利用三维激光扫描技术监测矿区桥梁结构形变

Deformation monitoring of bridge structure in mining area using 3D laser scanning technology

利用三维激光扫描技术测得的建筑物点云数据能够较清晰地表示建筑物的三维空间信息,提供高精度、高密度的建筑物表面描述。点云本身不直接显示自身所包含的特征信息,在进行局部形变提取时,需要进行点云分割工作。现有的应用于建(构)筑物的分割算法大多依赖于建(构)筑物特征设定突变阈值,当遇到复杂场景时,这些假设往往会导致错误。随着机器学习在点云处理领域的延伸,建(构)筑物点云数据边界的识别和分割有了新的实现思路。本文以某矿区工作面上方铁路桥两期三维激光扫描数据为例,采用神经网络方法对桥拱钢结构实行分割提取,在对1000万个标记桥梁点云数据进行训练后,神经网络模型可以学习操作人员识别点云中各点的属性并进行标记,并提取两期数据中的钢结构点云;对使用神经网络分割出的桥拱钢结构点云进行分析,通过对钢结构底边线进行特征线拟合、长度提取,计算钢结构的位移及拉伸量,并结合桥拱位移、形变量分析桥梁形变。研究表明:使用神经网络模型对标记数据进行训练可以有效识别建(构)筑物特征,并应用于建(构)筑物局部形变分析。

The point cloud data of buildings measured by 3D laser scanning technology can clearly represent the three-dimensional spatial information of buildings,and provide high-precision and high-density surface description of buildings.The point cloud itself does not directly display its own characteristic information,so when extracting local deformation,point cloud segmentation is needed.Most of the existing segmentation algorithms applied to buildings and structures rely on the characteristics of them to set the mutation threshold.When encountering complex scenarios,these assumptions often lead to errors.With the extension of machine learning in the field of point cloud processing,a new idea has emerged in the recognition of buildings and structures’boundaries and point cloud data segmentation.Taking the 3D laser scanning data of two phases of a railway bridge above the working face of a mining area as an example,this paper uses the neural network method to segment and extract the steel structure of bridge arches.After training 10 million marked bridge points,the neural network model can learn operators to recognize the attributes of points in the point cloud and mark them,then extract the steel structure point cloud in the two phases of data.In the next way,this paper analyzes the point cloud of steel structure of bridge arches segmented by neural network,through the characteristic line fitting and length extraction of the bottom edge line of steel structure,calculates the displacement and tension of steel structure,and analyzes the bridge deformation combined with arch displacement and shape variable.The research shows that using neural network to train the labeled data can effectively identify the characteristics of buildings and structures,and this technology can be applied to analyze local deformation of them.