在地下道中基于机器学习的分段关节故障的识别
Zhisheng Jin1, Zihai Yan1,2, Haoran Fu1
1MOE Key Laboratory of Soft Soils and Geoenvironmental Engineering, Department of Civil Engineering, Zhejiang University, Hangzhou 310058, People's Republic of China.
概括
这项研究引入了反向传播神经网络 (BPNN) 来识别道变形模式,区分曲与脱位. 这种人工智能方法有助于评估分段关节的完整性,并指导地下道的工程处理.
科学领域:
- 地质技术工程 地质技术工程
- 人工智能的人工智能
- 结构健康监测 结构健康监测
背景情况:
- 在软土壤中的屏蔽道面临纵向差异性沉积和变形.
- 在螺栓故障分析和工程处理中,区分曲和脱位变形至关重要.
- 目前的位或开口检测方法是劳动密集型和昂贵的.
研究的目的:
- 开发一种创新高效的方法,用于识别屏蔽道的纵向变形模式.
- 通过区分曲和脱位来准确检测断段关节故障.
- 为确定分段联合有效性和实施适当的工程处理提供基础.
主要方法:
- 收集来自中国东部软土壤地铁的道结算数据.
- 使用等价轴刚度模型计算结算-迁移和结算-开放数据集.
- 开发和应用反向传播神经网络 (BPNN) 回归模型来预测变形.
主要成果:
- BPNN模型成功地预测了道沉积曲线的位移和开放.
- 该方法根据预测的变形准确地确定了段接头的有效性.
- 通过应用于杭州地铁道,证明了效率.
结论:
- 开发的BPNN方法为识别道变形模式提供了一种有效且不那么劳动密集的方法.
- 准确识别变形模式对于确保盾道的长期运行安全至关重要.
- 这种人工智能驱动的技术有助于对运输基础设施的故障分析.
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