摘要
The employment of large-diameter shield machines has increased the likelihood of encountering composite formations,posing engineering challenges associated with excessive surface settlement.To tackle this issue,this study introduces a hybrid model which integrates the extreme learning machine(ELM)with the sparrow search algorithm(SSA)to predict longitudinal surface settlement.Based on on-site measurements.this study analyzed longitudinal surface settlement patterns across both homogeneous and composite formations.Tunneling parameters,geological parameters,and geometrical parameters were considered as input parameters.Furthermore,this study conducted a comparative analysis of the predictive performance among SSA-ELM,ELM,and SSA-back propagation(BP),with respect to coefficient of determination(R^(2)),mean absolute error(MAE),root mean square error(RMSE),and training time.Last,in anticipation of potential risks,a feasible optimization approach is provided.SSA-ELM outperforms both ELM and SSA-BP in terms of R^(2),MAE,and RMSE,with values of 0.8822,0.3357,and 0.4072,respectively.Regarding training time,SSA-ELM requires 0.2346 s,prior to SSA-BP with a value of 1.8427.Although it is not as fast as ELM,the discrepancy between SSA-ELM and ELM is only 0.1187 s.Overall,SSA-ELM demonstrates higher performance and serves as an effective tool to guide the construction process.
基金
sponsored by the National Natural Science Foundation of China(Grant Nos.52178386 and 52378336)。
