Abstract In this article, maximum surface settlement (MSS) of urban tunnels was investigated on the basis of three operational parameters of tunnel width, tunnel depth, excavation method, as well as three soil parameters of cohesion, friction angle and elasticity modulus. Seven intelligent methods of long short-term memory (LSTM), deep neural networks (DNNs), K-nearest neighbor (KNN), Gaussian process regression (GPR), support vector regression (SVR), decision tree (DT), and linear regression (LR) were used to perform investigation. The intelligent methods were studied on the basis of 300 datasets accessed from 8 urban tunnels in Iran. Two cross-validation methods of hold-out and 5-fold were utilized for analyzing the prediction results. Finally, the DNNs method with R2 = 0.9939 and RMSE = 3.396301689 mm in the hold-out cross-validation mode and R2 = 0.9937 and RMSE = 2.199337605 mm in the 5-fold cross-validation mode, was recommended and suggested as the best prediction method for MSS.

中文翻译：

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预测城市隧道开挖引起的最大地表沉降

摘要 本文基于隧道宽度、隧道深度、开挖方式三个操作参数，以及黏聚力、摩擦角和弹性模量三个土壤参数，研究了城市隧道的最大表面沉降（MSS）。长短期记忆（LSTM）、深度神经网络（DNNs）、K-最近邻（KNN）、高斯过程回归（GPR）、支持向量回归（SVR）、决策树（DT）、线性七种智能方法回归（LR）被用来进行调查。智能方法是在从伊朗 8 条城市隧道访问的 300 个数据集的基础上进行研究的。使用保留和5倍两种交叉验证方法来分析预测结果。最后，使用 R2 = 0.9939 和 RMSE = 3 的 DNNs 方法。