A reliable and accurate prediction of the tunnel boring machine (TBM) performance can assist in minimizing the relevant risks of high capital costs and in scheduling tunneling projects. This research aims to develop six hybrid models of extreme gradient boosting (XGB) which are optimized by gray wolf optimization (GWO), particle swarm optimization (PSO), social spider optimization (SSO), sine cosine algorithm (SCA), multi verse optimization (MVO) and moth flame optimization (MFO), for estimation of the TBM penetration rate (PR). To do this, a comprehensive database with 1286 data samples was established where seven parameters including the rock quality designation, the rock mass rating, Brazilian tensile strength (BTS), rock mass weathering, the uniaxial compressive strength (UCS), revolution per minute and trust force per cutter (TFC), were set as inputs and TBM PR was selected as model output. Together with the mentioned six hybrid models, four single models i.e., artificial neural network, random forest regression, XGB and support vector regression were also built to estimate TBM PR for comparison purposes. These models were designed conducting several parametric studies on their most important parameters and then, their performance capacities were assessed through the use of root mean square error, coefficient of determination, mean absolute percentage error, and a10-index. Results of this study confirmed that the best predictive model of PR goes to the PSO-XGB technique with system error of (0.1453, and 0.1325), R² of (0.951, and 0.951), mean absolute percentage error (4.0689, and 3.8115), and a10-index of (0.9348, and 0.9496) in training and testing phases, respectively. The developed hybrid PSO-XGB can be introduced as an accurate, powerful and applicable technique in the field of TBM performance prediction. By conducting sensitivity analysis, it was found that UCS, BTS and TFC have the deepest impacts on the TBM PR.

隧道掘进机（TBM）性能的可靠而准确的预测可以帮助最大程度地降低高资本成本的相关风险，并有助于安排隧道项目。这项研究旨在开发六个混合模型，分别通过灰狼优化（GWO），粒子群优化（PSO），社交蜘蛛优化（SSO），正弦余弦算法（SCA）和多诗句优化对极端梯度增强（XGB）进行优化（MVO）和飞蛾火焰优化（MFO），用于估算TBM穿透率（PR）。为此，建立了一个包含1286个数据样本的综合数据库，其中包含七个参数，包括岩石质量名称，岩石质量等级，巴西抗拉强度（BTS），岩石质量风化，单轴抗压强度（UCS），每分钟转数和每个刀具的信任力（TFC），被设置为输入，TBM PR被选择为模型输出。与提到的六个混合模型一起，还建立了四个单个模型，即人工神经网络，随机森林回归，XGB和支持向量回归来估计TBM PR，以进行比较。设计这些模型时，对它们的最重要参数进行了几项参数研究，然后通过使用均方根误差，确定系数，平均绝对百分比误差和a10指数来评估其性能。这项研究的结果证实，最佳的PR预测模型是PSO-XGB技术，其系统误差为（0.1453和0.1325），R 还建立了随机森林回归，XGB和支持向量回归来估计TBM PR，以进行比较。设计这些模型时，对它们的最重要参数进行了几项参数研究，然后通过使用均方根误差，确定系数，平均绝对百分比误差和a10指数来评估其性能。这项研究的结果证实，最佳的PR预测模型是PSO-XGB技术，其系统误差为（0.1453和0.1325），R 还建立了随机森林回归，XGB和支持向量回归来估计TBM PR，以进行比较。设计这些模型时，对它们的最重要参数进行了几项参数研究，然后通过使用均方根误差，确定系数，平均绝对百分比误差和a10指数来评估其性能。这项研究的结果证实，最佳的PR预测模型是PSO-XGB技术，其系统误差为（0.1453和0.1325），R 确定系数，平均绝对百分比误差和a10指数。这项研究的结果证实，最佳的PR预测模型是PSO-XGB技术，其系统误差为（0.1453和0.1325），R 确定系数，平均绝对百分比误差和a10指数。这项研究的结果证实，最佳的PR预测模型是PSO-XGB技术，其系统误差为（0.1453和0.1325），R²的（0.951，0.951），平均绝对误差百分比（4.0689，和3.8115），和（0.9348，和0.9496）在训练和分别测试阶段，A10-索引。可以将开发出的混合PSO-XGB作为一种准确，功能强大且适用的技术引入TBM性能预测领域。通过敏感性分析，发现UCS，BTS和TFC对TBM PR的影响最大。