查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators publish new report on Ma chine Learning. According to news reporting originating from Kalamazoo, Michigan , by NewsRx correspondents, research stated, “Concrete bridge decks are critical transportation infrastructure components where deterioration can compromise str uctural integrity and public safety. This study develops machine learning (ML) m odels using the National Bridge Inventory (NBI) to classify deck conditions and predict deterioration trajectories.” Our news editors obtained a quote from the research from Western Michigan Univer sity, “Models were tested and trained on inspection records from over 28,786 bri dges in Michigan over 23 years, from 1992 to 2015. Eleven approaches were evalua ted after hyperparameter optimization, based on 10-fold cross-validation, includ ing logistic regression, gradient boosting, AdaBoost, random forest, extra trees , Knearest neighbors, naive Bayes, decision tree, LightGBM, CatBoost, and baggi ng. 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 forecasti ng. The research sheds light on crucial factors such as deck age, area, and aver age daily traffic, contributing to a more comprehensive understanding of the fac tors influencing bridge deck condition ratings. These insights inform preventati ve 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 infrastruct ure. This paper explores the use of machine learning techniques for the deterior ation prediction of concrete bridge decks to estimate the remaining service life of bridges. These models will contribute to the safety, efficiency, and sustain ability of bridge infrastructure by providing timely information and evidence-ba sed decision making for bridge maintenance and management.”
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