## 3 Predictive Model Algorithm Principle
### Multiple Optimization Algorithm Model  
In the operation of LSTM, performing hyperparameter optimization is crucial as it identifies the optimal combination of hyperparameters that enhances model performance. By selecting appropriate hyperparameter combinations, it is possible to improve the model's generalization ability, resulting in superior performance on the test set. This study employs four widely applicable and practical hyperparameter optimization algorithms: Genetic Algorithm (GA), Differential Evolution (DE), Bayesian Optimization (BO), and Particle Swarm Optimization (PSO). Due to constraints in length, the principles of these algorithms are not extensively elaborated. Subsequently, the study will train these four hyperparameter optimization algorithms, compare the performance of evaluation metrics under different algorithms, and employ a ranking method to determine the optimal hyperparameter optimization algorithm for this model.  
After selecting the optimal hyperparameter optimization algorithm, in order to enhance the model's generalization ability, a multi-algorithm optimized model incorporating Time Series cross-validation (TSCV) and the Dropout algorithm was established to further improve the predictive performance of the model on tunneling speed.The Dropout algorithm is a widely used regularization technique for deep learning models proposed by [PERSON] and his team in 2012. Its primary objective is to prevent neural networks from overfitting, thereby improving the model's generalization ability across different data sets. This study incorporated the Dropout layer into the LSTM model, with a dropout probability set to 0.1. This implementation achieved random dropout of a portion of neurons in the neural network, preventing certain neurons from developing excessive dependence on specific features and thereby reducing the complexity of the neural network.  
Tunnel boring machine (TBM) excavation parameters constitute time-series data with temporal dependencies. In conventional cross-validation, data is randomly partitioned, making validating past data using future data highly inappropriate due to the temporal dependencies. Therefore, the data sequence should not be arbitrarily shuffled. This study employs time series cross-validation to evaluate prediction models' performance on time-series data. When assessing time-series prediction models, time-series cross-validation effectively measures the model's generalization ability across different periods. The model utilizes a five-fold time series cross-validation, and the operational workflow is depicted in Figure 1:The process begins by selecting a window size, which encompasses a specific number of observational values. Subsequently, the window is incrementally moved forward, providing distinct training and validation data subsets for the model. At each step, the model is trained on the data within the window and validated on the data outside the window. This allows for the computation of performance metrics, such as mean squared and absolute percentage errors, for the model on each validation set. Finally, the average of these metrics is calculated to assess the model's overall performance. By combining the above methods, the overall model process is shown in Figure 2.