Explainable Structural Health Monitoring of Reinforced Concrete Beams Using Probabilistic Image Models
Keywords:
Structural Health Monitoring, Probabilistic Image Models, Explainable Artificial Intelligence, Reinforced Concrete.Abstract
The structural integrity of critical civil infrastructure relies heavily on continuous and accurate condition assessment. Structural Health Monitoring of reinforced concrete beams has traditionally depended on manual inspections or localized sensor networks, both of which present significant scalability and reliability challenges. While recent advancements in deep learning and computer vision offer automated alternatives for damage detection, the deterministic nature and inherent opacity of these models limit their practical adoption in safety-critical engineering applications. This paper presents a comprehensive framework for the Explainable Structural Health Monitoring of reinforced concrete beams utilizing probabilistic image models. By integrating Bayesian principles into convolutional architectures, the proposed methodology not only identifies structural anomalies such as micro-cracks and spalling but also quantifies the predictive uncertainty associated with each diagnosis. Furthermore, the integration of explainability mechanisms allows for the generation of visual attribution maps, providing engineers with interpretable evidence of the model decision-making process. Extensive analysis demonstrates that the probabilistic approach effectively distinguishes between aleatoric uncertainty arising from image noise and epistemic uncertainty inherent in the model parameters. The results indicate that equipping image-based monitoring systems with uncertainty quantification and transparent decision pathways significantly enhances diagnostic reliability, ultimately fostering greater trust in automated inspection paradigms for civil infrastructure management.
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