基于LSSVM对盾构施工诱发地面塌陷变形预测

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摘要成都地铁1，2号线盾构在砂卵石地层施工诱发多次地面塌陷事故，其地面塌陷变形曲线受多种因素影响，且各因素对地面塌陷变形曲线的影响表现出非线性特性，因此地面塌陷变形曲线很难用显示的数学表达式进行求解。最小二乘支持向量机是基于统计学习理论的机器学习方法，该方法能避免传统神经网络诸多缺陷，能够分析复杂因素对结果影响的潜在规律，据此引入最小二乘支持向量机，以地层物理力学参数、盾构埋深和地层损失数量为输入参数，建立地面塌陷变形曲线预测模型。经过样本检验，预测模型具有较强的泛化能力，预测结果精度和可靠性较高。

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	白永学1
	漆泰岳1
	李有道2
	吴占瑞1

关键词 ：隧道工程, 最小二乘支持向量基, 地面塌陷, 变形曲线预测, 统计分析

Abstract：When shield crossed the sandy cobble stratum in Chengdu metro No.1 and No.2 lines，the induced surface subsidence reached as high as dozens of times. Surface collapse deformation curve is influenced by many factors，and the influences of the factors on surface collapse deformation curve show the nonlinear characteristics. So the surface collapse deformation curve is difficult to solve with mathematical formulas. Least squares support vector is a machine learning method based on the statistical learning theory. It can avoid shortcomings of traditional neural network and analyze influencing rule on the result with complicated factors. Thereby least squares support vector machine method was introduced to establish prediction model for surface collapse deformation. Prediction model took physico-mechanical parameters of stratum，buried depth of shield and ground loss value as input parameters. Though testing sample data，the prediction mode has strong generalization ability，and its prediction result has high accuracy and reliability.

Key words： tunnelling engineering least square support vector machine surface collapse prediction for deformation curve statistical analysis

引用本文:

白永学1，漆泰岳1，李有道2，吴占瑞1. 基于LSSVM对盾构施工诱发地面塌陷变形预测[J]. 岩石力学与工程学报, 2013, 32(s2): 3666-3674.
BAI Yongxue1，QI Taiyue1，LI Youdao2，WU Zhanrui1. PREDICTION FOR SURFACE COLLAPSE DEFORMATION OF SHIELD CONSTRUCTION BASED ON LSSVM. , 2013, 32(s2): 3666-3674.

链接本文:

https://rockmech.whrsm.ac.cn/CN/Y2013/V32/Is2/3666

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