PRIORITISATION METHODOLOGY FOR APPLICATION OF BRIDGE MONITORING
SYSTEMS FOR QUICK POST-EARTHQUAKE ASSESSMENT
Piotr Omenzetter (corresponding author)
The LRF Centre for Safety and Reliability Engineering
The University of Aberdeen, Aberdeen AB24 3UE, UK
piotr.omenzetter@abdn.ac.uk
Poonam Mangabhai
Watercare, 2 Nuffield St, Auckland 1023, New Zealand
Ravikash Singh
Beca, 6 Garden Place, Hamilton 3204, New Zealand
Rolando Orense
Department of Civil and Environmental Engineering, The University of Auckland
Private Bag 92019, Auckland, New Zealand
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ABSTRACT
To facilitate quick post-earthquake assessment of bridge condition, monitoring systems can be
installed onto structures. However, due to high cost it is impractical to monitor all bridges within a
network. Bridges which are exposed to increased hazards, are vulnerable and have high failure
consequences pose the greatest risk to network functionality should they fail in a seismic event, and
would therefore benefit the most from implementation of monitoring systems and quick condition
assessment methodologies. This paper outlines a methodology to prioritise bridges for monitoring
and quick condition assessment based on their seismic risk. The methodology uses four factors to
determine risk, i.e. seismic hazard, vulnerability, failure impact and uncertainty of available data
and assessment methods. The hazard factor accounts for the seismicity levels at bridge sites and
length of time of exposure to hazard. Structural and geotechnical aspects have been combined to
determine the vulnerability of each bridge. Impacts quantify the consequences of bridge failure on
safety and network functionality. The uncertainty premium accounts for the quality, variability and
limitations of data and risk assessment methods used. The overall risk calculated for each bridge
within a stock enables prioritisation of structures for monitoring and quick post-disaster assessment.
The whole spectrum of approaches to bridge monitoring and condition evaluation comprises bridge
specific monitoring data used for quick and accurate analyses for the most critical, high risk
bridges; data sourced from wide-area strong motion arrays used for quick but less accurate
assessment for medium risk structures; and traditional visual inspection based assessment of low
risk bridges. A discrete scoring system was adopted and detailed tables that enable scoring the
hazards, vulnerabilities, impacts and data and assessment uncertainties developed. The proposed
methodology was applied to a selection of bridges from the city of Wellington, New Zealand to test
its applicability and performance. A comparative study with another seismic risk assessment
method was also conducted. The results showed that the methodology effectively prioritised bridges
depending on seismic risk. The methodology was also able to determine if risk at a particular bridge
site was predominantly related to hazard, structural vulnerability, geotechnical vulnerability or
impact. The methodology is simple, quick and flexible and can be adapted based on the level of
accuracy required. The uncertainty premium allows risk to be determined given variable data and
assessment method quality which has the benefit of being able to tailor data collection and
assessment to the needs of each network and available resources.
KEY WORDS: bridges; condition assessment; disaster response; risk; road networks; structural
health monitoring.
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1.
INTRODUCTION
Today, the need to protect and maintain road assets and their functionality has become a necessity
for any local authority or national road and highway operator to ensure the needs of communities
and economy are adequately met. Bridges are critical and expensive components within the
transportation network providing essential infrastructure, services and interconnections within the
various road networks that underpin the life of communities. However, bridges are subject to
various natural hazards, of which earthquakes are, in many countries, one of the most important.
Legislative documents often require that all lifelines (including the road network) be able to
function to the fullest possible extent during and after an emergency (Seville and Metcalfe 2005).
Complex topography and constraints of built environment often dictate transportation networks
lacking in redundancy and failure of a small number of bridges may have significant negative
consequences at the time of natural disaster. Following an earthquake, bridges may be closed due to
safety concerns, and may only be re-opened for use once site investigations have been carried out.
Due to the large number of bridges within any network and limited resources for inspections, this
can be a time consuming process and may lead to traffic delays and congestion thus hampering
quick post-disaster recovery and rebuilt. Furthermore, adequate functionality of the critical links
within the transportation network of the affected area is necessary immediately in the aftermath of
an event to ensure access to such services as hospitals, evacuations centres and airports, and
operation of search and rescue, fire and emergency supply services and others. To exacerbate the
challenges brought about by limited resources, judging the soundness of a bridge stroked by an
earthquake is difficult because of the subjective and qualitative nature of visual inspections (Phares
et al. 2007).
Research into strategies, tools and technologies that will assist in quick post-earthquake assessment
of bridge damage, condition and performance and overcome, or at least lessen, the aforementioned
problems is urgently required. Monitoring systems can collect real time data and, with appropriate
and careful data interpretation, provide information about the condition and performance of bridges.
This will provide asset managers and emergency response centres with valuable information to
assist decision making following a seismic event. While it is not expected, or necessary, or practical
to completely replace visual inspections by monitoring systems, the latter can be a useful
component in the whole spectrum of assessment methods. However, to achieve the maximum
benefit from monitoring systems they need to be implemented in a strategic, planned and targeted
way, and well-integrated into the entire post-disaster response plans and practices.
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This research is a part of a larger vision to develop strategies and tools that will enable quick postearthquake assessment of bridge damage, condition and performance using data collected by
monitoring systems. To realise such a vision the following objectives need to be fulfilled:
 Developing a methodology for prioritisation of bridges for application of quick assessment
and sensing technologies. This will take into account bridge importance in the network and
seismic risks, including structural and geotechnical risks.
 Developing methodologies for using existing wide-area free-field seismic data for postearthquake bridge condition and damage assessment. This assessment will take into account
both structural and geotechnical failures affecting bridges. The focus will be on correlating
simple measures extracted from the strong motion data with structural, foundation and soil
performance and damage.
 Developing guidelines for instrumentation to be installed on bridge structures and in their
vicinity for measuring seismic excitation and responses (bridge specific instrumentation).
This instrumentation will record structural, foundation and soil responses as appropriate. The
focus will be on optimal, affordable hardware and simple measurements, such as accelerations
and tilts, which can help in assessment of seismic damage.
 Developing a methodology for quick condition and damage assessment based on correlating
simple measures extracted from data collected by bridge specific instrumentation with
structural, foundation and soil performance and damage.
 Developing guidelines for integration of monitoring and quick assessment results into the
emergency planning and response practices of organisations responsible for post-disaster
functionality of transportation networks.
This paper reports on the research related to the first objective, i.e. the development of a
prioritisation methodology for selection of bridges for strategic application of monitoring systems
and quick assessment using monitoring data. The need for such a methodology stems from the fact
that due to the cost of monitoring systems it is unrealistic, if ever necessary, to instrument all, or
even the majority, of the bridges on a network. Furthermore, immediate information about postearthquake condition is not necessarily required for all bridges but only for those whose failure is
more likely to result in larger consequences to network functioning. The question then arises as to
which bridge structures should be monitored and quickly assessed. Considering seismic risk of each
bridge at a network level provides a rational basis for selection and underpins the proposed
methodology.
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The outline of the remainder of the paper is as follows. In the next section, a discussion of the
benefits of bridge monitoring systems is presented. The following section contains a short review of
representative approaches to assessment of seismic risk to bridges. This is followed by the main
thrust of this paper, i.e. the presentation of the general philosophy and then details of the developed
risk-based prioritisation methodology that enables rational selection of bridges for monitoring and
quick post-earthquake condition assessment. An example of methodology application to a selection
of bridges within the road network of Wellington, New Zealand is provided and discussed. A
comparative study with another simple risk assessment method used in New Zealand is also
conducted for validation and exploration of the performance of the proposed method. Finally, a set
of conclusions rounds up the paper. The proposed methodology was developed in the New Zealand
context and the following discussions will make references to the local practices, however, it is
general enough to be applicable, after suitable modifications, in other countries and regions.
2. MONITORING SYSTEMS
The overall objective of using monitoring systems is to measure data and interpret them using
engineering knowledge so that structural condition and reliability can be quantified objectively
(Aktan et al. 2002). Traditional visual inspection techniques can be expensive and time consuming,
are qualitative, subjective, and only capable of assessing outward appearances (Phares et al. 2007).
It is, therefore, desirable to supplement them with monitoring. Over the past several decades,
considerable research has been conducted and marked progress achieved in that area, including
better sensor and system development, data storage and transmission, and data interrogation,
processing and interpretation for extracting information about structural performance and reliability
(Wenzel 2009).
Inaudi and Walder (2011) recommend monitoring to be undertaken in the following broad range of
situations:
 New structures with innovative design, construction techniques, or materials.
 New structures with poorly understood risks, including geological, seismic, meteorological,
environment, construction, and quality risks.
 New or existing structures which are representative of a larger population of similar
structures, where information derived from monitoring can be extrapolated to the wider
population.
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 New or existing structures that are critical at a network level, such that their failure or
deficiency would have a serious impact on the network functioning.
 Existing structures with known deficiencies, problems and/or very low rating.
 Candidate structures for replacement or refurbishment, where the real need for interventions
can be assessed a priori and repair efficiency evaluated a posteriori.
There are already numerous cases of planned and proactive integration of monitoring into newly
constructed innovative, landmark and/or record breaking structures (Abdelrazaq 2011). However,
the above list also envisages applications of monitoring to existing, potentially numerous, structures
in hope to better manage their risks. Despite the existing examples of successful applications of
monitoring to such cases (Tozser et al. 2011), it can be argued that most of such projects are ad hoc
in their nature and reactive to existing identified problems rather than proactive, are not well
integrated into the overall asset management or disaster/emergency response planning, and their
benefits are not always clearly demonstrated.
Potential general benefits of using monitoring are often summarised as follows (Abdelrazaq 2011):
 Reducing uncertainty about structural condition and performance.
 Discovering hidden structural reserves.
 Discovering deficiencies that may be missed by traditional assessment techniques.
 Increasing safety and reliability.
 Ensuring long term quality of aging infrastructure.
 Allowing better informed asset management.
 Increasing knowledge about in-situ structural performance.
The above list, examined from the point of view of organisations responsible for post-disaster
functioning of transportation networks, makes monitoring a useful tool as the potential benefits
address their key challenges, i.e., the need for advanced knowledge about bridge condition and
performance, and reliable data for ensuring that bridges can perform to the expected level.
Monitoring systems can collect data in real time and can help detect damage to the structure, which
can be in the form of changes to the material and/or geometric properties of the system (Gastineau
et al. 2009). They can aid decision making immediately following a seismic event or be used for
long term condition monitoring.
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In spite of these benefits, structural monitoring has only made limited transition from the research
domain into widespread practical applications. In order to achieve a widespread, planned and
proactive integration of monitoring into post-disaster response practice and realise its potential
benefits it is necessary to establish a sound philosophy guiding the implementation of monitoring
systems to bridges. By doing so, monitoring systems can be strategically deployed to enhance the
post-disaster response processes and help address their current limitations in a cost effective way.
This paper argues that such a philosophy should be based on considering the risk that failures of
individual bridges present to the functioning of the entire transportation system and outlines a riskbased method for prioritisation of bridges for implementation of monitoring systems and quick
condition assessment using monitoring data.
3. RISK ASSESSMENT METHODS FOR BRIDGES
This section presents a short introduction to bridge risk assessment. It is not intended to be wide, let
alone exhaustive, but instead presents only the general concepts and principles involved as well as
representative approaches, and also lays the ground for the proposed risk-based prioritisation
methodology. The examples are selected mostly from studies concerned specifically with seismic
risk investigations but also include more general methodologies.
The commonly accepted definition of risk is the probability of failure multiplied by the expected
impacts (or consequences) of failure. Failure probability itself is a function of hazard occurrence
probability and structural vulnerability to the given hazard (Standards New Zealand 2004a).
Most of the bridge seismic risk assessment schemes have been developed in the context of
prioritisation of structures for seismic retrofit or replacement. While not intended to contribute to
decisions pertaining to the selection of structures for monitoring, they nevertheless provide useful
examples of approaches and methodologies that can inform this study.
The main objectives of seismic retrofit of bridges (Kapur 2006) are to:
 Minimise the risk of bridge collapse via identifying bridges most vulnerable in a seismic
event.
 Prioritise refurbishment and replacement projects to minimise loss of life and disruption to
commerce, i.e. focusing on those structures with the greatest consequences of failure.
 Accept moderate damage in less important structures and give priority to bridges that pose a
greater risk to public safety and network functionality disruptions.
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Applied Technology Council (1983) published guidelines for retrofitting of highway bridges that
included a preliminary seismic risk assessment procedure and a detailed evaluation methodology.
The preliminary screening was based on assigning scores in the range between 0 and 10 for site
seismicity, bridge vulnerability and bridge importance. The risk was determined as a weighted sum
of the scores. The detailed evaluation required determining the demand to capacity ratios of key
structural components such as expansion joints and bearings, columns, piers, footings, abutments
and foundations via non-linear structural analysis.
Basöz and Kiremidjian (1995) developed a screening tool with the objective to identify high risk
bridges most in need of seismic retrofit based on vulnerability and importance. The methodology
incorporates tools such as network analysis, fragility analysis and value models to prioritise bridges.
The method is very comprehensive but it can be very time consuming and costly to collect the
required data and carry out detailed assessment; for example Bush et al. (2012) analysed the current
state of bridge asset management in New Zealand and concluded that many types of data would not
be readily available.
New Zealand Transport Agency (NZTA) (the operator of the country’s state highway network) uses
a two-stage methodology it inherited from its predecessor Transit New Zealand (1998). The first
stage is an initial screening procedure which ranks bridges based on seismic hazard, importance of
the bridge and vulnerabilities associated with the bridge, and uses relative weights to rank these
factors. The next stage in the methodology is a more detailed assessment of high ranking bridges
which requires a review of the results from the preliminary screening and specialist judgment. The
initial screening process requires less costly data and was considered useful as a preliminary
assessment tool. (The procedure for initial screening outlined in Transit New Zealand (1998) is
adopted in this paper for validation of the proposed new method of risk assessment in Section 6.1,
where it is also described in more detail.)
In real life applications of risk analysis to bridges detailed and refined probabilistic information
about both failure probability and consequences may often not be available. Many simple, yet
practical, risk assessment schemes circumvent these limitations by assigning numerical scores for
hazard, H, vulnerability, V, and impacts, I, and risk R can then be succinctly expressed in the
following form:
𝑅 =𝐻 ×𝑉×𝐼
(1)
However, even those scores can only be reasonably determined if enough information is available.
For example, if vulnerability is judged using only simple desktop revisions of as-designed
documentation there is considerably more uncertainty involved compared to a situation when more
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information is available such as as-built documentation, non-destructive testing and/or monitoring
results, structural analysis results etc. To address such uncertainties resulting from different data
quality and assessment practices, Moon et al. (2009) modified the above risk formula to:
𝑅 =𝐻 ×𝑉×𝐼×𝑈
(2)
where U is the uncertainty premium penalizing relative lack of information used for, and
simplifications in, risk assessment. Applying an uncertainty factor brings further insights into the
risk analysis as it accounts for data and assessment techniques which will likely differ between
bridges. One would expect that as bridge importance increases, for example due to high road usage,
more regular inspections and therefore more data will be available for this bridge compared to other
bridges. However, this may not always be the case in practice as there are gaps in the current
assessment process. Bush et al. (2013) proposed a move toward improving data collection by
strengthening the link between the data collected and its purpose in the assessment process. They
aimed to achieve this balance by linking data collected for bridges with their level of risk and
criticality within the transportation network. Bridges of low risk and criticality would only require
basic data collection regimes, whilst bridges with high criticality and risk demand advanced data
collection techniques. The risk assessment framework proposed by Moon et al. (2009) allows for
comparison of different levels of data quality. This has the benefit of reducing costly site
investigations, and encourages the use of data that may already be available, albeit acknowledging
their limitations.
In this research it was felt, based on inspection of available information that further differentiation
of uncertainty levels and premiums is required, and individual premiums related to the assessment
of hazards, UH, vulnerabilities, UV, and impacts, UI, were introduced. Furthermore, several different
aspects of vulnerability and impacts may receive different scores and to combine, or aggregate
those, root-mean-squares (RMS) is used. The adapted formula for the total risk for a bridge thus
becomes:
𝑅 = RMS(𝑈𝐻,𝑖 × 𝐻𝑖 ) × RMS(𝑈𝑉,𝑖 × 𝑉𝑖 ) × RMS(𝑈𝐼,𝑖 × 𝐼𝑖 )
(3)
where subscript i refers to individual vulnerabilities and impacts.
Omenzetter et al. (2011) considered uncertainties related to the available information about
structural and functional capacity and loads and other demands imposed on the structure. Even if
correct in their expected values, both are typically known only with some uncertainty. In some
cases, e.g. when undiscovered serious problems exist, even the mean values can be misestimated.
To account for these uncertainties and errors, conservative assumptions must be made that increase
the apparent risk. More data, and more importantly better quality and more reliable data, and
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information inferred from such data can reduce such uncertainties and eliminate erroneous
assumptions. Thus, better estimation of risk factors in most cases reduces the risk in the first place.
In some cases, when previously unknown and unexpected problems not covered by the
conservativeness of less refined risk estimations surface, the risk may actually increase, but this
increase is then underpinned by evidence. Monitoring systems can provide such additional data for
improved risk assessment. Omenzetter et al. (2011) also demonstrated that the overall networklevel-aggregated risk reduction is most efficient when efforts to collect better quality data focus
mostly on those structures that already present the highest risks, whilst not ignoring totally less atrisk ones.
As can be seen from the above discussions, monitoring systems have a potential for making a
positive contribution to better post-disaster response and recovery as they help to better understand
and manage the risks associated with operating an affected transportation network. The next section
outlines how we propose to prioritise bridges for monitoring and quick post-earthquake assessment.
4. TIERED, RISK-BASED PRIORITISATION OF BRIDGES FOR MONITORING
AND QUICK ASSESSMENT
The risk-based philosophy adopted in this study assumes that some bridges, i.e. those that pose
more risk to the operation of the transportation system, will be selected for monitoring and quick
post-disaster assessment of their condition. The whole spectrum of approaches to bridge condition
evaluation is presented in Table 1.
In the proposed framework, bridges with low seismic risk will be evaluated post-earthquake using
the currently prevailing approach based mostly on visual inspections scheduled depending on the
availability of inspectors and need.
Bridges in the intermediate risk category will not have dedicated instrumentation installed on them
or in their proximity. Instead, data recorded by wide-area free-field arrays will be used. However,
this will require interpolation of such data so that ground motion parameters can be estimated at the
bridge site. Initial research to develop suitable approaches to predict basic ground motion metrics
such as peak ground accelerations using artificial neural networks is reported in Ramhormozian et
al. (2013). This will be complemented by quick and simple methods for translating the hazard
metrics into damage estimates. The outcome will allow declaring a bridge as safe for immediate
continuous use, or requiring traffic restrictions, or closure. If required, further assessment
supplemented by data from visual inspections and technical analyses can be conducted at a suitable
time.
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Bridges in the high risk category will receive special consideration. They will have dedicated
monitoring systems with sensors measuring seismic excitation and bridge responses, including
those of super- and substructure, foundation and nearby soil. The amount, type and locations of
instrumentation will be individually tailored to the need of each bridge as determined by a prior
structural assessment study. Using the bridge specific monitoring data will enable much more
detailed and accurate appraisal of bridge condition. This research programme, in its later stages,
aims at providing guidelines for instrumentation and analysis of data from bridge specific
monitoring systems for quick condition assessment. If required, further assessment using visual
inspections, monitoring data and in-depth technical analyses can be conducted at a later stage.
5. RISK-BASED PRIORITISATION METHODOLOGY
This section explains the details of the proposed risk scoring methodology that enables bridge
prioritisation. It is based on the general concepts proposed by Moon et al. (2009). However, the
methodology presented herein extends those tools and differs in several aspects. While Moon et al.
(2009) considered a wide spectrum of hazards facing bridges, here only the seismic hazard is taken
into account. A new seismic hazard scoring method is proposed. Uncertainty scores for different
vulnerabilities and impacts are allowed to take independent values providing more flexibility to deal
with various types of data. Also, scoring tables recently proposed for multiple hazards by
Omenzetter et al. (2014) have been further developed in considerable detail and specified in this
paper for seismic hazards and vulnerabilities. Geotechnical and structural aspects have been
combined to determine the overall seismic vulnerability, treating the structure, foundation and soil
as a whole system.
The flow of the methodology developed to evaluate risk for each bridge is summarised in Figure 1.
The tables used for risk evaluation and scoring and referred to below are placed in Appendix at the
end of the paper. The procedural steps are as follows:
1. Data collection, archiving and retrieval.
2. Determination of uncertainty premium scores using Table A1.
3. Determination of hazard scores using Table A2.
4. Determination of structural vulnerability scores using Table A3 and geotechnical vulnerability
scores using Table A4.
5. Determination of impact scores using Table A5.
6. Calculation of aggregated risk for the bridge using Equation 3.
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7. Ranking of bridges using aggregated risk.
8. A re-evaluation step, involving additional data collection and/or analyses, is recommended to
reduce the uncertainty at important bridge sites that might have led to high risk as data used in
the assessment could have been of poor quality and only simplified analyses used.
Determination of the uncertainty premium, hazard, vulnerability and impact scores is based on a
discrete scoring system. Key areas and indicators of hazard, vulnerability and impacts have been
identified and ranked depending on their level. Table 2 shows the basic philosophy of ranking and
score assignment for seismic hazard, vulnerabilities and impacts. Discrete scores between 1 and 3
are used depending on the determined level of hazard, vulnerability and impact. A score of 0 is also
included for situations when a particular category is not applicable, e.g. foundation weakening by
scour for a bridge not located over a stream, river or canal. Tables A2-A5 in Appendix provide
detailed criteria for the rational judgement of the level of hazard, vulnerability and impact.
Following the original ideas of Moon et al. (2009) it was felt that a more refined uncertainty
premium scoring system was required and five scores between 1.0 and 1.4 were adopted for that
purpose as shown in Table A1. In the present form, the methodology does not use any additional
weighting of the factors that determine the scores. However, some weighting is implicitly present in
linking the factor qualitative descriptors and/or numerical values to the scores. If a particular factor
is deemed more important and influential, higher scores can be linked to relatively milder
descriptors or numerical values associated with the factor. Also, some factors may never be
assigned the lowest or highest scores, the latter exception actually adopted for seismic vulnerability
scores for the bridge deck (see Table A3). Developing of an explicit weighting scheme may be a
future enhancement of the methodology.
Individual raw scores related to the seismic hazard and each vulnerability and impact category are
multiplied by uncertainty premium scores, and the overall aggregated risk is obtained by taking the
RMS of these values as illustrated by Equation 3. The use of RMS rather than e.g. the arithmetic
average or maximum individual value, causes heightened individual scores to have more influence
on the aggregate risk score, while lower risks are not totally ignored. Based on the aggregated risk it
is possible to rank bridges. Individual scores are also still available to examine which of the seismic
hazard, vulnerabilities and impacts contribute most to the overall risk. It is also possible to
determine, based on input scores, if the risk is driven by uncertainty, hazard, vulnerability or
impacts. It is important to recognise that poorer data and assessment techniques results in larger
uncertainty premium scores and therefore larger risk estimates. If an original risk estimate is
thought intolerable, better data can be collected and more accurate assessment can be conducted for
selected bridges as envisaged in Figure 1. There is also need for updating the risk assessment
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periodically and following some discrete events. Some examples when risk assessment may need to
be updated include the following. Periodic updating is required because the condition of structural
elements and foundations will deteriorate with time. Also, patterns of traffic volumes may evolve
over time, e.g. due to emergence and growth of new suburbs or industrial areas, resulting in certain
bridges assuming more importance. On the other hand, discrete events such as e.g. bridge
refurbishment and repair or damage sustained in an earthquake will respectively decrease or
increase bridge vulnerability. Likewise, opening of a new bypass will lessen the traffic demand on a
bridge leading to its lower importance and therefore lower failure impact. Hazard estimates may
also be updated when new research results become available and infiltrate the engineering practice.
Finally, new or better quality data on, and estimates of, hazards, vulnerabilities and impacts may
become available reducing the uncertainty premiums.
The following sections provide details about the determination of the uncertainty premium, hazard,
vulnerability and impact scores.
5.1. Uncertainty Premium Score
The uncertainty premium accounts for the reality that data collection and risk assessment methods
vary and this will affect the level of risk associated with the bridge (Omenzetter et al. 2011). Five
levels of data collection have been included as shown in Table A1. Level 1 carries the highest
uncertainty premium as data used in the assessment is very general and qualitative, and lacks detail
and quality permitting only minimum standard assessment techniques to be applied. At the other
end, Level 5 uses a broad range of qualitative and quantitative data, including in-situ testing and
monitoring, and advanced analytical techniques to form a much better informed assessment of
hazards, vulnerabilities and impacts. As bridge importance increases, it is likely that more site
specific data will be present and this is where the higher risk assessment levels may be useful. If
data is missing or lacking in quality, it may also be advisable to carry out more intensive site
investigations and analyses to ensure that the data used in the assessment meets appropriate
standards and best practices.
Rather than applying one overall uncertainty premium score, it will often be the case that different
scores might need to be applied for different aspects of hazards, vulnerabilities and impacts. For
example, it is often found that geotechnical information is scarcer, perhaps nearly entirely missing,
and less precise than structural information. On the other hand, some, but not all, bridge or
foundation elements might have been subjected to an advanced evaluation in the past and better
information and knowledge about these may be available. Situations like these will drive up or
down the partial uncertainty premium scores to be used in Equation 3. The partial uncertainty
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premium scores have the advantage that data of variable quality can be used simultaneously. Site
investigations are time consuming and costly, and where some data is already available this
becomes very useful.
The range of uncertainty premium values between 1.0 and 1.4 in Table A1 was determined in such
a way that if the lowest or highest partial uncertainty premiums are applied to all partial scores for
hazard, vulnerabilities and impacts (see Equation 3) they give the overall premium of 1.0 or 2.74,
respectively, which are similar to the minimum and maximum overall premiums of 1.0 and 2.5
proposed by Moon et al. (2009). This range was found appropriate in the case study reported later to
differentiate risk in the bridge sample considered.
Extreme cases of poor quality or even lack of data may pose practical challenges to the application
of the methodology. In such cases, it is advisable to be conservative and assume maximum scores
for the aspects that cannot be precisely assessed and also use high uncertainty premiums from Table
A1. It is also envisaged that the engineer or asset manager will examine the full process of risk
assessment and risk profile and identify such bridges for special treatment, as our methodology
attempts to rationalise risk assessment process but does not seek to replace sound professional
judgment.
5.2. Hazard Score
The hazard score represents the level of seismic hazard at the bridge site. Seismic demands and
damage to structures and ground are closely related to the severity of seismic hazard (New Zealand
Geotechnical Society 2010) and therefore seismic demands on structures are a good representation
of the seismic hazard. The maximum design spectral acceleration (MDSA) was adopted in the
proposed methodology to develop a partial seismic hazards score. The probability of occurrence of
a given MDSA in the future depends on the time the bridge will remain in service, and hence the
remaining service life was also considered to assign another partial hazard score.
For the purpose of the proposed risk assessment methodology the MDSA was calculated by suitably
adapting the seismic horizontal design action formula from NZS1170 (Standards New Zealand
2004b). The full formula for seismic horizontal design action coefficient Cd(T) in units of g is given
in NZS1170 as:
𝐶𝑑 (𝑇) =
𝐶ℎ (𝑇)×𝑍×𝑅×𝑁(𝑇,𝐷)×𝑆𝑝
(4)
𝑘𝜇
where Ch(T) is the spectral shape factor depending on structural period T and soil type, Z is the
hazard factor quantifying regional seismicity levels, R is the earthquake return period factor, N(T,D)
is the near fault factor depending on T and distance to the fault D, and Sp and k are additional
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factors that depend on design structural ductility. It can be seen that Cd(T) as defined in Equation 4
in fact combines aspects of hazard, vulnerability and impacts. The dependence of Cd(T) on the
structural period and ductility is in fact related to vulnerability as these characterise the structure
rather than the hazard itself. The return period is in fact a proxy for failure impacts as more critical
structures are designed for shaking levels with higher return periods.
In order to have a simple formula for a hazard-only metrics Equation 4 has been adapted to express
MDSA as
𝑀𝐷𝑆𝐴 = 𝐶ℎ,𝑚𝑎𝑥 × 𝑍 × 𝑁𝑚𝑎𝑥 (𝐷)
(5)
where Ch,max is the maximum spectral shape factor value for a given soil type but independent of
structural period T, and Nmax(D) is the maximum value of near fault factor depending on the
distance to the fault D but again independent from T. The full range of possible MDSAs was
considered and divided into intervals which were then translated into seismic scores as shown Table
A2. It is noted that the boundaries between low, intermediate and high MDSAs in Table A2 were
chosen after consideration of the bridge stock in Wellington used in case study, where the bridges
are located on a variety of soil types and some very close to an active fault. These values are
adjustable and their calibration may be undertaken if they do not allow for sufficient differentiation
between bridges. This comment in fact applies to many scoring criteria, particularly those with
numerical values, and remarks in the text will highlight this.
Scores for the remaining service life are also provided in Table A2. They are based on the typical
design service life of 100 years and divide bridges into those that have more than 50, between 25
and 50, and less than 25 years of service life left.
It is acknowledged that this hazard score determination method (and the methods for vulnerability
and impact score determination detailed later) is suitable only for the simplest levels of analysis
attracting large uncertainty premium scores (see Table A1) and extensions to cover more advanced
cases will need to be developed in the future if required. If further accuracy is desired, a more site
specific hazard analysis would have to be carried out to determine location specific seismic hazard
probabilities. However, for the vast majority of bridges no such advanced information may
immediately be available and so for such reasons the simple approach proposed has a practical
value.
5.3. Vulnerability Scores
The vulnerability factor represents the conditional probability of failure of the bridge given seismic
hazard occurrence. In a seismic event, the structure, foundation system and supporting soil should
be considered together (Pender 2007). Due to this interdependence, structural and geotechnical
15
aspects are both considered to determine the overall bridge vulnerability at each site. It is
acknowledged that the proposed criteria for determination of vulnerability (and later impact) scores
are often descriptive and qualitative in nature. They also require considerable amount of holistic
professional engineering judgement to assign scores to a given structure. They are, however,
intended to be applied in situations where only limited data is available and sophisticated
assessment is impractical or unnecessary. As the price for such simplifications, they attract larger
uncertainty premiums. Nevertheless, the proposed scoring method is rational and comprehensive,
providing practitioner with a usable tool.
5.3.1. Structural Vulnerability Scores
Structural vulnerability scoring system is shown in Table A3. The main documents used to develop
Table A3 was Transit New Zealand’s ‘Bridge manual’ (Transit New Zealand 2003) and ‘Manual
for seismic screening of bridges’ (Transit New Zealand 1998) as they are concerned with how
bridges are affected in the event of an earthquake, analysis and design criteria, and bridge
vulnerabilities. The focus is on concrete structures as these clearly dominate in the New Zealand
bridge stock (New Zealand Transport Agency 2010a). The low, medium and high column
slenderness ratios were assumed after Wang (2000). Structural vulnerabilities have been subdivided
into three separate categories related to superstructure, substructure and deck as follows:
 Substructure: Contains the critical components such as columns, abutments and retaining
walls, structural geometry characteristics such as skew, and condition indicators such as
concrete spalling and cracking and reinforcement corrosion and other signs of distress. These
components are affected most in an earthquake and therefore their increased vulnerabilities
may be critical in risk assessment and need to be properly accounted for.
 Superstructure: Includes elements such as girders, expansion joints, holding down bolts and
bearings, and proper superstructure supports. Condition indicators include spalling, cracking
and corrosion, bearing, joints and bolts movement and damage. During an earthquake,
loadings of these elements are intensified and can cause major damage.
 Deck: Includes the deck slab and reinforcement. The deck contributes relatively less to
seismic vulnerabilities and hence maximum proposed scores were always lower than 3.
5.3.2. Geotechnical Vulnerability Scores
Geotechnical vulnerabilities in a seismic event are related to both the foundation structure as well as
the soil it is supported and surrounded by. The recent 2010 Darfield and 2011 Christchurch
earthquakes provided a good indication of the seismic performance of New Zealand’s bridges. The
road networks were fairly resilient taking only a week or two to reinstate infrastructure to
16
reasonable levels, however, this was highly dependent on the susceptibility to liquefaction
(Institution of Professional Engineers New Zealand 2012). The main cause of damage to both road
and foot bridges following the 2010 Darfield and 2011 Christchurch earthquakes was liquefactioninduced lateral spreading at the approaches behind bridge abutments (Waldin et al. 2012). In
liquefied areas, lateral spreading of approaches was observed mostly near river banks, where the
bridge acted as a rigid strut while foundations underwent forced rotations in the direction of ground
flow. Around the world many urban areas are built on land subject to liquefaction, and as it is
generally flat, it is often regarded as premium land (Institution of Professional Engineers New
Zealand 2012), making liquefaction and its effects a key geotechnical seismic consideration.
A geotechnical vulnerability scoring system (Table A4) has been developed to determine the
vulnerabilities to the various types of foundation and ground failure modes:
 Soil type and failure modes: In an earthquake the ground is subject to shaking that may lead
to liquefaction, lateral spread and bearing capacity failure. Amongst other qualitative criteria,
the liquefaction safety factor was used as a simple quantitative measure of liquefaction
potential. The factor can be calculated as the ratio of equivalent cyclic shear stress estimated
according to Seed and Idriss (1971) and cycling resistance ratio obtained from standard or
cone penetration tests (Idriss and Boulanger 2008). Possibility of fault rupture across bridge
site was also taken into account. Other factors, such as soil homogeneity, ground
improvements and stability of slopes adjacent to the bridge, have also been included as they
affect the seismic vulnerability at a bridge site.
 Foundation characteristics and condition: Factors such as foundation depth and soil type, pile
type and condition, and settlement were used to assess the vulnerability. Scour at foundations
was also included as it may reduce the strength of the foundation, with stream velocities
classified into low, medium and high based of Pemberton and Lara (1984).
5.4. Impact Scores
Impact scores represent the consequences associated with the failure to perform adequately. Bridges
that are critical, i.e. those with large failure consequences, carry higher risk than those with low
criticality to the network. Typical bridges in many countries and regions are not major structures,
for example bridges on local authority roads in New Zealand have an average span length of 17m
and state highway bridges an average span length of 35m (New Zealand Transport Agency 2010a).
It can be concluded that an ‘average’ New Zealand bridge has one or two spans (Bush et al. 2012).
It is, therefore, reasonable to assume that in case of damage, the direct costs of repair or
replacement will be less significant than the wider costs of bridge closure to the functioning of the
17
entire networks, for example due to traffic delays and congestion. In the aftermath of an earthquake,
other aspects of impaired network functionality, such as access to hospitals and emergency services,
are also more pronounced. Therefore, in the developed importance scoring methodology such wider
consequences are highlighted (Bush et al. 2013).
Table A5 scores impacts based on the following factors:
 Public safety: Traffic volumes (average annual daily traffic (AADT) and annual daily truck
traffic (ADTT)) on and under the bridge and facilities crossed are considered. Higher road
usage means potential higher consequences in terms of public safety should a bridge fail
leading to injuries and casualties. Importance levels according to NZS1170 (Standards New
Zealand 2004b) relate to structures that may have a significant numbers of people present in
or on them whose safety might be at risk during a seismic event (level 3), or have special
post-disaster functions assigned to them (level 4 and 5). (For the latter reason importance
levels are also considered when assigning a score related to emergency road use, see below.)
 Replacement/repair cost: Direct cost of replacement or repair of a bridge damaged in an
earthquake. Numbers are proposed in NZ$ taking into account New Zealand’s context. This is
another example of a parameter that can be calibrated depending on a particular transportation
network analysed.
 Typical road use: AADT and ADTT counts on the bridge (and below the bridge if relevant)
were considered to judge consequences of bridge failure as well as length of detour required
after closure. New Zealand Transport Agency (2010b) has identified roads of national
significance which carry large volumes of vehicles and underpin the economy of New
Zealand. Bridge damage along these roads may cause significant traffic disruptions and
delays and ultimately affect the economy to a significant extent and hence bridges on those
and other important roads received a high impact score.
 Emergency use: Some bridges may be located along designated emergency routes to essential
services, such as a hospital or airport. The structure’s importance level after NZS1170
(Standards New Zealand 2004b) has also been included in this category. In the event of an
earthquake, the availability of the road to these services will be vital.
 Utilities: Essential utilities (e.g. water, sewage, gas and telecommunications) may be
supported by the bridge structure and bridge failure will pose risk to their proper functioning.
Some factors are included in more than one impact category. For example AADT is assumed to be
correlated with the number of casualties expected in a strong seismic event, thus it is included in
18
‘Public safety’. However, AADT is also included in ‘Typical road use’ as it is assumed to be
correlated with the economic losses expected due to impaired functionality of the transportation
system.
6.
CASE STUDY: APPLICATION OF PROPOSED METHODOLOGY TO
WELLINGTON CITY COUNCIL BRIDGES
Bridge stock data for the city of Wellington, New Zealand’s capital was obtained from Wellington
City Council (WCC). This data set was used to test the methodology developed on a real, complex
network. The study area included the central business and administrative district and residential
areas. The data provided by WCC consisted of general information about each bridge, its structural
system, defects and condition and basic soil characterisation. Additional data and information to
carry out the risk assessment was obtained from a variety of other sources as shown in Table 3.
A total of nine bridges were selected for risk assessment and prioritisation. Table 4 lists the assessed
structures and provides a brief description of the bridges and associated hazards, vulnerabilities and
impacts. For the sake of brevity, only those aspects of hazards, vulnerabilities and impacts that
resulted in scores larger than one were mentioned. Most of the bridges run across the Wellington
Urban Motorway (a road of national significance) as it approaches the Wellington Central Business
District, with a few bridges in urban areas and at the outskirts of the city. The selection represents a
good spread of bridge types, sizes, soil types and liquefaction potentials, distances from a major
active fault, and importance in the transportation network.
Given the nature of data and risk assessment methods adopted, high uncertainty premium scores
were generally assumed (with few exceptions, e.g. for skew and remaining service life as these
were easily obtainable from bridge documentation): 1.2 for hazard, 1.3 for structural vulnerabilities,
1.3 for geotechnical vulnerabilities, and 1.2 for impacts, respectively (Table A1). Table 5 provides,
as an example, detailed risk assessment for Boulcott St. bridge, while Table 6 a summary of the
hazard, vulnerability, impact and final overall risk scores of the nine bridges.
Examination of Table 5 allows understanding which factors contributed most to the risk of Boulcott
St. bridge. The hazard score (including uncertainty premiums) was 2.72 driven mostly by the long
remaining service life and proximity (less than 2km) to the Wellington fault, but situation on soil
class C had a mitigating effect. For this bridge, the superstructure showed no signs of significant
distress, weakness or vulnerabilities (except for moderate skew) but because of the high potential
for liquefaction, lateral spread and fault rupture elevated vulnerability scores were assigned to the
majority of factors relating to geotechnical failures. A similar pattern was observed for several
19
analysed bridges that only occasionally had some intermediate, and ever more rarely important,
structural vulnerability issues, but had significant vulnerabilities associated with soil and foundation
failure modes. This confirms the importance of considering the whole soil-foundation-structure for
adequate seismic risk assessment.
It is not an intention to provide here any absolute risk score thresholds to judge if monitoring and
quick assessment should be used or not, but based on the results in Table 6, Aotea Quay North and
Hobson St. bridges would be the first to be considered for application of monitoring and quick
assessment, while Happy Valley Rd. bridge would have the lowest priority.
Examination of Table 6 shows that the proposed methodology is also able to identify whether risk
at a particular bridge is predominantly determined by hazard, vulnerability or impacts. For the two
bridges with highest overall scores, i.e. Aotea Quay North and Hobson St., the deciding factor was
the high hazard score due to their extreme proximity to the Wellington fault and location on soil
class E resulting in predicted large probability of strong shaking. Aotea Quay North had a lower
hazard score than Hobson St. because of a shorter remaining service life but higher overall score
because of the larger impacts reflecting its location over an important roads and railway and larger
replacement cost. The overall risk score for the remaining bridges was a combination of different
levels of vulnerabilities and impacts, as the hazard scores were the same (except for Helston Rd.
West bridge).
6.1. Comparative Study with New Zealand Transport Agency Methodology
In order to cross-validate the proposed risk assessment and ranking methodology and gain further
insights into its working and performance, a comparative study has been conducted where the same
selection of nine bridges was assessed using the preliminary seismic screening method used by
NZTA (Transit New Zealand 1998). This method calculates the overall risk score by multiplying
scores for hazard, vulnerability and importance. These hazard, vulnerability and importance scores
are sums of several partial scores multiplied by weighting factors. The hazard score depends on
peak ground acceleration (in fact on the hazard factor Z (Standards New Zealand 2004b) but
without considering distance to a fault), remaining life, soil condition and liquefaction potential.
The vulnerability score is a function of the design year, presence of structural hinges, span overlap
at supports, bridge length and skew, pier and abutment types and other secondary features. The
importance score is determined based on AADT on and under the bridge, detour length, facilities
crossed, strategic importance, and utilities carried by the bridge. Unlike in the proposed new
methodology, no uncertainty scores are considered. Also, the focus is on bridge sub- and superstructure failure modes and the potential soil failure is seen as hazard to the bridge sub- and super20
structure rather than the whole bridge-foundation-soil system vulnerability. Further details together
with the criteria for score determination can be found in Transit New Zealand (1998). The full risk
scoring process as applied to the nine analysed bridges is illustrated in Table 7.
Figure 2 shows the comparison between risk scores determined according to the NZTA method and
the proposed one. (Note both scores were divided by their respective maximum values so that they
do not exceed one and can be more easily compared.) Overall, the coefficient of determination R2
(Steel and Torrie 1960) for linear regression is 0.82 indicating a good correlation between the
scores yielded by the two methods. More importantly, two bridges singled out as the highest risks,
namely Aotea Quay North and Hobson St. bridges, are the same for both methods. There is in fact
difference in the order these two bridges are ranked; however, the scores by the proposed method
(14.55 for Aotea Quay North bridge and 14 27 for Hobson St. bridge, respectively; see Table 6) and
by the NZTA approach (0.109 and 0.117, respectively; see Table 7) are very close in both cases. It
can thus be concluded that neither of the two methods has a level of accuracy that would allow
distinguishing clearly between the two bridges, but they yield consistently close risk assessments.
Further differences in the order in which bridges appear on the risk spectrum can also be observed
amongst the medium and low risk structures. For example, the proposed method ranks Ghuznee St.,
Boulcott St. and Hawkeston St. bridge, respectively, as the third, fourth and fifth highest risk
bridges, whereas for the NZTA method the order is Hawkeston St., Helston Rd. West and Ghuznee
St. bridge, respectively. The swap between Ghuznee St. bridge and Hawkeston St. bridge is because
of the difference in vulnerability assessment, resulting from a stronger emphasis on the design year
(and associated prevailing practices), in the NZTA methodology. From the point of view of the
intended application of the proposed scoring method to prioritisation for monitoring and quick postevent assessment these differences within medium (or low) risk bridges will not be critical as all
medium (or low) risk bridges, according to the proposed strategy, would receive similar treatment.
Thus, as long as they are correctly classified into the medium (or low) risk category, bridge to
bridge differences would be of secondary importance, however, they may need to be resolved later
with a more specified decision tool if required. Taking a realistic view of the current resources
available to the transport asset managers, individually tailored monitoring systems will only be
considered for the most at-risk bridges, and here the proposed methodology is in good agreement
with the NZTA risk assessment method.
The comparative study also enabled making some additional observations. One is that while the
NZTA method tends to rather clearly cluster bridges into three groups of high, medium and low
risk, the proposed methodology spreads risk scores more evenly across the entire spectrum (see
Figure 2). This is a direct consequence of using RMS to aggregate various risks by the proposed
21
method discussed earlier in Section 5, where the highest risks receive more emphasis but lower
risks are not ignored either. The NZTA method also yields the lowest risk scores close to zero,
whereas the proposed method tends to yield relatively higher scores for such bridges. For example,
the Happy Valley Rd. bridge received a score of only 0.005 (or 4% compared to the highest scoring
structure) in the NZTA assessment, but a score of 5.09 (or 35% compared to the highest scoring
structure) according to the proposed methodology. While this may not have a strong effect on
relative risk assessment within a group of bridges, it nevertheless may create a psychological effect
that some bridges do not require much attention. It would appear that the NZTA method may
underscore some risks, creating an impression that some bridges are nearly risk free. This does not
appear to be desirable. Finally, the NZTA method yielded very low scores for combined AADT and
detour length despite high weighting associated to that factor influencing bridge importance (see
Table 7). This is because higher scores can only be obtained when detour lengths become
significant and reflects the origins of the NZTA method that was developed mainly for state
highways passing through rural or other sparsely populated areas with less developed road
networks. In contrast, the proposed method assigns higher impact scores for much shorter detours
(see Table A5). This is because the present method was developed with a focus on urban areas with
heavier traffic where even short detours can lead to significant congestion and delays. Both methods
will thus need to adjust how this factor of impact/importance is scored if used for new tasks they
were not yet calibrated for.
Overall, it is argued that the proposed methodology successfully differentiated between the levels of
seismic risk for the bridges considered as far as the selection of structures for different tiers of
monitoring and quick assessment is concerned. Otherwise, several of the structures examined might
have appeared very similar in size and structural form. Thus the proposed methodology can help to
pave the way for targeted application of monitoring and quick condition assessment.
7.
CONCLUSIONS
Monitoring systems gathering seismic ground motion and bridge response data can play an
important role in the management and restoration of transportation network functionality following
seismic events by providing objective and quantitative information for quick assessment of bridge
performance and condition. However, to fully harness their benefits in a cost effective way it is
necessary to prioritise bridges so that monitoring can be applied in a strategic and targeted way. To
that end a simple methodology has been proposed that assesses the overall bridge seismic risk by
assigning numerical scores for seismic hazard, vulnerabilities and impacts. An important feature of
the methodology is that is takes into account the entire soil-foundation-structure system and
considers associated vulnerabilities in these components. Furthermore, the use of uncertainty
22
premium scores provides flexibility to the risk assessment methodology as it enables using data and
assessment methods varying in quality and sophistication. Tables that enable scoring the
multifaceted aspects of seismic hazard, vulnerabilities, impacts and uncertainties were developed.
The method was applied to a selection of bridges from Wellington, New Zealand and enabled their
successful prioritisation for application of monitoring and quick condition assessment. Insights
were also gained about the contribution of hazard, vulnerability and impact to the overall seismic
risk of each bridge. A cross-validation study was conducted with another seismic risk assessment
method and results were found in good agreement.
8. ACKNOWLEDGEMENTS
The authors would like to express their gratitude to their supporters. Piotr Omenzetter’s work within
the Lloyd’s Register Foundation Centre for Safety and Reliability Engineering at the University of
Aberdeen is supported by Lloyd’s Register Foundation. The Foundation helps to protect life and
property by supporting engineering-related education, public engagement and the application of
research. Wellington City Council provided access to their bridge stock database for the case study.
Research work at the University of Auckland was partially supported by the Natural Hazards
Research Platform grant UAOM11/15-4.3.
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APPENDIX: TABLES FOR DETERMINATION OF VULNERABILITY SCORES
This Appendix provides the tables used to determine numerical values for uncertainty premium
scores (Table A1), hazard scores (Table A2), structural vulnerability scores (Table A3),
geotechnical vulnerability scores (Table A4), and impact scores (Table A5).
26
Table 1. Risk-based approaches to bridge monitoring and quick post-earthquake condition
assessment.
Seismic risk Data collection/monitoring system
level
use
Low
Data collected only via visual
inspections
No quantitative data collected via
monitoring
Intermediate Monitoring data from wide area
strong motion arrays
Additional data collected via visual
inspections
High
Condition assessment techniques
‘Slow’ assessment based only on inspectors’
reports from visual inspections
‘Quick’, less accurate assessment based on
wide area strong motion data interpolated to
the bridge site
Follow-up assessment based on visual
inspections and technical analyses as
required
Monitoring data from bridge ‘Quick’, accurate assessment based on
specific monitoring systems and monitoring data collected on the bridge and
wide area strong motion arrays
wide area strong motion data
Additional data collected via visual Follow-up assessment based on visual
inspections
inspections and in-depth technical analyses
as required
27
Table 2. Discrete scoring system for hazard, vulnerability and impact.
Hazard/vulnerability/impact level
Score
Not applicable
0
28
Low
1
Moderate
2
High
3
Table 3. Data used in risk assessment.
Source
New Zealand
Transport Agency
(2010b)
Wellington City
Council bridge
database
Wellington Regional
Council (1993)
Semmens et al.
(2011)
NZS1170 (Standards
New Zealand 2004b)
Google maps
(Google 2013)
Data type
Roads of national significance
Bridge name, location, facility/stream crossed, length, width, number of
spans
Materials used for beams, piers, abutments, deck and foundation
Structural condition
Soil characteristics
Replacement/repair cost
Photographs and as-built plans
Liquefaction hazard map
Wellington fault location
Soil characterisation according to the NZS1170
Z, Ch,max and Nmax(D) values for seismic hazard assessment, Equation 5
Distance from Wellington fault
General width of waterway beneath bridge
Slope of ground
Detour length
29
Table 4. Description of analysed bridges.
Bridge
Aotea Quay
North
Boulcott St.
Ghuznee St.
Happy Valley
Rd.
Description
15 spans, 211m long, longest span 16m, RC structure, 44° skew,
built in 1931
Overall good condition of substructure and superstructure
Situated on reclaimed land with fill consisting of domestic waste,
sand, boulders and rock
Soil class E
High susceptibility for liquefaction
Less than 2km to the active Wellington fault
Over an important railway line, important local road and on-ramp to
a road of national significance
Significant replacement cost NZ$1,477,000
Significant numbers of vehicle per day
2 spans, 58m long, RC structure, 10° skew, built in 1978
Overall good condition of substructure and superstructure
Situated on reclaimed land with fill consisting of domestic waste,
sand, boulders and rock
Soil class C
High susceptibility for liquefaction
Less than 2km to the active Wellington fault
Over road of national significance
Moderate replacement cost
Significant numbers of vehicle per day
3 spans, 41.4m long, RC structure, 20° skew, built in 1977
Overall good condition of substructure and superstructure
Some horizontal movement normal to bridge axis across an
expansion joint
Situated on reclaimed land with fill consisting of domestic waste,
sand, boulders and rock
Soil class C
High susceptibility for liquefaction
Less than 2km to the active Wellington fault
Over road of national significance
Moderate replacement cost
Significant numbers of vehicle per day
6.1m long, double culvert, RC with shotcreted steel beams, built
1990
Significant corrosion and spalling of superstructure
Beach deposits consisting of marine gravel with sand mud and
beach ridges
Soil class C
3km to the active Wellington fault
Over a stream with some scour vulnerability
30
Hawkeston St.
Helston Rd.
West
Hobson St.
Owhiro Bay
Pde.
The Terrace
2 spans, 67.5m long, RC structure, 32° skew, built in 1970
Overall good condition of substructure and superstructure
Undifferentiated weathered, poorly sorted loess-covered alluvial
gravel deposits
Soil class C
Less than 2km to the active Wellington fault
Over road of national significance
Moderate replacement cost
Significant numbers of vehicle per day
3 spans, 39.3m long, RC structure, built in 1959
Overall good condition of substructure and superstructure
Undifferentiated weathered, poorly sorted loess-covered alluvial
gravel deposits
Soil class B
Less than 2km to the active Wellington fault
Over road of national significance
Moderate replacement cost
Significant numbers of vehicle per day
3 spans, 55.3m long, RC structure, 16° skew, built in 1967
Overall good condition of substructure and superstructure
Situated on reclaimed land with fill consisting of domestic waste,
sand, boulders and rock
Soil class E
High susceptibility for liquefaction
Less than 2km to the active Wellington fault
Over road of national significance
Moderate replacement cost
Significant numbers of vehicle per day
2 spans, 13.8m long, prestressed/RC structure, built in 1996
Overall good condition of substructure and superstructure
Beach deposits consisting of marine gravel with sand mud and
beach ridges
Soil class C
Less than 6km to the active Wellington fault
Over stream
Moderate liquefaction potential
Single-span, 17.9m long, RC structure, built in 1978
Overall good condition of substructure and superstructure
Undifferentiated weathered, poorly sorted loess-covered alluvial
gravel deposits
Soil class C
Less than 2km to the active Wellington fault
Over an important road
Significant numbers of vehicle per day
31
Table 5. Example of detailed risk assessment and scoring, Boulcott St. bridge.
Row
no.
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
Hazard/vulnerability/impact
Hazard
MDSA
Service life
RMS hazard (1-2)
Vulnerability
Structural
Substructure
Superstructure
Deck
Geotechnical
Soil
Foundation
RMS vulnerability (4-27)
Impact
Raw
score, S
2
3
Uncertainty
premium, U
1.2
1.0
Piers
Abutments
Retaining walls
Spalling/cracking
Skew
Redundancy
Spalling
Cracks in girders
Bearing failures
Expansion joints
Overlap/linkages
Slab
Reinforcement
Soil homogeneity
Liquefaction
Lateral spreading
Bearing
capacity/settlement
Fault rupture
Ground improvement
Slope stability
Foundation/soil type
Piles
Foundation settlement
Scour
1
1
1
1
2
1
1
1
1
1
1
1
1
1
3
2
2
1.3
1.3
1.3
1.3
1.0
1.0
1.3
1.3
1.3
1.3
1.3
1.3
1.3
1.3
1.3
1.3
1.3
3
2
1
1
1
1
0
1.3
1.3
1.3
1.3
1.3
1.3
1.0
Public safety
Replacement/
repair cost
Typical road use
Emergency road use
Utilities
2
2
1.2
1.2
2
1
1
1.2
1.2
1.2
RMS impact (29-33)
Risk (3×28×34)
32
S×U
2.4
3.0
2.72
1.3
1.3
1.3
1.3
2.0
1.0
1.3
1.3
1.3
1.3
1.3
1.3
1.3
1.3
3.9
2.6
2.6
3.9
2.6
1.3
1.3
1.3
1.3
0.0
1.86
2.4
2.4
2.4
1.2
1.2
2.01
10.13
Table 6. Summary of risk assessment and scoring for nine analysed bridges using proposed method.
Bridge
RMS(𝑈𝐻,𝑖 × 𝐻𝑖 )
RMS(𝑈𝑉,𝑖 × 𝑉𝑖 )
RMS(𝑈𝐼,𝑖 × 𝐼𝑖 )
R
Aotea
Quay
North
2.64
2.09
2.63
14.55
Boulcott
St.
Ghuznee
St.
2.72
1.86
2.01
10.13
2.72
2.11
2.01
11.52
Happy
Valley
Rd.
2.72
1.56
1.20
5.09
33
Hawkeston
St.
2.72
1.72
2.01
9.38
Helston
Rd.
West
2.21
1.66
2.01
7.35
Hobson
St.
3.31
2.14
2.01
14.27
Owhiro
Bay
Pde.
2.72
1.57
1.78
7.61
The
Terrace
2.72
1.47
1.78
7.10
Table 7. Risk scoring for nine analysed bridges using NZTA method.
Row
no.
Risk scoring
Hazard
factors
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
*
Aotea
Quay
North
Boulcott
St.
Ghuznee
St.
Happy
Valley
Rd.
0.33
0.33
0.33
0.33
0.50
1.00
1.00
1.00
1.00
1.00
0.58
Bridge
Hawkeston
St.
Helston
Rd.
West
Hobson
St.
Owhiro
Bay
Pde.
The
Terrace
0.33
0.33
0.33
0.33
0.33
1.00
1.00
0.70
1.00
1.00
1.00
1.00
0.00
0.00
0.00
1.00
0.50
0.00
1.00
0.66
1.00
0.66
0.00
0.51
0.00
0.51
0.00
0.34
1.00
0.73
0.50
0.58
0.00
0.51
0.02
0.01
0.01
0.01
0.01
0.01
0.01
0.02
0.01
1.00
1.00
1.00
0.00
1.00
1.00
1.00
0.00
0.33
1.00
1.00
1.00
0.00
1.00
1.00
1.00
0.00
1.00
0.90
0.90
0.90
0.70
0.90
0.90
0.90
0.90
0.90
0.00
0.40
0.00
0.39
0.00
0.39
0.00
0.11
0.00
0.39
0.00
0.39
0.00
0.39
0.00
0.15
0.00
0.32
1.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.50
0.00
0.00
1.00
0.00
0.00
1.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
1.00
0.50
0.49
0.00
0.00
0.47
0.109
0.60
0.50
0.11
0.00
0.00
0.15
0.039
0.60
0.50
0.22
0.00
0.00
0.16
0.041
0.00
0.50
0.00
0.00
0.00
0.08
0.005
0.60
0.50
0.36
0.00
0.00
0.29
0.058
0.20
0.50
0.00
0.00
0.00
0.35
0.046
0.60
0.50
0.18
0.00
0.00
0.41
0.117
0.00
0.50
0.00
0.00
0.00
0.08
0.007
0.00
0.50
0.00
0.00
0.00
0.08
0.013
Weight
*
0.40
PGA
Remaining
0.30
life
Soil
0.15
condition
Liquefaction
0.15
Hazard score
Importance
Weight
factors
AADT ×
0.50
Detour
length
AADT
0.10
u/bridge
Facilities
0.15
crossed
Strategic
0.15
importance
Utilities
0.10
Importance score
Vulnerability Weight
factors
Design year
0.25
Hinges
0.08
Overlap at
0.10
supports
Length
0.12
Pier type
0.15
Skew
0.05
Abut. type
0.10
Other
0.15
Vulnerability score
Risk score (5×11×20)
PGA = peak ground acceleration; ** AADT = annual average daily traffic
34
Table A1: Risk assessment levels and associated uncertainty premium scores
Level
1
2
3
4
5
Data included and assessment techniques
No or very limited data, aerial photos, site
photos, GIS data, non-site specific reports,
document reviews
Aerial photos, site photos, as-built plans,
visual inspections, maintenance history,
traffic data, document reviews
Aerial photos, site photos, as-built plans,
visual inspections, maintenance history,
site-specific soil data, traffic data, analytical
techniques,
Aerial photos, site photos, as-built plans,
visual inspections, maintenance history,
site-specific soil data, traffic data, in-depth
analytical techniques
Aerial photos, site photos, as-built plans,
visual inspections, maintenance history,
traffic
data,
non-destructive
testing,
structural monitoring, site-specific soil data,
in-depth analytical techniques
35
Quality
assurance
Minimum
standards
Uncertainty premium
score
1.4
Adequate
practice
1.3
Adequate
practice
1.2
Best practice
1.1
Best practice
1.0
Table A2: Hazard scores
Hazard score
MDSA (g)
Remaining service life (years)
1
<1.2
<25
36
2
1.2-2.0
25-50
3
>2.0
>50
Table A3: Structural vulnerability scores
Vulnerability
score
Piers
Abutments
Retaining
walls
Spalling and
cracking
1
2
SUBSTRUCTURE
 Designed after 1972
 Designed between 1933
and 1972
 Slab-type piers

Multi-column piers
 High structural ductility
 Verticality is maintained  Intermediate structural
ductility
(no out of plumb
movement)
 Some signs of out of
plumb movement
 Primarily axial loads
applied
 Concentric axial forces
and moderate bending
 Slenderness ratio <22
moments applied to the
 Pier reinforcement
column
embedded in

superstructure/ foundation Slenderness ratio 22100
at least for the
development length
 Pier reinforcement
embedded in
superstructure/
foundation for less than
the development length
3
 Designed before 1933
 Single column piers
 Limited structural
ductility
 Significant out of
plumb movement
 Axial loads acting
eccentrically and/or
significant moments
producing P-delta
effects
 Slenderness ratio
>100
 Pier reinforcement
embedded in
superstructure/
foundation for less
than the development
length
 Monolithic abutments
 Monolithic abutments
 Non-monolithic
abutments
 Length of abutment <35m  Length of abutment 35for concrete
50m for concrete
 Length of abutment
superstructure, <20m for
superstructure, 20-40m
>50m for concrete
steel superstructure main
for steel superstructure
superstructure, >40m
members
main members
for steel
superstructure main
 Approach settlement slab  Approach settlement
members
>7m long
slab 4-7m long
 Approach settlement
slab <4m long
 Non-integral bridge
 Walls ≥3m in height
 Mechanically
abutment walls or
stabilised earth walls
 Anchored walls
independent walls <3m in  Factor of safety for
 Factor of safety for
height
sliding <1.2,
sliding 1.2-1.4,
overturning <1.5, and
 Gravity and reinforced
overturning 1.5-1.7, and
concrete cantilever walls
overall stability
overall stability against
against soil failure
soil failure 1.25-1.45
 Factor of safety for
<1.25
sliding >1.4, overturning
>1.7, and overall stability
against soil failure >1.45
 No visible sign of spalling  Evidence of concrete
 Excessive cracking
or excessive cracking
cracking, spalling and
and spalling and
some reinforcement
major exposure of
exposed
substructure
reinforcement
37
 Skew angle <5°
 Skew angle 5-15°
 Skew angle >15°
Skew
SUPERSTRUCTURE
 High structural
 Medium structural
redundancy: no hinges in
redundancy: no more
superstructure spans
than 1 hinge in
superstructure spans
 No structural
redundancy, e.g.
simply supported
Structural
span(s)
redundancy
 Very limited
structural redundancy:
2 or more hinges in
superstructure spans
 No visible sign of spalling  Evidence of concrete
 Excessive spalling
spalling and some
and major exposure of
Spalling
reinforcement exposed
superstructure
reinforcement
Fatigue
 No/low evidence of
 Evidence of significant  Excessive evidence of
cracks in
significant cracks in the
cracks in the
major cracks in the
girders
superstructure
superstructure
superstructure
 No/low evidence of
 Evidence of bearing
 Excessive evidence of
Bearing
bearing deflection or
deflection and signs of
bearing deflection and
failures
signs of damage
damage
signs of damage
 No/low evidence of
 Evidence of movement  Excessive evidence of
movement during
during earthquakes and
movement during
Expansion
earthquake or closing of
closing of expansion
earthquake and
joints
expansion joints
joints
closing of expansion
joints
Overlap on  Overlap >400mm if no or  Overlap 200-400mm if  Overlap <200mm if
supports,
loose linkage, >300mm
no or loose linkage,
no or loose linkage,
linkages,
for holding down bolts,
150-300mm for holding
<150mm for holding
shear keys,
>200mm for tight tension
down bolts, 100-200mm down bolts, <100mm
holding down linkage
for tight tension linkage
for tight tension
bolts
linkage
 Spacing of hold down
 Spacing of hold down
bolts <300mm
bolts 300-400mm
 Spacing of hold down
bolts >400mm
 Bolts develop clamping
 Bolts develop clamping
force >500kN per metre
force 300-500kN per
 Bolts develop
length
metre length
clamping force
<300kN per metre
 No/low evidence of
 Evidence of moderate
length
yielding, damage or
yielding, damage or
movement across
movement across
 Evidence of excessive
linkages/shear keys/
linkages/shear keys/
yielding, damage or
holding down bolts
holding down bolts
movement across
linkages/shear keys/
holding down bolts
DECK
 Fully cast in place
 Partially cast in place or
concrete deck
precast concrete deck
Slab
 Minimum slab thickness  Minimum slab thickness
>165mm
≤165mm
38
 2 layers of isotropic
reinforcement
 Reinforcing steel of
Grade 430 or higher
 The outer layer of
reinforcement in each
face of the slab placed
Reinforcement
normal to the beams
 The maximum spacing of
reinforcement ≤300mm
 Reinforcement lap splices
at least equal to
development length
 1 layer of isotropic
reinforcement
 Reinforcing steel of
Grade 300 or less
 The outer layer of
reinforcement in each
face of the slab placed
parallel to the beams
 The maximum spacing
of reinforcement
>300mm
 Reinforcement lap
splices less than
development length
39
Table A4: Geotechnical vulnerability scores
Vulnerability
score
Soil
homogeneity
1
 Homogenous soil
deposit under
foundations
 No/low liquefaction
potential from hazard
map or field test
 Pre-pleistocene soil
 Clay
 Liquefaction safety
Liquefaction factor FL>1.5
potential
Lateral
spreading
 >30m to liquefiable
layer
2
SOIL
 Non-homogenous
 Strongly non-homogenous
deposits, e.g. individual deposits, e.g. some
foundations on different foundations on man-made
in-situ soils
infill and some on in-situ
soil
 Moderate liquefaction
 High liquefaction potential
potential from hazard
from hazard map or field
map or field test
test
 Pleistocene soil
 Liquefaction observed in
the past
 Transition between
clay-like and sand-like  Holocene or less than 500
soil
years uncompacted fill
 Liquefiable deposit
 Sandy or silty soil
treated before
 Semi confined or confined
construction
aquifer near structure
 Liquefaction safety
 Liquefiable deposit not
factor FL =1.2-1.5
treated before construction
 Liquefaction safety factor
FL<1.2
 Ground slope <5%
 Ground slope >5%
 ≤30m to liquefiable
 Over water body or near
layer
steep slope
 ≤30m to liquefiable layer
 Medium density soil
 Loose soil
 Skew angle 5-15°
 Skew angle >15°
 Dense soil
 Skew angle <5°
 Raft foundations or
end bearing piles
 >10km away from
 Within 5-10km of an
any active faults with
active fault with an
Fault rupture an average recurrence
average recurrence
interval of 1000 years
interval of 1000 years
or less
or less
 Extensive ground
 Some ground
improvement at site
improvement at site
Ground
 Pile pinning
improvement technique used to pin
upper liquefiable soil
layers
 All slopes stable
 Mitigation of steep,
Slope
unstable slopes near
stability
bridge
FOUNDATION
 Founded on deep
 Founded on shallow
Foundation
foundation or bedrock
foundations on
and soil type
cohesive soil
Bearing
capacity,
settlement
3
40
 <5km of an active fault
with an average recurrence
interval of 1000 years or
less
 No ground improvement or
mitigation of liquefiable
deposits at site
 Steep, unstable unmitigated
slopes near bridge
 Founded on shallow
foundations on noncohesive soil
Piles
Foundation
settlement
Scour
 Piles reinforced along
their whole length
 Vertical piles
 No significant
movement between
the piles and soil, and
no relative movement
between the pile caps
 Cast-in-place concrete
piles,
precast/prestressed
concrete piles
 Centre-to-centre
spacing ≥770mm and
2.5 pile diameters
 No settlements in
medium-dense or
loose dry sands
 No post-earthquake
consolidation
settlements of clay
layers
 No settlements
induced by the postearthquake dissipation
of pore pressure in a
non-liquefiable sand
deposits
 Deep foundations (e.g.
long or drilled piles)
on durable rock
 Foundations outside of
channel and flood
plain and well above
water elevation
 Resistant material
 No evidence of scour
observed
 Low stream velocity
<1m/s
 Mild stream slope
 Spill-through
abutment
 Piles reinforced along
whole length
 Inclined piles
 Evidence of moderate
movement between the
piles and the soil, and
relative movement
between the pile caps
 Centre-to-centre
spacing <770mm or 2.5
pile diameters
 Sheet steel piles
 Piles not reinforced along
whole length
 Inclined piles
 Evidence of major
movement between the
piles and the soil, and
relative movement between
the pile caps
 Centre-to-centre spacing
<770mm or 2.5 pile
diameters
 Steel H piles
 Some settlements in
medium-dense or loose
dry sands
 Some post-earthquake
consolidation
settlements of clay
layers
 Some settlements
induced by the postearthquake dissipation
of pore pressure in a
non-liquefiable sand
deposits
 Foundations protected
by boulders or other
protective techniques
 Medium resistance
material
 Mild evidence of scour
observed
 Observed build-up of
debris reducing bridge
waterway at piers or
abutments
 Medium stream
velocity 1–2m/s
 Medium stream slope
 Vertical wall abutment
with wing walls
 Significant settlements in
medium-dense or loose dry
sands
 Significant post-earthquake
consolidation settlements of
clay layers
 Significant settlements
induced by the postearthquake dissipation of
pore pressure in a nonliquefiable sand deposits
41
 Foundations are inside the
channel and flood plain
 Foundations on scourable
material such as sand
 Visible scour observed
 High stream velocity >2m/s
 Steep stream slope
 Vertical wall abutment
Table A5: Impact scores
Impact score
Public safety
Replacement/repair
cost
1
 Importance level 1 or
2*
 AADT**<4,000 and
ADTT***<200 on the
bridge or road under
the bridge
 Crosses facilities
with no or minimum
human presence, e.g.
stream, parking or
storage facilities
 Low
replacement/repair
cost <NZ$100,000
 AADT<4,000 and
ADTT<200 on the
bridge
 AADT<4,000 and
ADTT<200 on road
under the bridge if
affected by bridge
failure/closure
 Detour routes <5km,
no congestion
Typical road use
Emergency road
use
2
 Importance level 3
 AADT=4,000-10,000
and AADT=200-600
on the bridge or road
under the bridge
 Crosses facilities
involving occasional
gathering of people,
minor commercial or
industrial facilities
 Crossing a secondary
road, railway line or
navigable channel
 Moderate
replacement/repair
cost NZ$100,000NZ$500,000
 AADT=4,000-10,000
and ADTT=200-600
on the bridge
 AADT=4,000-10,000
and ADTT=200-600
on road under the
bridge if affected by
bridge failure/closure
 Detour routes 510km, with some
congestion
 Crossing a secondary
road, railway line or
navigable channel
 Not along designated  Not along designated
emergency route or
emergency route or
route to essential
route to essential
lifelines
lifelines
 Importance level 1 or  Importance level 3
2
42
3
 Importance level 4 or 5
 AADT>10,000 or
ADTT>600 on the
bridge or road under
the bridge
 Crosses facilities
involving frequent
gathering of people,
major commercial or
industrial facilities
 Crossing a major road,
railway line or
navigable channel
 High
replacement/repair
cost >NZS$500,000
 AADT>10,000 or
ADTT>600 on the
bridge
 AADT>10,000 and
ADTT>600 on road
under the bridge if
affected by bridge
failure/closure
 Detour routes >10km,
or with significant
congestion
 Major transportation
route such as along
state highway
 Road of national
significance
 Critical link to major
transportation route
 Crossing a major road,
railway line or
navigable channel
 Along designated
emergency route
 Along route to
essential lifelines such
as hospital, ambulance
station, fire service,
key airport etc.
 Importance level 4 or 5
 No or limited water,
sewerage, gas,
telecommunications
or other utilities
supported by bridge
structure
 Medium importance
water, sewerage, gas,
telecommunications
or other utilities
supported by bridge
structure
 High/critical
importance water,
sewerage, gas,
Utilities
telecommunications or
other utilities
supported by bridge
structure
*
Importance level according to NZS1170 (Standards New Zealand 2004b); ** AADT = annual
average daily traffic; *** ADTT = annual daily truck traffic
43
Uncertainty
premium scores
Data
collection/
storage/retrival
 Amount, type and
quality of data
 Type of analyses
performed
 Quality assurance
practice
Hazard score
 Seismic design
action from
design code and
remaining service
life, or
 Site specific,
detailed study
Vulnerability
scores
 Structural
vulnerabilities
 Geotechnical
vulnerabilities
Impact scores
 Public safety
 Replacement/
repair costs
 Usual road use
 Emergency road
use
 Utilities
Calculate risk
R = RMS(UH,i×Hi) × RMS(UV,i×Vi) × RMS(UI,i×Ii)
Rank bridges
Re-evaluate bridges of
high risk
High
Level of
uncertainty?
Low
Select high risk
bridges for
monitoring
Figure 1. Bridge prioritisation methodology.
44
1.00
Ghuznee
0.80
Proposed method
Aotea
Hobson
R² = 0.82
Boulcott
0.60
Hawkeston
Owhiro
Helston
Terrace
0.40
Happy Valley
0.20
0.00
0.00
0.20
0.40
0.60
0.80
1.00
NZTA method
Figure 2. Normalized risk scores by proposed method vs. NZTA method.
45