Nondestructive testing algorithm of building concrete material defects based on machine learning  

作　　者：Jiayuan Chen 

机构地区：[1]School of Civil Engineering,Zhejiang University of Technology,Hangzhou,People’s Republic of China

出　　处：《Journal of Control and Decision》2023年第2期143-149,共7页控制与决策学报（英文）

摘　　要：In order to pay more attention to the quality of construction concrete and accurately judge whether concrete material meets the standard,a nondestructive testing algorithm of building concrete material defects based on machine learning is proposed.Through the ray tracing algorithm of Snell’s theorem,the shortest path between two random punctuation marks of building concrete is calculated.The original coordinate system and grid size were set,the trend and length of the line in the grid were calculated,and the coordinates between the grid corner points and the transmitting probe were calculated so as to obtain the position of the intermediate refractive points of the two probes.Finally,the vector dot product of the local defects is obtained by the optimal hyperplane calculation of the binary classification in the support vector machine.Experimental results show that the proposed method has the advantages of high precision.

关 键 词：Machine learning building concrete materials nondestructive testing SIMILARITY image acquisition 

分 类 号：TU528[建筑科学—建筑技术科学] TP181[自动化与计算机技术—控制理论与控制工程]