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An Objective Approach toReliability-Based Risk Assessmentand Mitigation for Coastal

Infrastructure Development

Wen Wu1Sik-Cheung Robert Lo2Xiao Hua Wang1

1 School of Physical, Environmental and Mathematical Sciences, University of New

South Wales at Australian Defence Force Academy, Canberra, Australia

2 School of Engineering and Information Technology, University of New South Wales at

Australian Defence Force Academy, Canberra ACT 2600, Australia


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AN OBJECTIVE APPROACH TO RELIABILITY_BASED RISK ASSESSMENT

AND MITIGATION FOR COASTAL INFRASTRUCTURE DEVELOPMENT

Wen Wu1*

, Sik-Cheung Robert Lo2, Xiao Hua Wang

1

1. School of Physical, Environmental and Mathematical Sciences, University of NewSouth Wales at Australian Defence Force Academy, Canberra ACT 2600, Australia.2 School of Engineering and Information Technology, University of New South Wales atAustralian Defence Force Academy, Canberra ACT 2600, Australia

Abstract

There are numerous kinds of uncertainties involved with coastal infrastructure projects,which may lead to a range of potential risks. The probabilistic approach, which is alsoreferred to as reliability analysis, is one of the essential ways in evaluating the impacts ofuncertainties involved in coastal infrastructure development. This article proposes an

objective approach incorporating uncertainty; risk assessment; and mitigation measuresin order to facilitate the decision making processes involved with coastal infrastructuredevelopment. Basic probability-related concepts, including reliability index, are firstlyintroduced. A stochastic cost-benefit analysis is presented using an example thatillustrates the modelling of uncertain future climate events. The general concept ofquantitative risk assessment, where risk is considered as a product of both the likelihoodof an adverse event and its consequences, are then demonstrated. This is the underpinningconcept for the use of ranking matrix in risk management. The importance of theconsequence analysis is also emphasized. Compared with the conventional deterministicevaluation method, the probabilistic approach incorporating uncertain type provides aneffective way to examine the feasibility and the reliability of coastal infrastructure

development. The methodology presented in this paper is in line with the United NationsInternational Strategy on Disaster Reduction (UNISDR) terminology to avoid the mix-up between low likelihood and low risk, and to avoid the confusion between extremedisaster and extreme events. Finally, the paper indicates a potential application of thismethod for other fields, such as environmental risk assessment in environmentalprotection and management activities.

Keywords: Uncertainty, Probability of failure, Reliability index, Risk assessment,Coastal infrastructure development

* Corresponding author: Wen WuPhD student in OceanographySchool of Physical, Environmental and Mathematical Sciences (PEMS), University ofNew South Wales at Australian Defence Force Academy, Canberra ACT 2600, AustraliaEmail:[email protected]
mailto:[email protected]:[email protected]:[email protected]:[email protected]


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1. Introduction

Uncertainties are becoming commonly recognized issues in various scientific fields, andrisks in carrying out a project are unavoidable under uncertainty. Since it is not alwayspossible to guarantee absolute safety and reliability of a project, it is necessary toconsider uncertainties and risks for an upcoming project (Ang & Tang, 1975; Dandy,1985; Ang & Tang, 1990; Ang & Tang, 2007; Omitaomu & Badiru, 2007). For coastalinfrastructure development, there is also an increasing trend of uncertainties associatedwith population growth, economic development, and climate change (e.g., naturalhazards, extreme events, sea-level rise). There is an increasing recognition that thetraditional expert-based decision making process is insufficient for the environmental orcontroversial risk context (Kalsnes et al., 2010).

How do uncertainties affect coastal infrastructure design and construction, and whatimpact uncertainties have on decision making? As these uncertainties and risks would beexplicitly identified and assessed only in terms of probability, it is essential to involve theconcept and methodology of probability and reliability (Ang & Tang, 1975, 2007). Atpresent, probabilistic method, which is usually referred to as reliability analysis, isdrawing increasing attention (Tung, 1992; Li & Lo, 2006; Wang & Kulhawy, 2008).Numerous relevant studies have been conducted, such as Carmichael & Balatbat (2010);Dandy (1985); Goicoechea et al. (1982); Low & Tang (1997); Nassar & Al-Mohaisen(2006); Omitaomu & Badiru (2007); Sivakumar Babu & Srivastava (2009); Tung (1992);Wang & Kulhawy (2008); and Wu & Lee (1988).

Coastal infrastructure is important for coastal zone development especially becausecoastal zones are costly to construct or rehabilitate. Coastal infrastructure is risk pronebecause it often needs to be built on difficult or weak ground conditions. Theinfrastructure loadings become more uncertain under the impact of environmentalpressures arising from factors such as wind, wave, and currents (Wu & Lee, 1988) Inaddition, uncertainties and risks might extend beyond the construction project in terms of

time and space, i.e., the uncertainties may relate to future events and with adverseconsequences extending well beyond the project site. Thus a subject assessment may bequestionable, and the reliability of coastal infrastructure can be better assessed byprobabilistic analysis.

This paper proposes an objective method to facilitate reliability analysis and riskassessment for coastal infrastructure development. The terminology used is in line withthe United Nations International Strategy on Disaster Reduction (UNISDR) in order toavoid confusion and mix-up (UNISDR, 2009). Basic probability and reliability conceptsare introduced in the first instance. A systematic procedure of reliability-based riskassessment is then provided for calculating the probability of failure (Pf) and thereliability index (). Consequence analysis isemphasized. The trade-off between project

safety and costs is also considered using an example to describe cost-benefit analysis.Finally, a possible application of reliability analysis is provided for an environmental riskassessment used in environmental protection and management activities.

2. Basic Concepts

2.1 Random Variables and Failure


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loading. In investment, the capacity is the benefit and the demand is the cost. The firstfunction of Equation (3) is linear, and thus may have lead to a series of desirablemathematical properties.

Figure 1 provides an intuitive example to describe the concept of failure defined

by a line in the R-S parameter space. The line is referred to as the Limit State Line as itdefines the onset of failure, an unwanted event (Ang & Tang, 1990). Evidently, P f =Pr(G0).

Figure 1. An example illustrating the concept of failure.

2.2 Reliability Index

The complexity of most systems, particularly those involving interacting with nature, makes thecalculation of Pfnot feasible. A simpler alternative is to calculate a reliability index, , asdiscussed below.

The early (and simplified) definition of reliability index, 0, as proposed by Cornell (1969)

is given by:0

G

G

= (4)

where and denote mean and standard deviation of G. Subscript 0 denotes that this is thesimplified definition. G represents a safety margin or safety factor, which is normalized withrespect to variability (as represented by G) to give 0. In general, 0depends on the choice of G(which is non-unique). The many drawbacks of 0are discussed by Lo & Li (1993). However, itsevaluation is relative simple. Techniques such as Monte Carlo simulation with an off-the-shelfplug-in to a spreadsheet application (such as @Risk) or first order second moment methodMostyn & Li (1993) are relatively well established. Furthermore, if the same performancefunction is used, 0can rank likelihood of failure.

The most general and invariant definition of as proposed by Hasofer & Lind (1974) is asfollows:

1( ) ( )Tx F

Min X m C X m

= (5)

whereXis the vector of random variables, mis mean value vector of the random variables, Ciscovariance matrix of the random variables, and F is failure domain. Its values differ from 0asproposed by Cornell (1969), but for the particular case of a linear performance function 0= .Geometrically, is the shortest distance from the transformed failure surface to the origin of


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reduced variate space (Li & Lo, 2006). Thus, the calculation of can be treated as solving anoptimization problem implemented using algorithms available within EXCEL or an off-the-shelfmathematical package (Low & Tang, 1997). A range of methods for calculating and 0havealso been discussed by Li (1991). It is evident that the calculation of or 0 does not requirecomplete knowledge of the pdfs. However, the effects of different pdfs can be compensated by

tail approximation techniques (Li et al., 1993).2.2 Relative Reliability

There are a few challenges in terms of reliability analysis; for example, more input data areneeded (Li & Lo, 2006). Additionally, Pfof most infrastructures is low, and it is rarely possibleto verify a computed Pfor . Another commonly known fact is the scarcity of statistical data,which is widely recognized. This issue cannot be avoided, and has to be confronted in riskassessment studies (Henley & Kumamoto, 1992; Tung, 1992). In order to effectively respond tothese challenges, the concept of relative reliability (or relative failure probability) needs to beapplied to facilitate risk assessment for coastal development. The relative reliability is calculatedwith the same assumptions, model, and input parameters, using the methods introduced

previously. A simpler approach of calculating or 0is preferred. Although the absolute valuesso computed are not necessarily correct, the relative values can be used for ranking purpose. Thisprovides an objective basis (as explained in a later section) for evaluating different options,noting that no action is itself an option.

3. Stochastic Cost-benefit Analysis

We will use an example to illustrate how uncertainties can be modeled in a cost-benefit analysisfor a major coastal infrastructure project. Cost-benefit analysis is a well developed tool, which isapplied to evaluate economic values of a project by determining its costs and benefits (Dandy,1985; Nassar & Al-Mohaisen, 2006; Omitaomu & Badiru, 2007). As described by Nassar & Al-Mohaisen (2006), cost-benefit analysis is to convert to the effects of an investment into financialterms and to account the benefits that usually accrue over a long period of time in contrast to thecapital costs (Nassar & Al-Mohaisen, 2006, p. 13). There has been a growing application ofcost-benefit analysis because of the demand for project efficiency (Nassar & Al-Mohaisen, 2006).The value of a project can be expressed as:

Value cost benefitst i

= + (6)

where i denotes sources of benefit, t denotes years from a projects start date, and thusbenefits are summed over both sources of benefit and time. Equation (6) intrinsically assumesthat benefits have been discounted to present value (PV). All the costs and benefits convertedinto dollars are equivalent as a common measurement. We would like to avoid V 0.Furthermore in comparing different design and development options, V is an important index to

guide management or policy decision. However, it is difficult to accurately predict the futurebenefits because of a range of reasons (Goicoechea et al. 1982; Dandy, 1985; Omitaomu &Badiru, 2007; Carmichael & Balatbat, 2010). Therefore, variables and parameters in the aboveequation should be treated as probabilistic (random variables) rather than deterministic.

In a stochastic cost-benefit analysis, input used to calculate costs and benefits areconsidered as random variables (i.e., characterized probabilistically). Therefore, V is also arandom variable. One may define failure as V0, and thus one can evaluate the investment

worthiness of the project by Pf, defined by P(V0). Different designs or development options


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have different Pf, which can be compared. As discussed in the previous section, one can alsoevaluate the design options by calculating their respective reliability indices, noting that there isan inverse relationship between Pfand .In the context of a stochastic cost-benefit analysis, thereliability index can also be considered an index for the upside of V.

The above paradigm is illustrated in Figure 2. The initial cost is represented by the red line,

whereas the projected benefits over time is given by the blue line. Since future return isintrinsically uncertain, its variability is indicated by a pair of dotted curves, noting that variabilityincreases with time into future. Evidently, one can then either calculate Pf or using themethodology presented in Section 2.1, noting that, more likely than not, the calculation of ismore feasible.

Figure 2. Original costs and benefits without consideration of uncertainties.For coastal infrastructure, there could be more uncertainties caused by climate change,

including increasing temperature, sea-level rise, and extreme weather events (Kalsnes et al.,2010). As a result of these driving forces, uncertainties and changes in coastal infrastructure willemerge, such as more severe loading; decreased longevity; more maintenance costs; altered landuse; changed construction location; and higher frequency destruction. A stochastic cost-benefitanalysis can take these factors into account as illustrated by the following example. Consider the

simple case of whether one needs to design for a significantly more severe storm surge/loadingfor a proposed coastal infrastructure, one may consider the following three design options:o[A] Now only: the design is based on present storm surge/loading criteria without

any allowance for a significantly more severe storm in the future.o[B] Future: the design can cope with a significantly more severe storm

surge/loading criteria that may (or may not) occur in the future.o[C] Adaptability: although the design can only cope with present storm

surge/loading criteria, but allowance is made to enable future strengthening.In option A, the infrastructure is only built for current conditions and may have to be

abandoned in the future due to climate change. This means assigning a finite probability of amore significantly severe storm conditions and the occurrence of such an event will trigger an

abandonment cost and reducing the future benefits after abandonment to zero. In option B, theinitial construction will be increased by a significant amount, but future benefits are kept. Inoption C, the initial construction cost will be increased by a modest amount. A finite probabilityof a more significantly severe storm condition also needs to be assigned, and the occurrence ofsuch an event will trigger a future strengthening cost which will then enable benefits be kept.The three options are illustrated schematically in Figure 3.


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[In Figure 3 Reviewer A suggests change to: Abandom & loss of future benefits??]Figure 3. Schematic diagram for the three options given by the cost-benefit example.

In a more general context, T, the year when more severe storm loading occurs due toclimate change, is uncertain. Thus, it should also be modelled as a random variable. Such a

general formulation [i.e., Equation (6)] can be mathematically expressed as:Option A: Value cost ( ). [1 ( )] benefits

t i

u L A u t = + T T (7)

where Ais the abandonment cost, L= design life of infrastructure, u(x) is a unit-step function

where its value jumps from 0 to 1 at x=0. In the above equation, at tT, the benefits for t > T

will be nullified, and A will be triggered if T< L. Furthermore, ifT> L, it is mathematically

equivalent to non-occurrence of significantly more severe storm loading.Option B: Value cost benefits

t i

B= + (8)

where B is the additional capital cost due to designing for a significantly more severe future

loading that may or may not occur.Option C: Value cost ( ). benefits

A S

t i

C u L t C = + (9)

where CAis the extra initial cost to ensure adaptability, and CSis the strengthening cost, which

will be triggered if T


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In a stochastic cost-benefit analysis, the relative Pf or of different options may provide thenecessary quantitative information for assessing the relative risk of an investment or policydecision. However, there are other failures, such as flooding or partial failure of a seawall section,the corresponding Pf or is not adequate for risk assessment or management. It is generallyaccepted that uncertainty would involve risk (Ang & Tang, 1975; Ang & Tang, 2007; Kalsnes et

al., 2010). However, the definition of risk may be somewhat elastic. Henley & Kumamoto (1992)defined risk as the possibility of loss or injury to people and property, which appears to suggestthat risk equate to probability of failure leading to injury or loss of property. However, Henley &Kumamoto (1992) also suggested that, if there might be failures in the system, risk assessmentwould be conducted to determine the consequence of the failure in terms of possible damage toproperty or people (Henley & Kumamoto, 1992, p. 8). This implies consequence also needs tobe considered as part of risk assessment.Wu & Lee (1988) stated that an ideal choice of factor ofsafety (against collapse of a structure) should be consistent with uncertainties magnitudes andtheir consequences. As uncertainties are also associated with P f, this implies both Pf andconsequence need to be considered. This paper adopts the approach of quantitative risk analysis(QRA), which defines risk by the following equation:

Risk=likelihood of a hazard consequence of the hazard (10)Noting that likelihood of a hazard occurring is in fact Pf, and therefore:Risk =Pf Consequences (11)

This approach is in line with the UNISDR (2009) and avoids many confusing statements aboutrisk (UNISDR, 2009). For example, if the hazard under consideration is the catastrophic failureof a deep sea oil drilling platform that may be extremely unlikely, the consequence is so high thatone cannot infer that the risk is negligibly low.

5. Risk Management

The Australian and New Zealand Standard on Risk Management (AS/NZS HB4360:2004), alongwith its companion handbook calledRisk Management Guidelines - Companion to AS/NZS 4360,provides a generic framework and guidance for effective risk assessment and management,which is applicable for any organizations. A systematic risk management process is establishedaccording to this standard (Figure 4). This process includes a series of phases, such asappropriate definition of context and hazard sources, risk assessment and treatment. There into,risk assessment is the key part of the process involving risk identification, analysis, andevaluation. Additionally, auditing means including communication, consultation, monitoring,and review are penetrated throughout the entire risk management process to ensure effectiveoperation of the process (Henley & Kumamoto, 1992; Broadleaf Capital International, 2007;Kalsnes et al., 2010; Standards Australia and Standards New Zealand, n.d.).
http://www.saiglobal.com/shop/script/Details.asp?DocN=AS0733759602AThttp://www.saiglobal.com/shop/script/Details.asp?DocN=AS0733759602AThttp://www.saiglobal.com/shop/script/Details.asp?DocN=AS0733759602AThttp://www.saiglobal.com/shop/script/Details.asp?DocN=AS0733759602AT


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Figure 4. Risk management process (modified from Broadleaf Capital International, 2007).

5.1 Risk Assessment Matrix

A ranking matrix as illustrated in Figure 5(a) is usually adopted in risk management. The risk, ormore precisely the relative risk, is determined by its position in the matrix. The column positionreflects Pf, whereas the row position reflects consequence. As the situation moves down thediagonal of the matrix, the risk increases. The off-diagonal positions are neither highest norlowest risk, because these positions do not have the most severe combination of P f andconsequence, as shown in Figure 5(b). This approach, although non-numeric in nature, reflectsthe spirit of Equation (10). It avoids the temptation of arbitrarily assigning, or shopping for, apre-conceived risk ranking, or of confusing about a highly unlikely hazardous event to low risk,or emotional debate on risk (or relative risk).

(a)

consequence 1 2 3 4

1

2

3

4

LikelihoodL H

H

L

(b) 1/ or (- )

Conse

quence high risk

low risk

Figure 5. (a): Risk assessment matrix; (b): Corresponding coordinates.


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Table 1. A quantitative example of risk assessment matrix.

Reliability index ()

$-Consequence 4 3 2 1

10 Million

100 Million

1 Billion

10 Billion

The ranking matrix can also be used in a quantitative form as illustrated in Table 1. In thisapproach, the column position is quantitatively determined by , whereas the column position isdetermined by dollar-equivalent of consequence.

5.2 Extreme EventsQuantifying Pfor is particularly important when one deals with allegedly extreme event. Anevent may be considered as extreme relative to our experience. But this presents the question ofwhether ones experience (of the past) is representative of new challenge. There is also thepossibility of confusion between an extreme event (like 1 in 1000 year storm) and an extremedisaster. The latter is the realization of extreme risk due to which may be caused by both anextreme event and poor risk management. Many may consider the 2004 Christmas tsunami in theIndian Ocean as an extreme event. Kokushu (2005) examined data reported by the Centre forResearch on the Epidemiology of Disasters (CRED) and arrived at some surprising findings. The2004 tsunami is an 1 in 100 year tsunami, not really such an extreme event. Even if one looks atthe disaster magnitude of this tsunami in terms of 200,000 deaths, which occurs 1 in 16 years,

which is relatively frequent. Indeed, the same database illustrated that various disasters have ledto more than 50,000 deaths occurred once every five years in the 20 thcentury. Thus, subjectiveperception of likelihood is influenced by our exposure (or lack of it) to these extreme disasters.The above discussion highlights the relevance of adopting a quantitative approach to riskassessment.

5.3 Consequence Analysis

Consequence analysis is clearly an important issue in risk assessment and management(Omitaomu& Badiru, 2007), and often expertise from different disciplines is needed in such anexercise. In a consequence analysis, both the immediate and longer term consequences of

failures need to be considered, and the term of reference in assessing consequences also needs tobe explicitly stated. This aspect is particularly important in assessing environmentalconsequences.

Often a failure may not directly equate to loss in property or life. For failure that may lead todamages to properties, Equation (11) is expanded into:

Risk ($) = Pf Vulnerability Property Value (12)


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where consequence depends on the vulnerability of the property, which in turn depends on theengineering design. Number of deaths often cannot be equated to dollar-equivalent. Thus, oneshould expand the basic equation for risk to life as:

Risk (L) = Pf P(S|f) P(T|S) P(L|T) N (13)where P(S|f) = probability of spatial impact (i.e., having an impact on an asset) conditional upon

the occurrence of failure; P(T|S) = probability of temporal impact (i.e., the asset being occupied)conditional upon spatial impact; P(L|T) = probability of non-survival of occupants; and N is thelikely number of occupants. An example is the failure of a coastal slope that may (or may not)impact coastal properties, which may (or may not) be occupied at the time of collapse, whichmay (or may not) survive. Thus, consequence analysis may also involve probability evaluation.

An infrastructure system contains various components, and the failures of different componentsmight lead to different failure modes, thus the system reliability would be affected by thesemultiple failure modes. Component failure and failure modes are central to the identification ofsystem hazards, and therefore it is essential to involve a systematic procedure to identify allpotential failure modes and their possible consequences. The event tree is such an analytical tool

to describe sequence of events leading to failures used in consequence analysis (Ang & Tang,1990; Henley & Kumamoto, 1992). An event might bring about various forms of failure withdifferent consequences; all possible paths follow the initiating event, these paths can be tracedthrough the subsequent events, and every path will lead to a distinct consequence (Ang & Tang,1990). Figure 6 provides a simple event tree. From the diagram it can be seen that a particularconsequence depends on the subsequent events following the initiating event (Ang & Tang,1990, p. 498). These multiple consequences of a system failure are often calculated by Equation(14) below, which in fact is the weighted sum of the final series of failures:

, ,1 , ,2 ,1 , 1Pr( ). Pr( | ) Pr( | )....Pr( | ).f trigger i f trigger i i if i ni ii

Consequence E E E E E E E C

= (14)

where trigger, Ef, trigger, is the triggering failure (i.e., initiating event), subscript i denotes the i-thbranch of the event tree that leads to the i-th consequence, Ci, Pr(Ej|Ej-1) is the probability of theoccurrence of the subsequentj-th conditional upon the occurrence of the (j-1)-th event, and Eifisthe final subsequent event on the i-th branch thus giving Ci.However, the above equation isbased on the assumption that subsequent events and failures on different branches of the treediagram are mutually exclusive.

Initiating

event

First

subsequent

events

Second

subsequent

events

n th

subsequent

events

p12

p11

p13

p21

p22

pnk Cijk

Consequence

Figure 6. A simple event tree diagram, where pnkis path probability, Cijk is consequences(modified from Ang & Tang, 1990).

A consequent analysis may also involve developing risk mitigation measures (Kalsnes et al.,2010). This is particularly important because we in general have no control on the occurrence ofan extreme event, only some control on Pftriggered by an extreme event. As discussed in Section


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5.2, an extreme disaster may be partly due to extreme consequences. The mitigation measuremay be in the form of better overall planning and design, such as the provision of a buffer zonefor coastal development, provision of flood resistant road pavement (to facilitate evacuation),readiness of disaster relief team, among others. It may also be in the form of monitoring systems(Kalsnes et al., 2010).

6. Application of Reliability Analysis into Environmental Risk AssessmentUncertainty and probability based reliability analysis might also be introduced intoenvironmental risk assessment (ERA) focusing on environmental protection and managementactivities. In this section, we will describe an ERA approach that is applied in environmentalmanagement of military activities using an example of Shoalwater Bay Training Area (SWBTA)in Central Queensland, Australia. An example of potential application of reliability analysis inERA will also be introduced.

SWBTA is one of the most significant military training areas in Australia. Specially, SWBTA isan important protected area due to its integrated marine and terrestrial ecosystem; diverse natural

resources; sensitive environment; and cultural significance (Sharp, 1998; Australian HeritageDatabase, 2009; Wu et al., 2010). It is therefore vital and complicated to manage militaryactivities in such a fragile environment.

Defence environmental managers recognize their responsibilities in maintaining environmentalsustainability while conducting military activities (Cuddy et al., 1990; Scott et al., 2000; Wark &Verrier, 2002; Department of Defence, 2003; Bioce, 2007; Beeby, 2008; Wu et al., 2010;Department of Defence, n.d.). ERA is an essential part of their Environmental ManagementSystem (EMS). Potential environmental hazards caused by military training exercises are firstlyidentified. In light of Equation (10), risk levels of these hazards are determined according to thefollowing risk assessment matrix shown in Table 2. This matrix is a ranking system similar to therisk assessment matrix shown in Figure 5(a).

Table 2. ERA matrix for SWBTA (adapted from Fendl & Sensese, 2007; SKM, 2009).

LikelihoodConsequence

Severe Major Moderate Minor NegligibleAlmostcertain

Very hi gh Very hi gh High Medium Low

Likely Very hi gh High Medium Medium Low

Possible High High Medium Medium Low

Unlikely High Medium Medium Low LowRare High Medium Low Low Low

The assessment process is conducted by a special operation panel including military personnel;environmental experts; consultants; and other stakeholders. The risk identifying criteria relies onthe panel members knowledge and experience. The measured risk levels have derived fromprevious risk assessment materials and information on previous military exercises conducted at


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SWBTA (Collie & Sensese, 2005; Fendl & Sensese, 2007; Wu et al., 2010). Thus, the riskidentification and assessment are based on collective judgement, rather than detailedcalculations (Collie & Sensese, 2005, p. 6). This indicates subjectivity, and is therefore notsufficient methodology for effective ERA. A quantitative evaluation process is therefore required(Ramos and de Melo, 2005; Wu et al., 2010).

The reliability analysis method proposed here may be applied in the SWBTA ERA process,using firebreak establishment in training area as an example. In SWBTA, there are variousuncertainties attributed to fire hazard and making this area fire prone. These uncertainties includeclimate change; increasing temperature; decreasing rainfall; dry season; inappropriate trainingtiming; increasing training frequency; improper operation; and environmental awareness.Consequently, the probabilistic approach considers that these uncertainties can be used in theERA of potential fire hazard. Based on the assumption made in Section 3, there can be also threeoptions for firebreak establishment: (a) only for now; (b) more fires in future (that may or maynot occur); and (c) adaptability that allows strengthening the firebreak during a long period.Referring to the cost-benefit analysis given in Section 3, firebreak values for options (a), (b), and

(c) can be obtained in terms of Equation (6), and Figures 2 and 3. Thus, the reliability ofdifferent firebreak establishments can be analysed, which will then provide more objectivedecision making for environmental managers of the SWBTA.

7 Discussion and Conclusions

We propose an objective approach incorporating uncertainties for risk assessment of coastalinfrastructure development. Probability-related concepts and the probabilistic approach areemphasised, and the methods to calculate the reliability index have been introduced. As foruncertain circumstances, a stochastic cost-benefit analysis is considered to be an effective toolfor assessment. An example of cost-benefit analysis has been used to illustrate the reliabilityanalysis process.

Uncertainties are inevitable in risk assessment. So far, probability is regarded as the onlysensible description of uncertainty and is adequate for all problems involving uncertainty(Omitaomu & Badiru, 2007, p. 162). Compared with conventional deterministic analysis,probabilistic approach incorporating uncertainty and reliability into the future has advantages. Ascoastal infrastructure is expected to have a long lifespan and there might be long-term impactscaused by uncertain events such as climate change, probabilistic analysis method is moreapplicable for risk assessment and management.

Consequence analysis is also emphasised in this paper. Besides working on Pfand/or , risks canbe reduced by working on consequences. For coastal infrastructure, natural hazards might beconsidered as acts of nature and unpreventable, whereas disasters can be partly preventablethrough consequence analysis.

In addition to coastal infrastructure development, the methods proposed in this paper can also beapplied in environmental risk assessments for environmental protection and management. As theexample in Section 6 illustrated, the qualitative approach appears to be subjective, and thereforethe systematic procedures and quantitative approach documented in this paper should guide the


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ERA in order to make the evaluation more quantitative and objective for more environmentally

resilient and adaptive management.

In summary, no systems will endure forever and nothing is extremely reliable because

uncertainties are inevitable in coastal infrastructure development. Reliability analysis

incorporating uncertainties is an effective method to reduce the probability of failure as low aspossible. However, further studies are required to model, analyse, and update various

uncertainties in coastal engineering. Through hazard identification; reliability-based risk

assessment; and cost-benefit analysis; we can predict potential failures and risks in the coastal

infrastructure system and formulate preventative measures for solutions. These allow responsible

organisations to be able to make educated decisions and establish reliable coastal infrastructure.

Acknowledgments

This paper is developed from a presentation given at the Workshop on Developing an Australia-

China Research Centre for Coastal Zone Sustainable Development, which was supported by the

University of New South Wales at Australian Defence Force Academy (UNSW@ADFA), Ocean

University of China (OUC), and grant (RG103046) from the Australia-China Council, Australia.

The first author has been sponsored by the China Scholarship Council (CSC) since August 2008

for her PhD program in Australia. This is a publication of the Sino-Australian Research Centre

for Coastal Management, paper number 6.

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