###### Abstract  
Insufficient investigations have been conducted on the analysis of shield tunneling parameters and the prediction of the tunneling excavation speed in formations composed of volcanic ash strata. To address this issue, we employ a comprehensive approach utilizing literature research, mathematical statistics, and other methodologies, centered on the analysis of the No. 1 Tunnel of the Jakarta-Bandung High-Speed Railway. Our focus is on examining the evolution patterns and inter-relationships of shield tunneling parameters within volcanic ash strata. Subsequently, we propose an optimized strategy for these tunneling parameters. By employing six machine-learning algorithms to construct prediction models, we compare and analyze their performance in predicting the tunneling excavation speed. The results indicate a positive correlation between slurry pressure and tunnel depth in volcanic ash strata, suggesting that the grouting pressure should exceed the slurry pressure by approximately 0.22 MPa. In the composite stratum of \"volcanic ash debris + round gravel\", the cutter torque exhibits a strong negative correlation with the total thrust (\(-0.77\)). Due to tool wear and ground resistance, the excavation speed and cutter speed are weakly negatively correlated. Compared to other strata, shield tunneling in volcanic ash strata exhibits larger grouting pressure fluctuations, slower tunneling excavation speed, greater total thrust, higher cutter torque, and lower cutter speed. Regarding shield tunneling excavation speed prediction, the ranking of the algorithm performance is RF \(>\) DNN \(>\) ANN \(>\) BPNN \(>\) MNR \(>\) SVM, with RF achieving a decision coefficient of 0.829. The RF model is well-suited for predicting the shield structure tunneling excavation speed. t 202  
Such predictions offer scientifically grounded references for the tunneling excavation speed during engineering projects, ensuring quality, enhancing construction efficiency, and mitigating engineering risks and costs. Currently, scholars have delved into the analysis of shield tunneling parameters and the forecasting of the tunneling excavation speed. Several scholars have engaged in pertinent research concerning shield machine tunneling parameters. Theoretical exploration primarily involves deriving expressions that link shield tunneling parameters, such as employing the shield thrust, cutter torque, and excavation speed to predict the surface settlement [7; 8; 9]. Statistical analyses have been conducted on tunneling parameters during shield tunneling, revealing variations across different strata [10; 11]. Scholars have scrutinized the sensitivity of shield tunneling to diverse parameter alterations and subsequently optimized shield machine tunneling parameters [12; 13]. In the realm of numerical simulation, scholars have employed finite element calculation models to investigate shield tunneling parameters in different strata or analyze the impact of varied tunneling parameters on the surface settlement and tunneling excavation speed [14]. In shield machine tunneling, a substantial volume of measured data are typically generated for tunneling parameters. Predicting the shield machine tunneling excavation speed involves addressing the challenge of multivariate time series regression [15]. Machine-learning algorithms, including the Multiple Linear Regression [16; 17] (MNR), Random Forest [18; 19] (RF), Backpropagation Neural Network [20; 21] (BPNN), Deep Neural Network [22; 23] (DNN), Support Vector Machine [24] (SVM), and other algorithms, demonstrate notable advantages in processing time series data. The prediction of the shield tunneling excavation speed is achieved through the implementation of artificial intelligence prediction models. Numerous research outcomes indicate that artificial intelligence models exhibit superior prediction performance compared to empirical models. Extensive research has been conducted on shield tunneling parameters.