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

Abstract

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.

KEYWORDS:

• Machine learning
• building concrete materials
• nondestructive testing
• similarity
• image acquisition

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Jiayuan Chen

Jiayuan Chen graduated from the Ningbo University of Technology in 2018. Studying at the Zhejiang University of Technology. His research interests include Concrete Durability.