BAYESIAN NEURAL NETWORKS FOR DYNAMIC STRUCTURAL RELIABILITY: ADVANCES IN UNCERTAINTY QUANTIFICATION, PHYSICS-GUIDED LEARNING, AND PROBABILISTIC MODELING

Abstract

Bayesian Neural Networks (BNNs) have emerged as a transformative approach for probabilistic modeling and uncertainty quantification in structural reliability analysis. Unlike deterministic neural networks, BNNs represent network weights as probability distributions, enabling natural integration of epistemic and aleatoric uncertainties. This comprehensive review examines BNN applications in dynamic structural systems, covering methodological advances, computational strategies, and real-world implementations. We synthesize findings from 45+ peer-reviewed studies demonstrating BNNs' superiority over traditional reliability methods including First-Order Reliability Method (FORM) and Monte Carlo Simulation (MCS). Key findings reveal that BNNs achieve 92-96% accuracy in failure probability estimation while reducing computational time by 65-98% compared to MCS. Physics-guided BNNs show particular promise for complex systems with sparse data, improving generalization across noise levels while maintaining model parsimony. Applications span structural health monitoring, remaining useful life prediction, seismic damage assessment, and design optimization. Variational inference and MCMC sampling dominate posterior approximation, each offering distinct computational-accuracy trade-offs. Current challenges include scalability for high-dimensional problems, hyperparameter tuning complexity, and limited interpretability. Future directions emphasize hybrid approaches integrating physics constraints, transfer learning mechanisms, and real-time edge deployment for autonomous infrastructure monitoring. This review provides practitioners and researchers actionable insights for implementing BNNs in safety-critical applications.