A Stress Measurement Method for Steel Strands Based on Spatially Self-Magnetic Flux Leakage Field

Author:

Liu Shangkai1,Cheng Cheng2,Zhao Ruiqiang3,Zhou Jianting1ORCID,Tong Kai1

Affiliation:

1. State Key Laboratory of Mountain Bridge and Tunnel Engineering, Chongqing Jiaotong University, Chongqing 400074, China

2. Chongqing Wukang Technology Co., Ltd., Chongqing 404000, China

3. School of Materials Science and Engineering, Chongqing Jiaotong University, Chongqing 400074, China

Abstract

Metal Magnetic Memory (MMM) exhibits the advantage of not requiring embedded sensors or external excitation, making it suitable for inspecting ferromagnetic components in engineering structures. This study introduced MMM into stress detection of steel strands. Graded tensile tests were conducted on the steel strands to investigate the correlation between Self-Magnetic Flux Leakage (SMFL) signals and stress levels. Different spatial detection positions with varying Lift-Off Values (LOV) and Rotation Angle Values (RAV) were set to examine the distribution of spatial SMFL field under load. Furthermore, a magnetic characteristic parameter AN was proposed to assess the stress level of the steel strands. The results indicate that the rate of change in the middle region of the SMFL curve was lower than that at the beginning and the end. Additionally, with increased applied load, the SMFL curve exhibited systematic variations, and the dispersion of the normal component curve gradually decreased. By utilizing the magnetic characteristic parameter AN, the stress in the steel strands can be calculated, with the parameters determined based on LOV and RAV. This achievement expanded the nondestructive testing methods for steel strands and holds significant research value.

Funder

National Natural Science Foundation of China

Chongqing Natural Science Foundation of China

Major Scientific Research Projects of China Railway Group Co., Ltd.

Publisher

MDPI AG

Subject

Building and Construction,Civil and Structural Engineering,Architecture

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