The advance rate (AR) of a tunnel boring machine (TBM) in hard rock condition is a key parameter for the successful accomplishment of a tunneling project, and the proper and reliable prediction of this parameter can lead to minimizing the risks associated to high capital costs and scheduling for such projects. This research aims at optimizing the hyper-parameters of the support vector machine (SVM) technique through the use of three optimization algorithms, namely, gray wolf optimization (GWO), whale optimization algorithm (WOA) and moth flame optimization (MFO), in forecasting TBM AR. In fact, the role of these optimization techniques is to optimize the hyperparameters ‘C’ and ‘gamma’ of the SVM model to get higher performance prediction. To develop the hybrid SVM-based models, 1,286 sample sets of data collected from a water transfer tunnel in Malaysia comprising seven input variables, i.e., rock mass rating, uniaxial compressive strength, Brazilian tensile strength, rock quality designation, weathering zone, thrust force and revolution per minute, and one output variable, i.e., TBM AR, were considered and used. Several GWO-SVM, WOA-SVM and MFO-SVM models were constructed to predict TBM AR considering their effective parameters. The accuracy levels of the proposed models were assessed using four statistical indices, i.e., the coefficient of determination (R²), root mean squared error (RMSE), mean absolute error (MAE), and variance accounted for (VAF). Modeling results revealed that the MFO algorithm can capture better hyper-parameters of the SVM model in predicting TBM AR among all three hybrid models. R² of (0.9623 and 0.9724), RMSE of (0.1269 and 0.1155), and VAF of (96.24 and 97.34%), respectively, for training and test stages of the MFO-SVM model confirmed that this hybrid SVM model is a powerful and applicable technique addressing problems related to TBM performance with a high level of accuracy.

硬岩条件下的隧道掘进机（TBM）的前进速度（AR）是成功完成隧道工程的关键参数，正确，可靠地预测该参数可以最大程度地降低与高资本相关的风险此类项目的成本和进度。这项研究旨在通过使用三种优化算法来优化支持向量机（SVM）技术的超参数，即灰狼优化（GWO），鲸鱼优化算法（WOA）和蛾火焰优化（MFO）。预测TBM AR。实际上，这些优化技术的作用是优化SVM模型的超参数“ C”和“γ”，以获得更高的性能预测。要开发基于混合SVM的模型，1，从马来西亚的输水隧道收集的286个样本数据集，包括七个输入变量，即岩体等级，单轴抗压强度，巴西抗拉强度，岩石质量标识，风化带，推力和每分钟转数，以及一个输出变量，即TBM AR，已被考虑并使用。考虑到它们的有效参数，构建了几个GWO-SVM，WOA-SVM和MFO-SVM模型来预测TBM AR。使用四个统计指标（即确定系数（R 考虑到它们的有效参数，构建了几个GWO-SVM，WOA-SVM和MFO-SVM模型来预测TBM AR。使用四个统计指标（即确定系数（R 考虑到它们的有效参数，构建了几个GWO-SVM，WOA-SVM和MFO-SVM模型来预测TBM AR。使用四个统计指标（即确定系数（R²），均方根误差（RMSE），平均绝对误差（MAE）和方差占（VAF）。建模结果表明，在预测所有三个混合模型中的TBM AR时，MFO算法可以捕获SVM模型的更好的超参数。对于MFO-SVM模型的训练和测试阶段，R ²（0.9623和0.9724），RMSE（0.1269和0.1155）和VAF分别为（96.24和97.34％）证实了这种混合SVM模型是强大且强大的高度准确地解决与TBM性能相关的问题的适用技术。