Use of Synthetic Input Parameters for Enhancing Prediction Performance of Rate of Penetration of TBM

Rapid population growth and the continuous restructuring of economic relationships have significantly increased global demand for efficient transportation systems. In this context, accurate prediction of the Rate of Penetration (ROP) of the Tunnel Boring Machine (TBM) is crucial for construction planning, cost estimation, and real-time operational decision-making in TBM tunneling. When TBMs are appropriately selected in terms of type and capacity according to route conditions and are operated in compliance with sound engineering principles, they enable the excavation of tunnels at very high rate of penetration while maintaining economic feasibility. Estimating tunnel completion time based on geological and geotechnical conditions along the tunnel alignment and the operational capacity of the TBM has been one of the most intensively studied topics in tunneling research over the past two decades. However, recent advances in artificial intelligence (AI) techniques offer significant potential for achieving higher predictive performance in ROP estimation. In light of these developments, this study evaluates the performance of various AI algorithms using data obtained from the T2 tunnel of the Bahçe–Nurdağ (Türkiye) twin tunnels, the longest railway tunnels in Türkiye. In addition, synthetic input parameters were generated to enhance prediction accuracy beyond that achieved in previous studies. The results demonstrate that incorporating these synthetic input parameters leads to improved model performance, with an increase of up to 2.65% in terms of the correlation coefficient. Given the already high predictive capability achieved without synthetic inputs (R² = 0.8637), the improvement obtained in this study (R² = 0.8866) is particularly noteworthy. Overall, the findings indicate that ensemble-based artificial intelligence models incorporating synthetic input data can predict ROP of TBM with very high accuracy, thereby offering a robust and reliable tool for estimating tunnel completion times in TBM tunneling projects.