随着人工智能技术的发展,一些机器学习模型逐渐被应用于岩土工程领域.为了准确预测盾构掘进引起的地表沉降,应该及时采取合理措施.依托上海市北横通道超大直径盾构掘进施工数据,使用随机森林(random forest,RF)、支持向量机(support vector machine,SVM)和极限梯度提升树(extreme gradient boosting,XGBoost)3类机器学习模型,以地质参数、几何参数和推进参数作为输入,预测超大直径盾构掘进地表最终沉降.使用遗传算法(genetic algorithm,GA)获取各机器模型的最优超参数,以区段内前70个数据点为基础训练集,对后27组数据点进行动态预测,获得各模型在地表最终沉降预测上的精度.研究结果表明,SVM模型的预测结果较差(MSE为5.06,R2为0.13),2类基于决策树的集成算法(RF和XGBoost)对地表预测的预测结果较好,其中RF模型的均方误差(mean squared error,MSE)为1.35,R2为0.70;XGBoost的MSE为2.20,R2为0.57,可以准确预测地表最终沉降,为软土地区超大直径泥水盾构施工控制及地表沉降预测提供一种新方法.
With the advancement of artificial intelligence technology, certain machine learning models have gradually been applied in the field of geotechnical engineering. These models aim to accurately predict the surface settlement caused by shield tunneling and facilitate the timely implementation of appropriate measures. Leveraging data from the construction of the Shanghai Beiheng Tunnel’s super large diameter shield tunneling, three types of machine learning models were employed: random forest (RF), support vector machines (SVM), and extreme gradient boosting (XGBoost). Geological, geometric, and advancement parameters were utilized as inputs to forecast the final surface settlement resulting from the excavation of a super large diameter shield tunnel. A genetic algorithm (GA) was used to obtain the optimal hyperparameters for each machine learning model. Using the initial 70 data points within a specific section as the foundational training set, dynamic predictions were made for 27 subsequent data points. This allowed for the assessment of the models’ accuracy in predicting the final surface settlement. The results indicated that the SVM’s predictive performance was relatively poor (with a mean squared error (MSE) of 5.06 and a coefficient of determination (R2) of 0.13). In contrast, the two decision-tree-based ensemble algorithms (RF and XGBoost) exhibited better predictive results for surface settlement. Specifically, RF demonstrated an MSE of 1.35 and an R2 of 0.70, whereas XGBoost had an MSE of 2.2 and an R2 of 0.57. These ensemble algorithms can accurately predict the final surface settlement and thereby provide a novel approach for controlling construction and predicting surface settlement in the context of super large diameter slurry shield tunneling in soft soil regions.
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