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Structural condition identification for civil infrastructure: an appraisal
based on existing literature reviews
Ying Wang and Marios Chryssanthopoulos
Dept. of Civil & Environmental Engineering, University of Surrey
Guildford, Surrey, GU2 7XH, United Kingdom
+44-1483684090
Abstract
Infrastructure performance is of great importance for a nation’s economy and its
people’s quality of life. For efficient and effective infrastructure asset management,
structural health monitoring (SHM) has been researched extensively in the past 20-30
years. With an increasing number of SHM systems being installed, the interpretation of
the large volume of monitoring data, i.e. often manifested as condition identification,
becomes essential in asset integrity management. This paper provides an appraisal of
existing literature reviews on SHM, considering both reviews on different types of
structures and those focused on different approaches for data interpretation. It explores
the evolution of research interests in this field and identifies the need for an integrated
physics-based and data-driven structural condition identification approach.
1. Introduction
Civil infrastructure systems are proving more vulnerable than many might have
expected, particularly in the light of technological advances. Recently, two collapsed
bridges, in the United States (1) and Myanmar (2), respectively, have attracted engineers’
and researchers’ attention, not only because both led to casualties but also owing to the
failure occurring at an early stage of the design life. Especially, the former, a pedestrian
bridge in Miami, collapsed just five days after a large segment had been constructed and
placed on fixed supports. Although it was reported to the authority that cracks were
observed (3), the time-varying condition of the structure appears not to have been
assessed properly. Although the exact failure mechanism is still under investigation, it
can be argued that if a structural condition identification approach had been
implemented, the catastrophic consequences of the collapse could have been mitigated.
Indeed, for efficient and effective infrastructure asset management, Structural Health
Monitoring (SHM) has been researched extensively worldwide in the past 20-30 years
(4-6), using different sensors to monitor the performance and behaviour of structures. The
research advancement in this field has been seen tremendous. Not only has a large
amount of papers been published, but also many civil structures have been equipped
with SHM systems (4), including bridges, buildings, wind turbines, offshore structures,
nuclear plants, dams, tunnels, etc. Once sensors are installed, real-time monitoring data,
affected by operational, structural and environmental conditions, can be collected. They
can provide more detailed information regarding the actual condition of a structural
system compared to traditional inspections. Therefore, the interpretation of the large
2
volume of monitoring data becomes increasingly more important in asset integrity
management. This data interpretation process has been extensively referred to in
literature as ‘damage detection/identification’. Indeed, although performance can be
closely related to structural condition, the change in structural condition may not be
necessarily due to damage. For example, in the case of the Miami bridge, an important
change in structural condition took place when the constructed segment was transferred
from temporary supports to its final position supported by piers. Therefore, “structural
condition identification” is the term used in this paper to encompass a data
interpretation process that might lead to an undesirable event being diagnosed or
predicted even before evidence of damage becomes apparent.
Due to the large amount of existing publications in this field, it has become increasingly
difficult to perform a comprehensive literature review based on primary sources, such as
those performed in Refs (5, 6). Moreover, the literature review type papers/books
published after 2000 are becoming specialised and can be categorised, in the main, into
the following groups: 1) review of a particular kind of structure or structural member; 2)
review of a type of structural damage; 3) review of the application of a particular type of
sensor; 4) review of a family of damage/condition identification approaches.
This paper provides an appraisal of (more than thirty) literature reviews, first with a
focus on reviews of different types of structures. Then, a review and discussion on
different structural condition identification approaches will be presented.
2. Reviews of SHM technologies for different structural types
2.1 Bridges
For civil infrastructure, research attention focused initially on bridges. In 2005, Ko and
Ni (7) summarised the technology development of 20 bridges in China which were
instrumented with long-term monitoring systems. Their sensing and data acquisition
systems, environmental effects on the measured data, and the linkage with bridge
maintenance and management were discussed in detail. Stemming from the collapse of
the I-35W Bridge in 2007, a report on bridge health monitoring and inspections was
published in 2009 (8). The focus of this report was to explore existing sensing systems
and their practical applications. A total of 25 systems were discussed, showing their
general characteristics, advantages and disadvantages. Further, 38 monitoring
companies provided their feedback on the application of these systems. These reviews
focus primarily on the availability and potential for implementation of different sensing
systems, as well as the data acquisition challenges in an SHM system.
After 2010, more specialised reviews, targeting different types of damage or using
various sensing technologies, can be seen. For example, Nair and Cai (9) gave a review
of the acoustic emission (AE) method and its application to bridge health monitoring.
They suggested that a statistical quantitative analysis technique, intensity analysis, is
promising in interpreting AE monitoring data. Yi et al. (10) reviewed the research and
development activities in the field of bridge health monitoring using Global Positioning
System (GPS). They suggested the integration of GPS receivers with other sensors to
improve the accuracy and reliability of the SHM system and to develop data processing
3
software packages for filtering, smoothing and synchronizing. Indirect bridge
monitoring methods using instrumented vehicles were reviewed by Malekjafarian et al. (11). The theoretical background related to live load monitoring and the link between
monitoring and condition identification methods were discussed. The advantages and
disadvantages of both modal-based and non-modal-based methods were summarised.
The monitoring of a special type of damage, i.e. bridge scour, was reviewed by
Prendergast and Gavin (12). Different equipment types were discussed, with focus placed
on how to use changes in structural dynamic properties for scour monitoring.
Overall, bridges appear to be the most mature structural type for the installation of SHM
systems. They have the potential not only to provide data streams for algorithm
development and validation but to serve as “live labs” in which important interactions
between operation and environment can be studied under real conditions. Significant
research efforts have already been placed on how to interpret data from specific sensing
systems in order to understand structural condition or damage location and severity.
2.2 Wind turbines
More recently, increasing research attention has been focused on the monitoring of wind
turbines. Ten damage detection techniques, including acoustic emission (AE), thermal
imaging, ultrasonic methods, and vibration-based methods, were discussed in detail by
Ciang et al. (13). Their conclusions suggested the use of fibre optic sensors to monitor
global load conditions and the use of AE methods for early warning of the onset of
damage or for registering an impact event. Hameed et al. (14) further divided the
monitoring systems for wind turbines into condition monitoring and fault detection.
Different techniques and algorithms were highlighted, though not systematically
grouped. This broad categorisation has been followed by others (15-17). They showed that
research and development is still at the level of selecting the most appropriate sensing
systems. With regard to condition identification methods, in spite of their large number,
their functions are still basic and further development is needed. This finding was
echoed by Wymore et al. (18), who identified that most SHM research in this field
focuses on instrumentation issues, even though the current barrier for more significant
industry uptake is data interpretation. It is suggested that statistical pattern recognition is
a promising structural condition identification scheme, a tool originally proposed by
Sohn et al. (19) and comprehensively covered by Farrar and Worden (20). It has also been
used to structure a review on the monitoring of offshore wind turbines (21). Five
supervised learning methods and three unsupervised learning methods were reviewed,
with the former preferred due to their damage classification and quantification
capabilities.
Reviews in this field have become more specialised since 2015. For example, the
monitoring of wind turbine blades was reviewed by Li et al. (22). Four promising damage
detection methods were discussed, namely AE, wave propagation, impedance, and
vibration-based methods. The conclusion was that damage detection for turbine blades
is far from mature and there are still many challenging issues to be resolved. The wind
turbine bearing condition monitoring was reviewed recently (23). Available techniques
were listed, however there was no conclusion on how to select between alternatives. A
wind farm-level health management was also reviewed (24), with the suggestion that data
4
from multiple sensing systems need to be integrated, for which data-based methods can
be promising for diagnosis and prognosis. Among various data sources, the use of data
from the turbine supervisory control and data acquisition (SCADA) is appealing (25),
since it has the potential to be used in different applications together with data-driven
algorithms for anomaly detection, damage modelling, etc.
The number of literature reviews on wind turbines is growing rapidly. However, as
many have indicated, neither the monitoring nor condition identification technologies
are mature. Further investigation on how to interpret data from different sources and
how to define/achieve structural condition identification is the current research hotspot.
2.3 Other structures
There are only few reviews of SHM research on other types of structures. In 2000, a
review of vibration-based health monitoring for composite material/structures was
published (26). It identified the limitations of modal-based methods, i.e. the difficulty in
identifying minor damage and in differentiating the type of damage. A later review
suggested the use of nonlinear features and/or statistical parameters for this kind of
structures (27).
In the railway industry, the wayside SHM technologies were reviewed according to
different sensing systems and/or targeted structural components (28), though the
interpretation of data was not discussed. More recently, the emerging economies related
to renewable energy have received attention, as demonstrated in reviews of wind
turbines. For example, the inspection and SHM techniques for concentrated solar power
plants were reviewed by Papaelias et al. (29). It focused on alternative non-destructive
evaluation (NDE) techniques for the inspection of solar receivers and insulated pipes,
though data interpretation methods were, once again, not discussed. Similarly, a review
on field monitoring of offshore structures focused on the sensing technologies,
including the design of a monitoring system, but did not address condition identification
problems (30). A survey on the condition-based maintenance in the nuclear power
industry systematically looked at monitoring, diagnostics and prognostics issues in this
highly specialised industry (31). A hybrid method, i.e. physics-based and data-driven,
was recommended as a future direction.
Based on above appraisal of reviews, it can be concluded that: 1) SHM research efforts
in civil infrastructure have been shifting from bridges to wind turbines, and other
renewable energy structures/devices; 2) for emerging structural types, research focuses
first on sensing technologies before tackling the next step, namely data interpretation, in
turn leading towards structural condition identification. Therefore, future research
efforts should be concentrated in this field, especially on various possibilities for
structural condition identification methods that can interpret monitoring data from
typical sensing systems implemented in specific structural types.
3. Reviews of structural condition identification approaches
Due to the large number and variation of structural condition/damage identification
approaches, comprehensive insights can be found in relevant textbooks (e.g. Refs (20,
5
32)), whereas literature reviews are relatively limited, compared to other types of review
papers. Generally, these approaches can be broadly classified into two categories:
physics-based (or model-based in many references) and data-driven (or data-based).
Although some overlapping does exist, this distinction is helpful in enabling a succinct
discussion of alternative techniques/methods and in allowing a perspective to be
presented on an integrated approach.
3.1 Physics-based methods
3.1.1 Physics-based damage indicators
Due to their sound theoretical background, computational efficiency, and easy
implementation, physics-based damage indicators received significant attention as soon
as SHM was proposed in the early 1990s. For vibration-based SHM systems, the
majority of those indices/indicators/features are derived from modal parameters
obtained from modal testing results. Theoretically, natural frequencies of a structure are
positively related to its stiffness. Thus, changes of natural frequencies can indicate an
alteration in structural stiffness from a prior value, which in turn can be linked to a
structural health condition. When damage severity increases, natural frequencies usually
decrease, while the degree of the reduction is dependent on the position of the damage
relative to the mode shape for each particular vibration mode. Therefore, information on
natural frequencies of different modes can be used to derive/quantify damage existence,
location and severity (33).
Similarly, other damage indices based on mode shapes have also been used to indicate a
change in structural stiffness. Mode shape curvature (MSC), modal strain energy, and
other indicators derived from basic modal parameters have been found to be more
sensitive to damage than some other indicators. However, they are also sensitive to the
environmental noise (usually high frequency), which needs careful consideration (20). To
exploit information from both natural frequencies and mode shapes, flexibility-based
methods have been developed. In this group of methods, natural frequency information
is intrinsically added in the form of weighting factors to mode shapes. By doing this, the
information on lower modes plays a more important role than that on higher modes,
based on the observation that physical test results for the former are usually more
reliable than for the latter. A comparative study (34), together with a review of different
methods, showed that MSC is more robust under different scenarios, e.g. when MSC is
derived from displacement mode shapes. Li (35) reviewed different types of strain-based
damage indicators and indicated that the strain frequency response function index is
good at data detection and localisation, while the strain energy-based index performs
well with noisy signals.
Although many of the above methods were satisfactory for numerical cases and thus
popular in early studies, some practical issues limit their application to damage
identification of real structures. In particular, less controlable factors, such as
temperature, excitation methods and ambient noise, may significantly affect the test
results of modal parameters. Therefore, the damage indicator methods suffer from the
difficulty of discerning the changes caused by other factors from those caused by
6
structural damage. Further, the computational accuracy of damage indicators becomes
increasingly unreliable for complex structures.
3.1.2 Model updating
Due to the limitations of the above methods, additional research efforts were placed on
model updating methods (or inverse methods) (36) from the late 1990s onwards. This
type of approach requires the integration of numerical models and experimental results.
Simply speaking, model updating is to find the best match of numerically derived
parameters to those obtained from the monitoring data, through iteratively changing
physical parameters in the numerical model using an optimisation algorithm.
First, a numerical model needs to be constructed for a real structure. Secondly, based on
numerical simulations, damage-sensitive features can be defined and quantified, usually
based on modal parameters. Thirdly, monitoring or vibration tests of the real structure
needs to be performed. Fourthly, damage-sensitive features can be derived based on the
monitoring/test results. The results from numerical simulation are almost always
different from those from tests in terms of the attributes of the damage-sensitive
features. Hence, the discrepancies are minimised through iteratively changing particular
parameters of the numerical model using optimisation algorithms. Selection of
appropriate parameters is non-trivial in many real applications. Finally, when the
discrepancies are small enough, the numerical model is regarded to be able to simulate
the behaviours of the real structure, i.e. a digital twin.
(a) (b)
Figure 1. Scheme of model updating. a) Model updating process; b) Condition
identification using two-step model updating
When this process has been completed, the parameters in the numerical model can
reliably represent those of the real structure. From then on, if there is any damage to the
structure, the monitoring/test results will change and, in turn, so will the features. By
repeating the model updating process, the corresponding parameters in the numerical
model can be re-updated and the new results will be correlated with damage existence,
location, and severity. This process is illustrated in Figure 1, where (a) and (b) represent
the model updating process and condition indeitification process, respectively. In Figure
Numerical model
Optimisation
Structures
Vibration test/monitoring
Updated numerical model
Features Features
Difference <
threshold?
Yes
No
Numerical model
Calibration
Existing test data
New test data
Condition identification
Updating
7
1(b), the box with dashed line includes the process to calibrate the numerical model (the
first step), while the box with solid line shows the process of condition identification
(the second step). Both steps need the model updating process as shown in Figure 1(a).
These methods are theoretically sound and have the potential to analyze complex
structures. However, the numerical models, typically finite element models, normally
have many variables. When the number of variables is large, it is very hard to find the
optimum set of updating variables. Furthermore, this kind of inverse problems is usually
underdetermined, which leads to a number of possible solutions. To differentiate the
solutions may need experts’ judgment or multiple broadly consistent events which
might allow elimination of some possible solutions. Last but not least, as the structures
become more complex, the computational costs become higher, and often unacceptable
for real-time monitoring.
3.2 Data-driven methods
3.2.1 Data-driven damage indicators (and unsupervised learning)
Damage identification can be achieved through non-modal-based data-driven damage
indicators. Since 2000, this family of methods/techniques has been receiving increasing
attention. First, wavelet transforms were researched extensively. Compared with
methods based on the Fourier transform, any particular local feature of the time domain
signal can be analysed based on the scale and translation characteristics of wavelets. For
example, Peng and Chu (37) reviewed the application of wavelet analysis to time-
frequency analysis of signals, fault feature extraction, singularity detection, etc. A
recently published book (32) compared the performances of different transforms
inlcuding Fourier, Wavelet, and Hilbert-Huang transform.
Depending on whether there is a training process, machine learning methods can be
classified into either supervised or unsupervised learning. Unsupervised learning
methods are usually based on statistical parameters of the monitoring/test data, without
training. Therefore, this method is recommended for novelty detection only (20). For
example, Sohn et al (19) presented two statistical pattern recognition techniques based on
time-series analysis successfully applied to fibre-optic strain gauge data by
distinguishing data sets from different structural conditions.
Since data-driven damage indicators exploit information from time-domain data directly
and flexibly, they are usually more sensitive to damage and may be able to discern
damage from features related to other factors. However, these methods may not be able
to deal with complex structures, thus exhibiting a similar disadvantage as the physics-
based damage indicators.
3.2.2 Supervised learning
With the development of machine learning algorithms, researchers have also started to
exploit supervised learning methods for structural damage identification. The
fundamental research hypothesis is that 1) the monitoring data embody various patterns
under different structural conditions; and 2) given a particular monitoring data set, the
8
structural condition can be identified through pattern recognition. Specifically, the
problem is to find the pattern 𝑦𝑝 (output) for a new monitoring data set 𝐱𝑝 (input), given
a system with different conditions. In this framework, the system is a machine learning
model based on 𝑁 training data sets {(𝐱1, 𝑦1), … , (𝐱𝑁 , 𝑦𝑀)} with the monitoring time-
domain data 𝐱𝑖 and the corresponding pattern label 𝑦𝑖 ∈ {1, … , 𝑀}. Here 𝑀 is the total
number of existing patterns and 𝑀 ≤ 𝑁, since more than one dataset may be associated
with one pattern.
(a) (b)
Figure 2. Scheme of supervised learning. (a) training based on calibrated
numerical simulation; (b) training based on physical test data
These methods can achieve damage identification for complex problems such as a pipe-
soil system (38, 39). The most important challenge is the training, i.e. how to reliably
acquire data representing a nearly unlimited number of conditions for a structure. There
are two possible training routes, numerical simulation or physical tests (20). Figure 2
presents the condition identification process for both routes based on supervised
learning. Although training based on test data is more direct and can, in principle, better
represent actual conditions, tests are usually expensive, time consuming and can be
affected by noise and lack of repeatability. For civil infrastructure, it is even more
difficult because: 1) to encounter real environmental conditions is rare (for example,
hurricanes and earthquakes are not predictable); 2) usually a scaled model is involved
and unpredictable size effect may affect the results; 3) some damage processes evolve
over very long periods (e.g. corrosion). Thus, training based on numerical models and
simulation appears more promising for civil structures, aided in the first phase of
training by the creation of physical test results obtained in relatively controlled
laboratory environments involving scaled prototypes.
3.3 Perspective
Despite their popularity, physics-based methods face two main challenges: first, it is
often difficult to find a feature that is sensitive to structural conditions while insensitive
to environmental noise. Second, model updating methods suffer from relatively low
computational efficiency due to their reliance on complex simulation models. To
address these challenges, efforts have switched to data-driven approaches in recent
years, where condition identification can be achieved through machine learning and
Training
Machine learning algorithm
Numerical model
Calibration
Simulation
Data “pool”
Existing test data
Condition identification
New test data
Training
Machine learning algorithm
Existing test data
Condition identification
New test data
9
artificial intelligence algorithms. Using such methods, the features can be generated
automatically and may achieve better structural condition identification results than
traditional methods, while computational costs can be significantly reduced. However,
existing data-driven methods face a main challenge, i.e. they often lack the complexity
embedded in numerous and diverse scenarios in real structures, considering different
possible conditions, environmental factors, and loading histories. Therefore, an
integrated approach using both physics-based and data-driven methods should, in
principle, be explored for structural condition identification to exploit the merits of the
two currently pursued approaches, while compensating their shortcomings. One
integration aspect is to use the results from physics-based methods as the training set for
the data-driven algorithm, as demonstrated in recent studies (38, 39). More innovative and
penetrating integration methods/techniques are needed to enhance the cost-effectiveness
of the training process. The fast pace of development in machine learning and artificial
intelligence areas in other sectors should pave the way for this to happen, though civil
infrastructure presents specific challenges, as indicated in the previous sections, for
which bespoke solutions will be needed.
It should be noted that many above methods have only been verified by numerical
examples and/or experimental studies in the laboratory. For the on-site monitoring of
structures, practical issues exist and there is still work in order to establish the
framework that will deliver consensus on how to select the most suitable structural
condition identification approach in the light of cost and reliability constraints.
4. Conclusions
In this paper, a succinct appraisal of literature review type papers/books in the field of
SHM of civil infrastructure is presented. First, focus is placed on the reviews of
different types of structures. Based on this, a transition of research interests from
bridges to renewable energy infrastructure can be clearly identified. More importantly,
the evolutionary nature of research objectives can be traced to reveal a similar sequence:
initially sensing technologies are trialled/evaluated, then data acquisition and basic
processing from different types of sensors is addressed, and lastly condition
identification for different damage types or different structural
component/assembly/system is undertaken. The paper then goes on to review different
structural condition identification methods, grouped as physics-based and data-driven.
Since both have their own advantages and disadvantages, innovative integration
methods using advanced data analytics algorithms are identified as one of the most
promising future research directions in this field.
Acknowledgements
The review work described in this paper was undertaken in the context of the EPSRC
First Grant: EP/R021090/1, “An integrated physics-based and data-driven approach to
structural condition identification”.
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