PI | |
Co-PIs | |
Project Type | Simulation |
Natural Hazard Type(s) | Earthquake |
Facilities | |
Awards | NIST Center for Risk-Based Community Resilience Planning | 70NANB15H044 and 70NANB20H008 | National Institute of Standards and Technology (NIST) |
Keywords | Seismic Vulnerability, Fragility Functions, Mainshock-Aftershock Sequences, Community Resilience, Seismic Risk Assessment, Structural Fragility Modeling, Monte Carlo Simulation (MCS), Multiple Strip Analysis (MSA), OpenSeesPy, IN-CORE Platform |
This project presents a data-driven methodology for developing single-variable fragility functions to assess the seismic vulnerability of buildings subjected to mainshock-aftershock sequences. The study focuses on reinforced concrete (RC) and woodframe structures, capturing incremental damage accumulation from successive seismic events. By explicitly incorporating aftershock effects—often overlooked in traditional fragility analyses—this research enhances seismic risk assessments and community resilience planning. Using Monte Carlo Simulations (MCS) and Multiple Strip Analysis (MSA), the project quantifies progressive structural damage under sequential seismic events. The methodology is implemented in OpenSeesPy, leveraging Nonlinear Time History Analysis (NTHA) and high-resolution finite element (FE) modeling. Findings are integrated into the IN-CORE (Interdependent Networked Community Resilience Modeling Environment) to evaluate community-wide risk and economic losses. Data Reusability and Applications: The datasets, fragility functions, and computational models developed in this project can be reused in various fields, including: - Seismic Risk Assessment: Researchers and engineers can use the fragility curves to predict damage probabilities for different building types under mainshock-aftershock sequences. - Community Resilience Modeling: Urban planners and policymakers can integrate these fragility functions into regional resilience assessments to improve post-earthquake recovery planning. - Structural Design and Retrofitting: The data supports performance-based earthquake engineering (PBEE), helping engineers design more resilient structures. - Machine Learning Applications: The fragility datasets can be used to train ML models for predicting seismic performance based on structural characteristics and seismic intensity measures. - Further Investigation: The project provides testbed information and a Jupyter Notebook for replicating and extending the analysis. This research offers a comprehensive, computationally efficient framework for integrating aftershock effects into seismic vulnerability assessments—a critical step toward improving community resilience against successive seismic events. Reference papers: Harati, M., & van de Lindt, J. W. (2024). Mainshock-aftershock building fragility methodology for community resilience modeling. Structures, 70, 107742. https://doi.org/10.1016/j.istruc.2024.107742 Harati, M., & van de Lindt, J. W. (2024). Community-level resilience analysis using earthquake-tsunami fragility surfaces. Resilient Cities and Structures, 3(2), 101–115. https://doi.org/10.1016/j.rcns.2024.07.006 Harati, M., & van de Lindt, J. W. (2024). Methodology to generate earthquake-tsunami fragility surfaces for community resilience modeling. Engineering Structures, 305, 117700. https://doi.org/10.1016/j.engstruct.2024.117700