Integrated platform to assess seismic resilience at the community level

Marasco, Sebastiano; Cardoni, Alessandro; Noori, Ali Zamani; Kammouh, Omar; Domaneschi, Marco; Cimellaro, Gian Paolo

doi:10.1016/j.scs.2020.102506

Cited by 62 publications

(26 citation statements)

References 43 publications

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“…The main responsibility of the department of traffic management is to give priority to ensuring that the physical engineering of the transportation infrastructure is intact and that the social services provided by the transportation infrastructure can operate continuously, stably, and safely [ 16 – 19 ]. Compared with traditional risk defensive measures, this demand for the transportation infrastructure system level will attract special attention from the decision-makers and the researchers [ 20 , 21 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Is Resilient Transportation Infrastructure Low-Carbon? Evidence from High-Speed Railway Projects in China

Wang

Chen

et al. 2022

Computational Intelligence and Neuroscience

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Establishing resilient transport infrastructure is an effective way for cities to deal with external disturbances and uncertainties during rapid urbanization. However, human society is presently facing a series of sustainable development obstacles, where the energy shortage and environmental pollution are catching significant concerns. Hence, it is imperative to investigate the carbon emission of the growing number of resilient transportation infrastructure (RTI) projects. Through extracting the carbon emission factor (CEF), this study built the carbon emission measurement model (CEMM) to evaluate the carbon emission of 26 resilient high-speed railway construction projects in China. The results indicated that the carbon emissions of the entire high-speed railway infrastructure projects in China show regional and social environmental differences. Meanwhile, there are potential correlations and positive relationships between the resilience of the high-speed railway infrastructure projects and their carbon emission. Suggestions and recommendations for governments and construction enterprises are put forward to further improve the resilient and low-carbon development of transportation infrastructure in China.

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Section: Literature Reviewmentioning

confidence: 99%

Is Resilient Transportation Infrastructure Low-Carbon? Evidence from High-Speed Railway Projects in China

Wang

Chen

et al. 2022

Computational Intelligence and Neuroscience

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“…Recent studies have also extended the scope of integrated infrastructure models by combining infrastructure-specific simulators with other urban system models for investigating various aspects of disaster resilience of urban regions. For example, [42] developed a Python-based integrated platform for assessing the vulnerability and resilience of urban power and water networks against seismic events by combining infrastructure simulators and agent-based socio-technical models. Similarly, [43] combined geographic information system (GIS) tools, building information modeling (BIM) tools, and domainspecific infrastructure simulation models to develop an integrated model to analyze the risks of urban flooding on both infrastructure and communities.…”

Section: Integrated Infrastructure Simulation Modelsmentioning

confidence: 99%

InfraRisk: An Open-Source Simulation Platform for Asset-Level Resilience Analysis in Interconnected Infrastructure Networks

Balakrishnan,

Cassottana

2022

Preprint

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Integrated simulation models are emerging as an alternative for analyzing large-scale interdependent infrastructure networks due to their modeling advantages over traditional interdependency models. This paper presents an open-source integrated simulation package for the asset-level analysis of interdependent infrastructure systems. The simulation platform, named 'InfraRisk' and developed in Python, can simulate disaster-induced infrastructure failures and subsequent post-disaster restoration in interconnected water-, power-, and road networks. InfraRisk consists of an infrastructure module, a hazard module, a recovery module, a simulation module, and a resilience quantification module. The infrastructure module integrates existing infrastructure network packages (wntr for water networks, pandapower for power systems, and a static traffic assignment model for transportation networks) through an interface that facilitates the network-level simulation of interdependent failures. The hazard module generates infrastructure component failure sequences based on various disaster characteristics. The recovery module determines repair sequences and assigns repair crews based on predefined heuristics-based recovery strategies or model predictive control (MPC) based optimization. Based on the schedule, the simulation module implements the network-wide simulation of the consequences of the disaster impacts and the recovery actions. The resilience quantification module offers system-level and consumer-level metrics to quantify

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“…In the future, resilience scenarios for other infrastructure systems, such as water, electrical, transportation, and telecommunication systems, as well as critical housing infrastructure systems, such as hospitals, markets, and schools, can be evaluated. Similarly, resilience values against other natural disasters, such as earthquakes and landslides, can be computed [87][88][89]. Outcome comparisons with other hierarchical-based methods, such as fuzzy AHP (Analytic hierarchy process) and Dempster-Shafer theory, can also be performed.…”

Section: Conclusion Limitations and Future Research Directionmentioning

confidence: 99%

Flood Resilience of Housing Infrastructure Modeling and Quantification Using a Bayesian Belief Network

2021

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Resilience is the capability of a system to resist any hazard and revive to a desirable performance. The consequences of such hazards require the development of resilient infrastructure to ensure community safety and sustainability. However, resilience-based housing infrastructure design is a challenging task due to a lack of appropriate post-disaster datasets and the non-availability of resilience models for housing infrastructure. Hence, it is necessary to build a resilience model for housing infrastructure based on a realistic dataset. In this work, a Bayesian belief network (BBN) model was developed for housing infrastructure resilience. The proposed model was tested in a real community in Northeast India and the reliability, recovery, and resilience of housing infrastructure against flood hazards for that community were quantified. The required data for resilience quantification were collected by conducting a field survey and from public reports and documents. Lastly, a sensitivity analysis was performed to observe the critical parameters of the proposed BBN model, which can be used to inform designers, policymakers, and stakeholders in making resilience-based decisions.

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Integrated platform to assess seismic resilience at the community level

Cited by 62 publications

References 43 publications

Is Resilient Transportation Infrastructure Low-Carbon? Evidence from High-Speed Railway Projects in China

Is Resilient Transportation Infrastructure Low-Carbon? Evidence from High-Speed Railway Projects in China

InfraRisk: An Open-Source Simulation Platform for Asset-Level Resilience Analysis in Interconnected Infrastructure Networks

Flood Resilience of Housing Infrastructure Modeling and Quantification Using a Bayesian Belief Network

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