To effectively optimize the shield construction parameters and achieve the goals of safety,efficiency,and energy-saving in the large-diameter slurry shield tunneling process,a hybrid intelligent algorithm combining categorical boosting(CatBoost)and decomposition was proposed based on a multi-objective evolutionary algorithm(MOEAD).The main shield construction parameters were set as the major research objects considering shield construction parameters and geological conditions,and the surface settlement,penetration rate,and tunneling-specific energy were determined as the prediction and control objectives.Moreover,the selected shield construction parameters were optimized,and a line of Wuhan rail transit was used to validate the hybrid algorithm performance.The results showed that the proposed CatBoost algorithm had great prediction performance for large-diameter slurry shields with the fitting accuracy(R2)of the three control objectives more than 0.9.The model's importance rank indicated that the total propulsion force and propulsion speed of the large-diameter slurry shield had significant influences on surface settlement,penetration,and tunneling-specific energy.The proposed CatBoost-MOEAD hybrid intelligent algorithm had an obvious optimization effect on the three control objectives,and the optimization ranges of surface settlement,penetration rate,and tunneling-specific energy reached 12.35%,7.47%,and 10.70%,respectively.Moreover,the control ranges of corresponding shield construction parameters were presented.
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