Seismic Resilience of Interdependent Built Environment for Integrating Structural Health Monitoring and Emerging Technologies in Decision-Making

Abstract

This article results from the joint work of the members of IABSE TG.8 “Design Requirements for Infrastructure Resilience” of Working Commission C1.Data Availability Statement: The data that support the findings of this study are available upon reasonable request. Contact the corresponding author for assistance.The functionality of interdependent infrastructure and resilience to seismic hazards has become a topic of importance across the world. The ability to optimize an engineered solution and support informed decision-making is highly dependent on the availability of comprehensive datasets and requires substantial effort to ingest into community-scale models. In this article, a comprehensive seismic resilience modeling methodology is developed, with detailed multi-disciplinary datasets, and is explored using the state-of-the-science algorithms within the interdependent networked community resilience modeling environment (IN-CORE). The methodology includes a six-step chained/linked process consists of: (a) community data and information, (b) spatial seismic hazard analysis using next-generation attenuation, (c) interdependent community model development, (d) physical damage and functionality analysis, (e) socio-economic impact analysis and (f) structural health monitoring (SHM) and emerging technologies (ET). An illustrative case study is presented to demonstrate the seismic functionality and resilience assessment of Shelby County in Memphis, Tennessee, in the United States. From the discussion of results, it is then concluded that data from structural health monitoring and emerging technologies is a viable approach to enhance characterising the seismic hazard resilience of infrastructure, enabling rapid and in-depth understanding of structural behaviour in emergency situations. Moreover, considering the momentum of the digitalization era, setting an holistic framework on resilience that includes SHM and ET will allow reducing uncertainties that are still a challenge to quantify and propagate, supported by sequential updating techniques from Bayesian statistics

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