Cross-Condition Prediction of Pointwise Surface Settlement Using Key Tunneling Parameter Selection and Transfer Learning

Construction-stage surface settlement prediction becomes challenging under changing geological conditions and limited monitoring data in target sections. This study proposes a cross-condition prediction framework integrating key tunneling parameter selection and transfer learning using shield construction data from Chengdu Metro Line 18. The prediction target is the pointwise cumulative surface settlement associated with a specific monitoring point and shield ring state. Ring-level operational records and settlement observations are temporally aligned, while grouped spatial-block splitting is adopted to preserve spatial dependence among samples. Key tunneling parameters are identified by combining Random Forest importance, SHAP or permutation importance, Slime Mould Algorithm optimization, Spearman correlation, and engineering interpretation. A Transfer Multilayer Perceptron is then constructed through source-domain pretraining, parameter transfer, and target-domain fine-tuning. Results show that the proposed T-MLP achieves an MAE of 3.18 mm, an RMSE of 4.08 mm, and an R2 of 0.852 in the target domain, reducing MAE by 39.54% relative to direct source-domain prediction. The framework provides an effective data-driven approach for cross-condition settlement prediction under limited target-domain observations.