An intelligent procedure for updating deformation prediction of braced excavation in clay using gated recurrent unit neural networks

Abstract

This paper aims to establish an intelligent procedure that combines the observational method with the existing deep learning technique for updating deformation of braced excavation in clay. The gated recurrent unit (GRU) neural network is adopted to formulate the forecast model and learn the potential rules in the field observations using the Nesterov-accelerated Adam (Nadam) algorithm. In the proposed procedure, the GRU-based forecast model is first trained based on the field data of previous and current stages. Then, the field data of the current stage are used as input to predict the deformation response of the next stage via the previously trained GRU-based forecast model. This updating process will loop up till the end of the excavation. This procedure has the advantage of directly predicting the deformation response of unexcavated stages based on the monitoring data. The proposed intelligent procedure is verified on two well-documented cases in terms of accuracy and reliability. The results indicate that both wall deflection and ground settlement are accurately predicted as the excavation proceeds. Furthermore, the advantages of the proposed intelligent procedure compared with the Bayesian/optimization updating are illustrated.