Hybrid GA-BP Neural Network for Accurate Prediction of TBM Advance Speed Under Complex Geological Conditions

TBM construction projects require substantial investment, making the accurate and rational prediction of TBM advance speed essential for cost control and timely project completion. To address this, the study develops a precise predictive model for TBM advance speed by integrating the BP neural network model with the genetic algorithm. Initially, raw TBM data were processed to remove non-operational records and anomalous values recorded during construction, resulting in a refined database of TBM operational parameters. The surrounding rock conditions were classified based on FPI and TPI, two key indices reflecting rock mass excavatability and rock-breaking efficiency. Using the K-means clustering algorithm, the dataset was segmented into three distinct groups. Seven tunneling parameters were selected as input features for the neural network model. Subsequently, three GA-BP neural network models were developed for different rock mass categories, with key parameters optimized for enhanced performance. Prediction results demonstrate that the GA-BP neural network exhibits superior accuracy and generalization capability. Compared to a conventional BP neural network, the GA-BP model reduces prediction errors by more than 10%.