DOI: 10.17603/ds2-d155-yt55
title: Machine Learning-Assisted Multi-Hazard Fragility Surface Dataset for Earthquake-Tsunami Resilience Analysis
title: in Development and Application of Machine Learning-Based Multi-Hazard Fragility Surfaces for Earthquake-Tsunami Resilience Analysis

description: This project presents a data-driven machine learning (ML) methodology for generating multi-hazard earthquake–tsunami fragility surfaces, enabling improved community resilience analysis. The ML model synthesizes 3D fragility surfaces from randomly selected 2D fragility curves for earthquakes and tsunamis, facilitating probabilistic damage assessment and loss estimation in seismic and coastal hazard scenarios.

Key features include:

- Physics-informed ML integration: Enhances the reliability of fragility surfaces for earthquake-tsunami hazard interactions.
- Computational efficiency: The ML algorithm significantly reduces computational time compared to traditional physics-based fragility generation.
- Structural retrofitting analysis: Fragility surfaces can be adjusted to evaluate the effectiveness of mitigation strategies.
- Applicability to community resilience modeling: The framework can be integrated into multi-hazard disaster risk reduction and urban planning strategies.

Reusability:

- The ML-based fragility model can be adapted to other multi-hazard scenarios, including sequential hazards such as hurricanes, floods, and wildfires.
- The dataset provides benchmark fragility functions for structural vulnerability modeling in performance-based seismic and tsunami engineering.
- The methodology supports Monte Carlo-based risk assessment frameworks and can be implemented in IN-CORE and other resilience modeling platforms.

References: 

Harati, M., & van de Lindt, J. W. (2024). "Data-Driven Machine Learning for Multi-Hazard Fragility Surfaces in Seismic Resilience Analysis." Computer-Aided Civil and Infrastructure Engineering. DOI: 10.1111/mice.13356.

Harati, M., & van de Lindt, J. W. (2025). "Fragility Function Development of RC Building Portfolio for Use in Earthquake-Tsunami Community Resilience Studies." Journal of Performance of Constructed Facilities, ASCE (Accepted - In Production).

Harati, M., & van de Lindt, J. W. (2024). "Mainshock-Aftershock Building Fragility Methodology for Community Resilience Modeling." Journal of Structures. DOI: 10.1016/j.istruc.2024.117700.

Harati, M., & van de Lindt, J. W. (2024). "Community-Level Resilience Analysis Using Earthquake-Tsunami Fragility Surfaces." Journal of Resilient Cities and Structures, 3(2), 101–115. DOI: 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(15), 1–15. DOI: 10.1016/j.engstruct.2024.117700.

description: This simulation investigates the development and application of machine learning (ML)-assisted multi-hazard fragility surfaces for earthquake-tsunami resilience analysis. The primary goal is to generate 3D fragility surfaces that integrate probabilistic damage states under sequential earthquake and tsunami loading, providing a computationally efficient alternative to physics-based simulations.

Methodology & Testing:
Data-Driven Fragility Surface Generation:

- The ML model was trained using randomly selected 2D fragility curves for earthquakes and tsunamis.
- A Random Forest (RF) regression algorithm was used to estimate joint failure probabilities across varying hazard intensity measures.
- The model was validated against physics-based fragility surfaces from finite element simulations.

Monte Carlo-Based Risk Assessment:

- Fragility surface predictions were integrated into a Monte Carlo simulation framework to compute structural damage probabilities.
- Multiple building archetypes, including reinforced concrete (RC) and unreinforced masonry (URM) structures, were tested under regular and long-duration earthquake motions.

Comparison with FEMA Combinational Rule:

- The ML-generated fragility surfaces were compared to FEMA’s superposition-based approach for estimating multi-hazard damage states.
- Results demonstrated that the ML-assisted fragility model provides more accurate estimates at higher hazard intensities, where FEMA’s approach tends to underestimate cascading damage.

Outcome & Key Findings:

- The ML-based approach successfully synthesized continuous earthquake-tsunami fragility surfaces from discrete 2D fragility curves, reducing computational costs by over 90% compared to traditional physics-based methods.
- The generated fragility surfaces improved predictions of community-level structural damage and economic loss assessments.
- The methodology supports structural retrofitting analysis, allowing for horizontally shifting fragility curves to represent mitigation scenarios.
- The results highlight the importance of accounting for hazard interactions in multi-hazard resilience planning.

Reusability & Applications:

- The dataset can be adapted to different structural types and hazard environments for multi-hazard fragility modeling.
- Researchers can use this dataset to benchmark ML-assisted fragility functions against traditional fragility methods.
- The ML-based fragility surfaces can be integrated into decision-support tools, such as IN-CORE, for urban disaster resilience planning.
- The approach can be extended to other multi-hazard scenarios, including hurricane-induced storm surges, landslides, and fire-following-earthquakes.

This simulation dataset provides a valuable resource for engineers, researchers, and policymakers interested in multi-hazard fragility analysis, structural risk assessment, and disaster resilience planning.