This study aims to develop machine learning (ML) models to predict the sufficiency rating (SR) of bridges, thereby supporting decision-making in infrastructure management, including repair, maintenance and reconstruction.
The National Bridge Inventory data was cleaned, labeled and normalized, and then the Boruta algorithm was used to select 22 significant features out of 40 original variables. A hybrid Bayesian-grid (HBG) technique was developed to optimize the hyperparameters of three predictive models: artificial neural networks (ANNs), support vector regression and k-nearest neighbors. Additionally, a multiple linear regression model was developed as a baseline for comparison. Model performance was evaluated using R², adjusted R², mean absolute error and root mean square error.
The ANN model outperformed the others. For classification tasks, the ANN model achieved 88.11% accuracy in categorizing bridges into three condition levels: good (SR = 80), need maintenance (80 > SR = 50) and need reconstruction (SR < 50). The HBG approach significantly improved predictive accuracy while reducing computational cost and processing time. The integration of the Boruta feature selection method enhanced both model accuracy and interpretability.
The HBG technique proposed in this study represents a significant advancement in the field by offering a robust and flexible framework for hyperparameter tuning that can be applied across a wide range of ML applications. These findings offer practical tools and insights for bridge management agencies to improve evaluation and maintenance decision-making, ultimately enhancing infrastructure sustainability and resource efficiency.
