- Email: [email protected]
Risk-based Resilience Assessment Model Focusing on Urban Infrastructure System Restoration
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Procedia Engineering 212 (2018) 1115–1122
7th International Conference on Building Resilience; Using scientific knowledge to inform policy 7th Conference on Building Resilience; scientific knowledge to inform policy andInternational practice in disaster risk reduction, ICBR2017, 27Using – 29 November 2017, Bangkok, Thailand and practice in disaster risk reduction, ICBR2017, 27 – 29 November 2017, Bangkok, Thailand
Risk-based Resilience Assessment Model Focusing on Urban Risk-based Resilience Assessment Model Focusing on Urban Infrastructure System Restoration Infrastructure System Restoration a a
a Citra S. Ongkowijoyoa* a* and Hemanta Doloia Citra S. Ongkowijoyo and Hemanta Doloi
University of Melbourne, Parkville-Melbourne, 3010-Victoria, Australia. University of Melbourne, Parkville-Melbourne, 3010-Victoria, Australia.
Abstract Abstract A number of metrics in the past studies have been proposed and numerically implemented to assess particular system resilience A number of metrics the past have proposed of andthe numerically implemented to assess particular system resilience during natural disasterin and theirstudies recovery in been the aftermath events. Among such performance measures, resilience is a during and their recoveryoninthe theurban aftermath of the events. such performance measures, resilience risk is a reliablenatural metric. disaster The resilience assessment infrastructure systemAmong facing disturbances depends on comprehensive reliable metric. The resilience the urban infrastructure system the facing comprehensive risk assessment. Nonetheless, it is assessment found that on previous studies lack of putting riskdisturbances assessment depends processesonwithin the resilience assessment. Nonetheless, is found that previous studies lack of putting risk assessment within the resilience assessment bodies. This itstudy proposes a risk criticality-based resilienttheassessment model processes for scenario-based assessment of bodies. This study proposes risk accounts criticality-based resilient assessment model for scenario-based resilience infrastructure systems. The amodel for uncertainties in the process including; the people expressions assessment of measures, infrastructure Theand model accounts uncertainties in the and process people propagation expressions towards risks riskssystems. magnitude its impact to for community estimation, the including; dynamic ofthe causality towards risks measures, risks magnitude andapplied its impact to community estimation, case and the dynamic causality propagation pattern simulation. The proposed model is to water supply infrastructure study with a ofhypothetical restoration pattern The level proposed modeland is applied to water infrastructure case study withsystem a hypothetical scenario.simulation. The resilience is assessed determined based supply on the maximum resilience level the can reach. restoration Results of scenario. Thehave resilience and integrated determinedmitigation based on the maximum resilience system can reach.phenomena Results of this analysis shownlevel thatisa assessed holistic and plans and strategies that level seek the to address complex this analysis have shown that holistic requirement. and integrated mitigation plans and strategies thattoseek to addressassess complex towards system restoration is a critical The model will enable stakeholders systemically the phenomena most-likely towards system restoration is a critical requirement. performance of the system during expected risk events.The model will enable stakeholders to systemically assess the most-likely performance of the system during expected risk events. © 2017 The Authors. Published by Elsevier Ltd. © 2017 2018 The The Authors. Published Ltd. © Authors. Published by by Elsevier Elsevier Ltd. committee of the 7th International Conference on Building Resilience. Peer-review Peer-review under under responsibility responsibility of of the the scientific scientific committee of the 7th International Conference on Building Resilience. Peer-review under responsibility of the scientific committee of the 7th International Conference on Building Resilience. Keywords: Risk assessment; Resilience analysis; Infrastructure system; Urban community. Keywords: Risk assessment; Resilience analysis; Infrastructure system; Urban community.
*Corresponding author: Tel: +61424578989 *Corresponding author: Tel: +61424578989 Email: [email protected] Email: [email protected]
1877-7058 © 2017 The Authors. Published by Elsevier Ltd. 1877-7058 2017responsibility The Authors. of Published by Elsevier Ltd. of the 7th International Conference on Building Resilience. Peer-review©under the scientific committee Peer-review under responsibility of the scientific committee of the 7th International Conference on Building Resilience.
1877-7058 © 2018 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the scientific committee of the 7th International Conference on Building Resilience 10.1016/j.proeng.2018.01.144
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1. Introduction The built environment such as urban infrastructure (UI) system provides the essential physical basis for modern societies, and have multi-dimensional impact on public safety and economic prosperity at regional and national levels. Past experiences have shown that such systems are exposed to various natural and manmade hazards. The resulting damage to the systems may cause human causalities and disrupt the normal day-to-day life of people in the short run. Worse, this damage may also impose significant direct and secondary economic losses due to business interruption that may not ever fully recover [1]. With extensive globalization and connectivity, the effects of natural and manmade disasters (intentional and unintentional) may no longer be restricted to any geographic or political vicinity. Severe disruptions are also becoming more unpredictable, more frequent and more damaging. When a disaster strikes particular UI systems, the community affected requires immediate help and action to survive, resources, and efforts to recover in a short time. Accordingly, the concepts of ‘risk management’ have become keywords when dealing with hazardous events. Meanwhile, resilience is an integrating concept that allows multiple risks, shocks and stresses and their impacts on ecosystem and vulnerable people to be considered together in the context of development programming. Resilience also highlights slow drivers of change that influence systems and the potential for non-linearity and transformation processes. It focuses attention on a set of institutional, community and individual capacities and particularly on learning, innovation and adaptation. Strengthening resilience can be associated with windows of opportunities for change, often opening after disturbance. The concept of resilience may enable the organizational philosophy changes needed to manage risk from a holistic picture and ensure safety and efficiency throughout the life cycle of the system [2]. Importantly, an alternative approach to resilience is to start from the basis of effective risk management, recognizing the inherent similarities between risk and resilience as organizing frames and the extent to which risk assessment and risk management provide a window on resilience. Therefore, an UI system that is effective in managing risk is likely to become more resilient to shocks and stresses event, though the exact relationship needs to be tested empirically. Managing risk in this context means reducing risk, transferring and sharing risk, preparing for impact and responding and recovering efficiently. It also involves being prepared for surprises-those disturbance events beyond the lived experience or occurring very infrequently. Thus, there is a need to go beyond the intuitive definition and provide a comprehensive quantitative of system. From the discussion above, it is ascertained that risk analysis plays pivotal roles as the foundation of resilience analysis. However, previous resilience system analysis lack of considering the risk dimension within the analysis processes. This issue prevents the development of a metric to measure resilience in a generic and consistent manner. Such a risk metric would greatly enable development of resilient systems, comparison of resilience strategies and support of resilience related decisions during design and operation. Therefore, there is an urge needs to develop a novel resilience analysis model particularly departing from a concept of uncertainty, dynamic and complex UI system environment. This research aims to develop a risk-based resilient analysis model for assessing and measuring the UI system robustness. The model retaining to use of risk criticality model for expressing the dynamic and complex risk characteristics about highly uncertain, unforeseeable and unknowable behavior in UI environment as well as the dynamic of people perceptions towards UI security. 2. Literature Review on Knowledge Gaps The advantages of adopting a risk management lens to strengthening resilience, may provide a cross-comparable matrix. Identification of overlaps at an early stage provides significant opportunities for integration of risks in diverse disciplinary and policy making. However, little known that risk and resilience analysis has very close relationship, which the integration between those two analysis models would give comprehensive UI system resilience analysis result in the pre-and post-hazard performance of the system. Thus, UI system resilience assessment has to go beyond conventional paradigm. Likewise, the existing quantitative approaches to measuring or computing resilience are also not consistent with the concept of risk management. A number of studies have developed and implemented such resilience analysis frameworks to quantify the resilience of various systems against natural hazards or the intelligent actions of adversaries. For instances,
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Cimellaro, G.P., A.M. Reinhorn, and M. Bruneau [3] presented the quantitative evaluation towards disaster resilience and proposed the unified terminology for a common reference framework. Henry, D. and J. Emmanuel Ramirez-Marquez [4] proposed generic metrics and formulae for quantifying system resilience. Ouyang, M., L. Dueñas-Osorio, and X. Min [5] proposed a new multi-stage framework to analyze infrastructure resilience. Further, Francis, R. and B. Bekera [2] proposed a resilience analysis framework and a metric for measuring resilience. The proposed framework consists of system identification, resilience objective setting, vulnerability analysis, and stakeholder engagement. Shafieezadeh, A. and L. Ivey Burden [1] proposed a probabilistic framework for scenario-based resilience assessment of infrastructure systems. Ouyang, M. and Z. Wang [6] adopted an existing resilience assessment framework for single system to interdependent systems and mainly focus in modelling and resilience contribution analysis of multi-systems’ joint restoration processes. The exercises of measuring resilience in previous studies are also highly variable, depending on the understanding and weight given to concepts (e.g., coping, capacity, vulnerability and adaptive capacity). Nonetheless, it is found that for the most part, there is no consistent quantitative approach towards UI system resilience analysis which consider mutual relationship between resilience framework with complex risk characteristic and its dynamic impact analysis which leads to a severe inefficiency. Therefore, this research suggests that resilience and comprehensive risk analysis are complementary and should be used in an integrated perspective. The context-specific nature of risk, the dynamic nature of change and the complexity of capacities associated with resilience make systemic measurement challenging and lead to proxies or a simpler frame for evaluation to be considered. Accordingly, there is a need for developing a novel and robust UI system resilience analysis model which capable to capture both the complex risk characteristic and impact dynamic that would be useable and useful for the development of effective recovery strategies [4]. 3. Proposed Robustness Analysis Model A basic requirement of resilience assessment is the UI system identification under study. After system identification, the analyst must determine the disruptive event(s) which make the system’s normal operating state susceptible to disruption. In this research, this step refers to risk identification. After identifying the disruptive event to which the identified system is vulnerable; it is important to incorporate the risk metric into the analysis. In this research, risk analysis applied as an entry point for operationalizing and measuring UI system robustness. The risk metric applied in this research is the ‘risk criticality’ which developed by Ongkowijoyo, C. and H. Doloi [7].
éæ
r ( Rn ) = êç G(Rn ) ´
ëè
(åg
( Rn ) )
[ R-R ]
Top
nÎN , GÎ[ 0,1]
[S-R ] ö ù ö æ ÷ + ç G(Rn ) ´ ( å g Top ( Rn ) ) ÷ ú ø è øû nÎN , GÎ[ 0,1]
(1)
where, G(Rn ) is the risk n magnitude which has the value of [0,1] after normalized. The conceptualization of G(Rn ) is
( å g ( R ) ) denotes the sum of the defined risk-risk [R-R] network topology indicators for the respective risk event, ( å g ( R ) ) denotes
modeled on the ideas of the risk triplet (likelihood, severity and the detectability). While,
[R-R ]
Top
n
[S-R ]
Top
n
the sum of the defined stakeholder-risk [S-R] network topology indicators for the respective risk event [7]. The next step is reviewing the basic understanding of system-of-interest resilience over time, which can be seen in [1-6, 8, 9] before continuing to build the further quantitative models supporting the resilience analysis. 3.1. Shock and Stress Event, and Robustness Model Development Computationally, when analyzing large complex system especially for UI system, it may not be possible to consider every possible failure/disruptive event. Therefore, a worst-case scenario approach and average scenario approach may have to be considered in order to develop a resilience strategy for the critical event in a class of events. To define the component recovery mechanism, this research firstly develops a main shock model. The main shock event refers to the maximum disturbance made by specific risk event affected system-of-interest until the system
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collapsed. In other words, it also refers to the maximum UI system capacity to withstand the disturbance impact in order to maintain its serviceability in the steady condition (t0 to t hz ) . In the condition after disturbance occurred, the delivery function of the system F (t ) is null (t he ) . This state remains constant since there is no recovery action
(the to t f ) . The main shock model, for each of the risk events can be calculated via equation 2 below. FSHOCK CAP. ( Rn ) = F (t0 ) - F (t he )
ét ù ét ù [ R-R ] [S-R ] [ R-R ] [S-R ] = ê ò f r ( R ) ( G( Rn ), g Top ( R ) , g Top ( R ) )dt ú - ê ò f r ( R ) ( G( Rn ), g Top ( R ) , g Top ( R ) )dt ú ët û ët û [S-R ] [ R-R ] = éë f r ( R ) ( G( Rn ), g Top ( R ) , g Top ( R ) ) ùû - 0 = j ( Rn ), n = 1, 2,..., N f
hz
n
n
n
n
n
where, G(Rn ) is the risk n magnitude, event within (R-R) network,
(g
n
n
(2)
he
0
n
(g
( Rn ) )
[ R-R ]
Top
( Rn ) )
[S-R ]
Top
n
denotes the network topology indicators for the respective risk
denotes the network topology indicators for the respective risk event
within (S-R) network. Further, the main shock capacity for respective risk event (Rn ) , need to find the objective of; maximum:[j (Rn ) ] .
The equation above indicates the three main conditions need to be maximized, that is; (i) risk magnitude, (ii) risk impact propagation (R-R), and (iii) risk impact to community (S-R). Further, the input parameters have to fulfilled requirements below.
ì f (O, S , D ), (O, S , D ) Î [1, 10], n = 1, ...., N ïï max {j ( R )} í [S-R ] = [S-R , (i , n ) Î 1 ] ï , ( n , m ) Î 1, where; ïî [ R-R ] = [ R-R ] G
R
n
IxN
n
( i , I )( n , N )
S ,R i
n
NxN
( n ,m )
R
n ¹ m otherwise 0
nm
Furthermore, the stress refers to the actual value of risk criticality r ( Rn ) mentioned in equation 1 for specific risk event (figure 4). The stress model for the system-of-interest resilience facing specific risk event denoted below;
FSTRESS CAP. ( Rn ) = r ( Rn ),
n = 1, 2,..., N
(3)
After the shock and stress capacity analysis model defined, the system robustness capacity analysis model can be determined. Robustness referring to engineering systems is, “the ability of elements, systems or other units of analyst to withstand a given level of stress, or demand without suffering degradation or loss of function [2, 4, 5]. It is therefore the robustness also explaining the residual functionality right after the extreme event and can be represented by the following relation. Following the general understanding of system robustness mentioned above, thus the generic system robustness equation towards particular (Rn ) at the time thd can be calculated using equation below:
j ( R)
FROB. ( Rn ) = é
ë
ù ù - é r ( Rn ) j ( R) û ëê j ( R) ûú
= 1 - n ( Rn ),
FROB. ( Rn ) Î [0,1],
(4)
n = 1,...N
where, j ( R) , r ( Rn ) , n ( Rn ) denotes to the main shock, stress and the normalized stress capacity based on main shock value determined previously. The normalized stress capacity is the element of [0,1] .
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3.2. Resilience Action and Simulation Model Development In this research, the recovery model focuses on the various recovery strategies scenario built for the system to restore and recover consider a number significant requirements, which is; the optimum resilience strategies towards achieving highest robustness capacity value of UI. The recovery model of system-of-interest over time depicts in figure 1 below.
Fig. 1. Restoration and recovery function transition over time
In the event of disruption, the recovery of UI system from its disrupted state will be dictated by the nature of the system as well as pre-determined policies and available facilities for repair/recovery. Given all of these, the resilience metrics only help compute the resilience of the system, the time for resilience and the total cost of resilience. Therefore, several assumptions and interpretations have been determined in this research. The final goal is to integrate the information from these different metrics into a unique system-of-interest robustness function leading to results that are unbiased by uniformed intuition or preconceived notions of risk. The UI system robustness evaluated at a specific time under disruptive event can be computed via the formula presented in the following equation. t ét ù j ( R) é ù F (t f ) = - ê ò ( (100% - a ) ×n ( Rn ) )dt + ò (n ( Rn ) )dt ú j ( R) û ë t ët û d
f
hd
d
é ù = 1 - ê F (td ) + ò (n ( Rn ) )dt ú , t ë û tf
(5)
"F (t f ) Î [0,1]
d
where, a refers to the coefficient applied in this research towards increases capacity of system-of-interest during the ‘Slack time’ period ( t hd to td ). In this research a determined as 2.5%. Meaning that, the system-of-interest increasing its robustness function during Slack time by 2.5% based on the previous robustness level. To find the most optimum resilience action strategy from various scenarios, the scenario-based robustness analysis for equation 5 above is developed and analyzed thoroughly. Thus, the objective function which need to be met is maximum : {F (t f )} , which is a non-linear objective function which could be solved using multi-objective optimization. However, this manuscript is not intending to discuss and develop both the optimization case and engaging metaheuristic methods respectively to find the optimum objective following its constrain.
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4. Model Validation: Numerical Example This research validates the model using the case of Surabaya (second largest city in Indonesia) water supply infrastructure system, in which its public amenities has ranked highest among the nation. The validation will exemplify and verify the applicability and usefulness the model approach to resilience. While the UI system as case study representation used here is representative of many similar infrastructure system, the quantitative approach per se is applicable to any UI system, even if it cannot be represented as same as the case study this research conducted. Furthermore, it is important to note that the assumptions made here are only representative for the cases presented. Several past studies have explored and discussed various problems and challenges that Surabaya water supply system [10-14]. In this research, the structured random sampling method is applied towards the data collection. A total of 126 respondents within eight stakeholder groups determined agreed to fill the design-based questionnaire disseminated. To exemplify the proposed robustness model, this research focus on the ‘pollution and contamination’ risk (R5) which found to be the most critical risk event among another risk events [7, 15-17]. Figure 2 depicts eights Surabaya water supply infrastructure system resilience analysis simulation facing R5 over time (i.e., shock capacity, stress capacity, expected recovery, and five different scenario-based recovery strategies). The five scenario-based recovery strategies formed the semi-linear recovery function. The reason semi-linear recovery function applied in this research is that; this function model generally applied when there is no information regarding the preparedness, resources available and societal response [3].
Fig. 2. Resilience analysis towards pollution and contamination risk. Table 1. System-of-Interest facing pollution and contamination risk simulation output. Condition faced by UI system
Resilience actions
Shock Stress Exp. Sc.1 Sc.2 Sc.3 Sc.4 Sc.5
Discrete time period ( t f )
thz
the
thd
td
1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00
0.00 0.50 0.50 0.50 0.50 0.50 0.50 0.50
0.00 0.50 0.50 0.50 0.50 0.50 0.50 0.50
0.00 0.50 0.51 0.51 0.51 0.51 0.51 0.51
1
2
3
4
5
6
7
8
9
10
11
12
t1
t2
t3
t4
t5
t6
t7
t8
t9
t10
t11
tf
0.00 0.50 0.54 0.52 0.58 0.51 0.59 0.67
0.00 0.50 0.58 0.57 0.60 0.63 0.62 0.68
0.00 0.50 0.65 0.60 0.62 0.65 0.63 0.69
0.00 0.50 0.72 0.64 0.64 0.67 0.64 0.71
0.00 0.50 0.79 0.68 0.65 0.68 0.66 0.73
0.00 0.50 0.85 0.72 0.66 0.70 0.67 0.75
0.00 0.50 0.90 0.76 0.67 0.71 0.68 0.78
0.00 0.50 0.94 0.80 0.70 0.74 0.69 0.82
0.00 0.50 0.96 0.81 0.70 0.74 0.72 0.81
0.00 0.50 0.98 0.83 0.74 0.77 0.72 0.83
0.00 0.50 0.99 0.87 0.77 0.80 0.75 0.85
0.00 0.50 0.99 0.90 0.77 0.80 0.78 0.87
To compare the scenario-based recovery strategies developed with the expected recovery action output, the expected recovery strategies function applied the trigonometric recovery function [3]. All values of resilience are
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normalized respect to the Surabaya urban water supply infrastructure system facing the shock impact of R5 and assumed equal to the largest capacity value. All values of resilience are comparable because all recovery strategies (i.e., searching the most optimum objective value) are equally effective in improving the resilience of the urban water supply infrastructure facing the R5. The shock event scenario shown that UI system reaching its lowest resilience state when R5 occurred with its devastating impact. The shock scenario intends to show readers that the R5 impact is enormous which affect both the community and other infrastructure system inherent risks (refers to the most critical risks [7]). This can be seen on respective infrastructure system FOM collapsed in the time of thz to the from 1.000 into 0.000. Then, there is no any resilience action (recovery strategy) applied which shown by its’ null F ( t ) value from the to tf. The stress scenario shown that UI system reaching its actual resilience capacity when R5 occurred. The stress scenario intends to show how respective UI system robust in the period of disturbance. The respective UI system FOM deteriorate in the time period of thz to the from 1.000 into 0.509 resilience state. This justifies that the respective UI system robustness is 0.491 which is less than 50% from the total UI system resilience capacity. Then, since there is no any resilience action (recovery strategy) applied its’ F ( t ) keep steady in the state of 0.509 within the time period of the the to tf. Simulating various scenarios for the resilience strategy model, five restoration strategies are considered (Sce.1-5). From both figure 6 and table 2, scenario 1 is considered the most desirable followed by scenario 5, scenario 3, scenario 4, and scenario 2 which is less desirable, based on the F ( t ) value reached in the time of t12. Scenario 1 is considered the optimum recovery strategy since the UI system robustness and resilience increase, reaching 0.997 out of 1.000. Nonetheless, scenario 1 still shows a loss to the UI system which refers to ‘residual loss’ as much as 0.003. In the simulation analysis, this loss can be expected to have no effect. In real world, however, this number can be very harmful and inflict severe financial (and non-financial) losses to both the urban community and respective UI system itself. Furthermore, even though scenario 1 is the most optimum scenario towards reaching resilience state in the time of t12, notwithstanding, this research is also paid attention and found that, resilience actions from recovery model scenario 1 is lower than the other recovery model scenarios between the time period of td and t4 . While, scenario 1 shown significant escalation F ( t ) value continuously during the time period of t4 to t9 until it reaches the highest F (t ) in the time period of t12 compare to other scenarios. During the post disturbance period, it is acknowledged that scenario 3 and 5 were attained higher F ( t ) compare with expected and other scenarios in the period time between t1 and t5. Even so, both scenario 3 and 5 obtained final F ( t ) value lower than scenario 1. Thus, it can be concluded that, recovery model scenario 3 and 5 has better robustness improvement towards F ( t ) in a short period of time specifically right after the disturbance occurred. On the other hand, recovery scenario 2 (also for scenario 4) perhaps the less prominent scenario which can be adopted. This findings supported by the facts that; (i) similar to previous discussion both scenario 2 and 4 were obtained higher F ( t ) value during time period of td and t2 compare to the expected recovery scenario and scenario 1, (ii) both scenario 2 and 4 obtained their F ( t ) value in the low state constantly during time period of t4 to t12 which explain that these scenarios deliver a slow pace of recovery processes. From the simulation results, scenario 2 has its steady F ( t ) value in the point of 0.770 when reached time t11 to t12. This indicates that resilience strategy for scenario 2 has not contributed and improve much to respective UI system during t11 to t12. The reason that the F ( t ) value unchanged over time is that the resilience strategy and action applied towards R5 lack of comprehensiveness. Correspond to the discussion above, the simulation output for scenario 3 also shown that its F ( t ) value keep steady in the point of 0.800 during t11 to t12.
In addition, scenario 5 well exceeds the expected recovery scenario by reaching highest F ( t ) value during td to t4 . Notwithstanding, during time period of
t4 till t12 , scenario 5 experienced obstacles and slowness towards obtaining
expected F ( t ) value. One of the deterioration can be found during t8 to t9 when scenario 5 undergone a downgrade of its F ( t ) value from 0.822 to 0.815. These analyses show that the recovery process is complex and it is influenced by time dimensions, spatial dimensions (e.g., different neighborhoods may have different recovery paths) and by interdependencies between different economic sector that are interested in the recovery process.
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5. Conclusions This research proposed and illustrated a novel scenario-based system robustness metric for resilience analysis in the context of UI systems facing disturbance. The model provides valuable insights into the likely performance of UI systems as a whole during and after specific disturbance (risk) scenario. The fundamental concepts of UI system resilience discussed herein provide a common frame of reference and a unified terminology. While the proposed resilience analysis metrics only provide a quantitative value for the system robustness, these metrics become useful and valuable only when used to devise effective resilience strategies for the system of interest. Preventive strategies or improvement resources may simultaneously affect the resilience under different hazard types. However, this research does not focus on maximizing the interacting mechanisms between the improvement resources (or strategies) for a given hazard and their impacts on different hazard types. Instead, the objective is to apply the emerging model to issue strategies to increase UI system resilience and illustrate their outcomes at different time associated with the simulation steps in the flowchart of Figure 1. The resilience analysis model developed in this research contributes to a new knowledge by supporting the computation of the system resilience as well as various strategies implications considered for different resilience strategies. This should help to systems engineers during overall system mitigation plan and design, or while devising restoration strategies. Evidently, the resilience analysis model presented provides an opportunity to consider UI system multi-objective optimization as a means to develop effective mitigation, recovery and restoration, or protective strategies. This research contributes as a practical tool for experts and decision makers to be able to determine the amount of investment that could be used for resilience strategies knowing (in quantitative terms), the level of resilience that they would now enable the UI system to exhibit in case of a disruption in future. References 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17.
Shafieezadeh, A. and L. Ivey Burden, Scenario-based resilience assessment framework for critical infrastructure systems: Case study for seismic resilience of seaports. Reliability Engineering & System Safety, 2014. 132: p. 207-219. Francis, R. and B. Bekera, A metric and frameworks for resilience analysis of engineered and infrastructure systems. Reliability Engineering & System Safety, 2014. 121: p. 90-103. Cimellaro, G.P., A.M. Reinhorn, and M. Bruneau, Framework for analytical quantification of disaster resilience. Engineering Structures, 2010. 32(11): p. 3639-3649. Henry, D. and J. Emmanuel Ramirez-Marquez, Generic metrics and quantitative approaches for system resilience as a function of time. Reliability Engineering & System Safety, 2012. 99: p. 114-122. Ouyang, M., L. Dueñas-Osorio, and X. Min, A three-stage resilience analysis framework for urban infrastructure systems. Structural Safety, 2012. 36–37(0): p. 23-31. Ouyang, M. and Z. Wang, Resilience assessment of interdependent infrastructure systems: With a focus on joint restoration modeling and analysis. Reliability Engineering & System Safety, 2015. 141: p. 74-82. Ongkowijoyo, C. and H. Doloi, Determining critical infrastructure risks using social network analysis. International Journal of Disaster Resilience in the Built Environment, 2017. 8(1): p. 5-26. McAllister, T., Research needs for developing a risk-informed methodology for community resilience. Journal of Structural Engineering, 2015: p. C4015008. Rochas, C., T. Kuzņecova, and F. Romagnoli, The concept of the system resilience within the infrastructure dimension: application to a Latvian case. Journal of Cleaner Production, 2014(0). Setiono, I., Using output-based aid to expand piped water supply to Surabaya urban poor, in Prakarsa Compendium, C.S. Walker and P. Punjabi, Editors. 2013, Indonesia Infrastructure Initiative: Jakarta. p. 169-173. Pranoto., A.H. and B. Suwarso. Community-Based Water Environmental Governance: A Community Movement in Surabaya City 2007. W.Dick, H., Surabaya City of Work. A Socioeconomic History, 1900-2000. 2002, Scott Quadrangle, Athens, OH, USA: Ohio University Press. 224-226. Ostojic, D.R., et al., Surabaya, Indonesia, in Energizing Green Cities in Southeast Asia. 2013, The World Bank: Washington DC. p. 139156. Kodoatie, R.J. and R. Sjarief, Pengelolaan Sumber Daya Air Terpadu. Edisi Revisi ed. Vol. 2. 2008, Yogyakarta: Penerbit ANDI. xii+420. Ongkowijoyo, C.S. and H.K. Doloi. Analyzing the Risk Criticality of an Infrastructure System on the Community. in Construction Research Congress 2016. 2016. Ongkowijoyo, C.S. and H. Doloi. Setting the Priority of Risk and Impacts to Community. in 6th International Conference on Building Resilience 2016. University of Auckland: Massey University and The University of Auckland. Ongkowijoyo, C.S. and H. Doloi. Analysing Community Hazard in Urban Infrastructure System. in 6th International Conference on Building Resilience 2016. University of Auckland: Massey University and The University of Auckland.
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Procedia Engineering 212 (2018) 1115–1122
7th International Conference on Building Resilience; Using scientific knowledge to inform policy 7th Conference on Building Resilience; scientific knowledge to inform policy andInternational practice in disaster risk reduction, ICBR2017, 27Using – 29 November 2017, Bangkok, Thailand and practice in disaster risk reduction, ICBR2017, 27 – 29 November 2017, Bangkok, Thailand
Risk-based Resilience Assessment Model Focusing on Urban Risk-based Resilience Assessment Model Focusing on Urban Infrastructure System Restoration Infrastructure System Restoration a a
a Citra S. Ongkowijoyoa* a* and Hemanta Doloia Citra S. Ongkowijoyo and Hemanta Doloi
University of Melbourne, Parkville-Melbourne, 3010-Victoria, Australia. University of Melbourne, Parkville-Melbourne, 3010-Victoria, Australia.
Abstract Abstract A number of metrics in the past studies have been proposed and numerically implemented to assess particular system resilience A number of metrics the past have proposed of andthe numerically implemented to assess particular system resilience during natural disasterin and theirstudies recovery in been the aftermath events. Among such performance measures, resilience is a during and their recoveryoninthe theurban aftermath of the events. such performance measures, resilience risk is a reliablenatural metric. disaster The resilience assessment infrastructure systemAmong facing disturbances depends on comprehensive reliable metric. The resilience the urban infrastructure system the facing comprehensive risk assessment. Nonetheless, it is assessment found that on previous studies lack of putting riskdisturbances assessment depends processesonwithin the resilience assessment. Nonetheless, is found that previous studies lack of putting risk assessment within the resilience assessment bodies. This itstudy proposes a risk criticality-based resilienttheassessment model processes for scenario-based assessment of bodies. This study proposes risk accounts criticality-based resilient assessment model for scenario-based resilience infrastructure systems. The amodel for uncertainties in the process including; the people expressions assessment of measures, infrastructure Theand model accounts uncertainties in the and process people propagation expressions towards risks riskssystems. magnitude its impact to for community estimation, the including; dynamic ofthe causality towards risks measures, risks magnitude andapplied its impact to community estimation, case and the dynamic causality propagation pattern simulation. The proposed model is to water supply infrastructure study with a ofhypothetical restoration pattern The level proposed modeland is applied to water infrastructure case study withsystem a hypothetical scenario.simulation. The resilience is assessed determined based supply on the maximum resilience level the can reach. restoration Results of scenario. Thehave resilience and integrated determinedmitigation based on the maximum resilience system can reach.phenomena Results of this analysis shownlevel thatisa assessed holistic and plans and strategies that level seek the to address complex this analysis have shown that holistic requirement. and integrated mitigation plans and strategies thattoseek to addressassess complex towards system restoration is a critical The model will enable stakeholders systemically the phenomena most-likely towards system restoration is a critical requirement. performance of the system during expected risk events.The model will enable stakeholders to systemically assess the most-likely performance of the system during expected risk events. © 2017 The Authors. Published by Elsevier Ltd. © 2017 2018 The The Authors. Published Ltd. © Authors. Published by by Elsevier Elsevier Ltd. committee of the 7th International Conference on Building Resilience. Peer-review Peer-review under under responsibility responsibility of of the the scientific scientific committee of the 7th International Conference on Building Resilience. Peer-review under responsibility of the scientific committee of the 7th International Conference on Building Resilience. Keywords: Risk assessment; Resilience analysis; Infrastructure system; Urban community. Keywords: Risk assessment; Resilience analysis; Infrastructure system; Urban community.
*Corresponding author: Tel: +61424578989 *Corresponding author: Tel: +61424578989 Email: [email protected] Email: [email protected]
1877-7058 © 2017 The Authors. Published by Elsevier Ltd. 1877-7058 2017responsibility The Authors. of Published by Elsevier Ltd. of the 7th International Conference on Building Resilience. Peer-review©under the scientific committee Peer-review under responsibility of the scientific committee of the 7th International Conference on Building Resilience.
1877-7058 © 2018 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the scientific committee of the 7th International Conference on Building Resilience 10.1016/j.proeng.2018.01.144
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1. Introduction The built environment such as urban infrastructure (UI) system provides the essential physical basis for modern societies, and have multi-dimensional impact on public safety and economic prosperity at regional and national levels. Past experiences have shown that such systems are exposed to various natural and manmade hazards. The resulting damage to the systems may cause human causalities and disrupt the normal day-to-day life of people in the short run. Worse, this damage may also impose significant direct and secondary economic losses due to business interruption that may not ever fully recover [1]. With extensive globalization and connectivity, the effects of natural and manmade disasters (intentional and unintentional) may no longer be restricted to any geographic or political vicinity. Severe disruptions are also becoming more unpredictable, more frequent and more damaging. When a disaster strikes particular UI systems, the community affected requires immediate help and action to survive, resources, and efforts to recover in a short time. Accordingly, the concepts of ‘risk management’ have become keywords when dealing with hazardous events. Meanwhile, resilience is an integrating concept that allows multiple risks, shocks and stresses and their impacts on ecosystem and vulnerable people to be considered together in the context of development programming. Resilience also highlights slow drivers of change that influence systems and the potential for non-linearity and transformation processes. It focuses attention on a set of institutional, community and individual capacities and particularly on learning, innovation and adaptation. Strengthening resilience can be associated with windows of opportunities for change, often opening after disturbance. The concept of resilience may enable the organizational philosophy changes needed to manage risk from a holistic picture and ensure safety and efficiency throughout the life cycle of the system [2]. Importantly, an alternative approach to resilience is to start from the basis of effective risk management, recognizing the inherent similarities between risk and resilience as organizing frames and the extent to which risk assessment and risk management provide a window on resilience. Therefore, an UI system that is effective in managing risk is likely to become more resilient to shocks and stresses event, though the exact relationship needs to be tested empirically. Managing risk in this context means reducing risk, transferring and sharing risk, preparing for impact and responding and recovering efficiently. It also involves being prepared for surprises-those disturbance events beyond the lived experience or occurring very infrequently. Thus, there is a need to go beyond the intuitive definition and provide a comprehensive quantitative of system. From the discussion above, it is ascertained that risk analysis plays pivotal roles as the foundation of resilience analysis. However, previous resilience system analysis lack of considering the risk dimension within the analysis processes. This issue prevents the development of a metric to measure resilience in a generic and consistent manner. Such a risk metric would greatly enable development of resilient systems, comparison of resilience strategies and support of resilience related decisions during design and operation. Therefore, there is an urge needs to develop a novel resilience analysis model particularly departing from a concept of uncertainty, dynamic and complex UI system environment. This research aims to develop a risk-based resilient analysis model for assessing and measuring the UI system robustness. The model retaining to use of risk criticality model for expressing the dynamic and complex risk characteristics about highly uncertain, unforeseeable and unknowable behavior in UI environment as well as the dynamic of people perceptions towards UI security. 2. Literature Review on Knowledge Gaps The advantages of adopting a risk management lens to strengthening resilience, may provide a cross-comparable matrix. Identification of overlaps at an early stage provides significant opportunities for integration of risks in diverse disciplinary and policy making. However, little known that risk and resilience analysis has very close relationship, which the integration between those two analysis models would give comprehensive UI system resilience analysis result in the pre-and post-hazard performance of the system. Thus, UI system resilience assessment has to go beyond conventional paradigm. Likewise, the existing quantitative approaches to measuring or computing resilience are also not consistent with the concept of risk management. A number of studies have developed and implemented such resilience analysis frameworks to quantify the resilience of various systems against natural hazards or the intelligent actions of adversaries. For instances,
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Cimellaro, G.P., A.M. Reinhorn, and M. Bruneau [3] presented the quantitative evaluation towards disaster resilience and proposed the unified terminology for a common reference framework. Henry, D. and J. Emmanuel Ramirez-Marquez [4] proposed generic metrics and formulae for quantifying system resilience. Ouyang, M., L. Dueñas-Osorio, and X. Min [5] proposed a new multi-stage framework to analyze infrastructure resilience. Further, Francis, R. and B. Bekera [2] proposed a resilience analysis framework and a metric for measuring resilience. The proposed framework consists of system identification, resilience objective setting, vulnerability analysis, and stakeholder engagement. Shafieezadeh, A. and L. Ivey Burden [1] proposed a probabilistic framework for scenario-based resilience assessment of infrastructure systems. Ouyang, M. and Z. Wang [6] adopted an existing resilience assessment framework for single system to interdependent systems and mainly focus in modelling and resilience contribution analysis of multi-systems’ joint restoration processes. The exercises of measuring resilience in previous studies are also highly variable, depending on the understanding and weight given to concepts (e.g., coping, capacity, vulnerability and adaptive capacity). Nonetheless, it is found that for the most part, there is no consistent quantitative approach towards UI system resilience analysis which consider mutual relationship between resilience framework with complex risk characteristic and its dynamic impact analysis which leads to a severe inefficiency. Therefore, this research suggests that resilience and comprehensive risk analysis are complementary and should be used in an integrated perspective. The context-specific nature of risk, the dynamic nature of change and the complexity of capacities associated with resilience make systemic measurement challenging and lead to proxies or a simpler frame for evaluation to be considered. Accordingly, there is a need for developing a novel and robust UI system resilience analysis model which capable to capture both the complex risk characteristic and impact dynamic that would be useable and useful for the development of effective recovery strategies [4]. 3. Proposed Robustness Analysis Model A basic requirement of resilience assessment is the UI system identification under study. After system identification, the analyst must determine the disruptive event(s) which make the system’s normal operating state susceptible to disruption. In this research, this step refers to risk identification. After identifying the disruptive event to which the identified system is vulnerable; it is important to incorporate the risk metric into the analysis. In this research, risk analysis applied as an entry point for operationalizing and measuring UI system robustness. The risk metric applied in this research is the ‘risk criticality’ which developed by Ongkowijoyo, C. and H. Doloi [7].
éæ
r ( Rn ) = êç G(Rn ) ´
ëè
(åg
( Rn ) )
[ R-R ]
Top
nÎN , GÎ[ 0,1]
[S-R ] ö ù ö æ ÷ + ç G(Rn ) ´ ( å g Top ( Rn ) ) ÷ ú ø è øû nÎN , GÎ[ 0,1]
(1)
where, G(Rn ) is the risk n magnitude which has the value of [0,1] after normalized. The conceptualization of G(Rn ) is
( å g ( R ) ) denotes the sum of the defined risk-risk [R-R] network topology indicators for the respective risk event, ( å g ( R ) ) denotes
modeled on the ideas of the risk triplet (likelihood, severity and the detectability). While,
[R-R ]
Top
n
[S-R ]
Top
n
the sum of the defined stakeholder-risk [S-R] network topology indicators for the respective risk event [7]. The next step is reviewing the basic understanding of system-of-interest resilience over time, which can be seen in [1-6, 8, 9] before continuing to build the further quantitative models supporting the resilience analysis. 3.1. Shock and Stress Event, and Robustness Model Development Computationally, when analyzing large complex system especially for UI system, it may not be possible to consider every possible failure/disruptive event. Therefore, a worst-case scenario approach and average scenario approach may have to be considered in order to develop a resilience strategy for the critical event in a class of events. To define the component recovery mechanism, this research firstly develops a main shock model. The main shock event refers to the maximum disturbance made by specific risk event affected system-of-interest until the system
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collapsed. In other words, it also refers to the maximum UI system capacity to withstand the disturbance impact in order to maintain its serviceability in the steady condition (t0 to t hz ) . In the condition after disturbance occurred, the delivery function of the system F (t ) is null (t he ) . This state remains constant since there is no recovery action
(the to t f ) . The main shock model, for each of the risk events can be calculated via equation 2 below. FSHOCK CAP. ( Rn ) = F (t0 ) - F (t he )
ét ù ét ù [ R-R ] [S-R ] [ R-R ] [S-R ] = ê ò f r ( R ) ( G( Rn ), g Top ( R ) , g Top ( R ) )dt ú - ê ò f r ( R ) ( G( Rn ), g Top ( R ) , g Top ( R ) )dt ú ët û ët û [S-R ] [ R-R ] = éë f r ( R ) ( G( Rn ), g Top ( R ) , g Top ( R ) ) ùû - 0 = j ( Rn ), n = 1, 2,..., N f
hz
n
n
n
n
n
where, G(Rn ) is the risk n magnitude, event within (R-R) network,
(g
n
n
(2)
he
0
n
(g
( Rn ) )
[ R-R ]
Top
( Rn ) )
[S-R ]
Top
n
denotes the network topology indicators for the respective risk
denotes the network topology indicators for the respective risk event
within (S-R) network. Further, the main shock capacity for respective risk event (Rn ) , need to find the objective of; maximum:[j (Rn ) ] .
The equation above indicates the three main conditions need to be maximized, that is; (i) risk magnitude, (ii) risk impact propagation (R-R), and (iii) risk impact to community (S-R). Further, the input parameters have to fulfilled requirements below.
ì f (O, S , D ), (O, S , D ) Î [1, 10], n = 1, ...., N ïï max {j ( R )} í [S-R ] = [S-R , (i , n ) Î 1 ] ï , ( n , m ) Î 1, where; ïî [ R-R ] = [ R-R ] G
R
n
IxN
n
( i , I )( n , N )
S ,R i
n
NxN
( n ,m )
R
n ¹ m otherwise 0
nm
Furthermore, the stress refers to the actual value of risk criticality r ( Rn ) mentioned in equation 1 for specific risk event (figure 4). The stress model for the system-of-interest resilience facing specific risk event denoted below;
FSTRESS CAP. ( Rn ) = r ( Rn ),
n = 1, 2,..., N
(3)
After the shock and stress capacity analysis model defined, the system robustness capacity analysis model can be determined. Robustness referring to engineering systems is, “the ability of elements, systems or other units of analyst to withstand a given level of stress, or demand without suffering degradation or loss of function [2, 4, 5]. It is therefore the robustness also explaining the residual functionality right after the extreme event and can be represented by the following relation. Following the general understanding of system robustness mentioned above, thus the generic system robustness equation towards particular (Rn ) at the time thd can be calculated using equation below:
j ( R)
FROB. ( Rn ) = é
ë
ù ù - é r ( Rn ) j ( R) û ëê j ( R) ûú
= 1 - n ( Rn ),
FROB. ( Rn ) Î [0,1],
(4)
n = 1,...N
where, j ( R) , r ( Rn ) , n ( Rn ) denotes to the main shock, stress and the normalized stress capacity based on main shock value determined previously. The normalized stress capacity is the element of [0,1] .
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3.2. Resilience Action and Simulation Model Development In this research, the recovery model focuses on the various recovery strategies scenario built for the system to restore and recover consider a number significant requirements, which is; the optimum resilience strategies towards achieving highest robustness capacity value of UI. The recovery model of system-of-interest over time depicts in figure 1 below.
Fig. 1. Restoration and recovery function transition over time
In the event of disruption, the recovery of UI system from its disrupted state will be dictated by the nature of the system as well as pre-determined policies and available facilities for repair/recovery. Given all of these, the resilience metrics only help compute the resilience of the system, the time for resilience and the total cost of resilience. Therefore, several assumptions and interpretations have been determined in this research. The final goal is to integrate the information from these different metrics into a unique system-of-interest robustness function leading to results that are unbiased by uniformed intuition or preconceived notions of risk. The UI system robustness evaluated at a specific time under disruptive event can be computed via the formula presented in the following equation. t ét ù j ( R) é ù F (t f ) = - ê ò ( (100% - a ) ×n ( Rn ) )dt + ò (n ( Rn ) )dt ú j ( R) û ë t ët û d
f
hd
d
é ù = 1 - ê F (td ) + ò (n ( Rn ) )dt ú , t ë û tf
(5)
"F (t f ) Î [0,1]
d
where, a refers to the coefficient applied in this research towards increases capacity of system-of-interest during the ‘Slack time’ period ( t hd to td ). In this research a determined as 2.5%. Meaning that, the system-of-interest increasing its robustness function during Slack time by 2.5% based on the previous robustness level. To find the most optimum resilience action strategy from various scenarios, the scenario-based robustness analysis for equation 5 above is developed and analyzed thoroughly. Thus, the objective function which need to be met is maximum : {F (t f )} , which is a non-linear objective function which could be solved using multi-objective optimization. However, this manuscript is not intending to discuss and develop both the optimization case and engaging metaheuristic methods respectively to find the optimum objective following its constrain.
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4. Model Validation: Numerical Example This research validates the model using the case of Surabaya (second largest city in Indonesia) water supply infrastructure system, in which its public amenities has ranked highest among the nation. The validation will exemplify and verify the applicability and usefulness the model approach to resilience. While the UI system as case study representation used here is representative of many similar infrastructure system, the quantitative approach per se is applicable to any UI system, even if it cannot be represented as same as the case study this research conducted. Furthermore, it is important to note that the assumptions made here are only representative for the cases presented. Several past studies have explored and discussed various problems and challenges that Surabaya water supply system [10-14]. In this research, the structured random sampling method is applied towards the data collection. A total of 126 respondents within eight stakeholder groups determined agreed to fill the design-based questionnaire disseminated. To exemplify the proposed robustness model, this research focus on the ‘pollution and contamination’ risk (R5) which found to be the most critical risk event among another risk events [7, 15-17]. Figure 2 depicts eights Surabaya water supply infrastructure system resilience analysis simulation facing R5 over time (i.e., shock capacity, stress capacity, expected recovery, and five different scenario-based recovery strategies). The five scenario-based recovery strategies formed the semi-linear recovery function. The reason semi-linear recovery function applied in this research is that; this function model generally applied when there is no information regarding the preparedness, resources available and societal response [3].
Fig. 2. Resilience analysis towards pollution and contamination risk. Table 1. System-of-Interest facing pollution and contamination risk simulation output. Condition faced by UI system
Resilience actions
Shock Stress Exp. Sc.1 Sc.2 Sc.3 Sc.4 Sc.5
Discrete time period ( t f )
thz
the
thd
td
1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00
0.00 0.50 0.50 0.50 0.50 0.50 0.50 0.50
0.00 0.50 0.50 0.50 0.50 0.50 0.50 0.50
0.00 0.50 0.51 0.51 0.51 0.51 0.51 0.51
1
2
3
4
5
6
7
8
9
10
11
12
t1
t2
t3
t4
t5
t6
t7
t8
t9
t10
t11
tf
0.00 0.50 0.54 0.52 0.58 0.51 0.59 0.67
0.00 0.50 0.58 0.57 0.60 0.63 0.62 0.68
0.00 0.50 0.65 0.60 0.62 0.65 0.63 0.69
0.00 0.50 0.72 0.64 0.64 0.67 0.64 0.71
0.00 0.50 0.79 0.68 0.65 0.68 0.66 0.73
0.00 0.50 0.85 0.72 0.66 0.70 0.67 0.75
0.00 0.50 0.90 0.76 0.67 0.71 0.68 0.78
0.00 0.50 0.94 0.80 0.70 0.74 0.69 0.82
0.00 0.50 0.96 0.81 0.70 0.74 0.72 0.81
0.00 0.50 0.98 0.83 0.74 0.77 0.72 0.83
0.00 0.50 0.99 0.87 0.77 0.80 0.75 0.85
0.00 0.50 0.99 0.90 0.77 0.80 0.78 0.87
To compare the scenario-based recovery strategies developed with the expected recovery action output, the expected recovery strategies function applied the trigonometric recovery function [3]. All values of resilience are
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normalized respect to the Surabaya urban water supply infrastructure system facing the shock impact of R5 and assumed equal to the largest capacity value. All values of resilience are comparable because all recovery strategies (i.e., searching the most optimum objective value) are equally effective in improving the resilience of the urban water supply infrastructure facing the R5. The shock event scenario shown that UI system reaching its lowest resilience state when R5 occurred with its devastating impact. The shock scenario intends to show readers that the R5 impact is enormous which affect both the community and other infrastructure system inherent risks (refers to the most critical risks [7]). This can be seen on respective infrastructure system FOM collapsed in the time of thz to the from 1.000 into 0.000. Then, there is no any resilience action (recovery strategy) applied which shown by its’ null F ( t ) value from the to tf. The stress scenario shown that UI system reaching its actual resilience capacity when R5 occurred. The stress scenario intends to show how respective UI system robust in the period of disturbance. The respective UI system FOM deteriorate in the time period of thz to the from 1.000 into 0.509 resilience state. This justifies that the respective UI system robustness is 0.491 which is less than 50% from the total UI system resilience capacity. Then, since there is no any resilience action (recovery strategy) applied its’ F ( t ) keep steady in the state of 0.509 within the time period of the the to tf. Simulating various scenarios for the resilience strategy model, five restoration strategies are considered (Sce.1-5). From both figure 6 and table 2, scenario 1 is considered the most desirable followed by scenario 5, scenario 3, scenario 4, and scenario 2 which is less desirable, based on the F ( t ) value reached in the time of t12. Scenario 1 is considered the optimum recovery strategy since the UI system robustness and resilience increase, reaching 0.997 out of 1.000. Nonetheless, scenario 1 still shows a loss to the UI system which refers to ‘residual loss’ as much as 0.003. In the simulation analysis, this loss can be expected to have no effect. In real world, however, this number can be very harmful and inflict severe financial (and non-financial) losses to both the urban community and respective UI system itself. Furthermore, even though scenario 1 is the most optimum scenario towards reaching resilience state in the time of t12, notwithstanding, this research is also paid attention and found that, resilience actions from recovery model scenario 1 is lower than the other recovery model scenarios between the time period of td and t4 . While, scenario 1 shown significant escalation F ( t ) value continuously during the time period of t4 to t9 until it reaches the highest F (t ) in the time period of t12 compare to other scenarios. During the post disturbance period, it is acknowledged that scenario 3 and 5 were attained higher F ( t ) compare with expected and other scenarios in the period time between t1 and t5. Even so, both scenario 3 and 5 obtained final F ( t ) value lower than scenario 1. Thus, it can be concluded that, recovery model scenario 3 and 5 has better robustness improvement towards F ( t ) in a short period of time specifically right after the disturbance occurred. On the other hand, recovery scenario 2 (also for scenario 4) perhaps the less prominent scenario which can be adopted. This findings supported by the facts that; (i) similar to previous discussion both scenario 2 and 4 were obtained higher F ( t ) value during time period of td and t2 compare to the expected recovery scenario and scenario 1, (ii) both scenario 2 and 4 obtained their F ( t ) value in the low state constantly during time period of t4 to t12 which explain that these scenarios deliver a slow pace of recovery processes. From the simulation results, scenario 2 has its steady F ( t ) value in the point of 0.770 when reached time t11 to t12. This indicates that resilience strategy for scenario 2 has not contributed and improve much to respective UI system during t11 to t12. The reason that the F ( t ) value unchanged over time is that the resilience strategy and action applied towards R5 lack of comprehensiveness. Correspond to the discussion above, the simulation output for scenario 3 also shown that its F ( t ) value keep steady in the point of 0.800 during t11 to t12.
In addition, scenario 5 well exceeds the expected recovery scenario by reaching highest F ( t ) value during td to t4 . Notwithstanding, during time period of
t4 till t12 , scenario 5 experienced obstacles and slowness towards obtaining
expected F ( t ) value. One of the deterioration can be found during t8 to t9 when scenario 5 undergone a downgrade of its F ( t ) value from 0.822 to 0.815. These analyses show that the recovery process is complex and it is influenced by time dimensions, spatial dimensions (e.g., different neighborhoods may have different recovery paths) and by interdependencies between different economic sector that are interested in the recovery process.
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5. Conclusions This research proposed and illustrated a novel scenario-based system robustness metric for resilience analysis in the context of UI systems facing disturbance. The model provides valuable insights into the likely performance of UI systems as a whole during and after specific disturbance (risk) scenario. The fundamental concepts of UI system resilience discussed herein provide a common frame of reference and a unified terminology. While the proposed resilience analysis metrics only provide a quantitative value for the system robustness, these metrics become useful and valuable only when used to devise effective resilience strategies for the system of interest. Preventive strategies or improvement resources may simultaneously affect the resilience under different hazard types. However, this research does not focus on maximizing the interacting mechanisms between the improvement resources (or strategies) for a given hazard and their impacts on different hazard types. Instead, the objective is to apply the emerging model to issue strategies to increase UI system resilience and illustrate their outcomes at different time associated with the simulation steps in the flowchart of Figure 1. The resilience analysis model developed in this research contributes to a new knowledge by supporting the computation of the system resilience as well as various strategies implications considered for different resilience strategies. This should help to systems engineers during overall system mitigation plan and design, or while devising restoration strategies. Evidently, the resilience analysis model presented provides an opportunity to consider UI system multi-objective optimization as a means to develop effective mitigation, recovery and restoration, or protective strategies. This research contributes as a practical tool for experts and decision makers to be able to determine the amount of investment that could be used for resilience strategies knowing (in quantitative terms), the level of resilience that they would now enable the UI system to exhibit in case of a disruption in future. References 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17.
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