Tunnel deformation monitoring is a crucial task to evaluate tunnel stability during the metro operation period.Terrestrial Laser Scanning(TLS)can collect high density and high accuracy point cloud data in a few minute...Tunnel deformation monitoring is a crucial task to evaluate tunnel stability during the metro operation period.Terrestrial Laser Scanning(TLS)can collect high density and high accuracy point cloud data in a few minutes as an innovation technique,which provides promising applications in tunnel deformation monitoring.Here,an efficient method for extracting tunnel cross-sections and convergence analysis using dense TLS point cloud data is proposed.First,the tunnel orientation is determined using principal component analysis(PCA)in the Euclidean plane.Two control points are introduced to detect and remove the unsuitable points by using point cloud division and then the ground points are removed by defining an elevation value width of 0.5 m.Next,a z-score method is introduced to detect and remove the outlies.Because the tunnel cross-section’s standard shape is round,the circle fitting is implemented using the least-squares method.Afterward,the convergence analysis is made at the angles of 0°,30°and 150°.The proposed approach’s feasibility is tested on a TLS point cloud of a Nanjing subway tunnel acquired using a FARO X330 laser scanner.The results indicate that the proposed methodology achieves an overall accuracy of 1.34 mm,which is also in agreement with the measurements acquired by a total station instrument.The proposed methodology provides new insights and references for the applications of TLS in tunnel deformation monitoring,which can also be extended to other engineering applications.展开更多
An integration processing system of three-dimensional laser scanning information visualization in goaf was developed. It is provided with multiple functions, such as laser scanning information management for goaf, clo...An integration processing system of three-dimensional laser scanning information visualization in goaf was developed. It is provided with multiple functions, such as laser scanning information management for goaf, cloud data de-noising optimization, construction, display and operation of three-dimensional model, model editing, profile generation, calculation of goaf volume and roof area, Boolean calculation among models and interaction with the third party soft ware. Concerning this system with a concise interface, plentiful data input/output interfaces, it is featured with high integration, simple and convenient operations of applications. According to practice, in addition to being well-adapted, this system is favorably reliable and stable.展开更多
Data augmentation is a widely used regularization strategy in deep neural networks to mitigate overfitting and enhance generalization.In the context of point cloud data,mixing two samples to generate new training exam...Data augmentation is a widely used regularization strategy in deep neural networks to mitigate overfitting and enhance generalization.In the context of point cloud data,mixing two samples to generate new training examples has proven to be effective.In this paper,we propose a novel and effective approach called Farthest Point Sampling Mix(FPSMix)for augmenting point cloud data.Our method leverages farthest point sampling,a technique used in point cloud processing,to generate new samples by mixing points from two original point clouds.Another key innovation of our approach is the introduction of a significance-based loss function,which assigns weights to the soft labels of the mixed samples based on the classification loss of each part of the new sample that is separated from the two original point clouds.This way,our method takes into account the importance of different parts of the mixed sample during the training process,allowing the model to learn better global features.Experimental results demonstrate that our FPSMix,combined with the significance-based loss function,improves the classification accuracy of point cloud models and achieves comparable performance with state-of-the-art data augmentation methods.Moreover,our approach is complementary to techniques that focus on local features,and their combined use further enhances the classification accuracy of the baseline model.展开更多
结构面分布对岩体的工程与力学性质具有重要影响,准确获取结构面信息对于分析岩体特性及其稳定性具有重要意义。通过三维激光扫描技术获取某高陡岩质边坡三维点云数据,通过对点云数据进行滤波前处理,采用开源程序Discontinuity Set Extr...结构面分布对岩体的工程与力学性质具有重要影响,准确获取结构面信息对于分析岩体特性及其稳定性具有重要意义。通过三维激光扫描技术获取某高陡岩质边坡三维点云数据,通过对点云数据进行滤波前处理,采用开源程序Discontinuity Set Extractor(DSE)对点云数据进行半自动化识别与分类,获取边坡岩体结构面的产状、迹长、间距等关键参数信息及点云聚类信息。通过对点云聚类信息进行拟合分析得到其概率分布模型并建立岩体的离散裂隙网络(DFN)模型,进一步基于点云数据采用“Rhino-Griddle-3DEC”联合建模方法建立了高陡岩质边坡的三维块体离散元模型,通过离散元模拟分析了该边坡的稳定性与潜在失稳区域。结果表明:在重力作用下,边坡整体安全系数约为1.5,坡顶突出危岩体竖向位移较大且安全系数较小,为潜在失稳区域。因此,采用该方法识别获取的结构面参数信息能够较好地反映岩体工程力学性质,对高陡岩质边坡稳定性分析与评价具有重要指导意义。展开更多
基金National Natural Science Foundation of China(No.41801379)Fundamental Research Funds for the Central Universities(No.2019B08414)National Key R&D Program of China(No.2016YFC0401801)。
文摘Tunnel deformation monitoring is a crucial task to evaluate tunnel stability during the metro operation period.Terrestrial Laser Scanning(TLS)can collect high density and high accuracy point cloud data in a few minutes as an innovation technique,which provides promising applications in tunnel deformation monitoring.Here,an efficient method for extracting tunnel cross-sections and convergence analysis using dense TLS point cloud data is proposed.First,the tunnel orientation is determined using principal component analysis(PCA)in the Euclidean plane.Two control points are introduced to detect and remove the unsuitable points by using point cloud division and then the ground points are removed by defining an elevation value width of 0.5 m.Next,a z-score method is introduced to detect and remove the outlies.Because the tunnel cross-section’s standard shape is round,the circle fitting is implemented using the least-squares method.Afterward,the convergence analysis is made at the angles of 0°,30°and 150°.The proposed approach’s feasibility is tested on a TLS point cloud of a Nanjing subway tunnel acquired using a FARO X330 laser scanner.The results indicate that the proposed methodology achieves an overall accuracy of 1.34 mm,which is also in agreement with the measurements acquired by a total station instrument.The proposed methodology provides new insights and references for the applications of TLS in tunnel deformation monitoring,which can also be extended to other engineering applications.
基金Project(51274250)supported by the National Natural Science Foundation of ChinaProject(2012BAK09B02-05)supported by the National Key Technology R&D Program during the 12th Five-year Plan of China
文摘An integration processing system of three-dimensional laser scanning information visualization in goaf was developed. It is provided with multiple functions, such as laser scanning information management for goaf, cloud data de-noising optimization, construction, display and operation of three-dimensional model, model editing, profile generation, calculation of goaf volume and roof area, Boolean calculation among models and interaction with the third party soft ware. Concerning this system with a concise interface, plentiful data input/output interfaces, it is featured with high integration, simple and convenient operations of applications. According to practice, in addition to being well-adapted, this system is favorably reliable and stable.
基金supported by the National Key R&D Program of China(No.2020YFB1708002)the National Natural Science Foundation of China(Grant Nos.62371009 and 61971008).
文摘Data augmentation is a widely used regularization strategy in deep neural networks to mitigate overfitting and enhance generalization.In the context of point cloud data,mixing two samples to generate new training examples has proven to be effective.In this paper,we propose a novel and effective approach called Farthest Point Sampling Mix(FPSMix)for augmenting point cloud data.Our method leverages farthest point sampling,a technique used in point cloud processing,to generate new samples by mixing points from two original point clouds.Another key innovation of our approach is the introduction of a significance-based loss function,which assigns weights to the soft labels of the mixed samples based on the classification loss of each part of the new sample that is separated from the two original point clouds.This way,our method takes into account the importance of different parts of the mixed sample during the training process,allowing the model to learn better global features.Experimental results demonstrate that our FPSMix,combined with the significance-based loss function,improves the classification accuracy of point cloud models and achieves comparable performance with state-of-the-art data augmentation methods.Moreover,our approach is complementary to techniques that focus on local features,and their combined use further enhances the classification accuracy of the baseline model.
文摘结构面分布对岩体的工程与力学性质具有重要影响,准确获取结构面信息对于分析岩体特性及其稳定性具有重要意义。通过三维激光扫描技术获取某高陡岩质边坡三维点云数据,通过对点云数据进行滤波前处理,采用开源程序Discontinuity Set Extractor(DSE)对点云数据进行半自动化识别与分类,获取边坡岩体结构面的产状、迹长、间距等关键参数信息及点云聚类信息。通过对点云聚类信息进行拟合分析得到其概率分布模型并建立岩体的离散裂隙网络(DFN)模型,进一步基于点云数据采用“Rhino-Griddle-3DEC”联合建模方法建立了高陡岩质边坡的三维块体离散元模型,通过离散元模拟分析了该边坡的稳定性与潜在失稳区域。结果表明:在重力作用下,边坡整体安全系数约为1.5,坡顶突出危岩体竖向位移较大且安全系数较小,为潜在失稳区域。因此,采用该方法识别获取的结构面参数信息能够较好地反映岩体工程力学性质,对高陡岩质边坡稳定性分析与评价具有重要指导意义。
