基于改进BiLSTM代理模型的基坑分步开挖变形计算方法OA

In this paper,a BO-BiLSTM-Att surrogate model is proposed to efficiently calculate the excavation-induced deformation.The BiLSTM network is integrated with Att to capture temporal mapping relationships between input and output parameters.The Bayesian optimization(BO)is implemented to identify optimal hyperparameters,thereby further enhancing the model's computational accuracy.Through a numerical case,the applicability and efficiency of BO in hyperparameter optimization are verified by comparison with PSO and GA.Subsequently,the surrogate model is trained by BO-optimized hyperparameters and its effectiveness in staged deformation calculation is demonstrated through the difference between predicted values and true values of test sets.Finally,the computational accuracy between BiLSTM network and BiLSTM-Att under identical hyperparameters is compared.It is shown that Att can improve the model's computational accuracy.The proposed surrogate model has enhanced the computational accuracy(R2=0.967)and generalization capability(mean average error 0.875 mm)in testing scenarios.