基于GA-BP神经网络的钢筋直径和埋深测量方法  

Measurement Method of Reinforcement Diameter and Buried Depth Based on GA-BP Neural Network

作　　者：吴恒 李潇逸 董兆林 代有权 WU Heng;LI Xiaoyi;DONG Zhaolin;DAI Youquan(Quality Supervision Department,State Grid Chongqing Electric Power Company,Chongqing 400014,China;School of Electrical Engineering,Chongqing University,Chongqing 400044,China)

机构地区：[1]国网重庆市电力公司物资分公司质量监督部,重庆400014 [2]重庆大学电气工程学院,重庆400044

出　　处：《重庆科技学院学报（自然科学版）》2023年第2期97-102,共6页Journal of Chongqing University of Science and Technology：Natural Sciences Edition

基　　金：国家自然科学基金项目“计及导体电晕流注光谱特性的冲击电压反演方法研究”(52007011)。

摘　　要：目前大多数钢筋质量测量方法难以准确有效地测量钢筋的直径和埋深,测量精度和效率难以满足实际需求。为此,提出一种基于GA-BP神经网络的钢筋直径和埋深测量方法,将不同直径和埋深的钢筋对应的混凝土表面磁感应强度作为样本数据,利用Matlab建立GA-BP神经网络模型对钢筋的直径和埋深进行测量。仿真实验结果显示,该方法的测量误差满足实际需求。At present,most of the testing methods of rebar quality are difficult to detect the diameter and depth of rebar accurately and effectively,and the measurement accuracy and efficiency are difficult to meet the actual demand.Therefore,a method of measuring the depth and diameter of rebar in concrete is presented based on GA-BP neural network.The magnetic induction intensity of concrete surface corresponding to rebars with different diameters and buried depths is taken as sample data.GA-BP neural network model is established in Matlab to predict the diameter and buried depth of reinforcement.The feasibility of the proposed method is verified by the finite element simulation model.The results show that the detection error of this method can meet the actual demand.

关 键 词：钢筋质量检测 电磁感应 有限元仿真 GA-BP神经网络 

分 类 号：TM154[电气工程—电工理论与新技术]