面向隧道变形检测研究的数字孪生方法
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国家重点研发计划项目(No.2022YFF0705704);国家市场监督管理总局技术保障专项项目(No.2023YJ10)资助。


A digital twin approach for tunnel deformation detection
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    摘要:

    针对隧道变形慢、难以获得有效实验检测数据导致隧道变形检测技术研究受限的问题,本文提出一种面向隧道变形检测研究的数字孪生方法,建立高保真隧道孪生模型;通过有限元模拟隧道的变形情况,输出隧道孪生模型的变形真值;搭建虚拟仿真平台,实现在虚拟环境中对隧道模型的三维激光扫描,以获得大样本检测数据,辅助训练变形检测方法;变形检测采用Geotransformer神经网络实现隧道点云配准,通过拟合隧道中轴线获取隧道断面点云,实现隧道变形分析。实验结果表明,该方法有效克服隧道变形检测技术研究受实验场地限制等问题,隧道模型表面重建平均误差为000253mm,最大误差为11325mm,与有限元方法输出变形真值对比,变形检测平均误差小于034cm,验证该变形检测方法具备较高的准确性,基本满足工程需求。

    Abstract:

    To solve the problem of slow tunnel deformation and difficulty in obtaining effective experimental detection data leading to limited research on tunnel deformation detection technology,a digital twin method for tunnel deformation detection is proposed,and a high fidelity tunnel twin model is establishedin this paper. The deformation of tunnel is simulated by finite element method,and the true value of tunnel twin model is obtained. A virtual simulation platform is built to realize the three dimensional laser scanning of tunnel models in a virtual environment to obtain large sample detection data and assist in training deformation detection methods. In deformation detection,Geotransformer neural network is used to realize tunnel point cloud registration,and tunnel section point cloud is obtained by fitting tunnel central axis to realize tunnel deformation analysis. Experimental results show that the proposed method can effectively overcome the problem of tunnel deformation detection technology research limited by experimental sites. The average error of tunnel model surface reconstruction is 0.00253mm and the maximum error is 1.1325mm. Compared with the true value of deformation output by finite element method,the average error of deformation detection is less than 0.34cm. It is verified that the deformation detection method has high accuracy and basically meets the engineering requirements.

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苏哲,罗哉,杨力,江文松,刘慧平.面向隧道变形检测研究的数字孪生方法[J].激光与红外,2024,54(9):139~138
SU Zhe, LUO Zai, YANG Li, JIANG Wen-song, LIU Hui-ping. A digital twin approach for tunnel deformation detection[J]. LASER & INFRARED,2024,54(9):139~138

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  • 在线发布日期: 2024-09-18
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