## 6 Conclusions  
This paper relies on the Shenzhen Metro Line 13 Left Tunnel Project from Liuxiandong Station to Baimang Station. It utilizes geological parameters and real-time monitoring of excavation parameters in the complex strata to establish a prediction model for the excavation rate of EPB/TBM dual-mode tunnel boring machines (TBMs) based on multi-algorithm optimization and recurrent neural networks. The accuracy of the prediction results is analyzed, and the main conclusions are as follows:  
1. The combined impact of the dual-mode TBM excavation mode, TBM operating state parameters, and geological parameter variations was considered in establishing the tunnel boring machine (TBM) excavation data set. The model is more interpretable under actual working conditions, supporting the planning and control of TBM construction. 2. Isolation Forest and an improved mean filtering algorithm were applied to handle TBM operating state parameters. This reduced the spatial variability of excavation parameters, making it easier for machine learning algorithms to learn the patterns of feature variations. The data set processed via denoising allows the construction of a more accurate prediction model for TBM tunneling speed. Figure 9: Time series cross-validation results. 3. A prediction model for TBM tunneling speed was established using multi-algorithm optimization and recurrent neural networks. The model integrates Bayesian optimization algorithms, dropout algorithms, and time-series cross-validation, demonstrating strong generalization capabilities and operational efficiency. The lowest MAPE prediction result is 8.3%, with an average MAPE prediction result below 15%. The established model primarily aims to explore the real-time operational patterns of tunneling speed in the dual-mode TBM excavation process under unsupervised training models and provide a foundational model for intelligent decision control. In practice, the data in the tunneling process is input into the model in real time, the model is trained, and the results are output in real time, which plays a guiding role in the field. In this study, the mechanical properties of rock have not been considered, and the model is not refined enough. In order to obtain a refined model that is more generalized and more in line with engineering practice, future research will consider more input of tunneling parameters and rock mechanics parameters. Conceptualization, T.Y. and T.W. ; methodology, T.Y., X.H., G.S. and B.L. ; software, T.W. ; validation, B.L., S.L., G.S., H.S. and T.W. ; formal analysis, X.P. and X.H. ; investigation, B.L., X.P., H.S. and S.L. ; resources, T.W. ; data curation, T.Y., H.S. and S.L. ; writing--original draft preparation, T.Y., G.S., T.W. and X.H. ; writing--review and editing, T.W. ; visualization, B.L., G.S. and X.P. ; supervision, X.H. and B.L. ; project administration, B.L., G.S., H.S. and S.L. ; funding acquisition, X.H. and S.L. All authors have read and agreed to the published version of the manuscript. It was supported by the National Natural Science Foundation of China Regional Joint Key Project (Grant No. U22A20234), Wuhan City Knowledge Innovation Special Project(Grant No. 2022010801010162), the National Natural Science Foundation of China (Grant No. 52074258), Hubei Provincial Key Research and Development Program (Grant No. 2021 BCA133), Hubei Provincial Natural Science Foundation Outstanding Youth Project (Grant No. 2022 CFA084) and China State Construction Engineering Corporation Science and Technology Research and Development Program Support (Grant No. CSCEC-2019-Z-19). Data are contained within the article. Authors [PERSON] was employed by China Construction Civil Infrastructure Corp., Ltd. Author [PERSON] was employed by China Construction South Investment Co., Ltd. Author [PERSON] was employed by Wuhan Urban Construction Group Construction Management Co., Ltd.