Seismic resilience of interdependent built environment for integrating structural health monitoring and emerging technologies in decision-making

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Makhoul, Nisrine and Roohi, Milad and van de Lindt, John W. and Sousa, Helder and Santos, Luís Oliveira and Argyroudis, Sotiris and Barbosa, Andre and Derras, Boumédiène and Gardoni, Paolo and Lee, Jong Sung and Mitoulis, Stergios and Moffett, Brittany and Navarro, Christopher and Padgett, Jamie and Rincon, Raul and Schmidt, Franziska and Shaban, Nefize and Stefanidou, Sotiria and Tubaldi, Enrico and Xenidis, Yiannis and Zmigrodzki, Stefan (2024) Seismic resilience of interdependent built environment for integrating structural health monitoring and emerging technologies in decision-making. Structural Engineering International, 34 (1). pp. 19-33. ISSN 1683-0350 (https://doi.org/10.1080/10168664.2023.2295901)

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Abstract

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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