about the strength of the dike.


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4. Use of WBI2017 The philosophy behind the development of the WBI2017 is translated in methods and procedures that

have to be used. In this chapter, the methods, specific for piping will be explained into more detail.

The last part of this chapter consists of the results that were gathered from interviews with experts.

4.1. Global Stochastic Subsoil schematization (WTI‐SOS) To determine the type of layers and their thickness in the subsoil different strategies are used in the

Netherlands. Usually a map is made to show the data gathered by Cone Penetration Tests (CPT’s) and

sounding charts in order to detect patterns in depth‐intervals and to mark the individual observations

as layer limits. To get a geotechnical profile those layer limits can be interpolated and the results can

be sketched on a map. In practice such sketches are sometimes used as a direct representation of the

subsoil and the schematization of the subsoil for the calculations is based on this representation 1‐on‐

1 (Hijma & Lam, 2015).

To include the uncertainty about the composition of the subsoil in an explicit way, the choice is made

by the initiators of the WBI to use an alternative approach, which focusses on naming and quantifying

the uncertainty (see chapter 3). This is done so that the choices made in the schematization of the

subsoil become more objective and rational. For each section of dike the subsoil is schematized with

a number of ‘subsoil scenarios’, or WTI‐SOS scenarios, which each have a probability of occurring. The

probabilities for each scenario are based on the observations in the area and on the insights in the

system of the subsoil (Kruse & Hijma, 2015). It is possible to adjust these scenarios, based on own

measurements by the dike manager.

In the WTI‐SOS the subsoil of the dike is presented with subsoil composition scenarios for

predetermined dike sections. Each of those scenarios is assigned a probability that this scenario will

be found in that dike section if, for example, a CPT is performed in the area. The assigned probabilities

are based on direct measurements and knowledge about the subsoil in the area, such as historical and

geological knowledge. In total 43 different units are defined in the WTI‐SOS. Those units describe the

composition of the layers that can be found. Each scenario consists of a number of those units. The

units each get a code based on their properties. In Table 4 the meaning of those codes is shown. The

first letter describes the stratigraphy of the layer. The second letter describes the depositional

environment on a regional scale. The third letter describes the depositional environment on a local

scale and the fourth letter describes the properties of the material. For example, the code H_mg_zk

means that the layer is deposited in the Holocene, in a marine environment, in a gutter and consists

of clayey sand. A scenario consists of multiple units with a certain thickness. On average, there are

ten scenarios defined for each dike section. There are however also dike sections with only 2 scenarios

and dike sections with up to 26 scenarios as well. The probabilities that are given to each scenario are

based on: (1) the relative frequency of occurring in a CPT and/or borehole, (2) characteristic size of a

unit (for example a gutter with a known width) and (3) how often a unit with a certain size can occur

in a segment of a known length. The probabilities that are determined for each of the scenarios are

classified from “very small chance” to “present everywhere”. For each class, indicative probabilities

are determined. For units that have a very low probability of occurring, the relevance is determined.

A very thin clay layer with a low probability of occurring, for example, is very relevant for piping, while

a sand layer of 6 meters, which in some cases has a thickness of 7 meters is not relevant (Diermanse,

2016a). Therefore, the thin clay layer will be included in a separate scenario, while the thicker sand

layer will not be include in a separate scenario (Diermanse, 2016a).


23

Table 4 ‐ Codes for the WBI‐SOS units (Hijma & Lam, 2015)

4.2. Calculation and models The WTI‐SOS is used as the input for the D‐Soil Model. This is the software used for the

schematization of the subsoil. In the WTI‐SOS, the subsoil is schematized, in subsoil segments, which

are dike sections with similar subsoil schematizations. These schematizations are built up from

several subsoil profiles. These profiles are consisting of ground layers, in which every layer consists of

a certain material with its respective parameters (Deltares, 2016). Based on measurements, the

segments, the profiles and the parameters of the ground layers can be adjusted to meet the data

retrieved from the measurements. In the D‐Soil Model other data, such as elevation metrics and

characteristic points are added (Deltares, 2016).

When this is done, the subsoil schematizations can be loaded into RisKeer (previously Ringtoets),

which is the software for the calculations of the WBI. Together with the profile of the dike and its

characteristic points, the schematizations and data from D‐Soil Model (which includes parameters for

the different layers of the subsoil) can be used to calculate the failure probability. In the result of the

calculation, three failure probabilities are shown (Deltares, 2017). The three failure probabilities

belong to the probability of occurring of uplift, heave and receding erosion respectively. The final

result is one failure probability for piping for this dike section, which is the smallest failure probability

of either three of the sub‐failure mechanisms (Deltares, 2017)..

4.3. Length effect The length effect plays an important role in the WBI2017. The length‐effect means that a longer chain,

or in this case a longer dike trajectory, has a higher probability of having a weak link (Vrijling, 2001).

According to the ENW Piping, the length‐effect is a physical reality. This means it is mainly influencing

the failure mechanisms, for which the strength is determined by the conditions of the subsoil, which

vary along the dike trajectory (Rosenbrand, 2017). Piping is such a failure mechanism as well, which

means that the length‐effect influences the failure probability for piping. The probability that piping

occurs along a dike at trajectory level becomes 5 to 10 times larger by the length‐effect compared to

the probability of piping along a dike on dike section level (Rosenbrand, 2017).

As described, the allowed failure probability for a dike trajectory is first divided over the failure

mechanisms based on the failure probability estimation. After that, the failure probability on

trajectory level per failure mechanism, is divided over the dike sections per failure mechanism by


24

using the length‐effect (Rijkswaterstaat Water, Verkeer en Leefomgeving, 2017c). By doing so a

maximum allowed failure probability per dike section per failure mechanism is calculated, as can be

seen in Figure 8.

Figure 8 ‐ Length‐effect (Adapted after: Rijkswaterstaat Water, Verkeer en Leefomgeving, 2017c)

With the inclusion of the length effect it is tried to exclude the uncertainty about weak spots in a dike.

After the failure probability of each failure mechanism per dike section is calculated the results can be

combined into one failure probability per dike trajectory. This process is called ‘assembling’ and is

explained in Appendix B.

4.4. Relevant uncertainties In the WBI2017, not all relevant uncertainties are included in the assessment. In the interviews with

experts, several uncertainties came up that are not (yet) included in the assessment at this moment.

One expert stated that all uncertainties that are known are included. If there are uncertainties that

have been missed, we do not know that. According to two other experts, there are still some

uncertainties that are not included in the assessment. These uncertainties are known, but the

methods to implement these uncertainties are lacking. They are kept in mind, but it is not possible to

apply them (yet). These uncertainties, however, do have a significant influence on the result of the

assessment.

According to another expert, the time dependence has to be included in the methods. This is

important in the areas where tides influence the water levels. According to Diermanse (2016b), this

will be implemented in the methodology in 2019. Besides the time dependence, also the damage

caused by digging in a dike, for example, could be included according to another expert. It is however,

very difficult to implement such an uncertainty. Besides that, the influence on the result of the

assessment will be very minimal. If such damage occurs, it will be on a very local scale, which makes

it hard to measure. According to the last expert, it should be possible to include different progress of

the high water, which would cause a longer relevant load. As a last source of uncertainty, the presence

or absence of sludge with high water velocities was mentioned. The sludge is important for the

intrusion resistance of the water into the soil.

4.5. Use by experts During the interviews with experts from Water Boards it has become clear that different Water Boards

use the WBI methods differently. Besides that, not every Water Board is equally experienced in the

discovery of how it works. One expert indicated that every user will make different choices in the


25

schematization of a dike. This causes that there will always be some subjective part in the result of

the assessment. As the schematization is done by people, there will always be some subjectivity to it.

The subjectivity is, however, kept as small as possible by using as many prescribed methods as

possible. This is also in line with the philosophy of the WBI, which states that the assessment has to

be done as objective and rational as possible (see chapter 3). Besides that, an assessor might use

different assumptions in the schematization of a dike for different failure mechanisms. At one Water

Board, they are trying to automate the process as much as possible. By doing so, it becomes possible

to perform sensitivity analysis. With these sensitivity analysis, it can be investigated what input

influences the assessment result the most and therefore, where investment would be useful.

At another Water Board, they indicated that the schematization is done in a conservative way for each

failure mechanism. By using a conservative approach, they try to stay on the safe side of the

assessment. Yet another Water Board does not use the WTI‐SOS. They use their own measurements,

which are translated into scenarios. This is done using the same methods as were used for the

development of the WTI‐SOS. They, however, find their own measurement to be more accurate than

the scenarios proposed in the WTI‐SOS.

Finally, one Water Board is not able to use the WBI methods. As their management area is highly

influenced by tides, time dependence has to be included, but that is not yet possible with the current

methods. For now, they will have to use the ‘customized assessment’, but from 2019 on it will be

possible to use the WBI methods. The WTI‐SOS the WBI‐method, but there are more methods for

other failure mechanisms2.

2 For more information on these methods, see (Rijkswaterstaat Water, Verkeer en Leefomgeving, 2017b, 2017c)


26

5. Acceptability of uncertainty In this chapter, the acceptable level of uncertainty will be identified. First, the term ‘acceptable’ will

be defined. Second, the acceptable level of uncertainty in the safety assessment for piping will be

identified.

5.1. Definition of acceptable The term ‘acceptable’ can be defined from two different points of view. The first point of view is from

the assessors at the Water Boards. Five of the interviewees are assessors from Water Boards. One of

them indicated that it is highly dependent on the costs and benefits of the reduction of uncertainty if

it is acceptable or not. If the costs of additional research exceed the possible benefits of reducing

uncertainty, the current level of uncertainty should be accepted and a reinforcement project has to

be started. An expert from another Water Board indicated that a result is acceptable if uncertainty

does not have a significant influence on that assessment result. As an example, uncertainty of ten

percent for wave height is rather accurate and does not have a significant influence on the assessment

result if that level of uncertainty applies. Therefore, this kind of uncertainty would be acceptable.

Three other experts from different Water Boards indicated that the result of an assessment is

acceptable if the methods and procedures as prescribed in the WBI are followed. With the methods

and procedures, they meant the methods and procedures as prescribed for the detailed assessment.

If these methods and procedures are followed, the assessment and the result of the assessment

complies with the prescriptions of the WBI and therefore it complies with Dutch Law.

From the experts that were involved with the development of the WBI, one stated that all

assessments are done with the same methods and procedures. Therefore, the level of uncertainty

does not matter for comparison between different stretches of dike. Even if the result is highly

uncertain, it is still possible to compare results, as they are all obtained using the same methods.

Based on these results an estimation can be made of what dikes has to get the highest priority

compared to other dikes.

5.2. Acceptability of uncertainty Based on the statements of the interviewees and the information gathered in the previous chapters,

three main boundary conditions can be identified in the acceptance of uncertainty. These three

boundary conditions will have to be satisfied to be able to declare a result of an assessment as

acceptable. These three boundary conditions are listed below.

1. The assessment is done according to the methods and procedures prescribed in the WBI2017

for the detailed assessment

2. The result of the assessment is reliable and traceable

3. There is at most a medium level of uncertainty in strength or load and a small level of

uncertainty in strength and/or load

5.2.1. Process In the interviews that were held, three of the experts indicated that a result and its uncertainty is

acceptable as long as the procedures prescribed in the WBI are followed. Therefore, this is the first

boundary condition for an acceptable result of the safety assessment. When the methods and

procedures of the WBI2017 are followed, the result of the assessment complies with Dutch Law.


27

The methods as prescribed in the WBI consist of three steps (Helpdesk Water, 2017b), namely:

1. Simple assessment

2. Detailed assessment

3. Advanced assessment

The three different steps are also shown in Figure 9. As can be seen, the steps are going from a very

generic and coarse level towards a detailed and location specific level.

Figure 9 ‐ Steps in the safety assessment (Adapted after: Helpdesk Water, 2017)

In the advanced assessment, the calculation rules and failure probability estimation of the detailed

assessment do not have to be used anymore (Rijkswaterstaat Water, Verkeer en Leefomgeving,

2017b). The assessor is free to use whatever method and calculation rules seem to fit the situation

best. If such a customized assessment is used, however, it has to be justified to the Minister why this

method was used and how it was executed. It depends on the type of analysis what information has

to be reported. If the assessor does not agree with the result of the calculations based on his

knowledge and experience for example, the assessment has to be substantiated with measurements

and/or historical data (Rijkswaterstaat Water, Verkeer en Leefomgeving, 2017b). An explanation of

the different steps of the safety assessment can be found in Appendix A.

5.2.2. Data The second boundary condition is considering the input data of the assessment. The results of the

assessment have to be “reliable and traceable” (Rijkswaterstaat Water, Verkeer en Leefomgeving,

2017b). It is the responsibility of the assessor to ensure this. The traceability of the results of the

assessment is ensured by the way the result has to be reported towards the Minister. How this has to

be done is prescribed in the WBI2017 in ‘Regeling veiligheid primaire waterkeringen 2017; Bijlage I

Procedure’.

For the reliability of the result there is less information given. The prescriptions only state that the

“relevant information has to be collected before the schematization, but this does not have to be done

exhaustive” (Rijkswaterstaat Water, Verkeer en Leefomgeving, 2017c). This means that there has to

be enough data of good quality for the input parameters, as the quality of the results of the

assessment are dependent on the quality of the input for a large part. The quantification of ‘enough

data’ and ‘good quality’, however, is unknown. It is not stated how many measurements have to be

done and with what kind of accuracy they have to be performed. This is left to the assessor to

determine when the input data, and with that the result of the assessment, is reliable enough. This

means the assessor is responsible for delivering a ‘reliable and traceable’ result. Especially the


28

traceability of the result ensures that the result can be reproduced by other assessors to check

whether the result is reliable or not.

5.2.3. Uncertainty in strength and load The third boundary condition is the (un)certainty in load and strength. To identify what uncertainty

might be acceptable in the result for load and strength interviews were held with experts. The experts

ranked the presented scenarios from one to nine. Based on their answers an average ranking was

calculated, which is shown in Table 5. As can be seen, certainty in load as well as strength is considered

very important. All experts agreed on the most acceptable scenario, which is a scenario with a small

curve for the load as well as the strength. This means that there is a high level of certainty in load and

strength. The second‐best scenario would be with a medium dispersion of the curve for the load and

a small curve for the strength. This scenario is, on average, ranked higher than the scenario with a

small curve for the load and a medium dispersion in strength. In general, this is true for all contrary

scenarios. From this it can be concluded that the interviewed experts consider certainty in strength to

be more important than certainty in load.

Table 5 ‐ Level of uncertainty in nine scenarios and their averaged ranks

Scenario Load Strength Average rank

1 Small Small 1

2 Medium Small 2.5

3 Small Medium 3.25

4 Medium Medium 4.75

5 Large Small 5

6 Small Large 6.25

7 Large Medium 6.25

8 Medium Large 7

9 Large Large 9

The results of the interviews are shown in the diagram of Figure 10. The answers show quite some

spreading. Especially in the scenarios with lower rankings and where certainty in load and strength

are far apart, the dispersion is remarkable.


29

Figure 10 ‐ Results of the interviews, the order of the scenarios is based on their average rank

From the results of the interviews it can be concluded that the result of the assessment is acceptable

if the load as well as the strength have at most a medium dispersion of their curves. This is, however,

an absolute minimum. As can be seen in Table 5, the average rank of the scenario in which the load as

well as the strength has a medium dispersion, is quite far from the first three scenarios. Therefore, it

is a wish that at least the strength or the load has a small curve.

5.3. Analysis In this paragraph the results of the interviews will be further analyzed. The first part is about the

achievability of the defined acceptability of the previous paragraphs. The second part will be about

the acceptable level of uncertainty.

5.3.1. Achievability of ‘acceptable’ In paragraph 5.1 the term ‘acceptable’ has been defined based on expert opinions. The experts

indicated that the result of an assessment, and with that its uncertainty, is acceptable as long as the

methods and procedures prescribed in the WBI are followed. Since it is possible to use whatever

method seems applicable in the customized assessment, it is assumed they mean the methods and

procedures of the detailed assessment. In this detailed assessment for piping the calculation rule of

Sellmeijer plays an important role (see Appendix E). In 2011, the calculation rule of Sellmeijer has been

improved, based on experiments. This new (or improved) calculation, combined with the new

methodologies of the WBI2017 causes that there is little experience with and insight in the new

methods. Therefore, there is also little insight in the uncertainties that come with the assessment


30

result and the influence of these uncertainties on the assessment result. The experts do, however,

indicate that they find it acceptable if there is as much certainty in load and strength as possible.

The used scenarios showed a situation with a standard deviation of the normal distribution which was

half (for the small curve) or double (for the large curve) of the medium curve. From the average ranks

of paragraph 5.2.3 it is clear that the scenarios where at least the load or the strength has a medium

uncertainty are considered ‘acceptable’. Since it was only asked to the interviewees to rank the

scenarios and not to indicate if they found the presented scenarios acceptable at all, it is difficult to

draw conclusions from this. It can only be concluded that the interviewees find a situation with more

certainty more acceptable than a situation with less certainty. This is in compliance with the

philosophy behind the norms, as the philosophy states that a result should be ‘reliable’. A reliable

result can only be achieved when there is a certain level of certainty about the input and the results.

Besides that, the scenarios with more certainty in strength are ranked higher on average than the

scenarios with a more certain load than strength.

The research of Van Stokkum (2016), however, shows that a high level of certainty in strength cannot

be reached using the current methods and models. Even if there is a measurement every meter, there

will always remain a significant level of uncertainty. This uncertainty is caused by the schematizations

and models used. As Van Stokkum (2016) and Sellmeijer et al. (2011) state, the model of Sellmeijer is

developed and calibrated on same scale laboratory tests for quite homogeneous types of sand. In

reality however, the layers are not that homogeneous (Van Stokkum, 2016). With the addition to the

formula in 2011, the outcome of large scale experiments, such as the IJkdijk tests, are quite good as

long as the subsoil consists of fine sand. If the subsoil consists of coarse sand, the model performs not

that good (Sellmeijer et al., 2011). An important conclusion from this is that, if better performing

models would be available, it may become possible to achieve the defined acceptability of

uncertainty. With the current models and methods, however, this is not possible.

5.3.2. Acceptable level of uncertainty Based on this research it is not possible to quantify the acceptable level of uncertainty in the

assessment result for piping. It is however possible to make a number of qualitative statements

regarding the acceptable level of uncertainty.

The cases used in this research all had the same failure probability. This was also said to the

interviewees. They did, however, still indicate a ranking from the most acceptable scenario to the

least acceptable scenario. This would be logical if the mean of the strength would have stayed the

same, as shown in Figure 3. In the interviews however, the cases used did not all have the same mean.

This means that, even though all scenarios had the same failure probability, the interviewed experts

prefer more certainty about the result.

In general, the experts indicated that all relevant uncertainties are included, except for the time

dependence. This also complies with the philosophy behind the norms, which states that

uncertainties are included more explicitly. From 2019, time dependence will be included as well, which

means that from then all known relevant uncertainties are included.

The interviewed experts find three factors important, which is that the prescribed methods are

followed, that the result is reliable and traceable and that there is as much certainty as possible about

the load and the strength. This also complies with the philosophy behind the WBI2017. The philosophy

states that the calculated failure probability is the ‘real’ failure probability. To be able to approach this

in such a way it is important to follow the right procedures and to have input that is of good quality.


31

By having that, it is ensured that the differences in the assessment results of different assessors are

as small as possible.

Based on this research the acceptable level of uncertainty cannot be quantified. Qualitatively it can

be said that the result of an assessment is acceptable if the methods of the detailed assessment have

been followed, the result is ‘reliable’ and ‘traceable’ and there is as much certainty as possible about

the strength and the load. As stated in paragraph 5.3.1, the wanted level of certainty about the load

and strength cannot be obtained with the current models and methods. Therefore, other methods

and models will have to be investigated to get methods and models that have less uncertainty.

Uncertainty in the result means that the result that is calculated can be different from the real failure

probability, which causes that a dike that is approved is weaker than expected. This means that,

according to the calculations the dike complies with the norm, while in reality it does not. The other

way around it can be the case that a dike is decried, while in reality it is strong enough to comply with

the norm. That would mean that the dike is reinforced, while that is not necessary. The money and

time spent on this unnecessary reinforcement should have been spent on a dike that needed the

reinforcement more. Therefore, it is important to ensure that the uncertainty in the assessment result

for load as well as strength is as small as possible.


32

6. Discussion Before drawing the final conclusions, the methods and results of this research will be discussed.

6.1. Methods The methods used in this research are a literature study for the first part and interviews for the second

part. The literature study was used to identify the theoretical input for the interviews. This has seemed

to be a decent method for the research questions posed at the start of the research. There are

however, some points of improvement.

Firstly, the visualizations used for the interviews are not based on a real case. This made it difficult for

some of the interviewees to interpret the figures. During the interviews it became clear, that location

and surroundings of a dike are very important factors for the accepted uncertainty. To determine a

quantitative acceptable level of uncertainty, several realistic cases have to be used. These cases would

have to be at different locations, with significantly different characteristics to be able to generalize

the outcome. Besides that, the interviewees did not look at the bandwidths very much. The main input

they used for their decision was the shape of the curves of the load and strength. Therefore, it might

be interesting to see if the answers would change if the shape of the curves would be kept the same,

but the bandwidths would change.

Secondly, the number of interviews done for this research is, due to time, limited to only seven. Of

these seven interviews, only five were held at Water Boards. For the qualitative part of the interviews

the number of interviews is sufficient. For the quantitative part (questions 8‐11), however, more

interviews would be useful. Since the Water Boards are the organizations that have to comply with

the WBI2017, it would be useful to have more input from different Water Boards than the five used

now. To have a better insight in the acceptability of uncertainty in the assessment for piping, an expert

from all Water Boards should be interviewed. It could be possible that experts from other Water

Boards have a different opinion, for example because there is a different water system in their

management area. Besides that, the representativeness of the research would improve if more

interviews with experts would be used. In this research, most interviewees were employees of a Water

Board. It is, however, very well possible that employees of other organisations, such as

Rijkswaterstaat, consultancy firms or knowledge institutes have a different opinion on the

acceptability of uncertainty.

As stated in the methodology, during the interviews, the questions 8 and 9 were dropped. By dropping

these questions there has not been obtained an answer to the question if the experts found the

scenarios acceptable at all. They only ranked the scenarios from most acceptable to least acceptable.

It is however possible that they did find none of them acceptable. Therefore, in future research,

questions 8 and 9 have to be included, or a question specifically asking the experts if they find it

acceptable has to be added.

When interviewing experts, the possibility of bias has to be considered. For this research there are

three types of bias that might have had an influence. These three types are motivational bias (Van Der

Sluijs et al., 2005), overconfidence bias (Kloprogge et al., 2007) and confirmation bias (Kloprogge et

al., 2007). When interviewing experts, they are likely to have an overconfidence bias. They

overestimate their own knowledge and abilities (Kloprogge et al., 2007). In this study that could be

the case as well, since the experts had to judge their own level of expertise. Besides that, there is the

possibility of a confirmation bias. This means that an interviewee is filtering out information to

support their own opinion. In this study, confirmation bias is not likely to be influential, since there

was not much information given to the interviewees. As a last type of bias there is the motivational


33

bias. Water Boards have to pay for measurements, while the Dutch state is paying (partly) for

reinforcement measures (Waterschap Brabantse Delta, 2017). This might cause the interviewees from

the Water Boards to answer the question of the interviews in such a way that reinforcement measures

are more likely to be beneficial. For example, accepting more uncertainty could mean less investment

in measurements, but more investment in reinforcing dikes. However, since the cases used in this

research were theoretical it is not likely that motivational bias has influenced the results. If, as

suggested before, real world cases would be used, this could become a problem that has a great

influence on the results.

6.2. Results As mentioned, the results are based on a limited number of interviews. Besides that, some of the

interviewees indicated they found it hard to appoint an acceptable level of uncertainty, due to the

novelty of the methods and procedures. Currently, they are trying to figure out what the influence of

the new methods and procedures is.

Most interviewees indicated that the level of uncertainty does not matter, as long as the assessment

is done according to the prescribed methods and procedures. If a dike does comply with the norm,

the level of uncertainty does not matter. However, if this is not the case, a more certain load and

strength is wanted. From the interviews it became clear that certainty in strength is considered

slightly more important than certainty in load. This is probably caused by the influence dike managers

can apply on the certainty in strength by doing more and other measurements. The load is based on

historical data. Doing more measurements to improve uncertainty in the load is therefore impossible.

In the philosophy behind the development of the WBI2017 it was stated that uncertainties are

included in a more explicit way. This was done to make the influence of these uncertainties clearer

and to make sure assessors are aware of the uncertainties. During the interviews, however, it has

become clear that some of the experts find the new methods very complicated and therefore they are

not fully aware of the influences uncertainties have on the results. It is however necessary to fully

understand the methods and procedures in order to give a proper assessment. Therefore, it is

important to check the understanding of the methods and the used statistics. Due to the newness of

the methods it is not yet possible to determine if the methods and statistics will be implemented in

the right way or that they are too complicated for the assessors to fully understand and use.

Therefore, it is important to keep this in mind and see in a year or two whether the methods are fully

understood and used in the right way. If this is not the case solutions for this problem will have to be

found and the question might arise if these new methods have contributed to the goal of water safety.

Another part of the philosophy is that the calculated failure probability is considered to be the ‘real’

failure probability. That means that all assumptions made, must be made conscious and by being fully

aware of the consequences of these assumptions. However, because not all experts are aware of the

different types of uncertainties and the methods, the results of the assessment are considered to be

the ‘right’ result. There is, however, no feeling for the reliability of that result and what the uncertainty

of that result is. Subsequently, this result is presented as the ‘real’ failure probability, while the level

of uncertainty around the result is unknown and therefore neglected. Therefore, assessors of the

water safety should be made aware of the uncertainties in the result. Besides that, it should be made

sure that assessors are understanding the methods they have to use, so they get a feeling for what a

certain result means.

Several experts indicated that the result of a safety assessment is acceptable if the methods and

procedures that are prescribed in the WBI2017 are followed. Indeed, the methods should be followed,

but this is never the only criteria to determine if a result can be considered acceptable. Besides


34

following the methods and procedure, there has to be enough input data of a certain, good, quality.

Besides that, there has to be certainty about the result for the load and the strength. Following the

methods and gathering ‘enough data of good quality’ as such seems achievable, but having certainty

about the load and strength is not. The load is determined by extrapolation of historical data, which

means there will always remain a certain level of uncertainty. The resistance (or strength) of a dike

against piping is highly dependent on the subsoil. In the Netherlands, the subsoil is heterogenic in

most places, while in calculations homogenic subsoils (from laboratories) are used. One of the experts

indicated that a heterogenic subsoil might actually contribute to the resistance against piping, since

the change in type of subsoil make the pipe longer or stops the pipe completely. Therefore, it is

important to investigate the influence of heterogenic and homogenic subsoil. Besides that, other

measurement techniques should be used for the determination of the type of subsoil underneath a

dike. By using other techniques, the certainty about the strength of a dike could be approved.

The results of this research are specific for piping. Since the results are gathered using theoretical

cases, it is difficult to generalize this for real world cases. The methods used in this research however,

can be used on real world cases. By doing so on many real‐world cases, it would become possible to

generalize these results and define a general accepted level of uncertainty, either per failure

mechanism or generalized for all failure mechanisms.


35

7. Conclusion and recommendations In this chapter, the final conclusions to this research will be drawn and some recommendations will

be given for future research as well as regarding the methods and models used for the safety

assessment of a dike.

7.1. Conclusion The philosophy of the WBI 2017 consists of two main parts. The first part contains the uncertainties

of the safety assessment and the way these uncertainties should be included in the safety assessment.

In the WBI2017 this is in a more explicit way than it was done before. The interviewed experts agree

with that. They indicated that, in their opinion, all relevant uncertainties are included in the methods

of the WBI2017, except for time dependency. Time dependency however, will be included in the

methods in 2019. From 2019 all relevant uncertainties that can be included in the methods and models

are included.

The second part of the philosophy is that the assessment result has to be ‘reliable’ and ‘traceable’.

This is realized by making the assessment as objective and rational as possible. One way of doing so

is by including uncertainties in a more explicit way, for example by using the WTI‐SOS. This WTI‐SOS

is used for the schematization of the subsoil. It uses different scenarios for the subsoil to include the

uncertainty about the subsoil. The experts agreed on this, as they stated that an assessment result is

acceptable for them if the methods and procedures prescribed in the WBI are followed. By following

these methods and procedures, the assessment result also complies with the conditions Dutch Law

puts on the safety assessment.

There are however still uncertainties and unclarities about the methods and procedures of the

WBI2017. Many Water Boards are still finding out what has changed and what the influence of these

changes is or will be on the safety assessment they have to do. This also means there is a lack of

experience with and insight in the methods and especially in the uncertainties that are now included

explicitly. If there is a lack of experience with these factors, the assessment result cannot be valued in

the right way. This can cause a wrong interpretation of the calculation result, and with that a wrong

assessment result.

According to the philosophy of the WBI2017 and the interviewed experts, there are three main

conditions an assessment result has to comply with to be acceptable. Firstly, the result has to be

obtained using the methods and procedures prescribed in the WBI2017. Second, the assessment

result has the be ‘reliable’ and ‘traceable’. As a last part, the assessment result, which is the calculated

strength and calculated load, has to be as certain as possible. The research of Van Stokkum (2016)

showed, however, that such a level of certainty is not possible with the current methods and models.

One reason for that is that the model of Sellmeijer is used for the calculations on piping. This model

is calibrated with small scale laboratory test in homogeneous subsoil, while in reality the scale is much

larger and the subsoil is way more heterogenic. In 2011, the calculation rule has been improved with

larger scale experiments, such as the IJkdijk, but this was still in a controlled environment. To be able

to reduce the current levels of uncertainty to an acceptable level, new models and methods will have

to be investigated.

The interviewees indicated that there are still unclarities about the methods and procedures. This

unclarity causes that the assessors lack insight into the uncertainties and their influence on the

assessment result. In the near future, however, the experience with and the insight into the methods

will grow. This will also cause that the insight in what the assessment result mean will grow. It is likely

that in a couple of years the Water Boards will also have more insight in wat the uncertainties that are


36

included mean and what influence these uncertainties have. This is, however, something that has to

be kept in mind when looking at assessment results. Due to the lack of experience the assessors might

have with the new methods, the results of the assessment can be unreliable. If this experience and

insight will not grow in the near future, the interpretation of the results is also not really possible.

Besides that, the uncertainties are then included in the methods and the assessment, but the

assessors have no idea what their influence is. With that, the philosophy of the WBI2017 is not

accomplished and the methodology is not reaching its intended goal.

The interviewees also indicated that the result of the assessment regarding load and strength has to

be as certain as possible (Figure 10). It can be concluded that the result of the assessment is acceptable

if the load as well as the strength have at most a medium dispersion of their curves. Together with the

results presented in Table 5, the average rank of the scenario in which the load as well as the strength

has a medium dispersion, is quite far from the first three scenarios. Therefore, it is a wish that at least

the strength or the load has a small curve, while the other one has at least a medium curve.

7.2. Recommendations With respect to future research it is recommended to do more interviews to identify the acceptable

level of uncertainty. Due to time limitations, it was not possible to conduct more interviews in this

research for the quantitative part (questions 8 through 11). The results of more interviews, however,

might give a different insight in the acceptability of uncertainty. As mentioned by the interviewees, it

is highly dependent on location and surroundings of a dike what level of uncertainty might be

accepted. Therefore, it will be useful to conduct additional interviews with experts of Water Boards.

Especially the Water Boards that have not been considered in this research might have a different

opinion, as their water system might be different from the Water Boards used for this research.

Furthermore, the use of real world cases for the visualization of uncertainty might help to make the

concept better understandable for the interviewees. As said, location and surroundings of a dike are

important factors in the assessment. Therefore, real world cases give a better view on the matter. If

several different dikes at different locations would be used the results could be generalized for all

dikes in the Netherlands later in the process. These different locations will have to have different,

known, uncertainties. By using a variety of situations, some with large uncertainties and other with

small uncertainties it will become possible to generalize the results. When using real world cases, the

addition of a failure probability can give more insight. By specifically mentioning what the failure

probability for a certain case is, it can be investigated if there is a difference in acceptability of

uncertainty between dikes with a high norm versus dikes with a lower norm.

In this research only piping was considered. In the interviews it was also clearly mentioned that the

cases presented were regarding piping. It might be interesting to see if the level of uncertainty that is

accepted changes for other failure mechanisms. It could be the case that for different failure

mechanisms other uncertainties are important. It could also be the case that for some failure

mechanisms more uncertainty is accepted than for other or that (un)certainty in either load or

strength is considered more important for other failure mechanisms. It would be interesting to know

if there is any differentiation between failure mechanisms, so research can be prioritized for the failure

mechanisms.

A last recommendation is to investigate in one or two years what level of uncertainty is accepted. At

that time, the Water Boards will have used the methods and procedures and they will have experience

with them. The experience with the methods and procedures will ensure that the interviewees have a

feeling for uncertainties in the methods and the results. The results of the interviews could be


37

different in a couple of years if the methods and procedures are fully incorporated into and

understood by the Water Boards.

With respect to the methods and the use of these methods, some recommendations can be made as

well. At this moment, most Water Boards are finding out how the new methods have to be used and

what the influence of the WBI2017 is on the assessment results. To get to a ‘reliable and traceable’

assessment result, however, it is very important to fully understand the methods and to have insight

into the influence uncertainties have on the assessment result. Therefore, it is recommended to have

some kind of check on the understanding of the methods by the assessors. This can, for example, be

done by having a course and a subsequent test for the assessors.

As mentioned before, research will have to be done into alternative methods and models for the

assessment of a dike on piping. As is shown in this research, experts/assessors from Water Boards

would like to have more certainty in the assessment result, but as Van Stokkum (2016) stated, this is

not possible with the current models and methods. Therefore, new models and methods have to be

investigated in order to get to the level of uncertainty that is acceptable for the experts/assessors.

In this research, the levels of uncertainty have only been quantified by ‘small’, ‘medium’ and ‘large’. In

future research it will however be useful to quantify this. For example, the ‘small uncertainty’ can be

set at 5% uncertainty, the ‘medium uncertainty’ can be set at 30‐40% uncertainty and the ‘large

uncertainty’ can be set at 80‐100% uncertainty. By quantifying the levels of uncertainty, the results of

the interviews might change. Besides that, the quantification gives the opportunity to also quantify

the results of the interviews into an acceptable level of uncertainty, expressed as a percentage.


38

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Appendices

APPENDICES


41

A. Steps of the safety assessment In this appendix, the steps of the safety assessment will be explained. In Figure 11 the general flow

chart for the safety assessment of a dike is shown. First a general filter on a trajectory level is applied

to filter out parts of dikes that are known to have a much higher or lower flood risk than the detection

value (‘signaleringswaarde’). For these dike trajectories the dike manager can immediately assess the

dike, based on expert judgement and the data available in the VNK (Veiligheid Nederland in Kaart)

(Rijkswaterstaat Water, Verkeer en Leefomgeving, 2017b). This results in the approval or disapproval

of the dikes that are having a much lower or higher flood risk respectively.

Figure 11 ‐ Flow chart of the safety assessment (Adapted after: Rijkswaterstaat Water, Verkeer en Leefomgeving, 2017b)

If this first general filter on a trajectory level is not applicable, a general filter per dike section has to

be applied. This filter has to be applied per dike section and per failure mechanism (toetsspoor). The

division of these dike sections is done with the help of the schematization manuals, which are

available through the Helpdesk Water. For example for piping, chapter 5 of the

‘Schematiseringshandleiding piping’ should be used to determine the division of the dike sections

(Rijkswaterstaat Water, Verkeer en Leefomgeving, 2017a). If this general filter does apply the dike

manager can decide to immediately conduct a customized assessment. If it does not apply, the

assessment procedure should be followed. In the assessment procedure three steps have to be

followed. First, a simple test has to be done in which with the help of simple and basic rules a check

will be performed whether the failure mechanism is of importance or not. This decision is made based

on safe dimensions of the dike. This means that, if the simple test results in a failure probability that

is negligible, a verdict can be given about that particular dike section for that particular test track. If

not, then the assessment should continue as shown in Figure 12 (Rijkswaterstaat Water, Verkeer en

Leefomgeving, 2017b).


42

Figure 12 ‐ Flow chart of the assessment after the first simple test (Adapted after: Rijkswaterstaat Water, Verkeer en Leefomgeving, 2017b)

In Figure 13 the continuation of the process towards the detailed assessment is shown. After the

simple assessment, a detailed assessment can be conducted when the failure probability of the

particular failure mechanism is negligible. In this detailed assessment, the dike is tested with the

failure probabilities that can be derived from the failure probability estimation (‘faalkansbegroting’).

The detailed test is done with probabilistic or semi‐probabilistic calculations. The calculation rules and

prescriptions are explained in ‘Bijlage III Sterkte en veiligheid bij de verschillende toetssporen’

(Rijkswaterstaat Water, Verkeer en Leefomgeving, 2017c).

Figure 13 ‐ Flow chart detailed assessment and customized test (Adapted after: Rijkswaterstaat Water, Verkeer en

Leefomgeving, 2017b)

After the detailed assessment has been conducted the results are combined with the results of the

customized tests to get to one final assessment. Based on this assessment the choices will be made

on what steps have to follow. There are four different possible next steps, which are (Rijkswaterstaat

Water, Verkeer en Leefomgeving, 2017b):

1. Conducting a detailed test per dike trajectory

This is done by using a probabilistic approach. The failure probability estimation does not

have to be used anymore, but the definition of failure and the models do not change.

2. Improve the schematization

The schematization of the dike is improved in order to have a better and more refined

schematization of the dike


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3. Conducting a customized assessment

The assessment has been done by using generic and widely applicable models and failure

definitions. By adjusting them and conducting a customized assessment the result gets more

reliable. This can be done by applying location specific information and more advanced

analysis.

4. Stopping the assessment

This step can be applied if the dike manager can substantiate that more analysis will not give

a different result and if the assessment at least contains the information as is described in

chapter 4 of ‘Bijlage I Procedure’ (Rijkswaterstaat Water, Verkeer en Leefomgeving, 2017b).

The assessment can also be stopped if it is not possible to get to a final assessment and the

dike manager can substantiate that the failure mechanism does not contribute to the total

failure probability. The last reason to stop the analysis would be if with a cost‐benefit analysis

demonstrates that further analysis is not cost effective.

For piping, the simple test will be conducted according to Figure 14. If the water defence consists of a

dune or artificial water retaining structure, piping will not be a problem, so the assessment can be

stopped. If the dike is made of sand and is situated on a sand subsurface, piping can never occur, which

means that the failure probability for piping can be neglected for this case (step E.2). If constructive

elements are present in the dike or subsoil, the assessment for piping can be stopped as well (step

E.3). If the dike or water retaining structure complies with a safe time dependent approach piping can

also be neglected (step E.4). If one of the following conditions is not met, the assessment should

continue with step E.5:

‐ In the past sand entraining wells have never been observed

‐ The seepage length is more than 50 meters

‐ The river discharge does not influence the height difference for the dike

‐ In the calamity plans two succeeding flood waves are taken into account

‐ There is no connection to a construction or pipe under the dike or water retaining structure

In step E.5 the dike is checked for its dimensions. If the dimensions of the dike are safe the assessment

can be stopped. The probability that piping occurs becomes negligible if the ratio between seepage

length and height difference reaches a certain value3. This value depends upon the thickness of the

coating, the length of the dike trajectory and the norm that has to be applied. If the ratio does not

reach the certain value, the assessment should continue with the detailed test.

3 These values can be found on pages 40 and 41 of ‘Regeling veiligheid primaire waterkeringen 2017; Bijlage III Sterkte en veiligheid’


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Figure 14 ‐ Flow chart of the simple test for piping (Adapted after: Rijkswaterstaat Water, Verkeer en Leefomgeving, 2017c)

The detailed test has to be conducted according to the calculation rules and formulas as described in

‘Regeling veiligheid primaire waterkeringen 2017; Bijlage III Sterkte en Veiligheid’ (Rijkswaterstaat

Water, Verkeer en Leefomgeving, 2017c) and using the WBI‐software. For this assessment, multiple

scenarios are taken into account (Rijkswaterstaat Water, Verkeer en Leefomgeving, 2017c).

The detailed assessment per dike section for piping is divided in three parts, as is shown in Figure 15.

First the assessment is performed for uplift (‘opbarsten’). If there is a chance of uplift, the assessment

for heave is performed. If heave can be a problem the dike is assessed for receding erosion

(‘terugschrijdende erosie’)4.

Figure 15 ‐ Fault tree of the assessment on dike section level for piping (Adapted after: Rijkswaterstaat Water, Verkeer en Leefomgeving, 2017c)

After the detailed assessment, an advanced or customized assessment can be done if needed. In this

customized assessment, the failure probability estimation and the calculation rules do not have to be

used anymore (Rijkswaterstaat Water, Verkeer en Leefomgeving, 2017b). In principle, the assessor is

free to use whatever methods he/she thinks suits the situation. If a customized assessment is used,

this has to be justified in the reporting to the Minister. Dependent on the type of analysis used,

different information has to be reported 5.

4 More information on Uplift, Heave and Receding erosion can be found in ‘Regeling veiligheid primaire waterkeringen 2017; Bijlage III Sterkte en veiligheid’ 5 Which information has to be reported for which analysis can be found on page 24 of ‘Regeling veiligheid primaire waterkeringen 2017; Bijlage I Procedure’


45

B. Assembling Not every failure mechanism gets a failure probability as a result of the assessment, and therefore the

final judgement is made by categories. Those categories are shown in Table 6. Dike trajectories in

category A and A+ are complying with both norms (signaling value and lower limit), which means no

action has to be taken. If a dike trajectory is put into category B, the dike trajectory has to be

subscribed to the HWBP as the dike trajectory does comply with the lower limit, but not with the

signaling value. The priority of such a dike trajectory, however, will be lower than the dike trajectories

that are put into category C or D. If a dike trajectory belongs to category D the priority for reinforcing

that dike trajectory will be very high, as it is the farthest away from the norm (Rijkswaterstaat Water,

Verkeer en Leefomgeving, 2017c).

Table 6 ‐ Categories of the safety assessment

Cat. Designation category safety assessment Category limits

A+ Probability of flooding of the dike section is much smaller than the detection value Dike section amply complies with the detection value

Ptrajectory < 1/30*Psignaling value

A Probability of flooding of the dike section is smaller than the detection value Dike section complied with the detection value

1/30*Psignaling value < Ptrajectory < Psignaling value

B Probability of flooding of the dike section is larger than the detection value, but smaller than the lower limit Dike section complies with the lower limit, but not with the detection value

Psignaling value < Ptrajectory < Plower limit

C Probability of flooding of the dike section is larger than the detection value and the lower limit Dike section does not comply with the detection value and the lower limit

Plower limit < Ptrajectory < 30 * Plower limit

D Probability of flooding of the dike section is much larger than the detection value and the lower limit Dike section does amply not comply with the detection value and the lower limit

Ptrajectory > 30*Plower limit

For the failure mechanism of piping, the assembling has to be done by combining the failure

probability contributions. This means that the probability contributions of the different dike sections

per failure mechanism are combined into one failure probability per dike trajectory for each failure

mechanism. This is done for all the failure mechanisms for which an estimation of the failure

probability is possible. For all the other failure mechanisms, the weakest dike sections are considered

to be normative (Rijkswaterstaat Water, Verkeer en Leefomgeving, 2017c).

For piping there are 4 steps that have to be followed for the ‘assembling’. The first three steps are for

the assembling of the dike sections per failure mechanism (Rijkswaterstaat Water, Verkeer en

Leefomgeving, 2017c):

Step 1a: An estimation has to be made of the failure probabilities of the dike trajectory, based

on mutual independence, so the failure probabilities of the dike sections have to be added up.


46

Step 1b: An estimation has to be made of the failure probabilities of the dike trajectory, based

on mutual dependence, so including the length‐effect. The failure probabilities of the dike

sections have to be multiplied with the length‐effect.

Step 1c: The minimum of the estimated failure probabilities from the previous two steps has

to be chosen.

After that, the failure probability of the dike trajectory for all failure mechanisms combined is

estimated:

Step 2a: An estimation has to be made of the failure probability of the dike trajectory, based

on mutual independence of the different failure mechanisms, so the failure probabilities have

to be added up.


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C. Questions interviews Allereerst bedankt dat u beschikbaar bent voor een interview. Graag zou ik eerst kort toelichten wat

het onderzoek inhoudt en vervolgens wat dit interview inhoudt.

Mijn onderzoek heeft betrekking op de onzekerheid die komt kijken bij de beoordeling van een dijk

op piping. Het aspect van de onzekerheid waar ik me met name op focus is welke mate van

onzekerheid nog geaccepteerd kan worden. De vragen in dit interview zullen hier ook betrekking op

hebben. De eerste vragen hebben betrekking op de beoordeling in het algemeen. De vragen die

daarop volgen zullen meer betrekking hebben op de onzekerheden in de beoordeling. De laatste

vragen hebben betrekking hebben op het accepteren van de onzekerheid in het

beoordelingsresultaat.

Vindt u het goed als ik het interview opneem? Dit zal alleen gebruikt worden voor de verwerking van

de resultaten door mij.

Heeft u voor we beginnen nog vragen? Dan verneem ik deze graag, liefst voordat het interview

plaatsvindt.

Hieronder zijn de vragen voor het interview opgenomen:

1. Hoe bekend bent u met de nieuwe beoordelingsmethodiek (WBI) op een schaal van 1 tot 5?

Een score 1 staat hierin gelijk aan een junior die net begint en 5 staat gelijk aan een expert die

vanaf het begin betrokken is geweest bij de ontwikkeling.

2. Hoe bekend bent u met de onzekerheden in de nieuwe beoordelingsmethodiek (WBI) op een

schaal van 1 tot 5?

In de nieuwe beoordelingsmethodiek wordt rekening gehouden met een aantal typen onzekerheden,

waaronder inherente onzekerheid (of natuurlijke variabiliteit), kennisonzekerheid (zowel statistische

als model onzekerheid) en ‘exogene onzekerheid’.

3. Hoe gaat u om met die onzekerheden? Worden die geaccepteerd, of wordt daar expliciet of

impliciet rekening mee gehouden?

4. Hoe vindt u dat er rekening wordt gehouden met de onzekerheden in de beoordeling op een

schaal van 1 tot 5?

5. Worden naar uw mening alle relevante onzekerheden meegenomen in de beoordeling?

6. Zo nee, welke onzekerheden zouden volgens u nog meer meegenomen moeten worden?

7. Wanneer zijn de onzekerheden in het beoordelingsresultaat voor piping voor u acceptabel?

Welk betrouwbaarheidsinterval zou u willen hebben voor het beoordelingsresultaat?

Ik heb 9 cases voorbereid, waarin de onzekerheid visueel inzichtelijk is gemaakt.

8. Ik wil u vragen deze cases een score te geven van 1 tot 5, waarbij 1 helemaal niet acceptabel

en 5 zeer acceptabel representeren.

9. Wil u de zojuist gemaakte indeling nog herzien?

10. Vervolgens wil ik u vragen dezelfde 9 cases te rangschikken van 1 tot 9, waarbij 1 het meest

acceptabel en 9 het minst acceptabel is.

11. Wil u nog iets aan deze rangschikking wijzigen?

Dit waren mijn vragen aan u, heeft u nog vragen of opmerkingen?

Dan wil ik u graag bedanken voor uw tijd en de antwoorden op mijn vragen. Na de verwerking van het

interview zal ik u de samenvatting opsturen ter verificatie als u dat goed vindt.


48

D. Cases interviews


49


50


51


52


53


54


55


56


57

E. Sellmeijer In the methods of WBI2017 for piping, the improved formula of Sellmeijer is used. The new formula

has been improved by using experiments in the real world. The difference with the original formula of

Sellmeijer from 1989 is that the resistance factor of the formula is influenced by relative density,

uniformity and particle roundness in the new calculation rule. The scale factor is influenced by the role

of particle size, which is measured in the experiments. The geometrical shape factor has not changed.

Below the calculation rule of Sellmeijer is shown, with in the red scares the improvements (or

additions) that have been made to the formula (Sellmeijer et al. , 2011).

1

In which:

. . .

√

.

0.91

.. .

In this formula the symbols represent the following characteristics:

Critical hydraulic head [m] Seepage length [m] Erosion coefficient [‐] Resistance factor [‐] Scale factor [‐] Geometrical shape factor [‐] Whites constant [‐] Unit weight of particles [N/m3]

Unit weight of water [N/m3] Bedding angle of sand [Degree] Relative density [%]

Uniformity [%]

Roundness of particles [%] 70th‐percentile grain size [m]

Intrinsic permeability [m2]

Hydraulic permeability [m/s] Dynamic viscosity [Ns/m2]

According to Sellmeijer et al. (2011), this calculation rule performs well when the subsoil is composed

of fine sand. When the subsoil is composed of coarse sand, the performance of the calculation rule is

still unknown6.

6 For more information on the calculation rule of Sellmeijer, see (Sellmeijer et al., 2011)


58

The improved calculation rule may only be applied within the following limits shown in Table 7

(Sellmeijer et al., 2011).

Table 7 ‐ Limitations for the use of Sellmeijer

Parameter Minimum Maximum Mean

50% 100% 72.5%

1.3 2.6 1.81

35% 70% 49.8%

150 μm 430 μm 208 μm

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FLOOD SAFETY: WHAT IS AN ACCEPTABLE LEVEL OF … · Civil Engineering & Management/Water Engineering ... of uncertainty in the safety assessment of a dike on piping. To answer this