Intelligent identification of defective regions of voids in tunnels based on GPR data

IF 4.1 2区材料科学 Q1 MATERIALS SCIENCE, CHARACTERIZATION & TESTING

Ndt & E International Pub Date : 2024-10-11 DOI:10.1016/j.ndteint.2024.103244

Guangyan Cui , Yanhui Wang , Yujie Li , Feifei Hou , Jie Xu

{"title":"Intelligent identification of defective regions of voids in tunnels based on GPR data","authors":"Guangyan Cui , Yanhui Wang , Yujie Li , Feifei Hou , Jie Xu","doi":"10.1016/j.ndteint.2024.103244","DOIUrl":null,"url":null,"abstract":"<div><div>Quantitatively detecting voids behind tunnel linings presents significant challenges in identifying the range of width and depth. This paper develops an innovative method for identifying defective regions of voids based on Ground Penetrating Radar (GPR) data. This method involves three steps: Firstly, the void-identifying-feature-set (VIFS) is constructed by extracting the Amplitude peak (AT), Signal energy (ET), and Amplitude peak of the first intrinsic mode function (IMF1) component (AH) of every A-scan signal. Secondly, the Support Vector Machine (SVM) is utilized to identify defect signals and normal signals, contributing to the width identification of void in the horizontal direction. Thirdly, an innovative Three-Stage-Boundary-Extraction (TSBE) algorithm is proposed to identify the depth range of voids in the vertical direction. Experimental results conducted on both field data and simulated data demonstrated that the Intersection over Union (IOU) value and consumption time of three groups of GPR data (Data I, Data II, and Data V) are 0.739 and 0.888 s, respectively. The average IOU and consumption time of the TSBE algorithm are 0.739 and 0.058 s, respectively.</div></div>","PeriodicalId":18868,"journal":{"name":"Ndt & E International","volume":"149 ","pages":"Article 103244"},"PeriodicalIF":4.1000,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Ndt & E International","FirstCategoryId":"88","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0963869524002093","RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MATERIALS SCIENCE, CHARACTERIZATION & TESTING","Score":null,"Total":0}

引用次数: 0

Abstract

Quantitatively detecting voids behind tunnel linings presents significant challenges in identifying the range of width and depth. This paper develops an innovative method for identifying defective regions of voids based on Ground Penetrating Radar (GPR) data. This method involves three steps: Firstly, the void-identifying-feature-set (VIFS) is constructed by extracting the Amplitude peak (A_T), Signal energy (E_T), and Amplitude peak of the first intrinsic mode function (IMF1) component (A_H) of every A-scan signal. Secondly, the Support Vector Machine (SVM) is utilized to identify defect signals and normal signals, contributing to the width identification of void in the horizontal direction. Thirdly, an innovative Three-Stage-Boundary-Extraction (TSBE) algorithm is proposed to identify the depth range of voids in the vertical direction. Experimental results conducted on both field data and simulated data demonstrated that the Intersection over Union (IOU) value and consumption time of three groups of GPR data (Data I, Data II, and Data V) are 0.739 and 0.888 s, respectively. The average IOU and consumption time of the TSBE algorithm are 0.739 and 0.058 s, respectively.

查看原文本刊更多论文

根据 GPR 数据智能识别隧道空洞缺陷区域

隧道衬砌背后空洞的定量检测在确定宽度和深度范围方面存在巨大挑战。本文基于地面穿透雷达 (GPR) 数据，开发了一种识别空洞缺陷区域的创新方法。该方法包括三个步骤：首先，通过提取每个 A 扫描信号的振幅峰值 (AT)、信号能量 (ET) 和第一本征模态函数 (IMF1) 分量 (AH) 的振幅峰值，构建空隙识别特征集 (VIFS)。其次，利用支持向量机（SVM）来识别缺陷信号和正常信号，有助于水平方向的空隙宽度识别。第三，提出了一种创新的三阶段边界提取（TSBE）算法，用于识别垂直方向的空洞深度范围。对现场数据和模拟数据进行的实验结果表明，三组 GPR 数据（数据 I、数据 II 和数据 V）的交集大于联合（IOU）值和消耗时间分别为 0.739 秒和 0.888 秒。TSBE 算法的平均 IOU 值和消耗时间分别为 0.739 和 0.058 秒。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Ndt & E International 工程技术-材料科学：表征与测试

CiteScore

7.20

自引率

9.50%

发文量

121

审稿时长

55 days

期刊介绍： NDT&E international publishes peer-reviewed results of original research and development in all categories of the fields of nondestructive testing and evaluation including ultrasonics, electromagnetics, radiography, optical and thermal methods. In addition to traditional NDE topics, the emerging technology area of inspection of civil structures and materials is also emphasized. The journal publishes original papers on research and development of new inspection techniques and methods, as well as on novel and innovative applications of established methods. Papers on NDE sensors and their applications both for inspection and process control, as well as papers describing novel NDE systems for structural health monitoring and their performance in industrial settings are also considered. Other regular features include international news, new equipment and a calendar of forthcoming worldwide meetings. This journal is listed in Current Contents.