2021 Volume 2 Issue J2 Pages 182-193
This study presents the rainfall data analyses for cut-slope failure caused by heavy rainfall, based on the actual failure cases of the expressway in the Tohoku district. The characteristics of the rainfall data indicated that the Radar/Raingauge-Analyzed Precipitation by JMA has conformance for deep learning and that it is difficult to judge the failure probability of a cut slope only from the rainfall data as the triggers. In addition, the authors developed a deep learning model by combining incitements and predisposing factors based on the rainfall analysis to judge the failure probability of cut-slope. As a result, the deep learning model with 92-96% of accuracy and 65-74% of sensitivities was constructed. However, the developed deep learning model showed a tendency to judge cut slopes with high risk based on predisposing factors slightly biased toward the failure side, and that the necessity of accumulating more training data was clarified for the deep learning.
