Advancing Structural Health Monitoring with Deep Belief Network-Based Classification

Structural health monitoring (SHM) plays a critical role in ensuring the safety and longevity of civil infrastructure by enabling the early detection of structural changes and supporting preventive maintenance strategies. In recent years, deep learning techniques have emerged as powerful tools for analyzing the complex data generated by SHM systems. This study investigates the use of deep belief networks (DBNs) for classifying structural conditions before and after retrofitting, using both ambient and train-induced acceleration data. Dimensionality reduction techniques such as principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE) enabled a clear separation between structural states, emphasizing the DBN’s ability to capture relevant classification features. The DBN architecture, based on stacked restricted Boltzmann machines (RBMs) and supervised fine-tuning, was optimized via grid search and cross-validation. Compared to traditional unsupervised methods like K-means and PCA, DBNs demonstrated a superior performance in feature representation and classification accuracy. Experimental results showed median cross-validation accuracies of 98.04% for ambient data and 96.96% for train-induced data, with low variability. Although random forests slightly outperformed DBNs in classifying ambient data (99.19%), DBNs achieved better results with more complex train-induced signals (95.91%). Robustness analysis under Gaussian noise further demonstrated the DBN’s resilience, maintaining over 90% accuracy for ambient data at noise levels up to σnoise=0.5. These findings confirm that DBNs are a reliable and effective approach for data-driven structural condition assessment in SHM systems.