Higher resolution input image of convolutional neural network of reinforced concrete earthquake-generated crack classification and localization

Abstract

According United States Geological Survey, Aceh is the northwestern part in Indonesia that has been affected by numerous strong earthquakes since 2004 tsunami. These earthquakes have generated massive impact to the buildings around the area, especially for the reinforced concrete based buildings. One of the most important problems to the reinforced concrete is the earthquake-generated crack. In this study, the dataset from the normal and cracked reinforce concrete are collected by taking the normal and cracked images. Several convolutional neural network models are implemented such as LeNet based models. These models are initially applied to recognize either normal or cracked conditions. Eventually, for the last stage, the localization of the crack is visualized by imposing the original images. For the localization, this study also evaluates the relatively smaller and bigger cracks. The results show the higher input image with modified LeNet generates better results compared to the basic model in superimposing the localized crack.