| 基于GA-SVR的地铁隧道沉降预测 |
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| 引用本文： | 周立俊,黄腾,王思捷,吴壮壮.基于GA-SVR的地铁隧道沉降预测[J].地理空间信息,2021,19(3):115-117. |
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| 作者姓名： | 周立俊 黄腾 王思捷 吴壮壮 |
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| 作者单位： | 河海大学地球科学与工程学院,江苏南京 210000;河海大学地球科学与工程学院,江苏南京 210000;河海大学地球科学与工程学院,江苏南京 210000;河海大学地球科学与工程学院,江苏南京 210000 |
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| 摘 要： | 利用支持向量回归(SVR)和遗传算法(GA)参数寻优,建立了基于GA-SVR的地铁隧道沉降预测模型,可提高地铁隧道沉降预测的精度。利用长期实测的地铁结构监测数据对SVR模型进行训练,并通过GA优化SVR模型的3个参数;利用训练模型均方误差结合留一交叉验证的方法确定GA的适应度。基于南京地铁2号线隧道结构沉降实测数据,将预测值与实测值进行了对比分析。结果表明,该模型预测的地铁隧道沉降预测值准确、可靠,其精度能满足工程实际要求。
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| 关 键 词： | SVR GA 变形预测 交叉验证 GA-SVR |
Subway Tunnel Settlement Prediction Based on GA-SVR |
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| Abstract: | In this paper,we used support vector regression(SVR)and genetic algorithm(GA)to optimize parameters,and established a subway tunnel settlement prediction model based GA-SVR,which could improve the accuracy of subway tunnel settlement prediction.Firstly,we used the long-term measured subway structure monitoring data to train SVR model,and used GA to optimize the three parameters of the SVR model.Then,we used the method for combining the mean square error of the training model with the leave-one-out crossvalidation to determine the fitness of GA.Finally,based on the measured data of tunnel structure settlement of Nanjing subway line 2,we compared and analyzed the predicted value and the measured value.The results show that the predicted value of the subway tunnel settlement predicted by this model is accurate and reliable,and its accuracy can meet the actual requirements of the project. |
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| Keywords: | SVR GA deformation prediction cross-validation GA-SVR |
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