| 基于神经网络的直接线性变换在地质力学模型试验中的应用 |
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| 引用本文： | 陈继光,张强勇,李术才,马国梁,张建国.基于神经网络的直接线性变换在地质力学模型试验中的应用[J].岩土力学,2008,29(4):897-900. |
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| 作者姓名： | 陈继光 张强勇 李术才 马国梁 张建国 |
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| 作者单位： | 1. 山东大学 土建与水利工程学院，济南 250061；2. 山东大学 岩土与结构工程研究中心，济南 250061 |
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| 基金项目： | 国家自然科学基金
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山东省自然科学基金 |
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| 摘 要： | 三维地质力学模型的变形数据主要由岩体内部变形和收敛变形两部分构成。寻找满足高精度位移测量的收敛变形观测方法已成为三维地质力学模型试验数据采集的重要研究方向。修正了传统的数码相机摄影测量基于图像坐标变换的计算思路，改善了试验中5×5小模板作业方式，采用大模板标准点布设方案和标志点相邻坐标数据点的选择方法，有效地减小了镜头畸变对标准点、标志点的影响。基于BP神经网络的直接线性变换原理进行了图像间的坐标识别和试验阶段测量观测点的坐标比较，实现了三维地质力学模型的收敛变形观测。将数码相机数字近景摄影方法应用于某大型分岔隧道的三维地质力学模型试验研究，取得了满意的研究成果。
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| 关 键 词： | 收敛变形 神经网络 直接线性变换 分岔隧道 三维地质力学模型试验 |
| 文章编号： | 1000-7598-(2008)04-897-04 |
| 收稿时间： | 2006-04-06 |
| 修稿时间： | 2006年4月6日 |
The application of direct linear transformation based on the neural network in geo-mechanics model test |
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| CHEN Ji-guang,ZHANG Qiang-yong,LI Shu-cai,MA Guo-liang,ZHANG Jian-guo.The application of direct linear transformation based on the neural network in geo-mechanics model test[J].Rock and Soil Mechanics,2008,29(4):897-900. |
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| Authors: | CHEN Ji-guang ZHANG Qiang-yong LI Shu-cai MA Guo-liang ZHANG Jian-guo |
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| Institution: | 1. College of Civil Engineering and Hydraulic Engineering of Shandong University, Jinan，Shandong 250061, china; 2. Geotechnical and Structural Engineering Research Center of Shandong University，Jinan，Shandong 250061，China |
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| Abstract: | The deformation date of 3D geo-mechanics model are composed of the internal deformation of rock mass and convergence deformation of boundaries. Searching the method of measuring the convergence deformation with high precision has been major direction of research in the area of data acquisition for 3D geo-mechanics model. In this paper, the calculation method based on the coordinates transformation of images in the traditional digital camera photogrammetry was revised the small template operation method of 5 ? 5 was improved. Instead, the big templates with the layout of standard points was used and a method of selection for coordinate data of neighboring marked nods was designed, thus, the influence of lens distortion was reduced effectively. The direct linear transformation mechanism based on the BP neural network was used for coordinates recognition of images and comparison of coordinates of marked nods during experiment period. The measurement of convergence deformation for 3D geo-mechanics model was thus achieved. A 3D geo-mechanics model applying the close shot photographic technique of digital camera was set up for a large scale forked tunnels, the results of measurements were satisfactory. |
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| Keywords: | convergence deformation neural network direct linear substitution bifurcation tunnels 3D geo-mechanics model test |
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