基于代理模型的地铁基坑承压含水层水文参数反演研究
叶如1狄宏规2朱之太3朱益龙1蒋斌4相龙胜5柴东升6姚琦钰7
Inversion Study of Hydrogeological Parameters for Metro Foundation Pit Confined Aquifiers Based on Surrogate Modeling
YE Ru1DI Honggui2ZHU Zhitai3ZHU Yilong1JIANG Bin4XIANG Longsheng5CHAI Dongsheng6YAO Qiyu7
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作者信息:1.宁波市轨道交通集团有限公司建设分公司， 315101， 宁波
2.上海市轨道交通结构耐久与系统安全重点实验室，201804， 上海
3.宁波市轨道交通物产置业有限公司， 315101， 宁波
4.上海市隧道工程轨道交通设计研究院，200235， 上海
5.中交隧道工程局有限公司， 100102， 北京
6.龙元建设集团股份有限公司， 200072， 上海
7.麦克马斯特大学, L8S 4L7, 汉密尔顿
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Affiliation:1.Construction Branch of Ningbo Rail Transit Group Co., Ltd., 315101, Ningbo, China
2.Shanghai Key Laboratory of Rail Infrastructure Durability and System Safety, Tongji University, 201804, Shanghai, China
3.Ningbo Rail Transit Property Co., Ltd., 315101, Ningbo, China
4.Shanghai Tunnel Engineering Rail Transit Design and Research Institute, 200235, Shanghai, China
5.CCCC Tunnel Engineering Co., Ltd., 100102, Beijing, China
6.Longyuan Construction Group Co., Ltd., 200072, Shanghai, China
7.McMaster University, L8S 4L7, Hamilton, Canada
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关键词:
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Key words:
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DOI:10.16037/j.1007-869x.20253117
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中图分类号/CLCN:U231+.3；TU46+3
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栏目/Col:土建工程
摘要:
［目的］为提高地铁车站基坑的施工安全，应准确掌握地下水的水文参数，尤其应提高承压含水层的渗透系数和储水系数取值的准确性，这是制定降水方案的重要前提。由此，需要对地铁基坑承压含水层水文参数进行更为深入的研究。［方法］以宁波轨道交通7号线体育馆站工程降水试验为背景，采用Python语言中的Flopy模块调用Modflow6软件，构建了三维地下水非稳定渗流模型。引入LSTM(长短期记忆网络)深度学习模型，构建了承压水水位变化的代理模型，并结合粒子群优化算法和现场实测数据，对承压含水层的渗透系数及储水系数进行了反演分析。提出了一种基于代理模型与优化算法的地铁基坑地下水文参数反演方法。［结果及结论］反演所得的垂直渗透系数为0.76×10-5 m/s，水平渗透系数为1.38×10-5 m/s，储水系数为6.42×10-5 m-1。将反演所得参数代入渗流数值模型，计算得到的数据与实测数据在抽水初期水位的快速下降、稳定期的缓变以及停止抽水后水位的逐步回升等阶段均吻合，验证了该反演方法的可行性。基于深度学习的代理模型与优化算法能够高效、准确地完成地下水参数反演工作。
Abstracts:
［Objective］ To enhance the safety of metro station foundation pit construction, it is essential to accurately determine the groundwater hydrogeological parameters, improving the accuracy of permeability and storage coefficient in confined aquifers particularly. This is a critical prerequisite for formulating dewatering schemes. Therefore, a more in-depth study of the hydrological parameters of confined aquifers in metro foundation pits is required. ［Method］ Based on the dewatering test at Sports Center Station on Ningbo Rail Transit Line 7, a three-dimensional transient groundwater seepage model is developed with Modflow6 software called via the Flopy module in Python language. An LSTM (long- and short-term memory) deep learning model is introduced to build a surrogate model of confined aquifer water level variations. Combined with a particle swarm optimization algorithm and based on field-measured data, an inverse analysis of the confined aquifer permeability and storage coefficients is conducted. Thereby a method for hydrogeological parameter inversion in metro foundation pits based on surrogate modeling and optimization algorithms is proposed. ［Result & Conclusion］ The obtained inverted vertical permeability coefficient is 0.76×10-5 m/s, the horizontal permeability coefficient is 1.38×10-5 m/s, and the storage coefficient is 6.42×10-5 m-1. When these parameters are input into the numerical seepage model, the calculated data closely matches the measured data in all process, including the stage of rapid water level drop in the initial pumping, the stage of gradual change during the stabilization period, and the stage of water level gradual recovery after pumping stops, validating the feasibility of the inversion method. The use of deep learning-based surrogate modeling combined with optimization algorithms enables efficient and accurate inversion analysis of groundwater parameters.
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