Development of a Machine Learning Model for Predicting River Water Levels Using Effective Rainfall and Evaluation of Residual Capacity of Piers in Response to Progress of Local Scour

Abstract

In recent years, the damage to bridge piers caused by the local scouring and the resulting bridge failures have been frequent in many parts of Japan as one of the torrential disasters that occur due to heavy rainfall. In order to prevent such disasters, it is necessary to identify bridge piers at high risk of damage, systematically implement preventive maintenance measures, and evaluate the residual capacity of the piers appropriately in order to determine their soundness. In this study, we have proposed a model for predicting river water level using adequate rainfall as an explanatory variable with machine learning and a model for evaluating the residual bearing capacity of bridge piers according to the progress of local scouring. By combining these models, it is possible to evaluate the residual bearing capacity of bridge piers in fluctuation in river water level and the progress of local scouring.