A proper planning schedule for tunnel boring machine (TBM) construction is considered as a necessary and difficult task in tunneling projects. Therefore, prediction of TBM performance with high degree of accuracy is needed to prepare a suitable planning schedule. This study aims to predict the advance rate of TBMs using optimized extreme learning machine (ELM) model with six particles swam optimization (PSO) techniques. Hence, six deterministically adaptive models, including time-varying acceleration (TAC)–PSO–ELM, improved PSO–ELM, Modified PSO–ELM, TAC–MeanPSO–ELM, improved MeanPSO–ELM, and Modified MeanPSO–ELM were developed. A number of performance criteria along with ranking system were used to identify the best model. The results showed that modified MeanPSO–ELM achieved the highest cumulative ranking (56), while the modified PSO–ELM achieved the lowest cumulative ranking (51). For training phase, improved PSO–ELM and TAC–PSO–ELM achieved the highest ranking (30) for each. The TAC–MeanPSO–ELM obtained the lowest ranking in the testing phase (29). Concerning the coefficient of determination (R²), modified PSO–ELM, improved PSO–ELM, TAC–PSO–ELM, and modified MeanPSO–ELM showed a similar behavior and achieved 0.97 for training and 0.96 for testing phases. Two models, including improved MeanPSO–ELM and TAC–MeanPSO–ELM achieved the same R² of 0.96 for both training and testing phases. The findings of this study suggest that the hybridization of ELM and PSO may result in more accurate results than single ELM model to predict the TBM advance rate.

在隧道项目中，适当的隧道掘进机（TBM）施工计划时间表被认为是必要且困难的任务。因此，需要以高精度来预测TBM性能，以准备合适的计划时间表。这项研究旨在使用六粒子游动优化（PSO）技术的优化极限学习机（ELM）模型来预测TBM的发展速度。因此，开发了六个确定性自适应模型，包括时变加速度（TAC）-PSO-ELM，改进的PSO-ELM，改进的PSO-ELM，TAC-MeanPSO-ELM，改进的MeanPSO-ELM和改进的MeanPSO-ELM。许多性能标准以及排名系统用于确定最佳模型。结果表明，改良的MeanPSO–ELM达到了最高的累积排名（56），而经过修改的PSO-ELM的累积排名最低（51）。在培训阶段，改进的PSO–ELM和TAC–PSO–ELM分别获得最高排名（30）。在测试阶段，TAC–MeanPSO–ELM排名最低（29）。关于确定系数（R ²），改进的PSO-ELM，改进的PSO-ELM，TAC-PSO-ELM和改进的MeanPSO-ELM表现出相似的行为，在训练阶段达到0.97，在测试阶段达到0.96。在训练和测试阶段，包括改进的MeanPSO-ELM和TAC-MeanPSO-ELM在内的两个模型均达到了0.96的相同R ²。这项研究的结果表明，与单个ELM模型相比，ELM和PSO的杂交可能会产生更准确的结果来预测TBM的进展速度。