Multi-disciplinary seismic resilience modeling for developing mitigation policies and recovery planning

Abstract

The multi-disciplinary data and information available at a community level comprise the foundation of natural hazard resilience modeling. These data enable and inform mitigation and recovery planning decisions prior to and following damaging events such as earthquakes. This paper presents a multi-disciplinary seismic resilience modeling methodology to assess the vulnerability of the built environment and economic systems. This methodology can assist decision-makers with developing effective mitigation policies to improve the seismic resilience of communities. Two complementary modeling strategies are designed to examine the impacts of scenario earthquakes from a combined engineering and economic perspective. The engineering model is developed using a probabilistic fragility-based modeling approach and is analyzed using Monte Carlo (MC) simulations subject to seismic multi-hazard, including simulated ground shaking and resulting liquefaction of the soil, to quantify the physical damage to buildings and electric power substations (EPS). The outcome of the analysis is subsequently used as input to repair and recovery models to quantify repair cost and recovery time metrics for buildings and as input to functionality models to estimate the functionality of individual buildings and substations by accounting for their interdependency. The economic model consists of a spatial computable general equilibrium (SCGE) model that aggregates commercial buildings into sectors for retail, manufacturing, services, etc., and aggregates residential buildings into a wide range of household groups. The SCGE model employs building functionality estimates to quantify the economic losses. The outcomes of this integrated modeling consist of engineering and economic impact metrics, which are used to investigate mitigation actions to help inform a community on approaches to achieve its resilience goals. An illustrative case study of Salt Lake County (SLC), Utah, developed through an extensive collaborative partnership and engagement with SLC officials, is presented. The results demonstrate the effectiveness of the proposed methodology in quantifying the loss and functional recovery of infrastructure systems, the impacts on capital stock, employment, and household income and the effect of various mitigation strategies in reducing the losses and functional recovery time subject to earthquakes with varying intensities.

Keywords

Community resilience / Infrastructure systems / Multi-disciplinary / Mitigation policy / Functional recovery

Relevance to resilience

Mitigation and recovery planning are central to enhancing community resilience. The paper's methodology facilitates the identification of vulnerabilities within the built environment and economic systems, thereby enabling decision-makers to prioritize interventions that reduce risk and enhance the capacity to recover post-disaster. Moreover, by incorporating repair and recovery models to estimate repair costs and recovery times, alongside functionality models for evaluating the post-event functionality of buildings and substations, the methodology offers a detailed understanding of the interdependencies within community systems. These insights are essential for developing resilient infrastructures that can maintain critical functions during and after a seismic event. The case study of Salt Lake County, Utah, underscores the practical application of the proposed methodology and its potential to inform community-level resilience goals. Through collaborative engagement with local officials, the study exemplifies how theoretical models can be grounded in real-world contexts, providing actionable insights for communities.

CRediT authorship contribution statement

Milad Roohi: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing - original draft, Writing - review & editing. Saeid Ghasemi: Data curation, Formal analysis, Investigation, Methodology, Software, Validation, Visualization, Writing - original draft, Writing - review & editing. Omar Sediek: Data curation, Formal analysis, Methodology, Software, Validation, Visualization, Writing - original draft, Writing - review & editing. Hwayoung Jeon: Data curation, Formal analysis, Investigation, Software, Validation, Visualization, Writing - original draft, Writing - review & editing. John W. van de Lindt: Conceptualization, Funding acquisition, Investigation, Project administration, Supervision, Writing - original draft, Writing - review & editing. Martin Shields: Funding acquisition, Investigation, Methodology, Software, Validation, Visualization, Writing - original draft, Writing - review & editing. Sara Hamideh: Funding acquisition, Investigation, Resources, Writing - review & editing. Harvey Cutler: Funding acquisition, Resources, Software, Writing - review & editing.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

The Center for Risk-Based Community Resilience Planning is a NIST funded Center of Excellence; the Center is funded through a cooperative agreement between the U.S. National Institute of Standards and Technology and Colorado State University (NIST Financial Assistance Award Numbers: 70NANB15H044 and 70NANB20H008). The views expressed are those of the authors and may not represent the official position of the National Institute of Standards and Technology or the U.S. Department of Commerce.

Supplementary materials

Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.rcns.2024.07.003.

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