Community-Resilience-Based Design of the Built Environment

Masoomi, Hassan; Lindt, John W. van de

doi:10.1061/ajrua6.0000998

Cited by 27 publications

(12 citation statements)

References 61 publications

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“…Many techniques have been employed in developing housing recovery models, such as linear regression (Sutley et al, 2019), system dynamics (Kumar et al, 2015), discrete event simulation (Huling and Miles, 2015), Markov chains (Lin and Wang, 2019), fuzzy-logic (De Iuliis et al, 2019), Monte Carlo simulation (Burton et al, 2017; Zeng and Zhang, 2019), and agent-based models (Grinberger and Felsenstein, 2014; Miles and Chang, 2011; Nejat and Damnjanovic, 2012). Nonetheless, there is growing agreement that housing recovery needs to be modeled in the context of the community, being influenced by infrastructural and socioeconomic factors, and constrained by the availability resources (Bilau et al, 2018; Davidson, 2015; Ellingwood et al, 2018; Lee et al, 2019; Masoomi and van de Lindt, 2018; Sutley et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Agent-based model for post-earthquake housing recovery

2020

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A framework of agent-based models for housing recovery is presented and used to investigate post-earthquake recovery in the City of Vancouver, Canada. Housing recovery is modeled for a portfolio of buildings, contrasting with the practice of assessing the reconstruction of buildings in isolation. Thus, the presented approach better captures the effect of competition for resources, infrastructure disruptions, and socioeconomic factors on recovery. The analyses include models for damage, inspection, financing, power infrastructure, and labor/materials for repairs. The presented approach is applied to simulate the recovery of 114,832 residential buildings in 22 neighborhoods in Vancouver. Results indicate that recovery after a strong earthquake will take more than three years. The density of old and rented buildings, and the income and immigration status of the homeowners are shown to be good predictors of the speed of recovery for a neighborhood. Mitigation measures are compared and it is shown that retrofitting the most physically vulnerable buildings or doubling the available workforce are effective at reducing housing recovery times. It is demonstrated that the equity in recovery between low and high socioeconomic status homeowners is improved if mitigation measures are implemented. The results presented in this article can inform disaster recovery plans and mitigation actions in Vancouver and similar communities.

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Section: Introductionmentioning

confidence: 99%

Agent-based model for post-earthquake housing recovery

2020

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“…Though queuing models are used extensively in manufacturing, material-handling systems, construction systems, and infrastructure recovery, only recently have they been used in the context of post-earthquake building portfolio recovery (Costa, 2019; Masoomi and Van De Lindt, 2019; Masoomi et al, 2020). Queuing models can capture real-world components of very complex processes and take into account uncertainties through probability distributions.…”

Section: Stochastic Queuing Modelmentioning

confidence: 99%

“…On the contrary, the PRI queue determines the processing order by the attributes of the customers. This type of queue has been implemented for electric power networks, where substations with a higher demand are prioritized first (Masoomi and Van De Lindt, 2019; Masoomi et al, 2020; Nozhati et al, 2019).…”

Section: Stochastic Queuing Modelmentioning

confidence: 99%

Modeling housing recovery after the 2018 Lombok earthquakes using a stochastic queuing model

Alisjahbana

Kiremidjian

2020

Earthquake Spectra

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Post-earthquake housing recovery monitoring is necessary, especially since the housing sector usually represents 50 percent of the total monetary disaster loss. However, very scarce recovery data, in addition to the complexities of the recovery process, make modeling housing recovery very difficult. Time-based stochastic models, which are commonly used in well-known frameworks such as the U.S. Federal Emergency Management Agency’s HAZARD-US (HAZUS), do not explicitly capture how the recovery process occurs in real life. In this article, we introduce a stochastic queuing model that considers the total number of damaged buildings, the damage distribution, resource constraints, and government-led reconstruction prioritization strategies. We applied our model to seven regions affected by the 2018 Lombok earthquakes, which destroyed over 226,000 residential buildings. In this study, we use publicly available daily data of the reconstruction progress obtained by local authorities for all damaged buildings. Using that dataset, we present recovery parameters for the Lombok region, including delay and reconstruction times. These parameters are an improvement over parameters currently available, which only apply to the U.S. region. Furthermore, we show that our model captures the observed recovery trajectory disaggregated by damage states, which provides insights into how the different building damage states recover individually. Results show that our queuing model reduces the root mean squared error (RMSE) of the recovery trajectory by 31.58% and is better able to represent the observed overall recovery trajectory compared to a time-based stochastic model.

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“…Lin and Wang (2017a, 2017b) developed a methodology for recovery modeling by aggregating building-level restoration using probabilistic damage assessment. Similar approach is generally utilized for the resilience evaluation (Koliou et al, 2018; Masoomi and Van de Lindt, 2018) and few studies utilize detailed component-based damage and consequence assessment approach to quantify resilience (Dong and Frangopol, 2016; Hashemi et al, 2019). In this article, nonlinear time history analysis is conducted to assess the performance at both component and system levels utilizing the next-generation PBEE.…”

Section: Introductionmentioning

confidence: 99%

Performance-based probabilistic framework for seismic risk, resilience, and sustainability assessment of reinforced concrete structures

Anwar

Dong

Zhai

2019

Advances in Structural Engineering

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Recent earthquakes have highlighted additional losses due to the lack of resilience of damaged structures. Environmental impact, as performance indicator, has also received increased attention within performance-based earthquake engineering. In this article, a combined probabilistic framework is proposed to assess seismic risk, sustainability, and resilience of a non-ductile reinforced concrete frame structure. The framework utilizes three-dimensional inelastic fiber-based numerical modeling approach to develop limit states associated with performance levels. The decision variables (i.e. repair cost, downtime, and equivalent carbon emissions) are quantified at both component level and system level and are compared considering seismic risk, sustainability, and resilience. In addition, the proposed approach considers uncertainties in the building performance and consequence functions of structural and non-structural components. Fast-track and slow-track schemes are utilized as a repair strategy and probabilistic resilience is quantified given the investigated time period. The proposed approach can aid the development of the next generation of performance-based engineering incorporating both resilience and sustainability.

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Community-Resilience-Based Design of the Built Environment

Cited by 27 publications

References 61 publications

Agent-based model for post-earthquake housing recovery

Agent-based model for post-earthquake housing recovery

Modeling housing recovery after the 2018 Lombok earthquakes using a stochastic queuing model

Performance-based probabilistic framework for seismic risk, resilience, and sustainability assessment of reinforced concrete structures

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