Abstract: To enhance the prediction accuracy of foundation pit dewatering level and strengthen the control of dewatering level, three optimization strategies, Logistic chaos mapping, adaptive dynamic weighting factor w and firefly disturbance optimal position, are introduced to improve standard pelican optimization algorithm (POA). Four benchmark test functions are selected to simulate and test the improved POA (IPOA), and five optimization algorithms are selected for comparison and analysis. Taking a foundation pit in Shenyang North Railway Station as an example, the IPOA algorithm is used to optimize the number of nodes in the hidden layer, the initial learning rate, and the L2 regularization coefficient in the convolutional neural networks (CNN)-bidirectional gated recurrent unit (BiGRU) model to form the IPOA-CNN-BiGRU water level prediction model. Additionally, the grey wolf optimizer (GWO), whale optimization algorithm (WOA), and POA are selected to optimize the CNN-BiGRU model and build the Midas GTS NX finite element numerical simulation model. The comparative results among these models in predicting dewatering level of the foundation pit reveal the following: (1) The IPOA optimization algorithm shows better performance in both single-peak and multi-peak function optimization tasks, and its optimization speed is the fastest in the four benchmark functions, and the optimization results are significantly better than the remaining five algorithms. (2) In the water level prediction task, the E_MAE, E_RMSE, and  E_MAPE of the IPOA-CNN-BiGRU model are 0.213 8, 0.253 9, and 0.771 3, respectively, and the prediction performances are 32.36%, 20.96%, and 15.79% higher than those of the CNN-BiGRU model under GWO, WOA, and POA optimization, respectively. (3) The IPOA-CNN-BiGRU model has a low dependence on the parameters compared with the Midas GTS NX finite element model, and its prediction accuracy is improved by 80.65%, which verifies the feasibility of the IPOA-CNN-BiGRU model in predicting dewatering level of the foundation pits.

Key words: foundation pit dewatering prediction, improved pelican optimization algorithm, hyperparameter optimization, numerical simulation, comparative validation