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首页> 外文期刊>Frontiers of structural and civil engine >Prediction of shield tunneling-induced ground settlement using machine learning techniques
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Prediction of shield tunneling-induced ground settlement using machine learning techniques

机译：使用机器学习技术预测盾构隧道引起的地面沉降

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摘要

Predicting the tunneling-induced maximum ground surface settlement is a complex problem since the settlement depends on plenty of intrinsic and extrinsic factors. This study investigates the efficiency and feasibility of six machine learning (ML) algorithms, namely, back-propagation neural network, wavelet neural network, general regression neural network (GRNN), extreme learning machine, support vector machine and random forest (RF), to predict tunneling-induced settlement. Field data sets including geological conditions, shield operational parameters, and tunnel geometry collected from four sections of tunnel with a total of 3.93 km are used to build models. Three indicators, mean absolute error, root mean absolute error, and coefficient of determination the (R2) are used to demonstrate the performance of each computational model. The results indicated that ML algorithms have great potential to predict tunneling-induced settlement, compared with the traditional multivariate linear regression method. GRNN and RF algorithms show the best performance among six ML algorithms, which accurately recognize the evolution of tunneling-induced settlement. The correlation between the input variables and settlement is also investigated by Pearson correlation coefficient.
机译：预测隧道诱发的最大地面沉降是一个复杂的问题，因为沉降取决于大量的内在和外在因素。这项研究调查了六种机器学习（ML）算法的效率和可行性，即反向传播神经网络，小波神经网络，通用回归神经网络（GRNN），极限学习机，支持向量机和随机森林（RF），预测隧道引起的沉降。现场数据集包括地质条件，盾构运行参数和从隧道的四个部分收集的总长度为3.93 km的隧道几何形状，用于建立模型。三个指标（平均绝对误差，均方根绝对误差和确定系数（R2））用于证明每个计算模型的性能。结果表明，与传统的多元线性回归方法相比，机器学习算法具有很大的潜力来预测隧道诱发的沉降。 GRNN和RF算法在六种ML算法中表现出最好的性能，它们可以准确地识别隧道诱发沉降的演变。输入变量和沉降之间的相关性也通过皮尔森相关系数进行研究。

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