1

Quantitative Risk Analysis of Linear Infrastructure on Permafrost: Arquluk Project Report

H. Brooks, PE

November 2015

1 Problem

Significant areas of northern Canada, including communities and business interests, and

their infrastructure, are underlain by permafrost. Also, business interests for oil and gas

exploration, and mining activities are increasing in the north (Allard et al. 2012).

Additionally, the terrestrial and/or aerial transportation infrastructure is the only, or a

significant piece of, infrastructure supporting communities and development in these

areas. Thus, the impacts of failing infrastructure can be significant and include higher

maintenance costs, performance reductions, and loss or decrease in capacity. Other less

tangible impacts include the decrease of quality of life and the increase in health and safety

risk.

Infrastructure constructed in permafrost areas was and is designed to meet the needs

of the communities and industries, which it connects. The designs are based on the site

conditions expected and the climatic conditions along the alignment, in order to preserve

the permafrost foundation soils supporting the embankment (TCCRE 1996). However,

existing infrastructure is increasingly exposed to climate conditions, which may be warmer

than the infrastructure’s design conditions, leading to increasing maintenance and likely

increased risk and hazards, due to thawing (BCG Engineering, Inc. 2011).

Risk analyses, quantitative or qualitative, are used as a decision tool to design and

manage monies for linear infrastructure. A quantitative risk analysis is conducted by

calculating the probability of failure (P) and the consequences of that failure (C), and

multiplying P and C to determine the risk (R) for each for the analyzed failure mode or

hazard (Baecher and Christian 2003). However, this analysis must be repeated for each of

the expected hazards and all hazards must be identified.

2

Based on a review of the literature of case studies of embankment infrastructure on

permafrost, some of the common problems observed consist of the following:

subsidence, lateral spreading and heaving in areas where subgrade conditions transition from bedrock to ice-rich permafrost or where the new roadway alignment crossed previous alignments (BGC Engineering, Inc. 2011, Lingnau 1985),

settlement and transverse cracking at culvert locations (BGC Engineering, Inc. 2011),

road surface and embankment stability problems in sections adjacent to pooled water in drainage ditches or near creek crossings with bridges or large culverts (BGC Engineering, Inc. 2011),

changed or damaged culvert gradients (BGC Engineering, Inc. 2011), and collapse of “small culverts” in fill sections grater than 5 meters in height (Lingnau

1985).

However, these problems are largely localized to the embankment structure. Additional

problems can impact the roadway from the surrounding area, including:

creep settlement, due to warm permafrost temperatures (M-Lepage, Doré, and Burn 2014),

thermal erosion, due to drainage conditions (M-Lepage, Doré, and Burn 2014), karsting, due to melting of subsurface ice (M-Lepage, Doré, and Burn 2014, Allard

2013), and active layer detachment landslides, due to increased thaw depth (M-Lepage, Doré,

and Burn 2014, Boucher et al. 2012, Trimble 2013).

Additionally, a fatality occurred on the Dempster Highway in North-West Territories

(NWT) in the fall of 1985. Hayley and Bowen (1987) noted “an unfortunate combination of

warm air temperatures, soil temperatures and wheel loading, ultimately resulted in

collapse of the road surface, causing the accident and fatality.”

Based on the failure modes observed in the literature, a fault tree for embankments on

permafrost is presented in Figure 1. Since the time allotted for the project is limited, the

project will focus on the failure modes highlighted in yellow. If additional failure modes are

known or are of enough importance to be addressed in the project, please contact the

project team.

3

Figure 1: Project Fault Tree (yellow text presents the failure modes which will be analyzed

in this project)

2 Literature Review on Risk, Probability of Failure and Consequence

Calculation

2.1 Risk Assessment Methods

As discussed previously, risk is evaluated by multiplying P and C. The process is

outlined in the All Hazards Risk Assessment Methodology created by Public Safety Canada

(2011). While not specifically an engineering risk analysis, the steps presented for risk

analysis are apt and include the following: 1) identify the hazards by defining the

parameters, internal and external, to be taken into consideration; 2) describe the risks and

document them; 3) calculate the nature and level of risk by determining or assigning a

value to P and C; 4) sort the events and compare the results with risk criteria to determine

tolerable risks; and 5) identify and recommend risks to mitigate.

The United States Army Corps of Engineers (USACE) uses a risk-based quantitative

analysis to improve financial decisions associated with dam and levee infrastructure by

4

creating an unbiased decision tool in support of planning studies. In order to characterize

and identify the risk events, the method utilizes an event tree, in which each node is an

event with two or more mutually exclusive braches, each with an assigned or calculated

probability of occurrence. The probability of a specific event’s occurrence is determined by

multiplying the probability of occurrence for all of the preceding events along the path of

the event tree (USACE 1999, Baecher and Christian 2003). This USACE guide is largely used

to analyze dam and levee infrastructure, where progressive failures can be of greatest

concern and the P has been determined based on past occurrences. However, the method

recommends First Order Second Moment (FOSM) methods or Monte Carlo simulation for

probability of occurrence calculation in the absence of past data (USACE 1999, Vick 2002).

The Public Infrastructure Engineering Vulnerability Committee (PIEVC) has created,

and validated via cased studies, a standardized protocol for use by engineering

professionals to assess infrastructure vulnerability to climate change (PIEVC 2009). In this

case, vulnerability is defined as “the degree to which a system is susceptible to or unable to

cope with, adverse effects of climate change, including climate variability and extremes,”

and risk is a value that characterizes vulnerability (Engineers Canada 2011). The possible

failure modes are paired with possible impacts from climate change, and each pairing is

analyzed using standard tables presenting P and C. The protocol can be either qualitative or

quantitative, depending on the assessor’s engineering judgment of the available data, and

scheduling and budgetary constraints (PIEVC 2009).

2.2 Permafrost Infrastructure Vulnerability – Case Study

A case study to test the PIEVC methodology was completed to analyze the vulnerability

of a 100 km section of Hwy 3 near Yellowknife, NWT. The highway consists of a gravel

embankment with Bituminous Surface Treatment (BST) over discontinuous permafrost.

The highway was recently reconstructed and the highway was “designed in anticipation

that the permafrost will be sustained to the greatest extent possible… over a 20 year

timeframe” (Arenson 2013). The focus of the project was to determine, how climate change

may impact the highway (BGC Engineering, Inc. 2011). Using the protocol, the analysis

team assessed “more than 1,100 highway infrastructure element-climate change” pairings.

5

The team used the qualitative analysis procedure due to insufficient information (BGC

Engineering, Inc. 2011). The qualitative analysis analyzed the geotechnical subgrade

conditions by classifying them into 5 categories based on the typical subsurface conditions

in the study area and Hwy 3 maintenance records were not “recorded in a structured

format, and therefore, only limited and anecdotal information” was available.

Based on the analysis, the highest risks generally included thermally sensitive

(temperatures near 0°C) ice-rich subgrade soils, and drainage systems sensitive to

changing rainfall conditions. However, five climate properties were generally associated

with the highest risk events. These include “ground and water temperature, average daily

air temperature, thawing index and the number of freeze/thaw cycles” (BGC Engineering,

Inc. 2011). Additional action was suggested to gather further climate data, investigate

subgrade soil conditions, project infrastructure capacity, and analyze operation and

maintenance records (BGC Engineering, Inc. 2011).

Additionally, Arenson (2013) noted that the PIEVC protocol helped identify critical

elements and data gaps need for the analysis. It was also noted that a 100 km section of

highway is a challenging infrastructure to be assessed in a two dimensional matrix (1:

infrastructure element, 2: climate condition). Arenson (2013) recommended the analysis

“include a third dimension in the risk matrix for linear infrastructure on heterogeneous

conditions to identify critical sections.” The “third dimension” in Arenson’s discussion was

to look at the combination of (1) infrastructure element, (2) climate condition, and (3)

subsurface conditions. The subsurface conditions, physical and thermal, vary considerably

in permafrost areas. To accurately identify critical sections, one must consider all the

physical conditions of the infrastructure and specifically incorporate the subsurface

conditions, as the thermal and soil conditions are the greatest driver of failure modes in

permafrost environments.

2.3 Probability of Failure Calculation

To perform a quantitative risk assessment, one must calculate P. This can be completed

two ways: 1) from past occurrence, or 2) using reliability calculations to analyze input

parameter uncertainty compared to a failure point (Duncan 2000, Banerjee and Datta

6

1991). Since little information is available for failure frequencies in permafrost

environments, the second calculation method can be used in permafrost environments.

This second methodology requires that one look at the statistical methods available for

calculating P beginning with the individual parameter uncertainty before aggregating the

uncertainty from multiple parameters in a calculation method. This discussion is presented

in further detail in Brooks, Doré and Lemieux (2015).

2.3.1 Variable Uncertainty

Uncertainty can be categorized into two types, that due to natural variation (aleatory)

and that due to systematic error (epistemic) (Baecher and Christian 2003). Vick (2002)

presented a very good metaphor for aleatory and epistemic uncertainty: “One never rolls

aleatory dice without epistemically believing they aren’t loaded.” Generally, epistemic

uncertainty may be reduced or quantified with the collection of further data, or better

theories or models (Vick 2002, Baecher and Christian 2003). Both types of uncertainty are

present within geotechnical engineering.

If a statistical analysis is performed, the evaluation of the data, the selection of a model

to represent the data, and the difference between the model and reality combine to form

the epistemic uncertainty in the analysis. However, these uncertainties may be based on

decisions made using engineering judgment, such as the layer boundaries used to

determine properties of different strata and borehole and sampling locations, introducing

human bias that can be difficult or impossible to measure (Lacasse and Nadim 1996, Vick

2002).

2.4 Reliability Analysis

New analysis methods have been developed to determine the reliability of FOS and

engineering calculations. These analysis methods utilize 1) input parameter uncertainty to

calculate P for an engineering calculation, and 2) in-depth statistical analysis to determine

P in relation to a critical value. Reliability is defined as the probability of success or unity

minus the probability of failure (Duncan 2000). In some engineering calculations, the

reliability index (β) is used as a measure of reliability, where the reliability index is the

mean value divided by the standard deviation of the parameter of factor of safety (FOS)

7

(Baecher and Christian 2003). Thus, the higher the reliability index, the less the

uncertainty.

2.4.1 Aleatory Uncertainty Analysis

Duncan (2000) proposes utilizing a Taylor series calculation method to determine the

FOS reliability. To use this method, each input parameter within the engineering

calculation requires a most likely value (mean) and a standard deviation. The mean value

for all parameters is used to determine the most likely FOS. The FOS variation is then

calculated by evaluating the FOS at the highest and lowest likely value of each parameter

(calculated by adding and subtracting the parameter’s standard deviation from its mean).

The difference between the two calculated FOS’s for each parameter are determined. The

mean and the standard deviation of the FOS difference for each parameter are calculated.

Using these and the most likely FOS, a reliability index can be determined and from that P,

with the assumption of a common PDF for the FOS. In this paper, Duncan (2000) uses the

lognormal reliability index and the normal distribution to determine P.

Due to the large amounts of statistical data required to accurately calculate the mean

and standard deviation of a parameter, usually more data than is within the scope of an

average geotechnical investigation, Duncan (2000) presents four methods to estimate the

standard deviation; including calculating the standard deviation from a published

coefficient of variation value. However, these published statistical parameters may not be

available for permafrost soils and their specific index and strength properties.

One of the problems in utilizing this method is its focus on only the aleatory

uncertainty. Vick (2002) presented an analysis completed by others researching the model

error and uncertainty in offshore pile design; the model uncertainty “far exceeded the

parameter uncertainty.” He suggests that in cases of extreme model bias, a subjective,

engineering judgment based probability can be used to “quantify” the model’s uncertainty.

However, this type of analysis adds a subjective and potentially inherently biased aspect

into the analysis.

8

2.4.2 Reliability Compared to a Critical Value

In order to include both aleatory and epistemic uncertainty, Banerjee and Datta (1991)

analyzed the reliability of thaw induced pore pressure calculations by Morgenstern and

Nixon (1971). First-order uncertainty analysis of the calculation method was used to

mathematically determine the functions of the total, epistemic and aleatory uncertainty in

the calculation of the excess pore water pressure. Once a total uncertainty function was

determined, the partial derivatives are evaluated with respect to each random variable, and

then each equation is evaluated at the mean value for each variable.

In Morgenstern and Nixon’s (1971) analysis method, the parameter R* denotes the

relationship between the rates of pore water generation and expulsion. Thus, if R* is

greater than unity, there is a potential instability due to excess pore water pressure

generation. The P calculated in their analysis will be the likelihood R* ≥ 1 given the

uncertainty in the calculation method. Banerjee and Datta (1991) validated their

mathematical analysis using a Monte Carlo simulation with 5,000 points for each

parameter. The coefficients of correlation between dependent parameters were assumed

and did not largely impact the final reliability calculation.

In order to utilize this analysis method, the following statistical parameters are

required: mean values for input parameters, coefficients of variation for each input

parameter, coefficients of correlation between parameters, uncertainty measures in the

theoretical relationship between parameters (from the first order uncertainty analysis),

and the nature of the relationship between parameters (Banerjee and Datta 1991).

2.5 Consequence Calculation

In order to allow for a completely quantitative analysis of risk, not only must P be

calculated, C must be determined as well. In some cases, this analysis can be relatively

simple, such as for maintenance activities due to settlement. The consequence in this case

is the calculated cost of materials and time for equipment and labor to re-grade and re-

compact the roadway as necessary. Calculating C gets more complicated when one

attempts to quantify the consequence of human fatalities and injuries, and indirect

economic impacts.

9

If a sudden collapse of the roadway surface occurs while a vehicle is travelling along the

highway, as has happened previously, causing a fatality (Hayley and Bowen 1987), how

does one quantify the loss? “Statistical methods (for risk analysis) may require a cost

function, which in turn may require the calculation of the cost of human life. This is a

difficult problem (Stamatis 2014).” Some suggested sources of information for quantifying

the potential costs due to fatality or injury include: insurance companies, due to their

actuarial information, and case law from similar situations. However, acquiring this

information may be very difficult, if not impossible, due to non-disclosure agreements,

privacy concerns and the potential for loss of competitive advantages between companies,

if the information was released to the public record. In the United States, cost/benefit

analyses are used to determine if Department of Transportation Highway Safety

Improvement Program expenditures are justified. This data may be available but may not

be applicable in Canada.

In the majority of published risk analysis studies reviewed by this author, the

consequence analysis consists of either monetary analysis or a casualty analysis for the

hazard (Lacasse and Nadim 1996, and Public Safety Canada 2011). The combination of

these two consequence analysis methods has not been observed within the engineering

literature. However, further data and studies may be present outside of the engineering

field.

Additional indirect economic impacts might also need to be considered. For example, if

a section of the Dempster Hwy from Dawson City, YT to Inuvik, NWT is closed, goods

normally transported via the highway must be flown. The cost difference between these

two transportation modes can be determined and possibly included in the risk analysis.

However, the amount of detail included in the calculation of the indirect cost depends on

the breadth and resources available for an analysis and should be at the discretion of the

agency or person conducting the risk analysis.

10

3 Project Methodology and Goals

In order for communities and industry to make informed unbiased assessments of their

infrastructure vulnerabilities, an objective risk analysis method is needed. The creation of a

tool to conduct risk analyses for embankment-supported linear infrastructure on

permafrost is the purpose of this project. In order to create a tool, the risk analysis method

must be derived from measureable data such as site conditions, physical and/or empirical

engineering calculations and consequences. Additionally, to address the analysis problem

discussed by Arenson (2013), the tool will focus on analyzing a single landform allowing

subsurface conditions to be analyzed individually and the risk calculated from the site’s

input parameters. Additional analyses can be conducted for the different landforms along

the alignment to create a risk profile.

Using the information above, a calculation process will be created for each failure mode

highlighted in Figure 1 (discussed in further detail in Table 1), a reliability analysis (using

First Order Second Moment, FORM, analyses or Monte Carlo Simulation) or other

quantitative metric will be used to determine P, and guidelines will be created to calculate

C from the existing literature review and from additional future multidisciplinary review.

Research still needs to be completed to determine the engineering calculation processes

for arching effects (discussed in further detail in section 0), lateral embankment spreading,

and sinkhole creation. The last two failure mode equations and failure conditions are still

under development by the project team. The engineering calculation of the arching process

requires further study and is discussed in Section 3.1, below.

Once the calculation methods are determined, a tool, likely in an Excel spreadsheet, will

be created to complete the risk analysis. The author has begun work in this regard focusing

on the calculation of thaw depth, an essential piece of many of the failure mode analyses.

The tool will be validated based on information from field data. The field sites will be

existing research sites, where data is readily available and may include sites along the

Dempster Highway, Salluit Airpot Access Road or Iqaluit Airport.

11

Table 1: Engineering Calculation Method and Consequences for Each Hazard

Hazard Detailed Hazard Heat Transfer Method

Engineering Calculations Consequences

Culvert Failure

Structural Collapse

N/A Bending failure or hoop stress failure causing collapse.

Increased maintenance, roadway repair and/or culvert replacement.

Gradient Failure Conductive or Convective

Changing gradients to the point the culvert no longer functions due to thaw settlements.

Increased maintenance and roadway repair.

Roadway Settlements

Unfrozen Soil Consolidation

N/A Consolidation properties of unfrozen soils.

Increased maintenance.

Thaw Settlement Conductive or Convective

Determined from depth of thaw, and empirical equations and/or thaw consolidation theory.

Increased maintenance.

Creep Settlement N/A Determined from mechanical properties of frozen soils.

Increased maintenance.

Ice Wedge Melting

Arching Occurs Convective See section 0. User safety risks (vehicle accidents, fatalities and injuries).

Localized Settlement

Convective Determined from depth of thaw, and empirical equations and/or thaw consolidation theory.

User comfort and user safety.

Lateral Embankment Spreading Conductive Due to settlement at the embankment toe, no calculation method yet determined.

Increased maintenance.

Sinkhole Creation Conductive or Convective

No calculation method yet determined.

User safety risks (vehicle accidents, fatalities and injuries).

Active Layer Detachment Convective Failure induced by deeper thaw than usual.

Increased maintenance, roadway failure from further sliding.

12

The tool created in this project must: 1) be applicable to practicing engineers, 2)

require the user to define the failure criteria for each failure mode, if necessary

(settlement restrictions for a roadway are very different from runways), and 3)

require the user to determine the extent of the consequence analysis. The form and

function of this tool will be based on the author’s discussion with Professor Doré

and input from the project committee.

The project’s final deliverables will include the tool, its background calculations

presented in a report with the validation from the field sites and a user’s guide.

3.1 Arching Effect Analysis

A literature review conducted on arching effects found striking inconsistencies

and a lack of information. Intuitively, the span of an arch or bridge in soils is largely

impacted by the size of the particles, their angularity, friction angle and effective

stress conditions. However, the majority of studies consist of very simple model

soils without cohesive effects (glass beads, aluminum rods or ball bearings) and

arching is observed over a trapdoor at the base of the testing apparatus. In these

studies, the ratio between the width of the trapdoor (B) and the average particle

size (D50) ranges from 4 to 8.5 depending on the analysis method and model

material (Ahamadi and Hosseininia 2013, Chevalier and Otani 2011, Guo and Zhou

2013, Ladanyi and Hoyaux 1969). Only one study was conducted to model soil

movement into thawing ice wedges. It was conducted at a model scale (1/30th) in a

centrifuge at 30g to model full-scale testing. Arching effects were observed and

ratios up to 45 were observed (Harris and Murton 2005, Harris, Murton and Davies

2005). This larger ratio may be due to the generation of pore water pressures lower

than hydrostatic observed within the system during thawing. The author feels that

the volume reduction during the thawing the model ice wedge created these lower

pore pressures.

While the problem of arching over ice wedges has not been thoroughly analyzed,

Kinney and Connor (1987) presented a solution to the hazard of collapse by

analyzing the bridging effects of geosythetics over voids. The presence of

13

geosythetics within the embankment structure can impact the risk by reducing

surface settlement and preventing collapse.

Given a previous fatality due to roadway collapse from arching effects, the

project team feels it is very important that the possibility of embankment collapse

be investigated further. The current ratios for arching potential range from 4 to 45,

based on previous works briefly discussed above. The project team would like to

further investigate this problem by conducting scale laboratory tests under plane

strain conditions. A model will be constructed consisting of a box with plexiglass

sides, where model ice wedges of various widths will be embedded in solid material

and covered with an embankment soil of various grain sizes, as shown in Figure 2. A

solid material will surround the ice wedge to focus the analysis on the arching ratio,

decrease the number of variables within the analysis, and increase the quantity of

data points.

Figure 2: Proposed laboratory model for arching ratio testing.

The model ice wedges will be covered in a model soil and allowed to thaw.

During thawing, insulation will be placed on the sides and base of the box to ensure

surficial conductive heat transfer. Measurements of the arch widths, depths and

surface movements will be taken after thawing with photographic documentation.

Additional measurements may be taken during or after thawing, as needed for a

thorough analysis. The results will be used to determine the arching ratio at which

arching is likely to occur to use as a basis for the risk analysis of this failure mode.

14

The surficial settlement characteristics will be used to determine the amplitude and

wavelengths of settlement areas to help determine the consequences of failure.

4 Project Schedule

A detailed schedule and list of tasks is presented in Figure 3 with completed

activities shaded in a darker color.

15

Figure 3: Project schedule with completed activities shaded.

16

5 References

Ahmadi, A., and E.S. Hosseininia (2013). “An Experimental Investigating the Width

and Height of a Stable Arch Formed in Granular Materials by Using a New

Developed Trapdoor Apparatus.” International Civil, Structural and

Environmental Engineering Conference, Zurich, Switzerland, October 12-13, pg.

20-24.

Allard, M., Lemay, M., Barrette, C., L'Hérault, E., Sarrazin, D., Bell, T., et al. (2012).

Permafrost and climate change in Nunavik and Nunasiavut: Importance for

municipal and transportation infrastructures. In From Science to Policy. An

Integrated Impact Study (IRIS) of Climate Change and Modernization (Vol.

Chapter 6, pp. 171-197).

Allard, M. (2013). The impacts of Climate Change on Permafrost: Merging Science

and Community Concerns. Presentation at the Pan-Territorial Permafrost

Conference, Yellowknife, NWT, Canada; November.

Arenson, L. (2013). NWT Highway 3 – Climate Change Vulnerability Assessment.

Presentation at the Pan-Territorial Permafrost Workshop, Yellowknife, NWT,

November 7, 2013.

Baecher, G. B., & Christian, J. T. (2003). Reliability and Statistics in Geotechnical

Engineering. Chichester, West Sussex, England: John Wiley & Sons, Ltd.

Banerjee, S., & Ditta, B. (1991). Reliability Analysis of Thaw-Induced Pore Pressures.

Journal of Cold Regions Engineering , 5 (3), 125-141.

BGC Engineering Inc. (2011). Climate Change Vulnerability Assessment for NWT

Highway 3. Final, Government of the Northwest Territories, Department of

Transporation.

Boucher, M., Grondin, G., & Paquet-Bouchard, B. (2012). Landslide in the Permafrost

near a Minitère des Transports du Québec Infrastructure in Salluit and

Stabilization Work. Cold Regions Engineering 2012: Sustainable Infrastructure

Development in a Changing Cold Environment (pp. 779-788). ACSE.

Brooks, H., G. Dore and C. Lemieux (2015). “Quantitative Risk Analysis of Linear

Infrastructure on Permafrost: State-of-the-Practice Literature Review.” Report to

Transport Canada, April 2015.

Chevalier, B. and J. Otani (2011). “Arching Observation in 3D Trapdoor Problem

with X-ray CT and Discrete Element Method.” Soils and Foundations, Japanese

Geotechnical Society, Vol. 51 No. 3, pg. 459-469

17

Duncan, J. M. (2000). Factors of Safety and Reliability in Geotechnical Engineering.

Journal of Geotechnical and Geoenvironmental Engineering , 126 (4), 307-316.

Engineers Canada (2011). Public Infrastructure Engineering Vulnerability Committee

(PIEVC) Engineering Protocol – Part 1, Version 10. Accessed online at

http://www.pievc.com/e/Part_I_-_PIEVC_Engineering_Protocol_-_Revision_10_-

_BETA_-_October_2011.pdf.

Guo, P. and S. Zhou (2013). “Arch in Granular Soils as a Free Surface Problem.”

International Journal for Numerical and Analytical Methods in Geomechanics.

Vol. 37, pg. 1048-1065.

Harris, C. and J.B. Murton (2005). “Experimental Simulation of Ice-Wedge Casting:

processes, products and Palaeoenvironmental Significance.” Cryospheric

Systems: Glaciers and Permafrost, Geologic Society of London, Special

Publication No. 242, pg. 131-143.

Harris, C., J.B. Murton and M.C.R. Davies (2005). “An Analysis of Mechanisms of Ice-

wedge Casting Based on Geotechnical Centrifuge Simulations.” Geomorphology,

Vol. 71, pg. 328-343.

Hayley, D.W. and J.A. Bowen (1987) “Ground Ice in Continuous Permafrost: a Hazard

to Safe Highway Operation.” Report associated with a presentation to Western

Association of Canadian Highway Officials, June 2, 1987, Yellowknife, NWT.

Kinney, T.C. and B. Connor (1987). “Geosynthetics Supporting Embankments Over

Voids.” Journal of Cold Regions Engineering. 1(4) pg. 158-170.

Lacasse, S., & Nadim, F. (1996). Uncertainties in Characterizing Soil Properties.

Uncertainty in the Geologic Environment: From Theory to Practice - Proceedings of

Uncertainty, (pp. 49-75). Madison, WI.

Ladanyi, B. and B. Hoyaux (1969). “A Study of the Trapdoor Problem in a Granular

Mass.” Canadian Geotechnical Journal, Vol. 6 No. 1, pg. 1-14.

Lingnau, B. E. (1985). Observation of the Design and Performance of the Dempster

Highway. Master's Thesis, University of Alberta, Department of Civil Engineering.

M-Lepage , J., Doré, G., & Burn, C. (2014). Advanced Seminar on Permafrost on

Engineering Applied to Transportation Infrastructure: Field Guide - Alaska

Highway. Yukon College .

Morgenstern, N. R., & Nixon, J. F. (1971). One-dimensional Consolidation of Thawing

Soils. Canadian Geotechnical Journal (8), 558-565.

PIEVC. (2009). PIEVC Engineering Protocol for Climate Change Infrastructure

Vulnerability Assessment. Public Infrastructure Engineering Vulnerability

Committee.

18

Public Safety Canada. (2011). All Hazards Risk Assessment - Methodology Guidlines.

Public Safety Canada.

Stamatis, D. H. (2014). Introduction to Risk and Failure. Boca Raton, Florida, USA: CRC

Press by Taylor and Fracis Group, LLC.

TCCRE. (1996). Roads and Airfields in Cold Regions. (T. S. Vinson, Ed.) New York, New

York, USA: ASCE.

Trimble, R. (2013, November). Recent Effects of CLimate Change on Permafrost and

Road Stability Dempster Hwy, NWT/YT. Yellowknife, NWT, Canada.

USACOE. (1999). Risk-Based Analysis in Geotechnical Engineering for Support of

Planning Studies. ETL 1110-2-547, United States Military, Department of the

Army.

Vita, C. L. (1984). Route Geotechnical Characterization and Analysis. Journal of

Geotechnical Engineering , 110 (12), 1715-1734.