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Advancing Structural Failure Analysis with Physics-Informed Machine Learning in Engineering Applications
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DOI：10.1016/j.eng.2025.10.003.png)
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Abstract
En 中文
While machine learning (ML) shows significant potential for structural-failure analysis, purely data-driven approaches face critical limitations, including data scarcity, lack of physical consistency, and poor interpretability in safety–critical applications. Physics-informed ML (PIML) addresses these challenges by integrating physical principles with data-driven methods, thereby enabling accurate and interpretable predictions, while maintaining physical consistency. This study presents a systematic categorization of PIML implementation strategies in structural-failure analysis, classifying the approaches into four distinct categories: physics-guided data manipulation, physics-inspired architectural design, physics-constrained loss functions, and hybrid physics–ML models. We examined the applications across the complete failure lifecycle, from mechanism analysis and fatigue-life prediction to structural-health monitoring and post-failure analysis, to demonstrate how different PIML strategies address specific engineering challenges. Through a critical evaluation of representative studies, we identified the current limitations, including data-integration complexities, physics-formalization difficulties, and computational trade-offs between accuracy and efficiency. Future research directions emphasize multisource knowledge fusion, transferable PIML frameworks, and enhanced post-failure analysis capabilities. This systematic framework provides clear guidance for selecting appropriate PIML strategies based on application requirements and available resources, thereby advancing the reliability and safety of engineering structures.
Keywords:
Machine learning
Physics-informed machine learning
Structural-failure analysis
Engineering applications
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