Convolutional Neural Network-Based Regression for Predicting the Chloride Ion Diffusion Coefficient of Concrete

作     者：Hyun Kyu Shin Ha Young Kim Sang Hyo Lee 

作者机构：Architectural EngineeringHanyang University ERICAAnsan15588Korea Graduate School of InformationYonsei UniversitySeoul03722Korea Division of Smart Convergence EngineeringHanyang University ERICAAnsan15588Korea 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2022年第70卷第3期

页      面：5059-5071页

核心收录：

学科分类：0202[经济学-应用经济学] 02[经济学] 020205[经济学-产业经济学] 

基　　金：This work was supported by the National Research Foundation of Korea(NRF)grant funded by the Korean government(MSIT)(NRF-2021R1A2C2007904) 

主　　题：Chloride ion diffusion coefficient convolutional neural network deep learning 

摘      要：The durability performance of reinforced concrete(RC)building structures is significantly affected by the corrosion of the steel reinforcement due to chloride penetration,thus,the chloride ion diffusion coefficient should be investigated through experiments or theoretical equations to assess the durability of an RC *** study aims to predict the chloride ion diffusion coefficient of concrete,a heterogeneous material.A convolutional neural network(CNN)-based regression model that learns the condition of the concrete surface through deep learning,is developed to efficiently obtain the chloride ion diffusion *** the model implementation to determine the chloride ion diffusion coefficient,concrete mixes with w/c ratios of 0.33,0.40,0.46,0.50,0.62,and 0.68,are cured for 28 days;subsequently,the surface image data of the specimens are ***,the proposed model predicts the chloride ion diffusion coefficient using the concrete surface image data and exhibits an error of approximately 1.5E−12 m^(2)/*** results suggest the applicability of proposed model to the field of facility maintenance for estimating the chloride ion diffusion coefficient of concrete using images.