固化现场管道缺陷的非破坏性表征
Richard Dvořák1, Luboš Jakubka1, Libor Topolář1
1Institute of Physics, Faculty of Civil Engineering, Brno University of Technology, 60190 Brno-střed, Czech Republic.
Materials (Basel, Switzerland)
|December 23, 2023
概括
这项研究引入了三种先进的非破坏性方法 - 冲击回声,地面透雷达和阻抗光谱学 - 以准确评估城市管道网络的状况,改进了传统的摄像头和激光扫描.
科学领域:
- 土木工程 土木工程是指土木工程.
- 材料科学 材料科学 材料科学
- 地质物理学 地质物理学
背景情况:
- 城市污水和水道网络是关键基础设施,需要定期维护.
- 没有沟的康复,特别是固化在现场管道 (CIPP) 技术,对城市地区至关重要.
- 目前的诊断方法,如摄像头扫描和激光扫描,缺乏材料表征能力.
研究的目的:
- 引入和评估三种创新的非破坏性测试 (NDT) 方法,以表征管道状况.
- 评估冲击回声,地面透雷达 (GPR) 和阻抗光谱在检测聚合物层管道缺陷方面的有效性.
- 用影响回声数据比较传统和深度学习机器学习算法用于缺陷表征.
主要方法:
- 冲击回声方法与深度学习相结合,在连续波量变换图像上进行缺陷表征.
- 具有启发式算法的地面透雷达 (GPR),用于检测管道后面的洞穴.
- 阻抗光谱用于表征由于不均固化而导致的聚合物层分层.
主要成果:
- 该研究成功地描述了分层,确定了CIPP背后的洞穴,并使用新型NDT方法评估了整体管道健康状况.
- 一个深度学习算法证明了从冲击回声信号中缺陷特征的有效性.
- GPR和阻抗光谱分别提供了地下缺陷和层分层的准确估计.
结论:
- 与传统技术相比,所介绍的NDT方法为评估城市管道网络提供了更高的准确性和材料表征能力.
- 这些创新方法对于有效的水和污水基础设施的维护和恢复规划至关重要.
- 机器学习与NDT数据的整合为自动缺陷分析和条件评估提供了强大的工具.
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