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Physics-Informed Neural Networks Fusion with Deep Learning for Structural Health Monitoring and Prognostics - A Review

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Abstract:

Physics-based neural networks and data-driven models for health monitoring of structures, remaining useful life, and prognostics have expanded over the past decade. This includes applications like computer vision and interpreting natural phenomena. Challenges mainly arise from limitations in applicability, such as the incomplete representation of complex industrial phenomena and data availability. The framework typically begins with damage detection, followed by classification and assessment to determine prognosis. Developing simulators for this process involves complex nonlinear parameters obtained from design space exploration of modular data, coupled with finite element models to predict damage and support decisions for preventive maintenance. This review provides a comprehensive overview of current advancements, challenges, potential solutions, and future research needs in the integration of deep learning with physics-informed neural networks for prognostics and structural health management.

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Periodical:

Construction Technologies and Architecture (Volume 23)

Pages:

75-83

DOI:

https://doi.org/10.4028/p-LSql3S

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Online since:

May 2026

Authors:

Muhammad Luqman*, Muhammad Arif

Keywords:

Bayesian Calibrations, Bayesian History Matching, Bayesian Information Criterion, Kernel Stein Discrepancy, Metropolis-Hasting’s Algorithm, Monte Carlo Simulations, Physics-Based Neural Networks, Prognosis

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© 2026 Trans Tech Publications Ltd. All Rights Reserved

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