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| Multi-algorithm fusion-optimization model and its engineering application for boreability evaluation of tunnel boring machine |
| YIN Xin1，2，GAO Feng1，2，LIU Quansheng1，2，WANG Xinyu1，2，HUANG Xing3，PAN Yucong1，2 |
| (1. School of Civil Engineering，Wuhan University，Wuhan，Hubei 430072，China；2. Key Laboratory of Safety for Geotechnical and Structural Engineering of Hubei Province，Wuhan University，Wuhan，Hubei 430072，China；3. State Key Laboratory of Geomechanics and Geotechnical Engineering，Institute of Rock and Soil Mechanics，Chinese Academy of Sciences，Wuhan，Hubei 430071，China) |
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Abstract Carrying out the evaluation of the boreability of tunnel boring machines(TBMs) has an important guiding significance for scientific planning of key construction factors. Based on 219 sets of data of Karaj water conveyance tunnel，Zagros water conveyance tunnel，and West-Qinling railway tunnel，this paper proposed a deep belief network model for the TBM boreability evaluation，improved by Bayes optimization algorithm and early-stopping strategy. This model used rock uniaxial compressive strength(UCS)，rock quality designation(RQD)，the angle between the dominant structural plane and the tunnel axis( )，and rock mass cuttability index(RMCI) as input variables，and used field penetration index(FPI) as the output variable. In the data preprocessing stage，Kriging interpolation and improved CRITIC algorithm were separately used to complement the missing values in the database and implement data weighting. Taking Yinsong water conveyance tunnel and LXB water conveyance tunnel as examples to test the practicality of the model：For 37 sets of test data of Yinsong water conveyance tunnel，the root mean square error (RMSE)，mean absolute percentage error(MAPE)，and coefficient of determination( ) were 2.18，8.25%，and 0.926 2，respectively；for the 49 sets of test data of LXB water conveyance tunnel，the RMSE，MAPE，and were 2.83，8.14%，and 0.981 7，respectively. Furthermore，by quantitatively comparing the RMSE，MAPE and of the model before and after data weighting，data weighting was an effective way to improve the prediction performance of the model. Finally，a comparative analysis of the model in this paper with BP neural network，support vector regression，K-nearest neighbor and random forest was conducted in terms of prediction accuracy and running speed，verifying the superiority of the model in this paper.
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2022, Vol. 41 
