Using machine learning algorithms for predicting bridge vertical deflection

Abstract

Machine learning (ML) is being increasingly used to ease structural health monitoring and management of bridges. Therefore, the main objective of this study is to apply two state-of-the-art ML algorithms, namely locally weighted learning (LWL) and K-Star for the prediction of vertical deflection of composite bridges. To accomplish the objective, 83 track loading tests were carried out at various bridges located in Vietnam and deflection data was collected for models’ development. Model's validation and comparison were carried out using different popular methods, namely mean absolute error, root mean squared error and R on both training (70%) and validation (30%) datasets. The results of this study indicate that the K-Star algorithm outperforms LWL in predicting the vertical deflection of composite bridges. Consequently, K-Star can serve as an effective tool for rapidly predicting vertical deflection, thereby facilitating more efficient bridge health monitoring and management. This efficiency can lead to significant time and cost savings in maintaining the structural integrity of composite bridges.