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Integrating resilience into asset management of infrastructure systems with a focus on building facilities
Abstract
Asset managers are facing the challenges of shrinking budgets from one side and the increasing frequency and impact of disturbances on their assets from the other side. To further equip asset managers to address these challenges, we propose an analytical framework for resilient asset management based on an extension to the established analytical framework of resilience. The core concept of this extension is to understand the significance of two types of couplings, including: (i) the coupled impact of different categories of disturbances, including shocks and stressors, and (ii) the chronological coupling of events in life cycle of an asset. A dynamic model of the proposed framework for integrating resilience into regular asset management is developed for building facilities and is integrated with mathematical modeling of stressors and randomly generated shocks. The model is then simulated as the results pointed to the need to offset the overestimation of the resilience of assets by integrating the coupled impact of shocks and stressors and the compounding impact of multiple potential disturbances in its life cycle. The proposed approach can extend the existing asset management models to future proof the plans for the life cycle of an asset and align the maintenance, repair, or rehabilitation actions with the actions associated with resilience enhancement retrofits.
... Their main objective is to account for temporal performance changes produced by natural hazard events. They evaluate a system's performance using different indicators such as network travel time (Izaddoost et al., 2021;Salem, Siam, El-Dakhakhni, & Tait, 2020), number of affected vehicles (Flannery, Pena, Katon, & Kennedy, 2015;Herrera et al., 2017), economic asset value (Do & Jung, 2018), loss of service cost over time (Baroud et al., 2015), road serviceability (Levenberg, Miller-Hooks, Asadabadi, & Faturechi, 2017), repair costs (Nicolosi, Augeri, D'Apuzzo, Evangelisti, & Santilli, 2022), among others. Some metrics consider well-known consequences after events (Alshboul et al., 2021;Do & Jung, 2018), while others propose stochastic models to integrate uncertainty in the potential consequences, usually through fragility models (Cartes, Echaveguren, Chamorro, & Allen, 2021;Flannery et al., 2015;Izaddoost et al., 2021;Levenberg et al., 2017;2017). ...
... They evaluate a system's performance using different indicators such as network travel time (Izaddoost et al., 2021;Salem, Siam, El-Dakhakhni, & Tait, 2020), number of affected vehicles (Flannery, Pena, Katon, & Kennedy, 2015;Herrera et al., 2017), economic asset value (Do & Jung, 2018), loss of service cost over time (Baroud et al., 2015), road serviceability (Levenberg, Miller-Hooks, Asadabadi, & Faturechi, 2017), repair costs (Nicolosi, Augeri, D'Apuzzo, Evangelisti, & Santilli, 2022), among others. Some metrics consider well-known consequences after events (Alshboul et al., 2021;Do & Jung, 2018), while others propose stochastic models to integrate uncertainty in the potential consequences, usually through fragility models (Cartes, Echaveguren, Chamorro, & Allen, 2021;Flannery et al., 2015;Izaddoost et al., 2021;Levenberg et al., 2017;2017). While most authors consider pristine conditions before the occurrence of a disruptive event, some authors introduce coupled indices (Izaddoost et al., 2021;Levenberg et al., 2017) that account for damage or pre-existing conditions in the infrastructure and the probabilistic occurrence of natural hazard events. ...
... Some metrics consider well-known consequences after events (Alshboul et al., 2021;Do & Jung, 2018), while others propose stochastic models to integrate uncertainty in the potential consequences, usually through fragility models (Cartes, Echaveguren, Chamorro, & Allen, 2021;Flannery et al., 2015;Izaddoost et al., 2021;Levenberg et al., 2017;2017). While most authors consider pristine conditions before the occurrence of a disruptive event, some authors introduce coupled indices (Izaddoost et al., 2021;Levenberg et al., 2017) that account for damage or pre-existing conditions in the infrastructure and the probabilistic occurrence of natural hazard events. Furthermore, some authors have proposed resilience metrics for a comprehensive long term analysis, which include the occurrence of several hazard events during the life cycle of each asset (Bruneau et al., 2017;Li et al., 2020;Yang & Frangopol, 2018. ...
Transportation asset management is a systematic process for the operation, maintenance, and upgrade of physical transportation assets over their life cycle. Although transportation asset management has continued to evolve over the years, the integration of transportation resilience into the process is an ongoing challenge for transportation agencies. Given that natural hazard events can result in significant damage to assets as well as cascading effects on other critical networks that depend on transportation systems to operate, the importance of this challenge cannot be overstated. Consequently, this paper presents a systematic literature review on the integration of resilience and risk into transportation asset management, with a focus on road networks, in order to understand the current state of the art and practice and to identify the ongoing challenges in the field. The review specifically focuses on the integration guidelines adopted by different countries and departments of transportation, as well as the different consequence modeling approaches used, resilience metrics, and methods to integrate transportation resilience into transportation asset management. A number of research gaps in the current state of the art and practice are then highlighted, and potential future research directions are presented.
... Merging these varied academic approaches through multiple forms of cross-disciplinary research has been identified as crucial [11]. Despite attempts to create theoretical bridges between these traditions (e.g., [12,13]), real-world implementations remain scarce [14]. ...
... The unique contribution of this integrated approach lies in its novel methodology and unique case application, as emphasized in [14]. Certain design requirements would have been overlooked without combining insights from different design traditions. ...
This study investigates the integration of customization and personalization approaches in aviation maintenance through Aviation Technical Support as a Service (ATSaaS) platform. Through a comprehensive survey of 86 small and medium-sized airlines, combined with mathematical modeling of fault detection systems, the study develops and validates a hybrid framework that integrates traditional maintenance approaches with AI-driven solutions. The comparative analysis demonstrates that the hybrid model significantly outperforms both pure customization and pure personalization approaches, achieving a 95% fault detection rate compared to 75% for customization-only and 88% for personalization-only models. The hybrid approach also showed superior performance in predictive maintenance effectiveness (96%), operational downtime reduction (92%), and cost optimization (90%). The research presents three architectural frameworks for ATSaaS implementation—customization-based, personalization-based, and hybrid—providing a structured approach for different airline categories. Large airlines, with their extensive technical expertise and complex operational requirements, benefit from enhancing their customized maintenance programs with personalization tools, improving overall maintenance efficiency by 23%. Simultaneously, smaller operators, often constrained by limited resources, can use ATSaaS platforms to access sophisticated maintenance capabilities without extensive in-house expertise, reducing operational costs by 35% compared to traditional approaches. The study concludes that the successful integration of customization and personalization through ATSaaS platforms represents a promising direction for optimizing aviation maintenance operations, supporting the industry’s movement toward data-driven, adaptive maintenance solutions.
... Among these factors, WE is the most influential. Adequate environmental facility management personnel ensure the maintenance and operation of infrastructure, thereby enhancing its disaster resistance [49]. Additionally, the establishment and maintenance of rainwater management systems and green spaces effectively reduce soil erosion and surface runoff, which in turn lowers the risk of geo-disasters [50]. ...
The increasing frequency of extreme climate events has posed severe challenges to China’s socio-economic development and ecological environment due to geological disasters. Therefore, there is an urgent need for effective adaptive strategies to enhance geo-disaster resilience. Environmental governance, as an effective measure to reduce risks from extreme climates and disasters while promoting high-quality social development, remains underexplored in terms of its impact on geo-disaster resilience. This study innovatively constructs a resilience assessment framework that considers extreme climate and geo-disaster intensity, integrating various statistical methods, including the Super-Efficiency Slacks-Based Measure Data Envelopment Analysis (SBM-DEA) model, spatial Markov chains, and methods such as Geodetector and the Geographically and Temporally Weighted Regression (GTWR), to reveal the spatiotemporal evolution of geo-disaster resilience in China from 2007 to 2022, while also analyzing the mechanisms through which environmental governance influences resilience and its spatiotemporal variations. The findings indicate that China’s geo-disaster resilience exhibits unstable growth with significant regional disparities. Spatially, resilience shows notable spillover effects and a tendency toward convergence within similar regions. Environmental governance unevenly enhances resilience over time and space: soil and water conservation and afforestation are generally effective measures, while the contributions of ecological water replenishment, environmental facility management personnel, fiscal expenditure, and nature reserve protection vary by region. This research offers key insights into improving geo-disaster resilience and optimizing environmental governance strategies to enhance China’s disaster response capacity and regional sustainable development.
... State-owned assets are an important material basis for teaching, research and other activities in higher vocational colleges and universities, and are also an important element in the management of higher vocational colleges and universities [14][15]. In recent years, with the growing scale of development of higher vocational colleges and universities and the increasing source of funds, the types and quantity of their assets are also increasing, but the problems of irrational asset allocation, idle assets, and waste of resources are also coming one after another [16][17]. Therefore, in order to achieve the goals of utilizing the existing assets, optimizing resource allocation, improving the effective utilization of assets, and enhancing the asset management level of higher vocational colleges and universities, the establishment of a set of scientific and standardized index systems suitable for the evaluation of asset performance of higher vocational colleges and universities is imminent [18][19][20][21]. ...
State-owned assets are an important guarantee for the daily teaching and research activities of higher vocational colleges and universities and serve an important role in ensuring the long-term development of higher vocational colleges and universities while also serving as an important task for talent cultivation and discipline development. In this paper, the construction process of the state-owned assets management performance assessment system is studied in depth, and the state-owned assets management level of higher vocational colleges and universities is analyzed by combining the hierarchical analysis method with the comprehensive fuzzy evaluation method. By constructing the index system and judgment matrix, the weights of “asset security level”, “asset management capacity”, “asset operation efficiency” and “asset management effect” are determined. The weights of “asset security level”, “asset management capacity”, “asset operation efficiency” and “asset management effect” are 0.41, 0.19, 0.18 and 0.22, respectively, and the judgment matrix satisfies the consistency test. The constructed index system is used to assess the management of state-owned assets of a higher vocational college, and the quantitative results show that its fuzzy comprehensive evaluation score is 66.6, reflecting the good level of comprehensive management of the school. The scores of specific level 1 indicators show that the school performs well in “asset management capacity” (70.08 points) but in “asset protection level” (66.72 points), “operational efficiency” (63 points) and “asset management capacity” (63 points), the school’s overall management level is good. However, there is still much room for improvement in “asset security level” (66.72 points), “operational efficiency” (63 points) and “management effectiveness” (66.32 points).
... SDG 9 focuses on building resilient infrastructure, promoting inclusive and sustainable industrialisation, and fostering innovation. Effective asset management is a fundamental component of resilient infrastructure (Izaddoost et al., 2021), ensuring that public assets such as buildings, roads, and facilities are well-maintained and utilised to their full potential (Park et al., 2016). By integrating governance, planning, and policy evaluation, this study identifies ways to enhance the management of these assets, which will improve infrastructure quality and sustainability in local governments across Indonesia (Jayawarsa et al., 2021). ...
Effective asset management is essential for enhancing operational efficiency and achieving sustainable development within local governments in Indonesia. This study explores key challenges in asset management, focusing on governance, planning, and policy evaluation. Several critical issues are identified: fragmented governance, insufficient transparency and coordination, outdated asset inventories, and misalignment between asset management practices and broader development goals. Policy evaluation mechanisms are also inadequate, failing to measure asset utilisation effectively. These challenges lead to inefficiencies in resource allocation, underutilisation of assets, and missed opportunities for local economic growth. The findings align with Sustainable Development Goals (SDGs), particularly SDG 11 (Sustainable Cities and Communities), which promotes efficient resource use, and SDG 9 (Industry, Innovation, and Infrastructure), which supports enhanced infrastructure management. Addressing these goals enables local governments to align asset management strategies with broader sustainability objectives, fostering equitable growth and long-term development. Through a qualitative literature review, this study analyses data from government reports and prior research to highlight these challenges and propose solutions. An integrated approach—aligning governance frameworks, improving asset management planning, and strengthening policy evaluation—can overcome the identified barriers. By adopting this model, local governments can optimise asset management, leading to improved efficiency, better resource utilisation, and sustainable development. Future research should explore applying this framework across regions in Indonesia to validate its effectiveness.
... There are numerous investigations and tremendous materials regarding portfolio management. The methodology of asset allocation and portfolio optimization the paper will use is relatively full-fledged, for example, Viviane used the investment-strategy model to analyze the environmental hazard of the global reserves [4]; Wolfgang et al. employed optimal asset allocation strategies for equity portfolio around the whole world [5]; Yuyeong et al. researched network for index-tracking portfolio optimization [6]; Zhi and Wang studied the portfolio optimization for inventory financing [7], Ali, Nader and Gholamreza investigated the importance of asset management in the field of building facilities [8]; As about the real assets, Jarosław and Janusz discussed the tactics based on mathematic and quantized way which can be really useful [9]. However, a mass of studies of portfolio optimization emphasize mainly on enterprise level, financial literature and different industries but there are still limited studies in the portfolio management and the specific investment strategies for individualities especially for the retirees. ...
... A service assessment for AM and decision-making distributes a limited budget for a long-term maintenance and rehabilitation plan based on the assessment of the level of service [5]. A dynamic analytical framework for resilient AM extends the established conventional AM to future proof the WLC functionality and aligns maintenance, repair, or rehabilitation tasks associated with resilience enhancement retrofits [6]. An empirical framework supports production companies to implement value-based AM by committing to operational excellence where the key decision criteria is the value delivered by assets to the organisation [7]. ...
Smart or intelligent built assets including infrastructure, buildings, real estate, and cities provide enhanced functionality to their different users such as occupiers, passengers, consumers, patients, managers or operators. This enhanced functionality enabled by the Internet of Things (IoT), Artificial Intelligence (AI), Big Data, Mobile Apps, Virtual Reality (VR) and 5G does not only translate into a superior user experience; technology also supports sustainability and energy consumption to meet regulation (ESG, NZC) while optimising asset management and operations for enhanced business economic performance. The main peculiarity is that technology is standardised, ubiquitous and independent from the physical built assets whereas asset users including humans, machines and devices are also common to different assets. This article analyses the atomic differences between built assets and proposes an asset omni-management model based on micro-management of services that will support the macro-functionality of the asset. The proposed key concept is based on the standardisation of different assets based on common and specific functionality and services delivered by the technology stack that is supporting already the transition to Industry 5.0 based on Web 3.0 and Tokenisation.
... With the input from the list of components of the infrastructure system (assets and their quantity) and the related condition inspections data, deterioration curves were provided for normal aging coupled with the impact of a sudden extreme event (Izaddoost 2021). This information was paired with the predicted cost to maintain the system to a targeted level. ...
SAFETY4RAILS is the acronym for the European Union Horizon 2020 co-funded innovation project entitled: "Data-based analysis for safety and security protection for detection, prevention, mitigation and response in trans-modal metro and railway networks" which started in October 2020. Its focus is to support the increase of security and resilience against combined cyber-physical threats including natural hazards to railway and metro systems. Its objectives target capabilities to support the characteristics of resilient systems; resilience represented by cycles containing phases of identification, protection, detection, response and recovery (Department of Communications 2019) (or similarly named phases); even if in practice it is not always possible to consider these phases sequentially. An ESREL paper in 2021 offered a very first look into the SAFETY4RAILS project and the SAFETY4RAILS Information System platform as well as some of the tools that are included in the platform. This paper will describe the architectural solution implemented for the platform in the last year and the demonstration of representative capabilities from the first simulation exercise with Madrid Metro at the beginning of 2022.
... With the input from the list of components of the infrastructure system (assets and their quantity) and the related condition inspections data, deterioration curves were provided for normal aging coupled with the impact of a sudden extreme event (Izaddoost 2021). This information was paired with the predicted cost to maintain the system to a targeted level. ...
SAFETY4RAILS is the acronym for the European Union Horizon 2020 co-funded innovation project entitled: "Data-based analysis for safety and security protection for detection, prevention, mitigation and response in trans-modal metro and railway networks" which started in October 2020. Its focus is to support the increase of security and resilience against combined cyber-physical threats including natural hazards to railway and metro systems. Its objectives target capabilities to support the characteristics of resilient systems; resilience represented by cycles containing phases of identification, protection, detection, response and recovery (Department of Communications 2019) (or similarly named phases). An ESREL paper in 2021 introduced the SAFETY4RAILS project and the SAFETY4RAILS Information System platform as well as some of the tools that are included in the platform. This paper will describe the architectural solution implemented for the platform in the last year and the demonstration of representative capabilities from the first simulation exercise with Madrid Metro at the beginning of 2022.
... Rehabilitation and maintenance are recognised as key drivers for the sustainability of constructions and a possible means to improve the resilience of the built environment, thus fitting SDG 11 (Barrelas, Ren, & Pereira, 2021;Izaddoost, Naderpajouh, & Heravi, 2021). Therefore, governments worldwide are issuing specific frameworks for designing an implementation plan of corrective measures focusing on rehabilitation and maintenance. ...
The prioritisation of public facilities’ maintenance is a necessary but complex task due to the need of considering both physical and socio-economic criteria. This study addresses this problem by quantifying the improvement in the delivery of social benefits that the corrective maintenance of an urban area’s public facilities could yield. Based on this, a decision framework is proposed to design and schedule corrective maintenance plans at a municipal scale. The methodology integrates multi-criteria assessment with an analytical method for evaluating the contribution of an area’s public facilities to its sustainable urban development based on their type of social infrastructure and their maintenance condition. The decision framework is implemented as a software to facilitate its application to a case study, consisting in building urban regeneration strategies aligned with governmental guidelines. The results revealed that decision-making is more efficient when considering the facilities’ type of social infrastructure. In addition, a cost-efficient prioritisation of corrective measures yields better results than neglecting the economy.
Purpose
This paper aims to review the elements of facilities condition index (FCI) as one of the most widely used metrics for describing the physical condition of facilities and as a measure of financial indicator related to maintenance activity. This research will benefit future studies that focus on implementation of FCI and encourage the best practice when assessing the physical condition to evaluate the performance of facilities as well as to plan for maintenance action to be taken and financial implication involved based on the findings from FCI.
Design/methodology/approach
A total of 33 studies from 2012 to 2022 were identified and extracted from four academic databased, named Scopus, Google Scholar, Web of Science and Mendeley. These published studies were selected because it matches with the inclusion requirements for research question, “What are the elements of facilities condition index discussed in the literature from 2012 to 2022?”. To answer the research question, the studies in ATLAS.ti were grouped into five major themes based on the codes and categories found.
Findings
This paper presents the findings of a thematic analysis of the current literature discussed about FCI. A total of 33 studies from 2012 to 2022 were identified and extracted from four academic databased, named Scopus, Google Scholar, Web of Science and Mendeley. A themed review was conducted, and five themes were identified as elements of FCI, which are named as follows: “Condition Assessment”, “Deferred Maintenance”, “Facilities Performance”, “Financial and Fund Allocation” and “Decision Making”.
Originality/value
This paper fulfils the fundamental elements on current FCI practices as well as intended to highlight existing practices that are essential to evaluate facilities performance and planning for maintenance strategies.
Many negative occurrences in any system cannot be linked to a single failure cause or system malfunction. They usually occur as a result of unforeseen combinations of typical performance variability. Disasters that human civilization has had to deal with in the past have highlighted the need of being ready and having the ability to quickly recover from a sudden and unexpected change in technological, organizational, social, and economic status of the community. Continuous emergences of business continuity disruptions lead to the global orientation towards resilience management application replacing the conventional risk assessment. The research studies risk assessment data prior to and after the emergence of resilience affecting shocks, and conducts a set of questions that will formulate resilience evaluation. The study proposed an evaluation framework of the current drainage system that will subsequently institutes a framework for a resilient system complimenting the main resilience attributes into three main models, and continuously improve its vulnerability status. The originality of this study lies on the unique demographical, geographical status of Qatar, the relative newness of drainage authority in Qatar and recent formation of its risk team.
Purpose
This paper extends the authors’ previous research work investigating resilience for municipal infrastructure from an asset management perspective. Therefore, this paper aims to formulate a pavement resilience index while incorporating asset management and the associated resilience indicators from the authors’ previous research work.
Design/methodology/approach
This paper introduces a set of holistic-based key indicators that reflect municipal infrastructure resiliency. Thenceforth, the indicators were integrated using the weighted sum mean method to form the proposed resilience index. Resilience indicators weights were determined using principal components analysis (PCA) via IBM SPSS®. The developed framework for the PCA was built based on an optimization model output to generate the required weights for the desired resilience index. The output optimization data were adjusted using the standardization method before performing PCA.
Findings
This paper offers a mathematical approach to generating a resilience index for municipal infrastructure. The statistical tests conducted throughout the study showed a high significance level. Therefore, using PCA was proper for the resilience indicators data. The proposed framework is beneficial for asset management experts, where introducing the proposed index will provide ease of use to decision-makers regarding pavement network maintenance planning.
Research limitations/implications
The resilience indicators used need to be updated beyond what is mentioned in this paper to include asset redundancy and structural asset capacity. Using clustering as a validation tool is an excellent opportunity for other researchers to examine the resilience index for each pavement corridor individually pertaining to the resulting clusters.
Originality/value
This paper provides a unique example of integrating resilience and asset management concepts and serves as a vital step toward a comprehensive integration approach between the two concepts. The used PCA framework offers dynamic resilience indicators weights and, therefore, a dynamic resilience index. Resiliency is a dynamic feature for infrastructure systems. It differs during their life cycle with the change in maintenance and rehabilitation plans, systems retrofit and the occurring disruptive events throughout their life cycle. Therefore, the PCA technique was the preferred method used where it is data-based oriented and eliminates the subjectivity while driving indicators weights.
Research communities across multiple disciplines have demonstrated an increasing concern about variations in the performance of social-ecological systems. In response to this concern, holistic research on resilience explores explanations for the performance of the systems under both predictable and unknown stressors and shocks. Embedded in broader systems, projects - which often involve a broad range of uncertainty and variability in performance outcomes - provide a fertile context in which to study resilience. On the other hand, projects involve temporary organising that is crucial in the extreme and changing contexts. In this essay, we frame a roadmap for the new theoretical domain of research at the intersection of resilience and projects. This framework intends to spark new research directions and can be used by scholars to investigate resilience at and across multiple levels-- individuals, groups/teams, projects, organisations, industries, and societies.
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.
Power distribution systems in the US are commonly supported by wood utility poles. These assets require regular maintenance to enhance the reliability of power delivery to support many dependent functions of the society. Limitations in budget, however, warrant efficient allocation of limited resources based on optimal preventive maintenance plans. A few studies have developed risk-based metrics to support risk-informed decision making in preventive maintenance planning for power distribution systems. However, integration of risk-based metrics and optimization for enhancing the life-cycle resilience of distribution systems has not been explored. To address this gap, this paper proposes a mixed-integer nonlinear programming (MINLP) model to maximize the life-cycle resilience of aging power distribution systems subject to multi-occurrences of hurricane events using an optimal risk-based maintenance planning. For this purpose, a risk-based index called the Expected Outages is proposed and integrated into the optimization problem to minimize the total expected number of power outages in the entire planning horizon. Various uncertainties in the performance of poles under stochastic occurrences of hazards are taken into account through advanced fragility models and an efficient recursive formulation that models the uncertainty of precedent pole failures. The proposed approach is applied to a large, realistic power distribution system for long-term maintenance planning given a total budget limit and different levels of periodic budget constraints. The resulting optimization problems are solved through the branch and bound algorithm. Results indicate that applying the presented methodology leads to a significant enhancement of the life-cycle resilience of distribution systems compared to the commonly implemented strength-based maintenance strategy set by National Electric Safety Code.
As the adoption of smart water systems increases, so does the importance of building resilience in these critical systems. Smart systems are often optimized to increase efficiency of water infrastructure and reduce costs, while resilience is required to enhance the systems's ability to recover from disruptions and usually requires additional investment. Thus, there is an inherent trade off between resilience and efficiency that needs to be reconciled for development of efficient and resilient water infrastructure. This paper utilizes a resilience assessment framework (Linkov et al. 2013a) to identify and organize the various functions of smart water systems that improve resilience.
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.
Building communities’ resilience to natural weather hazards requires the appropriate assessment of such capabilities. The resilience of a community is affected not only by social, economic, and infrastructural factors but also by natural factors (including both site characteristics and the intensity and frequency of events). To date, studies of natural factors have tended to draw on annual censuses and to use aggregated data, thus allowing only a limited understanding of site-specific hot or cold spots of resilience. To improve this situation, we carried out a comprehensive assessment of resilience to typhoon disasters in Nansha district, Guangzhou, China. We measured disaster resilience on 1×1-km grid units with respect to socioeconomic and infrastructural dimensions using a set of variables and also estimated natural factors in a detailed manner with a meteorological modeling tool, the Weather Research and Forecast model. We selected typhoon samples over the past 10 years, simulated the maximum typhoon-borne strong winds and precipitation of each sample, and predicted the wind speed and precipitation volume at the 100-year return-level on the basis of extreme value analysis. As a result, a composite resilience index was devised by combining factors in different domains using factor analysis coupled with the analytic hierarchy process. Resilience mapping using this composite resilience index allows local governments and planners to identify potential hot or cold spots of resilience and the dominant factors in particular locations, thereby assisting them in making more rational site-specific measures to improve local resilience to future typhoon disasters.
The lack of a comprehensive decision-making approach at the community-level is of significant importance that must be garnered immediate attention. Network-level decision-making algorithms need to solve large-scale optimization problems that pose computational challenges. The complexity of the optimization problems increases when various sources of uncertainty are considered. This research introduces a sequential discrete optimization approach, as a decision-making framework at the community-level for recovery management. The proposed mathematical approach leverages approximate dynamic programming along with heuristics for the determination of recovery actions. Our methodology overcomes the curse of dimensionality and manages multi-state, large-scale infrastructure systems following disasters. We also provide computational results which suggest that our methodology not only incorporates recovery policies of responsible public and private entities within the community, but also substantially enhances the performance of their underlying strategies with limited resources. The methodology can be implemented efficiently to identify near-optimal recovery decisions following a severe earthquake based on multiple objectives for an modeled Electrical Power Network of a testbed community coarsely modeled after Gilroy, California, United States. The proposed optimization method supports risk-informed community decision-makers within chaotic post-hazard circumstances.
This paper proposes an optimization model, using Mixed-Integer Linear Programming (MILP), to support decisions related to making investments in the design of power grids serving industrial clients that experience interruptions to their energy supply due to disruptive events. In this approach, by considering the probabilities of the occurrence of a set of such disruptive events, the model is used to minimize the overall expected cost by determining an optimal strategy involving pre- and post-event actions. The pre-event actions, which are considered during the design phase, evaluate the resilience capacity (absorption, adaptation and restoration) and are tailored to the context of industrial clients dependent on a power grid. Four cases are analysed to explore the results of different probabilities of the occurrence of disruptions. Moreover, two scenarios, in which the probability of occurrence is lowest but the consequences are most serious, are selected to illustrate the model’s applicability. The results indicate that investments in pre-event actions, if implemented, can enhance the resilience of power grids serving industrial clients because the impacts of disruptions either are experienced only for a short time period or are completely avoided.
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.
To mitigate the impact of natural or man-made hazards on the services of an infrastructure facility, it is important to quantitatively assess its available capacity. For example, in a post-disaster scenario, critical infrastructure is likely to experience (i) excessive demand for the service of an infrastructure and/or (ii) compromised capacity because of damage to the infrastructure and the failure of infrastructure interdependencies. As the demand grows and nears the capacity limit of an infrastructure facility, a shortage of services required for the community’s recovery will occur. The development of mitigation strategies and an assessment of their effectiveness require a systematic approach. In this paper, a functional stress–strain principle for infrastructure facilities is proposed to quantitatively assess their serviceability in post-disaster scenarios. Functional stress in infrastructure management represents a service-related demand on an infrastructure facility, while strain indicates its coping capacity. The dynamic nature of infrastructure services will be considered depending on the relationship between demand and available capacity. The allowable range of functional stress is then defined, considering plastic and elastic patterns of responses of a facility during recovery to explore strain capacity variations. The proposed principle facilitates a systematic understanding of how infrastructure facilities can adapt themselves to growing stress and the maximum level of stress they can handle. The application of the proposed functional stress–strain principle is demonstrated through case studies of two infrastructure facilities in a post-earthquake scenario: a medical facility and a power facility.
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.
Improving urban resilience could help cities better cope with natural disasters, such as neighborhood flood events in Mexico City pictured here. Data source: Unidad Tormenta, Sistema de Aguas de la Ciudad de Mexico.
Evaluating and enhancing resilience in water infrastructure is a crucial step towards more sustainable urban water management. As a prerequisite to enhancing resilience, a detailed understanding is required of the inherent resilience of the underlying system. Differing from traditional risk analysis, here we propose a global resilience analysis (GRA) approach that shifts the objective from analysing multiple and unknown threats to analysing the more identifiable and measurable system responses to extreme conditions, i.e. potential failure modes. GRA aims to evaluate a system's resilience to a possible failure mode regardless of the causal threat(s) (known or unknown, external or internal). The method is applied to test the resilience of four water distribution systems (WDSs) with various features to three typical failure modes (pipe failure, excess demand, and substance intrusion). The study reveals GRA provides an overview of a water system's resilience to various failure modes. For each failure mode, it identifies the range of corresponding failure impacts and reveals extreme scenarios (e.g. the complete loss of water supply with only 5% pipe failure, or still meeting 80% of demand despite over 70% of pipes failing). GRA also reveals that increased resilience to one failure mode may decrease resilience to another and increasing system capacity may delay the system's recovery in some situations. It is also shown that selecting an appropriate level of detail for hydraulic models is of great importance in resilience analysis. The method can be used as a comprehensive diagnostic framework to evaluate a range of interventions for improving system resilience in future studies.
Sea-level rise (SLR) causes estimates of flood risk made under the assumption
of stationary mean sea level to be biased low. However, adjustments to flood
return levels made assuming fixed increases of sea level are also inaccurate
when applied to sea level that is rising over time at an uncertain rate. To
accommodate both the temporal dynamics of SLR and their uncertainty, we develop
an Average Annual Design Life Level (AADLL) metric and associated Design Life
SLR (DL-SLR) allowances. The AADLL is the flood level corresponding to a
time-integrated annual expected probability of occurrence (AEP) under
uncertainty over the design life of an asset; DL-SLR allowances are the
adjustment from 2000 levels that maintain current average probability over the
design life. Given non-stationary and uncertain sea-level rise, AADLL flood
levels and DL-SLR allowances provide estimates of flood protection heights and
offsets for different planning horizons and different levels of confidence in
SLR projections in coastal areas. Here we employ probabilistic sea-level rise
projections to illustrate the calculation of AADLL flood levels and DL-SLR
allowances for a set of long-duration tide gauges along U.S. coastlines.
In (FEMA 308, 1998) a general framework facilitating decisions on appropriate course of action
(accept damage, restore, or upgrade) for damaged buildings after an earthquake was presented.
Such Performance-Based Policy Framework relies on performance index of the
building in its intact and damaged state and on the relative performance loss as significant
indicators for repair and/or upgrade decisions; however, no specific guidance for the
establishment of PL and IP thresholds governing damage acceptability was given. This
paper proposes an improvement of the PBPF introduced in FEMA 308 by providing it with
a set of performance thresholds that can be established based on a clear quantitative
approach and presents a set of tools for its practical implementation with reference to
Reinforced Concrete frame buildings. In addition to the Repair and Repair and Retrofit
options, also the Demolish one is introduced, because it corresponds to a not infrequent
decision in case of severely damaged buildings in European-Mediterranean regions. Criteria
to establish decision thresholds IP and PL are proposed that allow to clearly connect
them to reconstruction costs and probability of failure of the building in the intact and
damaged state. Finally, a case study demonstrates the application of the hypothesized
policy framework for a Municipality in southern Italy and investigates on the effects of
varying decision thresholds on the total reconstruction costs and mean safety level for the
considered building stock
full article available online at:
http://link.springer.com/article/10.1007/s10518-015-9824-0
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.
Accidental events that may lead to a progressive collapse (e.g., explosions and collisions) and earthquakes are two types of destructive hazards for super-tall buildings. The study of the resilience of super-tall buildings against these hazards has become a crucial issue. Corresponding research outcomes have important social, political, and economic value for damage/loss assessment and the post-disaster recovery plans for important buildings. A resilient structural system usually exhibits significantly reduced consequences and recovery time after hazards. Therefore, based on the resilience concept and the widely adopted “frame–truss–core tube” system, a novel seismic–progressive collapse resilient super-tall (SPCRST) building system and the corresponding design method are proposed in this paper. Detailed case study results indicate that, compared with the conventional system, the proposed system has significant advantages in controlling the dynamic responses of super-tall buildings during accidental events and earthquakes (e.g., vertical displacement after local column failure, and floor accelerations and inter-story drifts under earthquakes) and in improving the seismic–progressive collapse resilience of the structure.
Purpose
This paper critically reviews economic impact assessment methods adopted in construction-related projects, to develop and present a novel bottom-up approach suitable to estimate regional economic impacts of building maintenance projects.
Design/methodology/approach
A thorough literature review of economic impact assessment in construction projects is carried out to identify the most relevant approach to estimate wider economic impacts of building maintenance projects. Based on these findings, a model based on the bottom-up approach to estimate wider economic impacts is developed. The applicability and face validity of the developed model is demonstrated through a case of cladding replacement program in Australia.
Findings
The literature review revealed that bottom-up models are better suited for estimating regional economic impacts of maintenance projects, given the challenges of obtaining micro-level economic data in the maintenance sector. In relation to the total economic impacts (direct and indirect), the results show that for every 1.34. In terms of employment impact, over 70% of the direct economic value addition is driven by the increase in labour, where close to 3 FTE jobs will be required for each $1 million of spending on cladding replacement projects.
Originality/value
This paper presents a model to estimate the wider economic impacts of building maintenance projects, which is typically overlooked in the construction management field. The proposed model is developed to incorporate the variability of different building maintenance projects so that the economic impact resulting from these projects could be estimated more accurately. This model can be used by local government decision-makers to justify and prioritise maintenance projects in a similar manner to new construction projects.
Given the ever-increasing number of aged residential buildings in Hong Kong, green retrofit provides a sustainable solution for improving existing aged residential buildings' performance instead of constructing new buildings. Studies on green retrofit technologies (GRTs) and green retrofit policies (GRPs) have received great attention globally. However, few studies have been done to understand the applicability and importance of GRTs and GRPs, and the impacts of GRPs on the application of GRTs in a particular region, such as Hong Kong. Therefore, based on a questionnaire survey conducted in Hong Kong, this study aims to further examine the GRTs and GRPs that have been identified in a previous study. The applicability of the GRTs and the importance of the GRPs for green retrofit of aged residential buildings in Hong Kong were studies, the correlations between the GRTs and GRPs were analyzed, and a priority guide was developed for promoting green retrofit of aged residential buildings in Hong Kong. The findings can help various stakeholders to have a better understanding of the GRTs and provide valuable references for the local government to develop future GRPs. Furthermore, this study also provides a fundamental guide for future green retrofit research and development in the local and global contexts.
This paper presents a model for parallel replacement and improvement for a fleet of assets to minimize both the economic costs and greenhouse gas (GHG) emissions where the emissions are limited by an emissions trading system also known as cap-and-trade. The firm which owns the assets has the options of using, storing, improving, or salvaging them. Different technological types and their performances have been considered for the assets. The firm has the option of purchasing new assets from varying technologies and/or improving its existing assets to a higher-performance type. The model considers the possibility of both banking the emission allowances or trading them in the market. The model was applied to data from a fleet of excavators in Ontario, Canada. The model and the findings of this case study could help emitter firms to simultaneously manage the emissions and costs of their assets in a jurisdiction regulated by cap-and-trade.
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.
The absorptive and restorative abilities of a community are two key elements of community resilience following disasters. The recovery of communities relies on an efficient restoration planning of damaged critical infrastructure systems, household units, and impaired supporting social and economic functions. These interdependent systems form a dynamic system of systems that changes continuously during restoration. Therefore, an effective and practical recovery planning process for a community can be modeled as a sequential dynamic optimization problem under uncertainty. This paper seeks to enhance our understanding of dynamic optimization concepts and their role in formulating post-disaster, community-level recovery strategies. Various methods of classic dynamic programming and reinforcement learning are examined and applied. Simulation-based approximate dynamic programming techniques are introduced to overcome the curse of dimensionality, which is characteristic of large-scale and multi-state systems of systems. The paper aims not only to study the unexplored topic of dynamic optimization in community resilience, but also to be a practical reference for policymakers, practitioners, engineers, and operations analysts to harness the power of dynamic optimization toward assessing and achieving community resilience.
Highway bridges play a significant role in maintaining safety and functionality of the society. The immediate damage of highway bridges caused by natural hazards can disrupt transportation systems, impede rescue and recovery activities. This disruption may result in tremendous financial and societal losses. Therefore, assessing vulnerability, recovery capability, and potential losses of bridges under natural hazards becomes a primary concern to decision-makers to facilitate the emergency response and recovery efforts. Under these concerns, resilience is a paramount performance indicator to evaluate and recover the functionality of structural systems under extreme events. In this article, an integrated framework for long-term resilience and loss assessment of highway bridges under multiple independent natural hazards is presented. The impacts of extreme events such as earthquakes, hurricanes and floods on the life-cycle performance of bridges are illustrated. A stochastic renewal process model of the random occurrence of hazard events is used to compute the expected long-term resilience and damage loss by considering both time-independent and time varying occurrence characteristics. The proposed approach is applied to a bridge by considering the impacts of earthquake and hurricane hazards. The proposed framework can be implemented to the design, maintenance and retrofit optimisation of infrastructure systems under multiple extreme events.
A probabilistic decision support framework is developed in this study for community resilience planning under multiple hazards using performance goals based guidelines such as the Oregon Resilience Plan and the National Institute of Standards and Technology Community Resilience Planning Guide. Herein, resilience of community infrastructure systems is defined as the joint probability of achieving robustness and rapidity based performance goals, which is quantified using Bayesian networks. The framework assesses the effects of decision support options such as selection of hazards, resilience goals, and mitigation (ex-ante) and response (ex-post) strategies to identify measures that can improve infrastructure performance to meet community defined resilience goals. This framework is applied for resilience assessment of building, transportation, water, and electric power infrastructure systems in Seaside, Oregon, under combined earthquake ground shaking and tsunami inundation hazards corresponding to different return periods. Uncertainties in damage, restoration, and economic losses are explicitly considered and propagated in the framework using Monte Carlo simulation (MCS). The MCS results are then used to inform the Bayesian network, which evaluates the joint resilience of infrastructure systems in Seaside. Results highlight the impact of considering different performance goals, introduction of ex-ante and ex-post measures, and interdependencies between various infrastructure systems on infrastructure resilience.
Disasters are becoming increasingly frequent, calamitous, and expensive. The constant increase of disasters and more exposure of people, facilities, built environment and assets are alarming indicators for strengthening disaster preparedness to response and ensuring that capacity planning are in place for effective response and recovery at emergency level. Ad hoc and an unplanned capacity planning in disaster events can result in further losses or exacerbate the disaster-stricken population. Despite the importance of understanding disaster risk, few empirical studies have been conducted over the last decade in terms of analyzing the factors that determine the public hospitals and healthcare centers effective responses to disasters. Therefore, this paper aims to introduce optimization model for effective restructuring and reformatting of hospital and healthcare facilities under calamitous situations that can be led to human loss reduction. A public hospital complex located in Tehran, Iran has been considered as a real case for resource allocation at the time of a devastating disaster so that it can encounter the sudden surge of injured people. Thmain concern in this hospital is converting the non-emergency wards into the emergency wards and adding extra capacity to the existing ones to enhance the hospital preparedness for effective response. The results show that policymakers can improve the resource planning for cost-effectiveness of disaster risk reduction and capability self-assessment. The findings can promote the resilience of hospitals, to ensure that they remain safe, effective and proactive during and after disasters.
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.
Building performance is an important yet surprisingly complex concept. This book presents a comprehensive and systematic overview of the subject. It provides a working definition of building performance, and an in-depth discussion of the role building performance plays throughout the building life cycle. The book also explores the perspectives of various stakeholders, the functions of buildings, performance requirements, performance quantification (both predicted and measured), criteria for success, and the challenges of using performance analysis in practice.
Building Performance Analysis starts by introducing the subject of building performance: its key terms, definitions, history, and challenges. It then develops a theoretical foundation for the subject, explores the complexity of performance assessment, and the way that performance analysis impacts on actual buildings. In doing so, it attempts to answer the following questions: What is building performance? How can building performance be measured and analyzed? How does the analysis of building performance guide the improvement of buildings? And what can the building domain learn from the way performance is handled in other disciplines?
• Assembles the current body of knowledge on building performance analysis in one unique resource
• Offers deep insights into the complexity of using building performance analysis throughout the entire building life cycle, including design, operation and management
• Contributes an emergent theory of building performance and its analysis
Building Performance Analysis will appeal to the building science community, both from industry and academia. It specifically targets advanced students in architectural engineering, building services design, building performance simulation and similar fields who hold an interest in ensuring that buildings meet the needs of their stakeholders.
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.
Reliability-centered maintenance is a process used to determine - systematically and scientifically - what must be done to ensure that physical assets continue to do what their users want them to do. Widely recognized by maintenance professionals as the most cost-effective way to develop world-class maintenance strategies, RCM leads to rapid, sustained and substantial improvements in plant availability and reliability, product quality, safety and environmental integrity. The author and his associates have helped users apply RCM and its more modern derivative, RCM2, on more than 700 sites in 34 countries. These sites include all types of manufacturing (especially automobile, steel, paper, petrochemical, pharmaceutical, and food manufacturing), utilities (water, gas, and electricity), armed forces, building services, mining, telecommunications, and transport. This book summarizes this experience in the form of an authoritative and practical description of what RCM2 is and how it should be applied. This book will be of value to maintenance managers, and to anyone else concerned with the reliability, productivity, safety, and environmental integrity of physical assets. Its straightforward, plant-based approach makes the book especially well suited to use in centers of higher education.
The deterioration of the U.S. highway system has received significant attention from scholars, industry practitioners, and policymakers over the last several decades. This growing interest has encouraged the production of multiple reports highlighting the challenges of enhancing system conditions in the long term. Because government agencies do not have sufficient resources to take care of roads in a timely manner, deterioration worsens, and available funds are primarily used for previously deferred maintenance and rehabilitation activities. The current work seeks to gain insight into the dynamics of capital investments and maintenance expenditures in U.S. road infrastructure. Based on a system dynamics model, the authors argue that the highway system is stuck in a capability trap (failure to achieve sustained improvements) because authorities tend to promote short-term reactive efforts over long-term proactive actions. The study contributes to the existing literature by highlighting the feedback mechanisms that connect maintenance and rehabilitation expenditures with aging and deterioration processes. Building on a counterfactual analysis between 1994 and 2010, the research reveals that incentivizing preventive practices not only enhances system conditions but also reduces major rehabilitation expenses and, in the long term, frees up resources for capacity expansion. Conclusions point to the difficulties associated with escaping the trap and the impacts of implementing reactive and proactive policies throughout the highway system.
This article establishes a tri-level decision-making model supporting critical infrastructure (CI) resilience optimization against intentional attacks. A novel decomposition algorithm is proposed to exactly identify the best pre-event defense strategy (protecting vulnerable components and building new lines), the worst-case attack scenario, and the optimal postevent repair sequence of damaged components. As different types of CIs have different flow models, this article mainly considers the direct current power flow model and the maximal flow model for illustrative purposes. The proposed framework is illustrated by a simple but representative case system with nine nodes, and main results include: (1) the marginal value of extra defense investment under low defense budget is more considerable for mitigating system resilience loss, especially under large intentional attacks; (2) no defense strategy is always the best under different attack budgets; (3) increasing amount of repair resources can dramatically enhance CI resilience, but makes the pre-event defense strategy less effective; (4) the use of maximal flow model can provide a lower bound estimation of the resilience loss from the power flow model; (5) the optimum defense strategy and the worst-case attack identified by minimizing CI resilience loss largely differ from those by minimizing CI vulnerability, where the latter does not consider the recovery actions. Finally, the algorithm complexity is analyzed by comparing with the enumeration method and by testing two larger electric power systems.
Enhancing community resilience in the future will require new interdisciplinary systems-based approaches that depend on many disciplines, including engineering, social and economic, and information sciences. The National Institute of Standards and Technology awarded the Center for Risk-Based Community Resilience Planning to Colorado State University and nine other universities in 2015, with the overarching goal of establishing the measurement science for community resilience assessment. The Centerville Virtual Community Testbed is aimed at enabling fundamental resilience assessment algorithms to be initiated, developed, and coded in a preliminary form, and tested before the refined measurement methods and supporting data classifications and databases necessary for a more complete assessment have fully matured. This paper introduces the Centerville Testbed, defining the physical infrastructure within the community, natural hazards to which it is exposed, and the population demographics necessary to assess potential post-disaster impacts on the population, local economy, and public services that are described in detail in the companion papers of this Special Issue.
Methodologies that analyze intersections between resilience and sustainability over a building's lifecycle are important to consider within the context of performance-based design. Life-cycle analysis (LCA) often incorporates building maintenance as part of use-phase environmental impacts; however, hazard-induced repairs are largely ignored. Furthermore, “sustainable” design strategies can lead to more damage and higher life-cycle impacts if the selected materials are less hazard resistant. This study presents an integrated sustainability and resilience assessment model for flood hazards for coastal, single-family residential (SFR) building designs that incorporates a probabilistic procedure for determining flood-related repairs during the useful life of the building and an LCA-based method for measuring the environmental impact of the building considering initial construction and flood-induced repairs. For a case study located in Saint Petersburg, Florida, the model shows that with minor changes in component configuration and materials, environmental sustainability indicators of embodied energy, carbon footprint, and water consumption can be reduced by approximately 40-60% when considering coastal flood damage repairs over a 30 year building life. The case study demonstrates that the model is a useful framework to support effective decision making considering life-cycle environmental impacts within an integrated sustainability and resilience performance-based design construct for residential buildings subjected to flood hazards within coastal areas.
https://authors.elsevier.com/a/1T~Tm8MyS8ixgM
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.
Resilience has been defined as "the ability to prepare and plan for, absorb, recover from, and more successfully adapt to adverse events." The term resilience is applied to a range of topics including physical security, business continuity, emergency planning, hazard mitigation, and the built environment's (e.g., facilities, transportation systems, and utilities) ability to resist and rapidly recover from disruptive events. This paper focuses on research needs for achieving community resilience of the built environment. Community resilience depends upon the capacity of facilities and infrastructure systems to maintain acceptable levels of functionality during and after disruptive events and to recover full functionality within a specified period of time. Natural, technological, and human-made hazards in the United States continue to be responsible for significant losses and damage to the built environment. To improve the disaster resilience of communities to hazard events, each community needs to develop plans based on a risk-informed methodology that addresses multiple hazards, system performance levels, recovery of functionality, and dependencies between systems. However, quantitative tools and metrics and risk-informed guidance for communities are not presently available. A risk-informed methodology that supports decision making among alternatives for community resilience is proposed. Research needs are outlined for short-term and long-term development plans that are based on two national workshops in 2011. Research needs include risk-informed tools to support resilience planning at the community level, performance goals including functionality and recovery levels, multiple resilience levels, and standardized tools and metrics for community resilience and the built environment.
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.
Public infrastructure management agencies in the United States are facing multiple challenges, including aging infrastructure, reduction in capacity of existing infrastructure, and limited funds for the maintenance of infrastructure. Limited funding makes infrastructure maintenance investment decision making a challenging task. Generally, such decisions are based only on physical condition. However, for better utilization of limited funds, multiple decision parameters such as strategic importance, socioeconomic contribution, and infrastructure utilization in addition to physical condition should be considered for infrastructure maintenance investments decisions. Considering this context, a decision support framework for infrastructure maintenance investment decision making is presented in this paper. The framework measures performance of candidate infrastructure [i.e., candidate for maintenance, repair, and rehabilitation (MRR)] based on the previously mentioned multiple decision parameters. Multiattribute utility theory (MAUT), Markov decision process (MDP), and portfolio management approach have been adopted to provide decision support outcomes including performance curves, decision logic maps, and network-level maintenance investment plan. The framework has been implemented through case study on a set of bridges candidates for MRR investment.
A framework is presented for incorporating probabilistic building performance limit states in the assessment of community resilience to earthquakes. The limit states are defined on the basis of their implications to post-earthquake functionality and recovery. They include damage triggering inspection, occupiable damage with loss of functionality, unoccupiable damage, irreparable damage, and collapse. Fragility curves are developed linking earthquake ground motion intensity to the probability of exceedance for each of the limit states. A characteristic recovery path is defined for each limit state on the basis of discrete functioning states, the time spent within each state, and the level of functionality associated with each state. A building recovery function is computed accounting for the uncertainty in the occurrence of each recovery path and its associated limit state. The outcome is a probabilistic assessment of recovery of functionality at the building level for a given ground motion intensity. The effects of externalities and other socioeconomic factors on building-level recovery and ways to incorporate these in the framework are discussed. A case study is presented to demonstrate the application of the proposed framework to model the post-earthquake recovery of the shelter-in-place housing capacity of an inventory of residential buildings. This type of assessment can inform planning and policy decisions to manage the earthquake risk to residential housing capacity of communities.
The concept of disaster-resilient communities has gained considerable acceptance and attention over the past decade, requiring the assessment of not only the monetary losses surrounding a hazard, but also the complex, time-dependent factors that influence community resilience. This paper presents an analytical, reliability-based approach to quantify seismic resilience based on the robustness and restoration rapidity of a portfolio of buildings following an earthquake event. The reliability problem is formulated using random variables to describe the spatially correlated seismic intensity, structural response, and duration of posthazard recovery for predefined building combinations within a portfolio. Based on these random variables, the first-order reliability method (FORM) is used as a basis to develop a new algorithm to evaluate a probability distribution of resilience for a suite of spatially distributed buildings. In addition, sensitivity measures are computed within FORM and used to prioritize cost-effective mitigation strategies to increase portfolio resilience. This assessment puts prehazard retrofit and posthazard restoration measures into a common preposterior framework to determine the most optimal allocation of resources to improve resilience given budgetary constraints. Preliminary results indicate that prehazard retrofit is often most cost-effective for increasing resilience; however, posthazard restoration efficiency is more cost-effective for achieving high resilience thresholds characterized by longer return periods.
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.
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.
