Аннотации:
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.