Investigation of effects of hazard geometry and mitigation strategies on community resilience under tornado hazards using an Agent-based modeling approach

Abstract

A large number of communities are impacted annually by the increasing frequency of tornado hazards resulting in damage to the infrastructure as well as disruption of community functions. The effect of the hazard geometry (center and angle of tornado path as well as the tornado width) is studied herein on how it influences the recovery of physical and social systems within the community. Given that pre-disaster preparedness including mitigation strategies (e.g., retrofits) and policies (e.g., insurance) is crucial for increasing the resilience of the community and facilitating a faster recovery process, in this study, the impact of various mitigation strategies and policies on the recovery trajectory and resilience of a typical US community subjected to a tornado is investigated considering different sources of uncertainties. The virtual testbed of Centerville is selected in this paper and is modeled by adopting the Agent-based modeling (ABM) approach which is a powerful tool for conducting community resilience analysis that simulates the behavior of different types of agents and their interactions to capture their interdependencies. The results are presented in the form of recovery time series as well as calculated resilience indices for various community systems (lifeline networks, schools, healthcare, businesses, and households). The results of this study can help deepen our understanding of how to efficiently expedite the recovery process of a community.

Keywords

Community resilience / Agent-based modeling (ABM) / Tornado / Mitigation strategy

Relevance to resilience

The present study investigates (i) the effect of hazard geometry (tornado center and angle as well as width) and (ii) the effect of structural retrofits and economic policies on the community recovery process and resilience (via calculation of resilience indices) using the agent-based modeling (ABM) approach applied to the virtual Centerville testbed. The subject of this study correlates to the scope of the Journal of Resilient Cities and Structures.

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

Financial support for this work was provided by the US Department of Commerce, National Institute of Standards and Technology (NIST) under the Financial Assistance Award Number (FAIN) #70NANB20H008. This financial support is gratefully acknowledged. 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 US Department of Commerce. The parametric analyses were conducted at the Texas A&M's High-Performance Research Computing Center. This support is greatly appreciated. Dr. Yong Yoo is acknowledged for his support of the data analysis adopted in this study.

References

[1] NOAA. U.S. tornadoes 2023. https://www.ncei.noaa.gov/access/monitoring/tornadoes/12/1?fatalities=true (accessed July 10, 2023).

[2] NOAA. The 25 deadliest U.S. tornadoes 2023. https://www.spc.noaa.gov/faq/tornado/killers.html (accessed July 10, 2023).

[3] Changnon SA. Tornado losses in the United States. Nat Hazards Rev 2009;10:145-50. doi: 10.1061/ASCE1527-6988200910:4145.

[4] Standohar-Alfano CD, van de Lindt JW. Tornado risk analysis for residential woodframe roof damage across the United States. J Struct Eng 2015; 142 04015099. doi: 10.1061/(ASCE)ST.

[5] Masoomi H, van de Lindt JW. Tornado fragility and risk assessment of an archetype masonry school building. Eng Struct 2016;128:26-43. doi: 10.1016/j.engstruct.2016.09.030.

[6] Koliou M, Masoomi H, van de Lindt JW. Performance assessment of tilt-up big-box buildings subjected to extreme hazards: tornadoes and earthquakes. J Perform Constr Facil 2017; 31 04017060. doi: 10.1061/(asce)cf.1943-5509.0001059.

[7] Masoomi H, van de Lindt JW. Restoration and functionality assessment of a community subjected to tornado hazard. Struct Infrastruct Eng 2018;14:275-91. doi: 10.1080/15732479.2017.1354030.

[8] Koliou M, van de Lindt JW. Development of building restoration functions for use in community recovery planning to tornadoes. Nat Hazards Rev 2020; 21 04020004. doi: 10.1061/(asce)nh.1527-6996.0000361.

[9] Wang W. (Lisa), van de Lindt JW. Quantitative modeling of residential building disaster recovery and effects of pre- and post-event policies. Int J Disaster Risk Reduct 2021;59:102259. doi: 10.1016/j.ijdrr.2021.102259.

[10] Basu N, Pryor R, Quint T. ASPEN: a Microsimulation model of the economy. Comput Econ 1998;12:223-41.

[11] Gutierrez Soto M, Adeli H. Multi-agent replicator controller for sustainable vibration control of smart structures. J Vibroengineering 2017;19:4300-22. doi: 10.21595/jve.2017.18924.

[12] Gutierrez Soto M, Adeli H. Many-objective control optimization of high-rise building structures using replicator dynamics and neural dynamics model. Struct Multidiscip Optim 2017;56:1521-37. doi: 10.1007/s00158-017-1835-9.

[13] Hung SL, Adeli H. Object-oriented backpropagation and its application to structural design. Neurocomputing 1994;6:45-55. doi: 10.1016/0925-2312(94)90033-7.

[14] Karim A, Adeli H. CONSCOM: an OO construction scheduling and change management system. J Constr Eng Manag 1999;125:368-76.

[15] Yu BG, Adeli H. Object-oriented finite element analysis using EER model. J Struct Eng 1993;119:2763-81.

[16] Cimellaro GP, Mahin S, Domaneschi M. Integrating a human behavior model within an agent-based approach for blasting evacuation. Comput Aided Civ Infrastruct Eng 2019;34:3-20. doi: 10.1111/mice.12364.

[17] Batouli M, Mostafavi A. Multiagent simulation for complex adaptive modeling of road infrastructure resilience to sea-level rise. Comput Aided Civ Infrastruct Eng 2018;33:393-410. doi: 10.1111/mice.12348.

[18] Nejat A, Damnjanovic I. Agent-based modeling of behavioral housing recovery following disasters. Comput Aided Civ Infrastruct Eng 2012;27:748-63. doi: 10.1111/j.1467-8667.2012.00787.x.

[19] Nasrazadani H, Mahsuli M. Probabilistic framework for evaluating community resilience: integration of risk models and agent-based simulation. J Struct Eng 2020;146:04020250. doi: 10.1061/(asce)st.1943-541x.0002810.

[20] Aghababaei M, Koliou M. An agent-based modeling approach for community resilience assessment accounting for system interdependencies: application on education system. Eng Struct 2022;255:113889. doi: 10.1016/j.engstruct.2022.113889.

[21] Aghababaei M, Koliou M. Community resilience assessment via agent-based modeling approach. Comput Aided Civ Infrastruct Eng 2022. doi: 10.1111/mice.12916.

[22] Masoomi H, Ameri MR, van de Lindt JW. Wind performance enhancement strategies for residential wood-frame buildings. J Perform Constr Facil 2018; 32 04018024. doi: 10.1061/(asce)cf.1943-5509.0001172.

[23] Almufti I, Willford M. Resilience-based earthquake design initiative (REDiTM ) rating system. London: Arup; 2013.

[24] Ellingwood BR, Cutler H, Gardoni P, Peacock WG, van de Lindt JW, Wang N. The centerville virtual community: a fully integrated decision model of interacting physical and social infrastructure systems. Sustain Resilient Infrastruct 2016;1:95-107. doi: 10.1080/23789689.2016.1255000.

[25] Aghababaei M. Quantitative models and framework for evaluating community resilience accounting for its interdependent systems. Texas A&M Univ 2021.

[26] Standohar-Alfano CD, van de Lindt JW. Empirically based probabilistic tornado hazard analysis of the United States using 1973-2011 data. Nat Hazards Rev 2015; 16 04014013. doi: 10.1061/(asce)nh.1527-6996.0000138.