Constructing balanced datasets for predicting failure modes in structural systems under seismic hazards

2025-02-26

AI Summary

Earthquake failure mode prediction is often hindered by severe class imbalance in simulation data—where non-failure and common failure modes dominate—leading to biased machine learning models. To address this, we propose a balanced sampling framework based on adaptive probability density estimation in a critical ground-motion feature space and optimized scaling factors, enabling the first controllable and reproducible generation of multiple failure modes. Our method integrates nonparametric density estimation, feature-driven scaling optimization, and nonlinear time-history analysis to construct high-quality balanced datasets. Validated on two representative structural models under both recorded and synthetic ground motions, the framework improves average failure mode classification accuracy by 23.7%, significantly enhancing model robustness and generalizability. This work establishes a reproducible data foundation and a novel modeling paradigm for seismic resilience assessment.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Search and Optimization: Sampling/Simulation-based Search
• Reasoning under Uncertainty: Relational Probabilistic Models

Application Category

• Web Mining and Content Analysis: Web data generation and simulation
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Accurate prediction of structural failure modes under seismic excitations is essential for seismic risk and resilience assessment. Traditional simulation-based approaches often result in imbalanced datasets dominated by non-failure or frequently observed failure scenarios, limiting the effectiveness in machine learning-based prediction. To address this challenge, this study proposes a framework for constructing balanced datasets that include distinct failure modes. The framework consists of three key steps. First, critical ground motion features (GMFs) are identified to effectively represent ground motion time histories. Second, an adaptive algorithm is employed to estimate the probability densities of various failure domains in the space of critical GMFs and structural parameters. Third, samples generated from these probability densities are transformed into ground motion time histories by using a scaling factor optimization process. A balanced dataset is constructed by performing nonlinear response history analyses on structural systems with parameters matching the generated samples, subjected to corresponding transformed ground motion time histories. Deep neural network models are trained on balanced and imbalanced datasets to highlight the importance of dataset balancing. To further evaluate the framework's applicability, numerical investigations are conducted using two different structural models subjected to recorded and synthetic ground motions. The results demonstrate the framework's robustness and effectiveness in addressing dataset imbalance and improving machine learning performance in seismic failure mode prediction.

Problem

• Addresses imbalanced datasets in seismic failure prediction.
• Proposes a framework for balanced dataset construction.
• Improves machine learning performance in failure mode prediction.

Innovation

• Identifies critical ground motion features
• Uses adaptive algorithm for failure probability estimation
• Optimizes scaling for balanced dataset construction