基于时间反演法和深度学习的 GFRP 锚杆杆体缺陷评估

金属矿山 ›› 2025, Vol. 55 ›› Issue (8): 175-183.

基于时间反演法和深度学习的 GFRP 锚杆杆体缺陷评估

白逸轩¹ 　刘　洋^1,2 　陈文超³ 　胡南燕¹ 　吕亚菲¹

1. 武汉科技大学资源与环境工程学院,湖北武汉 430081;2. 清华大学合肥公共安全研究院,安徽合肥 230601; 3. 中钢集团马鞍山矿山研究总院股份有限公司,安徽马鞍山 243000

出版日期:2025-09-15 发布日期:2025-09-16
通讯作者: 刘　洋(1990—),男,副教授,博士。
作者简介:白逸轩(2000—),男,硕士研究生。
基金资助:
深地国家科技重大专项(编号:2024ZD1003808);国家自然科学基金项目(编号:42307237);长江科学院开放研究基金项目(编号:CKWV20241189 / KY)。

Defect Assessment of GFRP Anchor Based on Time Reversal Method and Deep Learning

BAI Yixuan ¹ 　 LIU Yang ^1,2 　 CHEN Wenchao ³ 　 HU Nanyan ¹ 　 LÜ Yafei ¹

1. School of Resource and Environmental Engineering,Wuhan University of Science and Technology,Wuhan 430081,China; 2. Hefei Institute of Public Safety Research,Tsinghua University,Hefei 230601,China; 3. Sinosteel Maanshan General Institute of Mining Research Co. ,Ltd. ,Maanshan 243000,China

Online:2025-09-15 Published:2025-09-16

摘要/Abstract

摘要： 针对玻璃纤维增强塑料(GFRP)锚杆杆体易发生剪切破坏且难以检测的问题,提出了一种基于时间反演法和深度学习的 GFRP 锚杆杆体缺陷评估方法,旨在实现缺陷的精准识别与定量评估。基于 COMSOL 数值模拟和实验室相似试验,采用时间反演法对含有不同缺陷的 GFRP 锚杆锚固结构进行检测,获取聚焦信号。结果表明,聚焦信号波形随杆体缺陷变化较小,信号波形重合度较高;聚焦信号幅值随杆体缺陷程度增大而减小。将试验得到的聚焦信号通过小波变换生成时频图,作为卷积神经网络(CNN)—支持向量机( SVM)模型的输入,以 GFRP 锚杆杆体缺陷程度作为输出,构建缺陷评估模型。模型训练结果表明,GFRP 锚杆杆体缺陷的评估准确率达到 100%。研究提出的方法能够实现对 GFRP 锚杆杆体缺陷程度的快速、准确评估,为 GFRP 锚杆的缺陷检测提供了重要的理论依据和技术支持。

关键词: GFRP 锚杆　杆体缺陷　时间反演法　小波变换　 CNN-SVM

Abstract: Aiming at the problem that the glass fiber reinforced plastic (GFRP) anchor rod body is prone to shear failure and difficult to detect,a GFRP anchor rod body defect evaluation method based on time inversion method and deep learning is proposed to achieve accurate identification and quantitative evaluation of defects. Based on COMSOL numerical simulation and laboratory similar test,the time inversion method is used to detect the GFRP anchor anchorage structure with different defects, and the focusing signal is obtained. The results show that the focused signal waveform changes little with the rod defect,and the signal waveform coincidence degree is high. The amplitude of the focused signal decreases with the increase of the defect degree of the rod body. The time-frequency diagram of the focused signal obtained by the test is generated by wavelet transform,which is used as the input of the convolutional neural network (CNN)-support vector machine (SVM) model,and the defect degree of the GFRP anchor bolt body is used as the output to construct the defect evaluation model. The model training results show that the evaluation accuracy of GFRP anchor bolt rod defects reaches 100%. The method proposed in this paper can realize the rapid and accurate evaluation of the defect degree of GFRP anchor,which provides an important theoretical basis and technical support for the defect detection of GFRP anchor.

Key words: GFRP anchor,rod body defects,time reversal method,wavelet transform,CNN-SVM

中图分类号:

TD350

白逸轩　刘　洋, 　陈文超　胡南燕　吕亚菲. 基于时间反演法和深度学习的 GFRP 锚杆杆体缺陷评估[J]. 金属矿山, 2025, 55(8): 175-183.

BAI Yixuan 　 LIU Yang , 　 CHEN Wenchao 　 HU Nanyan 　 LÜ Yafei . Defect Assessment of GFRP Anchor Based on Time Reversal Method and Deep Learning [J]. Metal Mine, 2025, 55(8): 175-183.

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