基于ISBOA-KELM模型的隧道挤压大变形预测及应用

Prediction and Application of Large Squeezing Deformation in Tunnel Based on ISBOA-KELM Model

为提高软岩隧道挤压大变形预测的准确性,提出一种改进的蛇鹫优化算法(ISBOA)优化核极限学习机(KELM)的隧道挤压大变形预测方法。设计多融合种群改进策略ISBOA算法,并建立ISBOA-KELM隧道挤压大变形预测模型,基于公开的隧道大变形数据集和某隧道工程案例验证ISBOA-KELM预测模型的有效性和工程适用性。结果表明,与其他方法相比,所提出的模型能够准确地实现软岩隧道挤压大变形预测,且具有良好的预测精度,可为隧道工程变形预测提供一种高效的新方法。

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.