Application of a convolutional neural network associated with influences the number of training data for deformation detection of concrete bridges

Abstract

Social infrastructures in Fukushima Prefecture, including bridges, is affected by natural disasters such as earthquakes and heavy rain. Since the durability and deterioration of an infrastructure are to accelerate under those influences, it is necessary to establish efficient inspection methods. A convolutional neural network (CNN) which is one of the methods of machine learning, the civil engineering filed is also known to as an effective maintenance method in recent year. Previous studies reported learning models as deformation detectors for concrete bridges were developed for each construction and civil engineering offices with using photos taken from road bridge maintenance results in Fukushima Prefecture as training data, and developed models could classified deformations with practical accuracy. Therefore, we tried to improve an accuracy for deformation detections by developing learning models with expansion of training data from maintenance results providing two offices. As a result, we found the improvement of eight points in the classification accuracy of the exposed rebar class and that of four points in the overall accuracy by the learning model with training data expansion.