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Modelling Principal-Agent Dilemma for Management of Resilience in Interdependent Infrastructure Systems
Abstract
Temporal dilemmas are observed between the short-term incentives of the asset managers, as agents, versus the long-term resilience driven goals of the organisations, who own and operate the infrastructure systems as principals. That is, the incentive mechanisms designed based on the short-term tenure of an asset manager may structurally deprive the infrastructure owner/operator organisations from directing resources towards their long-term resilience driven goals. Lack of long-term incentives by asset managers adds another layer to management of resilience in the context of infrastructure systems in their life cycle. We proposed a framework for integrating resilience into asset management of infrastructure systems and modelled it to explore the impact of such temporal dilemmas in principal-agent relations and potential strategic responses. The model reflects the interdependency of the sub-systems of infrastructures to assess each sub-system's periodic coupled resilience. The proposed integrated simulation reflects coupled impacts of shocks and stressors within regular asset management regimes. The simulation results provide quantitative evidence of how managerial and political dimensions can impact management of resilience in the context of infrastructure systems and open another discourse towards considering the complexities of organising for resilience in socio-technical systems.
... However, the dark side of projects as effective mechanisms to enhance resilience is the temporary nature of organizing that has implications for long-term goals and accountability of the involved actors (e.g., in retrofit projects, Izaddoost et al., 2023) as well as potential resilience traps (Rachunok & Nateghi, 2021) that lock the impacted communities in certain future scenarios. In this context, project scholars can go beyond studying the potential of projects in response to global grand challenges, but also critically review how projects did or did not deliver the long-term intended targets, what is the impact of projects in future scenarios, and if there were effective accountability mechanisms to ensure long-term resilience driven goals through temporary and ephemeral organizing. ...
Increasing concerns about disruptions and the need for adaptation to drastic changes in social and ecological
systems is reflected in the growing interest in resilience science. At the same
time, the surge in medium to large scale projects around the world, known as projectification of societies,
has resulted in considerable interest in the discipline of project studies. The disciplinary
idiosyncrasies of project studies motivate a reflection on theoretical and methodological considerations in
resilience science.” This reflection rests on the experience of instigating research at the juncture of
resilience and projects in this special collection. Specifically, we framed these observations as a
set of principles that can inform resilience science, including the need for
theoretical parsimony, deliberate attention to system boundaries in conceptualization of
resilience, as well as critical considerations in measuring resilience. We posit that in its
essence, project studies has to navigate a paradox through: (i) investigating projects as efficient form of
organizing for a resilient, sustainable and just future, while (ii) unpacking the governance and accountability
limitations of temporary organizing within projects that aim to ensure these long-term goals. The
proposed principles are core to the broader research stream that we call “projects of future,” a
collective inquiry to unpack these paradoxes and a research stream to engage project scholarship with the
contemporary phenomena in Anthropocene.
This paper presents a probabilistic framework for life-cycle seismic resilience assessment of aging bridges and transportation road networks subjected to infrastructure upgrade. The proposed framework accounts for the uncertainties in damage occurrence of vulnerable deteriorating bridges and restoration rapidity of the overall system functionality. The time-variant bridge fragilities and the damage combinations probability are evaluated considering different earthquake magnitudes and epicenter locations that define the seismic scenario. Traffic analyses are carried out to assess in probabilistic terms the network functionality profiles, the corresponding resilience levels, and a damage-based measure of life-cycle resilience. The effects of structural deterioration, seismic damage, and post-event repair actions under uncertainty are related to traffic restrictions applied over the network. The framework is applied to reinforced concrete bridges exposed to chloride-induced corrosion and simple road networks with a single bridge or two bridges in series under different earthquake scenarios. The effect of network upgrading is also investigated by adding road segments with a vulnerable bridge to strengthen the network connectivity and improve the lifetime system resilience. The results show the capability of the proposed resilience framework in quantifying the detrimental effects of structural deterioration at the network scale and the beneficial consequences of infrastructure investments, such as enhancing the network redundancy with the construction of an additional highway branch.
The performance of lifelines under damage and emergency conditions induced by sudden extreme events, such as earthquakes, can be assessed based on the concept of resilience. Damage also arises in time due to aging, affecting the structural performance of each network component and, consequently, impairing the overall system functionality. Furthermore, the sparse location over the network of vulnerable deteriorating structures, such as spatially distributed bridges, should be taken into account to establish proper infrastructure management policies. In this article, a probabilistic approach is proposed to assess the seismic performance of transportation networks considering the uncertainties involved in the lifetime structural response of aging bridges under different earthquake scenarios. The time-variant seismic capacity associated with prescribed limit states is evaluated by nonlinear incremental dynamic analysis. The initial damage induced by seismic events and its recovery process through structural repair is related to traffic restrictions to different road users. Traffic flow distribution analyses are carried out over the road network to assess the post-event system functionality and the corresponding seismic resilience. The role of different factors such as aging, earthquake scenario, and seismic capacity correlation is investigated by considering spatially distributed reinforced concrete bridges exposed to corrosion and highway networks with detours and re-entry links.
The objective of this study is to establish a framework for analyzing infrastructure dynamics affecting the long-term steady state, and hence resilience in civil infrastructure systems. To this end, a multi-agent simulation model was created to capture important phenomena affecting the dynamics of coupled human-infrastructure systems and model the long-term performance regimes of infrastructure. The proposed framework captures the following three factors that shape the dynamics of coupled human-infrastructure systems: (i) engineered physical infrastructure; (ii) human actors; and (iii) chronic and acute stressors. A complex system approach was adopted to examine the long-term resilience of infrastructure based on the understanding of performance regimes, as well as tipping points at which shifts in the performance regime of infrastructure occur under the impact of external stressors and/or change in internal dynamics. The application of the proposed framework is demonstrated in a case of urban water distribution infrastructure using the data from a numerical case study network. The developed multi-agent simulation model was then used in examining the system resilience over a 100-year horizon under stressors such as population change and funding constraints. The results identified the effects of internal dynamics and external stressors on the resilience landscape of infrastructure systems. Furthermore, the results also showed the capability of the framework in capturing and simulating the underlying mechanisms affecting human-infrastructure dynamics, as well as long-term regime shifts and tipping point behaviors. Therefore, the integrated framework proposed in this paper enables building complex system-based theories for a more advanced understanding of civil infrastructure resilience.
The resilience of a system is related to its ability to withstand stressors, adapt, and rapidly recover from disruptions. Two significant challenges of resilience analysis are to (1) quantify the resilience associated with a given recovery curve; and (2) develop a rigorous mathematical model of the recovery process. To quantify resilience, a mathematical approach is proposed that systematically describes the recovery curve in terms of partial descriptors, called resilience metrics. The proposed resilience metrics have simple and clear interpretations, and their definitions are general so that they can characterize the resilience associated with any recovery curve. This paper also introduces a reliability-based definition of damage levels which is well-suited for probabilistic resilience analysis. For the recovery modeling, a stochastic formulation is proposed that models the impact of recovery activities and potential disrupting shocks, which could happen during the recovery, on the system state. For illustration, the proposed formulation is used for the resilience analysis of a reinforced concrete (RC) bridge repaired with fiber-reinforced polymer.
Resilience, the ability to withstand disruptions and recover quickly, must be considered during system design because any disruption of the system may cause considerable loss, including economic and societal. This work develops analytic maximum flow-based resilience models for series and parallel systems using Zobel’s resilience measure. The two analytic models can be used to evaluate quantitatively and compare the resilience of the systems with the corresponding performance structures. For systems with identical components, the resilience of the parallel system increases with increasing number of components, while the resilience remains constant in the series system. A Monte Carlo-based simulation method is also provided to verify the correctness of our analytic resilience models and to analyze the resilience of networked systems based on that of components. A road network example is used to illustrate the analysis process, and the resilience comparison among networks with different topologies but the same components indicates that a system with redundant performance is usually more resilient than one without redundant performance. However, not all redundant capacities of components can improve the system resilience, the effectiveness of the capacity redundancy depends on where the redundant capacity is located.
This chapter proposes a novel general stochastic formulation for the Life-Cycle Analysis (LCA) of deteriorating engineering systems. The formulation rigorously formalizes the different aspects of the life-cycle of engineering systems. To capture the probabilistic nature of the proposed formulation, it is named Stochastic Life-Cycle Analysis (SLCA). The life-cycle of an engineering system is shaped by deterioration processes and repair/recovery processes, both characterized by several sources of uncertainty. The deterioration might be due to exposure to environmental conditions and to both routine and extreme loading. The repair and recovery strategies are typically implemented to restore or enhance the safety and functionality of the engineering system. In the SLCA, state-dependent stochastic models are proposed to capture the impact of deterioration processes and repair/recovery strategies on the engineering systems in terms of performance measures like instantaneous reliability and resilience. The formulation integrates the state-dependent stochastic models with the previously developed Renewal Theory-based Life-Cycle Analysis (RTLCA) to efficiently evaluate additional system performance measures such as availability, operation cost, and benefits. The proposed SLCA can be used for the optimization of the initial design and mitigation strategies of engineering systems accounting for their life-cycle performance. As an illustration, the proposed SLCA is used to model the life-cycle of a reinforced concrete bridge, subject to deteriorations caused by corrosion and earthquake excitations. The deteriorated bridge column is repaired using Fiber Reinforced Polymer (FRP) composites. The results show that the deterioration processes significantly affect the performance measures of the example bridge.
Resilience and sustainability have both gained traction in civil engineering. There is significant overlap between both fields, but practitioners tend to remain confined to their niche. This paper clarifies the link between both fields, reflects on the underlying concepts, and identifies challenges and opportunities in understanding complex problems involving both resilience and sustainability. A conceptual framework is proposed for understanding resilience and sustainability together. The example of a coastal town subject to sea-level rise and large storms is used to motivate the framework. The example is used to evaluate the use of discount rates for events in the distant future. The results are discussed to determine our ability to decide whether such scenarios are sustainable. The conclusion is that computational approaches will be inadequate. Rather, there is a need for qualitative thinking that embraces ambiguity and unmeasurable uncertainty.
Preparation and Restoration is the second volume of Resilience Engineering Perspectives within the Ashgate Studies in Resilience Engineering series. In four sections, it broadens participation of the field to include policy and organization studies, and articulates aspects of resilience beyond initial definitions:
- Policy and Organization explores public policy and organizational aspects of resilience and how they aid or inhibit preparation and restoration
- Models and Measures addresses thoughts on ways to measure resilience and model systems to detect desirable, and undesirable, results
- Elements and Traits examines features of systems and how they affect the ability to prepare for and recover from significant challenges
- Applications and Implications examines how resilience plays out in the living laboratory of real-world operations.
Preparation and Restoration addresses issues such as the nature of resilience; the similarities and differences between resilience and traditional ideas of system performance; how systems cope with varying demands and sometimes succeed and sometimes fail; how an organization's ways of preparing before critical events can enable or impede restoration; the trade-offs that are needed for systems to operate and survive; instances of brittle or resilient systems; how work practices affect resilience; the relationship between resilience and safety; and what improves or erodes resilience.
This volume is valuable reading for those who create and operate systems that must not only survive, but thrive, in the face of challenge.
This paper explores the operational and physical resilience of acute care facilities, recognizing that the key dimension of acute care facilities is not a simple engineering unit. Quantification of resilience is first approached from the broader societal context, from which the engineering subproblem is formulated, recognizing that, to operate, hospitals depend intricately on the performance of their physical infrastructure from the integrity of structural systems and nonstructural systems, lifelines, components, and equipment. Quantification relates the probability of exceeding floor accelerations and interstory drifts within a specified limit space, for the structural and nonstructural performance. Linear and nonlinear structural responses are considered, as well as the impact of retrofit or repair. Impact on time to recovery is considered in all cases. The proposed framework makes it possible to relate probability functions, fragilities, and resilience in a single integrated approach, and to further develop general tools to quantify resilience for sociopolitical-engineering decisions. DOI: 10.1193/1.2431396
This paper sets out the foundations for developing robust models of community recovery from earthquake disasters. Models that anticipate post-disaster trajectories are complementary to loss estimation models that predict damage and loss. Such models can serve as important decision support tools for increasing community resilience and reducing disaster vulnerability. The paper first presents a comprehensive conceptual model of recovery. The conceptual model enumerates important relationships between a community's households, businesses, lifeline networks, and neighborhoods. The conceptual model can be operationalized to create a numerical model of recovery. To demonstrate this, we present a prototype computer simulation model and oraphical user interface. As the model is intended for decision support, it is important to involve potential users in model development. We conducted a focus group involving Puget Sound, Washington, area disaster management practitioners to elicit local insight about community recovery and model development needs, using the prototype as stimulus. Important focus group issues included potential model inputs, useful recovery indicators, potential uses of recovery models, and suitable types of software systems.
This paper presents a comprehensive model to quantify disaster resilience of systems that is defined as the capability to sustain functionality and recover from losses generated by extreme events. The model combines loss estimation and recovery models and can be applied to critical facilities (e.g. hospitals, military buildings, etc.), as well as utility lifelines (e.g. electric power systems, transportation networks, water systems etc.) that are crucial to the response of recovery processes, decisions and policies. Current research trend leads toward the definition of complex recovery models that are able to describe the process over time and the spatial definition of recovery (e.g. meta-models for the case of health care facilities). The model has been applied to a network of hospitals in Memphis, Tennessee. The resilience framework can be used as a decision support tool to increase the resilience index of systems, such as health care facilities, and reduce disaster vulnerability and consequences.
SUMMARY This paper presents a conceptual framework to define seismic resilience of communities and quantitative measures of resilience that can be useful for a coordinated research effort focusing on enhancing this resilience. This framework relies on the complementary measures of resilience: "Reduced failure probabilities," "Reduced consequences from failures," and "Reduced time to recovery." The framework also includes quantitative measures of the "ends" of robustness and rapidity, and the "means" of resourcefulness and redundancy, and integrates those measures into the four dimensions of community resilience — technical, organizational, social and economic.
The objective of bridge management is to allocate and use the limited resources to balance lifetime reliability and life-cycle cost in an optimal manner. As the 20th century has drawn to a close, it is appropriate to reflect on the birth and growth of bridge management systems, to examine where they are today, and to predict their future. In this paper, it is attempted to shed some light on the past, present, and future of life-cycle management of highway bridges. It is shown that current bridge management systems have limitations and that these limitations can be overcome by using a reliability-based approach. It is concluded that additional research is required to develop better life-cycle models and tools to quantify the risks, costs, and benefits associated with highway bridges as well as their interrelationships in highway networks.
A novel formulation is proposed for uncertainty propagation in risk and resilience analysis of hierarchical systems. The main challenges are related to the complexity of hierarchical systems’ computational workflow and high-dimensional probability space. The computational workflow in regional risk and resilience analysis consists of many interconnected sub-models to predict future hazards, the reliability and functionality of physical systems, and the recovery of disrupted services. The complexity of the computational workflow limits the number of model evaluations for uncertainty propagation. In contrast, the computational workflow contains many sources of uncertainty that demand extensive model evaluations to accurately estimate their effects. The proposed formulation in this paper consists of a multi-level uncertainty propagation approach to reduce the problem dimensionality and a variables-grouping approach to reduce the number of model evaluations. The idea of the multi-level uncertainty propagation is to break down the high-dimensional problem into several low-dimensional ones, one for each level of the hierarchy in the computational workflow. The proposed variables-grouping approach provides an adaptive refinement of uncertainty propagation to identify the influential uncertain input data and computational sub-models. The paper illustrates the proposed formulation through a well-known academic problem and regional risk and resilience analysis of a community.
The spatial and temporal extent of disruptions to services provided by infrastructure following disruptive events is directly related to the instantaneous state of the infrastructure and their post-disruption recovery. This paper develops a novel formulation to model the effects of infrastructure deterioration on their time-varying ability to recover after disruptive events. By unifying available models for deterioration and recovery, the paper proposes a general formulation to model the physical state and functionality of deteriorating infrastructure throughout its service life. The paper further develops resilience measures to quantify the temporal and spatial variations of infrastructure's ability to recover after disruptive events. The proposed formulation has a hierarchical structure that enables exploiting readily available data at the lower level of hierarchy to improve the prediction capability of models at the infrastructure level. Incorporating the governing physical laws in the proposed formulation also enables customizing the models to emulate the reality of infrastructure deterioration and recovery. While the formulation is general, the emphasis is on modeling potable water infrastructure as a case in which the deterioration of pipelines grows mostly undetectable until extensively developed. By the time the deterioration becomes visible, a substantial portion of the infrastructure service life has already been depleted, and costly repair or replacement would be inevitable. To illustrate, the proposed formulation has been implemented to model the time-varying reliability and resilience of the potable water infrastructure of the city of Seaside in Oregon, United States. The example highlights the effects of spatially varying exposure conditions and pipelines’ age on the reliability, functionality, recovery, and resilience of the potable water infrastructure.
Risk and resilience analysis research has favored simpler models such as topological connectivity and maximum flow algorithm to model infrastructure performance. However, modeling of dependencies and interdependencies requires high-fidelity flow analyses. Developing a rigorous mathematical formulation to model interdependent infrastructure encounters three sets of challenges: (1) Identifying and understanding the different types of interactions within and across the infrastructure, (2) Modeling the time-varying performance of each infrastructure accurately, and (3) Modeling the interdependencies among infrastructure. This paper presents a mathematical formulation that models infrastructure as a set of generalized flow network objects. The proposed formulation then models infrastructure interdependencies using dynamic interfaces among the network objects, enabling infrastructure-specific multi-fidelity analyses. This formulation is the first in the literature that can model bilateral and looped interdependencies. The paper also presents guidelines for selecting boundaries and resolutions and introduces novel aggregated performance measures that address the disparity in the performance of spatially distributed infrastructure. Finally, the paper illustrates the proposed formulation using two examples. First, a simple example conceptually illustrates the formulation’s details. Second, a real-world example models the performance of interdependent infrastructure for Shelby County, Tennessee, in a post-earthquake scenario to illustrate scalability.
Fire resilience is a measure that quantifies functional continuity of a building damaged by a fire. Despite its numerous advantages, only a few studies have attempted to assess fire resilience. In this study, a computational framework using the multi-layer zone model was developed for assessing the fire resilience of buildings. The multi-layer zone model is an advanced form of the classical one-layer and two-layer zone models; the model divides rooms of analysis into multiple horizontal control volumes, called zones, for the governing equations of the fire induced environment behavior. This model is suitable for evaluating damage of building components in the fully developed stage of a fire with almost uniform temperature distribution inside the rooms and in the earlier stages with vertically stratified temperature distributions. This is an important feature of fire hazard evaluation because a small rise in temperature or dispersion of smoke can cause damage to certain types of building components, such as non-structural members, equipment systems, and stored items with relatively low fire resistivity. All the components should remain undamaged for the functional continuity of buildings. The framework assesses the damage ratio of a building by aggregating the building components of each zone that is calculated using vulnerability functions. Based on recent statistics on fire incidents and building refurbishment in Japan, the damage ratio is further converted to recovery cost and time required for calculating the fire resilience. Hazard mitigating functions of fire protection equipment systems, i.e., fire extinguisher, indoor fire hydrant, sprinkler system, mechanical smoke exhaust system, and fire alarm system, were incorporated in the framework considering occupants’ response to a fire. As a case study, the fire resilience of a five-story office building was assessed using the Monte Carlo approach, where uncertain parameters associated with the fire source, fire protection equipment systems, occupants, and fire service were considered as variables. As a result, although a building component with relatively high fire resistivity (i.e., structural members) is a major factor influencing the cost and time required for recovery, extinguishment in the early stage of a fire was particularly important to improve the fire resilience of buildings. In this study, the damage ratios of building components were evaluated by the multi-layer zone model; the proposed framework provides an overview of the computational procedure for assessing the fire resilience of buildings, which can be a guide for the other types of fire hazard models.
Resilience curves are used to communicate quantitative and qualitative aspects of system behavior and resilience to stakeholders of critical infrastructure. Generally, these curves illustrate the evolution of system performance before, during, and after a disruption. As simple as these curves may appear, the literature contains underexplored nuance when defining “performance” and comparing curves with summary metrics. Through a critical review of 273 publications, this manuscript aims to define a common vocabulary for practitioners and researchers that will improve the use of resilience curves as a tool for assessing and designing resilient infrastructure. This vocabulary includes a taxonomy of resilience curve performance measures as well as a taxonomy of summary metrics. In addition, this review synthesizes a framework for examining assumptions of resilience analysis that are often implicit or unexamined in the practice and literature. From this vocabulary and framework comes recommendations including broader adoption of productivity measures; additional research on endogenous performance targets and thresholds; deliberate consideration of curve milestones when defining summary metrics; and cautionary fundamental flaws that may arise when condensing an ensemble of resilience curves into an “expected” trajectory.
Resilience is becoming a key concept for risk assessment and safety management of interdependent critical infrastructures (ICIs). This work proposes a resilience enhancement framework for ICIs. With reference to the accidental event, ex-ante and ex-post solutions for enhancing system resilience are analysed and included into a hierarchical model of resilience enhancement strategies (RES). To provide specific resilience enhancement solutions for ICIs, we integrate the hierarchical model with a model predictive control-based dynamic model of ICI system operation. The relationships between the solutions implemented and their impacts on the system parameters are discussed. A multi-objective optimization (MOO) problem is defined, with the objectives of simultaneously minimizing RES cost and maximizing ICIs resilience. The fast non-dominated sorting genetic algorithm NSGA-II is used to solve the MOO problem. For exemplification, a case study is considered, involving interdependent natural gas network and electric power grid. The results show that the resilience enhancement framework is effective in finding optimal RESs for given ICIs.
An efficient and safe transportation system is essential to communities during the long-term recovery period after earthquakes. A disrupted transportation network due to infrastructure damage or failure affects the functionality of the traffic system and poses increased traffic safety risks. A rational assessment of the traffic network performance in terms of both traffic efficiency and safety cannot only provide comprehensive quantification of system resilience, but also enable more risk-informed post-hazard recovery planning. A new methodology to assess the resilience performance of transportation networks during post-earthquake long-term recovery period is developed in this study with two main innovative contributions: (1) modeling the traffic performance of partially closed road segments in the network simulation and optimization, which offers useful tool to capture the time-progressive recovery process; and (2) integrating both traffic efficiency and safety into the resilience assessment and recovery prioritization. After a resilience indicator is introduced to characterize the overall traffic efficiency and safety of the transportation network using probabilistic sampling method, a new restoration priority measure is proposed to support post-earthquake restoration of damaged bridges. A demonstrative study is conducted on a hypothetical network system located in an earthquake-prone area.
The random occurrence of natural and manmade hazards causes significant interruption to normal functionality of engineered systems and critical infrastructures (CIs). Given their ubiquitous nature and the necessity to maintain their performance, there is a global need to quantify these systems’ resilience under multi hazard, particularly when the hazards exhibit random and dynamic frequency and severity. Discussed in this study is the resilience quantification of engineered systems and CIs as a function of their performance during and after the multi hazard; to account for the uncertainty of the hazard and recovery resource availability, stochastic performance metrics such as availability and recovery time are investigated across time and adopted to quantify resilience as a piecewise function. The property of resilience is thoroughly investigated with respect to systems’ reliability metrics. Extensive simulation studies and a numerical example are provided to validate the effectiveness and applicability of the proposed resilience models.
Modern earthquake engineering has to evolve toward resilient-based design, where structures are expected to survive major earthquakes with reduced or null damage that could be easily reparable in a reasonable time. With such design strategy, three important goals are warranted: a) life safety of people, b) preserve building property within a reasonable investment to repair light damage, and c) minimize economic losses because of building´s usage interruption. One way to achieve such goal is using hysteretic energy dissipation devices as structural fuses. Although any global strategy could be used for the successful resilient design of buildings with structural fuses, the authors have work extensively to define global design parameters to introduce resilient-based design in the current force-oriented format of most seismic building codes worldwide. Then, global design parameters obtained from very comprehensive parametric studies for special moment-resisting steel frames with hysteretic fuses are presented and discussed. Also, it is shown that the proposed code-oriented methodology is successful to achieve resilient seismic design when subjected to strong earthquake records that may even surpass those considered in their design spectra.
Reducing hazard‐induced disruptions to infrastructure functionality is cardinal to regional resilience. Specifically, effective strategies to enhance regional resilience require: (a) developing mathematical models for infrastructure recovery; (b) quantifying resilience associated with the developed recovery process; and (c) developing a computationally manageable approach for resilience optimization. This paper proposes a rigorous mathematical formulation to model recovery, quantify resilience, and optimize the resilience of large‐scale infrastructure. Specifically, a multiscale model of the recovery process is proposed that significantly reduces the computational cost, while favoring practical and easily manageable recovery schedules. To quantify regional resilience, resilience metrics are proposed that capture the temporal and spatial variations of the recovery process. The paper then formulates a multiobjective optimization problem that aims to improve regional resilience in terms of the proposed metrics, while minimizing the recovery cost. Finally, the paper illustrates the proposed formulation by considering interdependent infrastructure in Shelby County, Tennessee, United States.
For the life-cycle analysis (LCA) of deteriorating engineering systems, it is critical to model and incorporate the various deterioration processes and associated uncertainties. This paper proposes a renewal-theory life-cycle analysis (RTLCA) with state-dependent stochastic models (SDSMs) that describe the deterioration processes. The SDSMs capture the multiple deterioration processes and their interactions through modelling the changes in the system state variables due to different deterioration processes. Then proper capacity and demand models that take the time-variant state variables as input are adopted to fully capture the impact of deterioration processes on the capacity, demand, and other time-variant performance indicators of the engineering system. The SDSMs are then integrated into RTLCA to efficiently evaluate various life-cycle performance quantities such as availability, operation cost and benefits of the engineering system. To implement the proposed formulation, a sampling-based approach is adopted to simulate samples from the relevant probability density functions (PDFs) to estimate the life-cycle performance quantities, while stochastic simulation-based approach is adopted to estimate the time-variant performance indicators needed to inform intervention activities. As an illustration, the proposed formulation is used to analyse the life-cycle performances of an example reinforced concrete bridge subject to deterioration due to corrosion and seismic loading.
Civil infrastructure systems and communities are continually subjected to changes in environmental and urban settings, evolving expectations and preferences of the public, tightening budgets, and unpredictable political circumstances over their service lives. Conventional decision methods, which determine optimal strategies based on stationary risk assessments and current knowledge, are not well suited for infrastructure systems/communities situated in evolving environments. There is a growing need to account for such evolutions in life-cycle risk assessment through adaptable, learning-based decision-making. Adaptive decision-making is a structured process of learning, improving understanding, and ultimately adapting management decisions in a systematic and efficient way, aimed at reducing uncertainties over the course of the management timeframe. This approach to risk management holds great potential for dealing with future challenges, by explicitly recognizing situational evolutions and improving decisions through learning.
This paper investigates possible dynamic changes in the conditions of civil infrastructure systems/communities and their potential effects on life-cycle performance. A systematic adaptive decision process is proposed as a way to continually reevaluate the risks and provide more adaptive and flexible management actions to enhance infrastructure/community resilience under evolving conditions. Sequential Bayesian updating is utilized to reduce uncertainty and leverage the continuous accumulation of knowledge. Finally, the proposed adaptive decision methodology is illustrated with a benchmark problem based on a testbed residential community in Kathmandu, Nepal, to examine its feasibility and effectiveness in managing evolving risks. The results show that evolving exposure and vulnerability would increase future risks to the community and such risks could be effectively managed through adaptive decision-making.
For performance analysis of deteriorating engineering systems, it is critical to model and incorporate the various deterioration processes and associated uncertainties. This paper proposes state-dependent stochastic models (SDSMs) for modeling the impact of deterioration on the performance of engineering systems. Within the stochastic framework, the change of the system state variables due to different deterioration processes and their interaction is modeled explicitly. As a candidate model to be used in the framework, a new general age and state dependent stochastic model for gradual deterioration is proposed, and its calibration based on data is also discussed. Once the time-variant system state variables are modeled, proper capacity and demand models that take them as inputs can be adopted to fully capture the impact of deterioration processes on the capacity, demand, and other system performances. The proposed framework is first demonstrated through a simple example, and then is used to model the deterioration of an example reinforced concrete (RC) bridge considering deterioration caused by both corrosion and earthquake including their interaction. The results show the importance of modeling the interaction between different deterioration processes, and also verify the advantages of the proposed framework.
Infrastructure systems in coastal areas are exposed to episodic flooding exacerbated by sea-level rise stressors. To enable assessing the long-term resilience of infrastructure to such chronic impacts of sea-level rise, the present study created a novel complex system modeling framework that integrates: (i) stochastic simulation of sea-level rise stressors, based on the data obtained from downscaled climate studies pertaining to future projections of sea level and precipitation; (ii) dynamic modeling of infrastructure conditions by considering regular decay of infrastructure, as well as structural damages caused by flooding; and (iii) a decision-theoretic modeling of infrastructure management and adaptation processes based on bounded rationality and regret theories. Using the proposed framework and data collected from a road network in Miami, a multiagent computational simulation model was created to assess the long-term cost and performance of the road network under various sea-level rise scenarios, adaptation approaches, and network degradation effects. The results showed the capabilities of the proposed computational model for robust planning and scenario analysis to enhance the resilience of infrastructure systems to sea-level rise impacts.
In recent years, significant advances have been accomplished in the fields of modeling, analysis, and design of deteriorating civil engineering systems, and novel approaches to life-cycle assessment, maintenance planning, and optimal design of structural systems have been proposed. Despite these advances, life-cycle concepts are far from being explicitly addressed in design and assessment codes and effectively implemented into practice. There is, therefore, a need to promote further research in the field of life-cycle performance of structural systems under uncertainty and to fill the gap between theory and practice by incorporating life-cycle concepts in structural design and assessment codes. An effort is currently ongoing within the Structural Engineering Institute (SEI)/ASCE to meet this need. This paper is part of this effort and is aimed at presenting a review of the main principles, concepts, methods, and strategies for life-cycle assessment and design of deteriorating structural systems under uncertainty. General criteria for deterioration modeling are first presented, with emphasis on the effects of corrosion and fatigue in steel structures and chloride-induced corrosion in concrete structures. The time-variant structural performance is then investigated with reference to a set of probabilistic performance indicators, and the structural lifetime associated with a reliability target is formulated. The role of inspection and monitoring, the effects of maintenance and repair interventions, and the definition of cost-effective maintenance strategies are discussed. The concepts of life-cycle performance assessment and maintenance planning are used to formulate the life-cycle reliability-based design problem in an optimization context.
Rehabilitation programs are essential for efficiently managing large networks of infrastructure assets and sustaining their safety and operability. While numerous studies in the literature have focused on various aspects of infrastructure rehabilitation, limited efforts have investigated the overall dynamics of the process. The research presented in this paper, therefore, takes a holistic view to investigate the dynamics that affect rehabilitation decisions and the long-term performance of an infrastructure network. First, the interactions among the main parameters related to asset deterioration, rehabilitation actions, and cost accumulation have been analyzed using causal loop diagrams (CLDs). Afterward, a system dynamics (SD) model has been developed based on the CLDs and the underlying mathematical relations among the various parameters. The SD model was then tested on a network of 1,000 assets over a 50-year plan, considering a range of rehabilitation policies regarding budgets, possible rehabilitation actions, and fund allocation options. The model proved to be a practical and effective tool for quick assessment of the long-term impact of rehabilitation policies on infrastructure performance and costs.
Bridge retrofitting is a common approach to enhance resilience of highway transportation systems. The study uses a multiobjective evolutionary algorithm to identify retrofit design configurations that are optimal with respect to resilience and retrofit cost. Application of this algorithm is demonstrated by retrofitting a bridge with column jackets and evaluating resilience of the retrofitted bridge under the multihazard effect of earthquake and flood-induced scour. Three different retrofit materials are used: steel, carbon fiber, and glass fiber composites. The inherent disparity in mechanical properties and associated costs of these different materials gives rise to a trade-off between cost and performance when it comes to retrofit operations. Results from the optimization, the Pareto near-optimal set, include solutions that are distinct from one another in terms of associated cost, contribution to resilience enhancement, and values of design parameters. This optimal set offers the best search results based on selected materials and design configurations for jackets.
A probabilistic approach to lifetime assessment of seismic resilience of concrete structures under corrosion is presented. The seismic capacity is assumed as time-variant functionality indicator and the seismic resilience is evaluated based on a suitable analytical representation of the functionality recovery profile. The proposed approach is applied to a four-span continuous bridge with box cross-section piers exposed to corrosion. A parametric analysis is performed to highlight the influence of the main factors of the recovery process. The results show that the detrimental effects of structural deterioration may affect the effectiveness of the recovery process and lead over time to a significant increase of uncertainty of the seismic resilience.
A probabilistic approach to lifetime assessment of seismic resilience of deteriorating concrete structures is presented. The effects of environmental damage on the seismic performance are evaluated by means of a methodology for lifetime assessment of concrete structures in aggressive environment under uncertainty. The time-variant seismic capacity associated with different limit states, from damage limitation up to collapse, is assumed as functionality indicator. The role of the deterioration process on seismic resilience is then investigated over the structural lifetime by evaluating the post-event residual functionality and recovery of the deteriorating system as a function of the time of occurrence of the seismic event. The proposed approach is applied to a three-story concrete frame building and a four-span continuous concrete bridge under corrosion. The results show the combined effects of structural deterioration and seismic damage on the time-variant system functionality and resilience and indicate the importance of a multi-hazard life-cycle-oriented approach to seismic design of resilient structure and infrastructure systems. Copyright © 2015 John Wiley & Sons, Ltd.
Planning retrofit actions on bridge networks under tight budget constraints is a challenging process. Because of the uncertainties associated with this process, a probabilistic approach is necessary. In this paper, a probabilistic methodology to establish optimum pre-earthquake retrofit plans for bridge networks based on sustainability is developed. A multicriteria optimization problem is formulated to find the optimum timing of retrofit actions for bridges within a network. The sustainability of a bridge network and the total cost of retrofit actions are considered as conflicting criteria. The sustainability is quantified in terms of the expected economic losses. The uncertainties associated with seismic hazard and structural vulnerability are considered. The methodology is illustrated on an existing bridge network. Genetic algorithms are used to solve the multicriteria optimization problem. The effects of deterioration on bridge seismic performance are considered. The effects of the time horizon on the Pareto optimal solutions are also investigated.
In recent years, the concepts of resilience and sustainability have become very topical and popular. The concept of sustainability rose to prominence in the late 1980s and became a central issue in world politics, when the construction industry began to generate the first sustainable building assessment systems with more or less equally weighted environmental, economic, and social aspects for office buildings over their life cycles. On the other hand, resilience is usually connected to the occurrence of extreme events during the life cycle of structures and infrastructures. In the last decade, it has been used to minimize specifically direct and indirect losses from hazards through enhanced resistance and robustness to extreme events, as well as more effective recovery strategies. A detailed comparison of the studies dealing with either infrastructure sustainability or resilience presented in this paper leads to the conclusion that they have a vast number of similarities and common characteristics. For instance, they both combine structural analyses with social and economic aspects; they both rely on techniques for the life-cycle analysis and decision making; they both are in an early stage, where the academic world is trying to find the best way to promote the application of the scientific results among professional engineers and the industry. Indeed, both approaches try to optimize a system, such as a civil infrastructure system, with respect to structural design, utilized material, maintenance plans, management strategies, and impacts on the society. However, for the most part, researchers and practitioners focusing on either resilience or sustainability operate without a mutual consideration of the findings, which leads to a severe inefficiency. Therefore, this paper suggests that resilience and sustainability are complementary and should be used in an integrated perspective. In particular, the proposed approach is rooted in the well-established framework of risk assessment. The impact of the infrastructure and its service states on the society in normal operational conditions (assessed by sustainability analysis) and after exceptional events (assessed by resilience analysis) should be weighted by the associated probabilities of occurrence and combined in a global impact assessment. The proposed perspective and assessment technique is applicable to various types of civil infrastructure systems, but the case of transportation networks and bridge systems is emphasized herein. A numerical application dealing with the comparative analysis of two possible bridge layouts is presented to exemplify the approach. The results show that both resilience and sustainability analyses assess a relevant amount of the impact of the bridge on the community where it is built, so neither one can be neglected. (C) 2014 American Society of Civil Engineers.
The concept of disaster resilience has received considerable attention in recent years and is increasingly used as an approach for understanding the dynamic response to natural disasters. In this paper, a new performance index measuring the functionality of a gas distribution network has been proposed, which includes the restoration phase to evaluate the resilience index of the entire network. The index can also be used for any type of natural or artificial hazard, which might lead to the disruption of the system. The gas distribution network of the municipalities of Introdacqua and Sulmona, two small towns in the center of Italy that were affected by the 2009 earthquake, has been used as a case study. The pipeline network covers an area of 136 km(2), with three metering pressure reduction (M/R) stations and 16 regulation groups. Different analyses simulating different breakage scenario events due to an earthquake have been considered. The numerical results showed that the functionality of the medium-pressure gas distribution network is crucial for ensuring an acceptable delivery service during the postearthquake response. Furthermore, the best retrofit strategy to improve the resilience index of the entire network should include emergency shutoff valves along the steel pipes. (C) 2014 American Society of Civil Engineers.
The significant advances recently accomplished in the fields of modeling, analysis, and design of deteriorating civil engineering systems are far from being explicitly addressed in design codes and effectively implemented into practice. There is, therefore, a strong need to promote further research in the field of life-cycle performance of structural systems under uncertainty and to fill the gap between theory and practice by incorporating life-cycle concepts in structural design codes and standards. An effort is currently ongoing within SEI/ASCE to meet this need. This paper is part of this effort and it is aimed at presenting a short overview of the main principles, concepts, methods and strategies associated with life-cycle assessment and design of deteriorating structural systems under uncertainty.
SUMMARY In the design and assessment of structures, the aspects regarding the future performance are gaining increased attention. A wide range of performance measures is covered by ‘sustainability’ to reflect these aspects. There is the need for well established methods for quantifying the metrics of sustainability. In this paper, a framework for assessing the time-variant sustainability of bridges associated with multiple hazards considering the effects of structural deterioration is presented. The approach accounts for the effects of flood-induced scour on seismic fragility. Sustainability is quantified in terms of its social, environmental, and economic metrics. These include the expected downtime and number of fatalities, expected energy waste and carbon dioxide emissions, and the expected loss. The proposed approach is illustrated on a reinforced concrete bridge. The effects of corrosion on reinforcement bars and concrete cover spalling are accounted. The seismic fragility curves at different points in time are obtained through nonlinear finite element analyses. The variation of the metrics of sustainability in time is presented. The effects of flood-induced scour on both seismic fragility and metrics are also investigated. Copyright © 2013 John Wiley & Sons, Ltd.
This paper proposes a probabilistic approach for the pre-event assessment of seismic resilience of bridges, including uncertainties associated with expected damage, restoration process, and rebuilding/rehabilitation costs. A fragility analysis performs the probabilistic evaluation of the level of damage (none, slight, moderate, extensive, and complete) induced on bridges by a seismic event. Then, a probabilistic six-parameter sinusoidal-based function describes the bridge functionality over time. Depending on the level of regional seismic hazard, the level of performance that decision makers plan to achieve, the allowable economic impact, and the available budget for post-event rehabilitation activities, a wide spectrum of scenarios are provided. Possible restoration strategies accounting for the desired level of resilience and direct and indirect costs are investigated by performing a Monte Carlo simulation based on Latin hypercube sampling. Sensitivity analyses show how the recovery parameters affect the resilience assessment and seismic impact. Finally, the proposed approach is applied to an existing highway bridge located along a segment of I-15, between the cities of Corona and Murrieta, in California. Copyright © 2013 John Wiley & Sons, Ltd.
In order to evaluate the seismic risk of transportation networks, it is necessary to develop a methodology that integrates the probabilities of occurrence of seismic events in a region, the vulnerability of the civil infrastructure, and the consequences of the seismic hazard to the society, environment, and economy. In this article, a framework for the time-variant seismic sustainability and risk assessment of highway bridge networks is presented. The sustainability of the network is quantified in terms of its social, environmental, and economic metrics. These include the expected downtime, expected energy waste and carbon dioxide emissions, and the expected loss. The methodology considers the probability of occurrence of a set of seismic scenarios that reflect the seismic activity of the region. The performance of network links is quantified based on individual bridge performance evaluated through fragility analyses. The sustainability and risk depend on the damage states of both the links and the bridges within the network following an earthquake scenario. The time-variation of the sustainability metrics and risk due to structural deterioration is identified. The approach is illustrated on a transportation network located in Alameda County, California.
The comparison of alternative construction methods is one of the principal reasons for using simulation to model construction processes. The efficiency and effectiveness of such comparisons can be greatly improved by the prudent use of "matched pairs," a variance reduction technique based on dedicated and fully synchronized random number streams. The basic methodology is illustrated by using the STROBOSCOPE simulation system to compare two alternative construction methods for rock tunneling (Conventional versus the New Austrian Tunneling Method (NATM)). For this example the effects are dramatic. The probability of identifying and choosing the cheaper construction method based on a single run increases from 55% to 96%, the variance of the cost difference decreases by two orders of magnitude, and the 95% confidence interval for the true cost difference given by 4,000 independent runs can be obtained by performing only seven replications using matched pairs. Besides this improvement in statistical efficiency, the use of matched pairs is a necessity for this example in order to compare the alternatives on a logical and equitable basis.
Considers how the life expectancies of building components in a life cycle cost calculation can be determined. Makes comparisons with initial capital cost estimating, where forecasts or estimates of cost have been carried out for many years. By definition an estimate is unlikely to be spot-on. Also recognizes that life expectancy is not just a mathematical calculation but also requires the use of expert judgement. Any forecast of a future event, while utilizing previously recorded performance data, will always be influenced by prevailing conditions and future expectations. The initial quality and standards of the building project are important characteristics in determining component life expectancy as is the type of project itself. Identifies a range of different sources of published information on building component life expectancies. Different techniques are also discussed that have a potential in assisting with the prediction of the lives of building components.
Major natural disasters in recent years have had high human and economic costs, and triggered record high postdisaster relief from governments and international donors. Given the current economic situation worldwide, selecting the most effective disaster risk reduction (DRR) measures is critical. This is especially the case for low- and middle-income countries, which have suffered disproportionally more economic and human losses from disasters. This article discusses a methodology that makes use of advanced probabilistic catastrophe models to estimate benefits of DRR measures. We apply such newly developed models to generate estimates for hurricane risk on residential structures on the island of St. Lucia, and earthquake risk on residential structures in Istanbul, Turkey, as two illustrative case studies. The costs and economic benefits for selected risk reduction measures are estimated taking account of hazard, exposure, and vulnerability. We conclude by emphasizing the advantages and challenges of catastrophe model-based cost-benefit analyses for DRR in developing countries.
The concepts of disaster resilience and its quantitative evaluation are presented and a unified terminology for a common reference framework is proposed and implemented for evaluation of health care facilities subjected to earthquakes. The evaluation of disaster resilience is based on dimensionless analytical functions related to the variation of functionality during a period of interest, including the losses in the disaster and the recovery path. This evolution in time including recovery differentiates the resilience approach from the other approaches addressing the loss estimation and their momentary effects. The recovery process usually depends on available technical and human resources, societal preparedness, public policies and may take different forms, which can be estimated using simplified recovery functions or using more complex organizational and socio-political models. Losses are described as functions of fragility of systems that are determined using multidimensional performance limit thresholds. The proposed framework is formulated and exemplified for a typical Californian Hospital building using a simplified recovery model, considering direct and indirect losses in its physical system and in the population served by the system. A hospital network is also analyzed to exemplify the resilience framework. Resilience function captures the effect of the disaster, but also the results of response and recovery, the effects of restoration and preparedness. Therefore, such a function becomes an important tool in the decision process for both the policy makers and the engineering professionals.
One of the findings of the research looking at the application of integrated logistics support (ILS) to the existing building stock carried out by the authors was that many factors have an effect on maintenance cost. In an attempt to uncover the underlying factors, a questionnaire survey was conducted among 50 local authority and housing associations throughout Scotland. The aim of the questionnaire was to identify the extent to which 24 factors impede the optimum application of maintenance cost. This paper describes the objectives of building maintenance and the principal elements of housing maintenance cost. The study revealed that maintenance cost is greatly influenced by factors which can only be evaluated subjectively, such as high expectations of tenants and improper use of the property.