TY - JOUR
T1 - Seismic Resilience of Interdependent Built Environment for Integrating Structural Health Monitoring and Emerging Technologies in Decision-Making
AU - Makhoul, Nisrine
AU - Roohi, Milad
AU - van de Lindt, John W.
AU - Sousa, Helder
AU - Santos, Luís Oliveira
AU - Argyroudis, Sotiris
AU - Barbosa, Andre
AU - Derras, Boumédiène
AU - Gardoni, Paolo
AU - Lee, Jong Sung
AU - Mitoulis, Stergios
AU - Moffett, Brittany
AU - Navarro, Christopher
AU - Padgett, Jamie
AU - Rincon, Raul
AU - Schmidt, Franziska
AU - Shaban, Nefize
AU - Stefanidou, Sotiria
AU - Tubaldi, Enrico
AU - Xenidis, Yiannis
AU - Zmigrodzki, Stefan
N1 - Publisher Copyright:
© 2024 International Association for Bridge and Structural Engineering (IABSE).
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
KW - SHM
KW - community resilience
KW - emerging technologies
KW - functionality
KW - interdependency infrastructure
KW - strategies optimization
UR - http://www.scopus.com/inward/record.url?scp=85183722067&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85183722067&partnerID=8YFLogxK
U2 - 10.1080/10168664.2023.2295901
DO - 10.1080/10168664.2023.2295901
M3 - Article
AN - SCOPUS:85183722067
SN - 1016-8664
VL - 34
SP - 19
EP - 33
JO - Structural Engineering International: Journal of the International Association for Bridge and Structural Engineering (IABSE)
JF - Structural Engineering International: Journal of the International Association for Bridge and Structural Engineering (IABSE)
IS - 1
ER -