###### Abstract
However, too much simplification is used in these models mentioned above, which means that these models need a lot of empirical parameters, and this may make them unable to accurately predict the parameters of the shield machine, cause safety hazards, or require more time. Numerical simulations based on finite difference and finite element methods, as a popular method in modern times, can effectively simulate the shield tunneling process, and then the force and deformation at different times and states can be obtained. [PERSON] et al. [11] proposed a computational framework for the simulation of the advancement and the excavation process of tunnel boring machines (TBM) in mechanized tunneling. [PERSON] et al. [12], using a coupled discrete element method (DEM) and a finite difference method (FDM), simulated EPB shield tunneling and the torque, thrust, chamber pressure, discharge, and settlement data were successfully simulated. However, due to the large amount of calculation in numerical simulation, the calculated results are only applicable to specific strata, and implementing additional models is necessary for different strata to reduce the robustness of the model, which make it unable to be used in mixed ground conditions. In addition to the two methods above, artificial intelligence methods have also played a vital role in the construction field in recent years. Machine learning (ML) advances the traditional observational method by employing data analysis and pattern recognition techniques, so the nonlinear relationship between shield tunneling parameters can be analyzed, which can make the model show the high levels of accuracy [13]. In the machine learning area, [PERSON] et al. [14], [PERSON] et al. [15], [PERSON] [16], and [PERSON] et al. [17] used artificial neural networks (ANNs) or deep neural networks (DNNs) to predict the penetration rate of the TBMs and could realize a highly accurate estimation. [PERSON] et al. [18] compared the accuracy and computational time of back-propagation neural network (BPNN), radial basis function neural network (RBFNN), and general regression neural network (GRNN), showing the power of neural networks. In order to cover the time series effect of shield tunneling data, [PERSON] et al. [19] and [PERSON] et al. [20] used long-short term memory (LSTM) networks, which can combine the previous learning information with the current input to generate the current output and it can effectively overcome the drawback of gradient explosion or gradient vanish caused by recurrent neural networks (RNNs). [PERSON] et al. [6] presented a hybrid model that used particle swarm optimization (PSO) algorithm to determine the hyperparameters for the LSTM neural network which performed better than the single model of LSTM. Despite all of the aforementioned studies, it is believed that machine learning can achieve good predictions of shield tunneling parameters. Though great advances have been made in shield tunneling parameters prediction, most of these studies focus on a single formation, which means the geological conditions do not change much or even change with the advancement of shield tunneling. However, the mixed ground, as one of the adverse geological conditions [2], is very common in engineering. Adverse geological conditions can strongly affect TBM advance rate and cutter wear, therefore it is necessary to propose a model that can predict the construction parameters of shield tunneling under the condition of mixed ground in order to better ensure the safety of shield construction. [PERSON] et al. [5] mentioned that their model was based on mixed ground, but the input parameters in the advance rate predictive model include slurry pressure, cutterhead torque, total thrust, rotation speed, pump pressure of thrust cylinders and flow rate in the feed and return line, and those parameters cannot reflect the effects of mixed ground. Fortunately, some scholars have found some relationships between mixed ground and penetration rate through data analysis, and these findings can provide some inspiration.