Predicting Concrete Bridge Deck Deterioration: A Hyperparameter Optimization Approach

Concrete bridge decks are critical transportation infrastructure components where deterioration can compromise structural integrity and public safety. This study develops machine learning (ML) models using the National Bridge Inventory (NBI) to classify deck conditions and predict deterioration trajectories. Models were tested and trained on inspection records from over 28,786 bridges in Michigan over 23 years, from 1992 to 2015. Eleven approaches were evaluated after hyperparameter optimization, based on 10-fold cross-validation, including logistic regression, gradient boosting, AdaBoost, random forest, extra trees, K-nearest neighbors, naive Bayes, decision tree, LightGBM, CatBoost, and bagging. Model effectiveness was assessed using accuracy, recall, F1-score, and area under the curve. Results indicate the optimized CatBoost classifier achieved 96.66% testing accuracy in rating deck conditions. The incorporation of hyperparameter optimization has significantly enhanced the overall predictive performance of the models, ensuring robust and reliable deterioration forecasting. The research sheds light on crucial factors such as deck age, area, and average daily traffic, contributing to a more comprehensive understanding of the factors influencing bridge deck condition ratings. These insights inform preventative maintenance planning to extend service life. This work pioneers a data-driven framework to forecast concrete deterioration, empowering officials with precise predictions to optimize infrastructure management under budget constraints. The approach provides a promising decision-support tool for sustainable infrastructure. This paper explores the use of machine learning techniques for the deterioration prediction of concrete bridge decks to estimate the remaining service life of bridges. These models will contribute to the safety, efficiency, and sustainability of bridge infrastructure by providing timely information and evidence-based decision making for bridge maintenance and management. Such prediction models have several practical applications such as (1) predicting when maintenance or repairs are likely to be needed; (2) assessing the risk of failure or deterioration of different components of a bridge; (3) effectively managing the bridge life cycle by providing insights into the aging process and helping authorities plan for rehabilitation or replacement strategies; (4) enabling ongoing monitoring of the performance of a bridge under various conditions such as heavy traffic loads, environmental factors, and seismic events; and (5) assisting in effective asset management by allowing for the prioritization of investments, the efficient allocation of budgets, and the planning for the long-term sustainability of the bridge infrastructure.