Prediction and Application of Large Squeezing Deformation in Tunnel Based on ISBOA-KELM Model
In order to enhance the accuracy of prediction of large squeezing deformation in soft rock tunnels,an improved secretary bird optimization algorithm(ISBOA)is proposed to optimize the kernel extreme learning machine(KELM)for prediction of large squeezing deformation in tunnels.Firstly,a multi-fusion strategy is designed for the ISBOA algorithm.Secondly,an ISBOA-KELM model is established to predict large squeezing deformation in tunnels.Finally,the effectiveness and engineering applicability of the ISBOA-KELM prediction model are validated using publicly available tunnel large deformation datasets and a case study of a tunnel project.The results demonstrate that compared to other methods,the proposed model can accurately predict large squeezing deformation in soft rock tunnels with good precision,providing an efficient new approach for tunnel deformation prediction in tunnel engineering.
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