Mobile QR Code QR CODE : Korea Journal of Construction Engineering and Management
Title Image based Concrete Compressive Strength Prediction Model using Deep Convolution Neural Network
Authors Jang, Youjin ; Ahn, Yong Han ; Yoo, Jane ; Kim, Ha Young
DOI http://dx.doi.org/10.6106/KJCEM.2018.19.4.043
Page pp.43-51
ISSN 2005-6095
Keywords Facility Management ; Concrete Compressive Strength Prediction ; Deep Convolution Neural Network
Abstract As the inventory of aged apartments is expected to increase explosively, the importance of maintenance to improve the durability of concrete facilities is increasing. Concrete compressive strength is a representative index of durability of concrete facilities, and is an important item in the precision safety diagnosis for facility maintenance. However, existing methods for measuring the concrete compressive strength and determining the maintenance of concrete facilities have limitations such as facility safety problem, high cost problem, and low reliability problem. In this study, we proposed a model that can predict the concrete compressive strength through images by using deep convolution neural network technique. Learning, validation and testing were conducted by applying the concrete compressive strength dataset constructed through the concrete specimen which is produced in the laboratory environment. As a result, it was found that the concrete compressive strength could be learned by using the images, and the validity of the proposed model was confirmed.