IMAGE ANALYSIS BY DEEP LEARNING FOR DISCRIMINATION OF ALKALI-SILICA REACTION

Abstract

This paper presents the development of a learning model to judge the Alkali Silica Reaction in concrete bridges from image data using the CNN algorithm. Gray scaling processing and brightness adjustment improve the accuracy of the judgment compared with color-image-based CNN learning. The learning model combined additional information other than image information, such as river system, name of structural components, and so on. As a result, the authors developed the learning model with 86.7% classification accuracy.