Abstract
Scientific and reasonable prediction of tunneling parameters is the premise to ensure the safety of the project. To further improve the accuracy and reliability of the prediction of tunneling parameters. To address the shortcomings of the kernel limit learning machine parameter selection affecting the prediction ability and the characteristics of volatility and non-stationarity of load data, a prediction model based on variational modal decomposition with a hunter-prey algorithm optimized for the kernel limit learning machine improved by the population chaos strategy and the Levy flight strategy is proposed. Relying on the Xinjiang YEGS tunnel project, 6,900 tunneling data sets were selected after data processing, and the data sets were divided according to the construction sequence. Grey correlation analysis was used to select four dimensions of cutter torque, cutter speed, penetration degree, and total thrust to predict the tunneling speed of full-section tunnel boring machines. The results show that under the same conditions, the VMD-LHPO-KELM model shows a 385% and 28% improvement in RMSE value simulation and 50% and 47.7% improvement in MAPE simulation, respectively, compared to the whale algorithm and the unimproved hunter-prey algorithm seeking algorithm, showing a better iteration rate. Compared with the most commonly used neural network VMD-LSTM model in engineering simulation, the goodness of fit coefficient is increased by 9.2%. The established VMD-LHPO-KELM model can better restore the real environmental impact of TBM construction.
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Acknowledgements
The author thanks Xinjiang Shuifa Group Co., Ltd. Saltuohai KS Tunnel management Office for providing data support, Xinjiang Agricultural University for providing research practice platform.
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Kebin Shi and Zhipeng Lu writing and editing; Zhipeng Lu chart editing and preliminary data collection. All authors read and approved the final manuscript.
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Communicated by: H. Babaie
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Lu, Z., Shi, K. A novel VMD-LHPO-KELM machine learning-based TBM boring parameter prediction. Earth Sci Inform 16, 2925–2938 (2023). https://doi.org/10.1007/s12145-023-01043-2
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DOI: https://doi.org/10.1007/s12145-023-01043-2