使用机器学习模型对连接管的非侵入式检测
Ayomide Zul Kazeem1, Xiong Bill Yu2
1Department of Civil and Environmental Engineering, Case Western Reserve University, 2104 Adelbert Road, Bingham 275, Cleveland, Ohio 44106-7201, United States.
概括
准确的管道检测对于基础设施升级至关重要. 一种新的非侵入性方法使用基于物理的建模和机器学习以99.9%的准确度识别管道,提供一个具有成本效益的解决方案.
科学领域:
- 环境工程 环境工程
- 数据科学数据科学数据科学
- 材料科学 材料科学 材料科学
背景情况:
- 美国环境保护局要求在10年内更换服务线.
- 准确识别埋藏的管道对水利公司来说是一个重大挑战.
- 由于环境干扰,目前的检测方法往往是昂贵的,破坏性的或不可靠的.
研究的目的:
- 开发一种非侵入性,高效和具有成本效益的方法来检测埋藏的管道.
- 将基于物理的建模与机器学习相结合,以改进管道材料分类.
- 为公用事业公司提供可扩展的解决方案,以符合监管要求.
主要方法:
- 开发了一个基于物理的有限元素分析 (FEA) 替代模型来模拟埋藏的管道的动态行为.
- 将现实的负载条件,如停止门开口,纳入FEA模型.
- 产生了超过13,000个合成加速信号,模拟了现实世界的噪音和信号限制.
- 在模拟数据上训练了七个机器学习 (ML) 模型,包括K-最近邻居 (KNN) 和极端梯度增强 (XGBoost).
主要成果:
- 集成的FEA-ML框架在管道材料的分类中实现了高精度.
- K-最近邻居 (KNN) 和极端梯度提升 (XGBoost) 模型显示了99.9%的分类准确性.
- 该方法在确定各种条件和噪声水平下的管道加速度方面被证明是有效的.
结论:
- 开发的非侵入性方法为管道检测提供了一个可扩展和具有成本效益的解决方案.
- 这一框架使公用事业公司能够有效地定位和更换管道,确保监管合规.
- 与传统技术相比,该方法最大限度地减少了运营中断和资源支出.
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