Construction Research Congress 2020
Empirical Assessment of Household Susceptibility to Hazards-Induced Prolonged Power Outages
Publication: Construction Research Congress 2020: Infrastructure Systems and Sustainability
ABSTRACT
The objective of this study is to empirically assess household susceptibility to the power disruptions during disasters. In this study, a service gap model is utilized to characterize household susceptibility to infrastructure service disruptions. The empirical household survey data collected from Harris County, Texas, in the aftermath of Hurricane Harvey was employed in developing an appropriate empirical model to specify the significance of various factors influencing household susceptibility. Various factors influencing households’ susceptibility were implemented in developing the models. The step-wise algorithm was used to choose the best subset of variables, and availability of substitutes, previous hazards experience, level of need, access to reliable information, race, service expectations, social capital, and residence duration were selected to be included in the models. Among three classes of models, accelerated failure time (AFT)-loglogistic model yielded the best model fitness for estimating households’ susceptibility to disaster-induced power disruption. The model showed that having a substitute, households’ need for the service, race, and access to reliable information are the most significant factors influencing household susceptibility to the power disruptions. Understanding households’ susceptibility to infrastructure service disruptions provides useful insights for prioritizing infrastructure resilience improvements in order to reduce societal impacts.
Get full access to this article
View all available purchase options and get full access to this chapter.
ACKNOWLEDGMENT
The authors would like to acknowledge the funding support from the National Science Foundation under grant number 1846069 and National Academies’ Gulf Research Program Early-Career Research Fellowship. Any opinions, findings, conclusion, or recommendations expressed in this research are those of the authors and do not necessarily reflect the view of the funding agencies.
REFERENCES
Agresti, A. (2007). An introduction to categorical Data Analysis. Annu. Rev. Sociol.
Aldrich, D. P. (2011). “The power of people: Social capital’s role in recovery from the 1995 Kobe earthquake.” Natural Hazards, 56(3), 595–611.
Applied Technology Council. (2016). “Critical assessment of lifeline system performance: understanding societal needs in disaster recovery.” Prepared for U.S. Department of Commerce National Institute of Standards and Technology, Engineering Laboratory, Gaithersburg, MD., NIST CGR(16-917–39).
Berkeley III, A. R., and Wallace, M. (2010). A Framework for Establishing Critical Infrastructure Resilience Goals. National Infrastructure Advisory Council.
Cameron, A. C., and Trivedi, P. K. (2013). Regression Analysis of Count Data. Econometric Society Monographs, Cambridge University Press.
Coleman, N., Esmalian, A., and Mostafavi, A. (2019). “Equitable Resilience in Infrastructure Systems: Empirical Assessment of Disparities in Hardship Experiences of Vulnerable Populations during Service Disruptions.” Natural Hazards Review.
Dong, S., Wang, H., Gao, J., and Mostafavi, A. (2019a). “Robust component: a robustness measure that incorporates access to critical facilities under disruptions.” Journal of Royal Society Interface.
Dong, S., Wang, H., Mostafizi, A., Gao, J., and Li, X. (2019b). “Measuring the topological robustness of transportation networks to disaster-induced failures: A percolation approach.” Journal of Infrastructure System.
Esmalian, A., Dong, S., Coleman, N., and Mostafavi, A. (2019a). “Determinants of risk disparity due to infrastructure service losses in disasters: a household service gap model.” Risk Analysis.
Esmalian, A., Rasoulkhani, K., and Mostafavi, A. (2019b). “Agent-Based Modeling Framework for Simulation of Societal Impacts of Infrastructure Service Disruptions during Disasters.” (June), 15–23.
Hosmer, D. W., and Lemeshow, S. (1999). Applied Survival Analysis: Regression Modeling of Time to Event Data. John Wiley & Sons, Inc., New York, NY, USA.
James, G., Witten, D., Hastie, T., and Tibshirani, R. (2014). An Introduction to Statistical Learning: With Applications in R. Springer Publishing Company, Incorporated.
Lindell, M. K., and Perry, R. W. (2000). “Household adjustment to earthquake hazard. A review of research.” Environment and Behavior, 32(4), 461–501.
Liu, H., Davidson, R. A., and Apanasovich, T. V. (2007). “Statistical forecasting of electric power restoration times in hurricanes and ice storms.” IEEE Transactions on Power Systems, 22(4), 2270–2279.
Nateghi, R., Guikema, S., and Quiring, S. M. (2014). “Power Outage Estimation for Tropical Cyclones: Improved Accuracy with Simpler Models.” Risk Analysis, 34(6), 1069–1078.
Rasoulkhani, K., and Mostafavi, A. (2018). “Resilience as an emergent property of human-infrastructure dynamics: A multi-agent simulation model for characterizing regime shifts and tipping point behaviors in infrastructure systems.” Plos One, 13, e0207674.
Stein, R., Buzcu-Guven, B., and Subramanian, D. (2014). “The Private and Social Benefits of Preparing For Natural Disasters.” Private and Social Benefits of Preparing International Journal of Mass Emergencies and Disasters, 32(3), 459–483.
Tabandeh, A., Gardoni, P., and Murphy, C. (2018). “A Reliability-Based Capability Approach.” Risk Analysis, 38(2), 410–424.
Information & Authors
Information
Published In
Construction Research Congress 2020: Infrastructure Systems and Sustainability
Pages: 933 - 941
Editors: Mounir El Asmar, Ph.D., Arizona State University, Pingbo Tang, Ph.D., Arizona State University, and David Grau, Ph.D., Arizona State University
ISBN (Online): 978-0-7844-8285-8
Copyright
© 2020 American Society of Civil Engineers.
History
Published online: Nov 9, 2020
Published in print: Nov 9, 2020
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.