2022 Volume 3 Issue J2 Pages 1017-1023
Gradient Boosting Decision Tree (GBDT), a machine learning technique, is widely used in both practice and research because of its superior accuracy and computation speed. In this research, with the aim of improving the accuracy of repair plans for bridges with longer service lives, the specifications and inspection data of bridges managed by Tochigi Prefecture and GIS data such as climate and topography (GIS data: National Land Information) are combined using convolutional neural networks (CNNs) and GIS as teacher data. The model to determine which bridges are likely to develop deterioration was developed using GBDT by teacher data. As a result of the verification, we were able to construct a GBDT that accurately estimates the presence or absence of damage progression. Based on the estimation results of the constructed GBDT, it was confirmed that grouping bridges and deriving a deterioration curve for each group improves the accuracy compared to the conventional method.
