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浙江大学学报(工学版)  2025, Vol. 59 Issue (6): 1233-1240    DOI: 10.3785/j.issn.1008-973X.2025.06.014
土木工程、交通工程     
深基坑开挖致高铁桥墩位移的SVM预测方法
宋旭明1(),李小龙1,唐冕1,王天良2,程丽娟3
1. 中南大学 土木工程学院,湖南 长沙 410075
2. 河南省交通规划设计研究院股份有限公司,河南 郑州 451450
3. 湖南省交通规划勘察设计院有限公司,湖南 长沙 410200
SVM prediction method for displacement of high-speed railway piers caused by deep foundation pit excavation
Xuming SONG1(),Xiaolong LI1,Mian TANG1,Tianliang WANG2,Lijuan CHENG3
1. School of Civil Engineering, Central South University, Changsha 410075, China
2. Henan Communications Planning and Design Institute Co. Ltd, Zhengzhou 451450, China
3. Hunan Province Communication Planning, Survey and Design Institute Co. Ltd, Changsha 410200, China
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摘要:

为了研究邻近基坑开挖引起的高铁桥梁墩顶附加位移对铁路运营安全的影响,依托某深基坑开挖工程,建立考虑地下水影响的土体-桥梁三维有限元模型. 分析高铁桥墩附加位移的单因素敏感性. 采用Box-Behnken design(BBD)试验设计方法结合支持向量机算法(SVM)建立高铁桥墩墩顶位移预测模型,结合蒙特卡洛法,对参数进行107次抽样计算,得到墩顶附加位移的可靠概率. 研究结果表明:基坑与高铁桥墩距离的变化对墩顶横向位移和竖向位移的影响最大. 在8组不同超参数组合的SVM模型中,最优模型的预测值与有限元计算值的最大误差小于6%,最优模型可代替有限元进行计算. 在墩顶横向位移为2 mm的限值下,背景工程基坑与桥墩距离为35 m时,墩顶横向附加位移的可靠概率为33.12%;当基坑与桥墩距离增加到39 m时,墩顶横向附加位移的可靠概率为99.68%. 所采用的分析方法可以削减因土层力学参数离散性大而产生的评估结果不确定性,为类似工程的安全评估提供参考.

关键词: 高速铁路深基坑墩顶附加位移支持向量机(SVM)可靠度    
Abstract:

A three-dimensional finite element model considering the influence of groundwater on soil and bridges was established based on a deep foundation pit excavation project, in order to study the impact of additional displacements of high-speed railway bridge piers caused by adjacent foundation pit excavation on railway operation safety. The single factor sensitivity of additional displacements of high-speed railway bridge piers was analyzed. The Box-Behnken Design (BBD) experimental design method combined with the support vector machine algorithm (SVM) was used to establish a displacement prediction model for the top of high-speed railway bridge piers. By combining the Monte Carlo method, 107 sampling calculations were performed on the parameters to obtain the reliable probability of additional displacements at the pier top. The research results showed that the change in the distance between the foundation pit and the high-speed railway bridge pier had the greatest impact on the horizontal and vertical displacements at the pier top. Among the eight different combinations of hyperparameters of the SVM model, the maximum error between the prediction values of the optimal model and the finite element calculation values was within 6%, indicating that the optimal model could replace the finite element for calculation. Under the limit of 2 mm lateral displacement at the pier top, the reliability probability of lateral additional displacement at pier top was 33.12% when the distance between the background engineering foundation pit and the bridge pier was 35 m; when the distance increased to 39 m, the reliability probability of lateral additional displacement at pier top reached 99.68%. The analysis method used can avoid uncertain evaluation results caused by large discretization of soil layer mechanical parameters, providing reference for safety evaluation of similar projects.

Key words: high-speed railway    deep foundation pit    additional displacement of pier top    support vector machine (SVM)    reliability
收稿日期: 2024-04-15 出版日期: 2025-05-30
CLC:  U 24  
基金资助: 国家自然科学基金资助项目(52078486).
作者简介: 宋旭明(1974—),副教授,博士,从事桥梁极限承载力、既有高铁桥梁风险评价研究. orcid.org/0009-0007-8438-1849. E-mail:ctysxm@163.com
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引用本文:

宋旭明,李小龙,唐冕,王天良,程丽娟. 深基坑开挖致高铁桥墩位移的SVM预测方法[J]. 浙江大学学报(工学版), 2025, 59(6): 1233-1240.

Xuming SONG,Xiaolong LI,Mian TANG,Tianliang WANG,Lijuan CHENG. SVM prediction method for displacement of high-speed railway piers caused by deep foundation pit excavation. Journal of ZheJiang University (Engineering Science), 2025, 59(6): 1233-1240.

链接本文:

https://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2025.06.014        https://www.zjujournals.com/eng/CN/Y2025/V59/I6/1233

图 1  SVM、SVR最优超平面示意图
图 2  SVM算法预测及可靠度分析流程
图 3  工点平面布置
图 4  基坑开挖后的有限元模型
代号参数名称基准值
1基坑距离/m35
2围护桩等效板厚/m0.832
3素填土内摩擦角/(°)10.00
4素填土黏聚力/kPa12
5素填土泊松比0.31
6素填土卸载模量/MPa35.0
7粉质黏土内摩擦角/(°)18.80
8粉质黏土黏聚力/kPa29.6
9粉质黏土泊松比0.3
10粉质黏土卸载模量/MPa43.4
11强风化泥质砂岩内摩擦角/(°)35.61
12强风化泥质砂岩黏聚力/kPa71.26
13强风化泥质砂岩泊松比0.26
14强风化泥质砂岩卸载模量/MPa175.7
15中风化泥质砂岩内摩擦角/(°)35.02
16中风化泥质砂岩黏聚力/kPa84.34
17中风化泥质砂岩泊松比0.24
18中风化泥质砂岩卸载模量/MPa261.6
表 1  模型参数基准值
参数
代号
横向
位移
竖向
位移
参数
代号
横向
位移
竖向
位移
13.29321.5331100.60350.3548
20.08770.0260110.02180.0344
30.01070.0252120.05780.0453
40.03220.0335130.13140.2072
50.03390.0696140.24950.1854
60.04800.0461150.04090.0755
70.02280.0579160.05040.0889
80.08200.0855170.25680.5057
90.47530.3690180.12010.2642
表 2  墩顶附加位移的参数敏感度
水平A/mBC/MPaDE/MPaFG/MPa
最低水平300.28328.780.238111.590.227180.48
平均水平350.30043.400.260175.700.240261.10
最高水平400.31758.020.282239.810.253341.72
表 3  BBD试验因素与水平
组号CγYZ
R2RMSE/mmR2RMSE/mm
11005.0000.99990.00320.99990.0004
2702.5000.99990.00310.99990.0004
3100.6000.99980.00600.99530.0048
4110.0890.99680.02910.98070.0097
5300.1000.99890.01760.98490.0086
620.5000.99820.02200.98720.0079
70.051.0000.87710.18170.83680.0283
80.20.0500.84250.20570.74040.0357
表 4  超参数设计及相应训练模型回归评价指标
图 5  训练模型预测结果与有限元计算结果对比
组号YZ
R2RMSE/mmemax/%R2RMSE/mmemax/%
10.88460.147116.070.79170.02809.27
20.98280.05686.720.82950.02537.98
30.99280.03683.200.92090.01735.48
40.99590.02792.230.93380.01584.93
50.99540.02942.470.93450.01575.06
60.98690.04954.430.94330.01465.77
表 5  预测模型回归评价指标及最大相对误差
图 6  SVM模型预测值与有限元计算结果对比
图 7  缩减样本后SVM模型预测值与有限元计算结果对比
图 8  预测精度指标-核函数参数关系曲线
图 9  多项式拟合值与有限元计算结果对比
因素代号μCVσ
A350.0511.785
B0.3000.020.006
C43.400.125.208
D0.2600.030.0078
E175.700.1322.841
F0.2400.020.0048
G261.100.1128.721
表 6  参数指标统计特征
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