Predictions of Deep Excavation Responses Considering Model Uncertainty: Integrating BiLSTM Neural Networks with Bayesian Updating
Yuanqin Tao, Honglei Sun, Yuanqiang Cai
Abstract
Predictions of excavation responses frequently differ from monitoring data due to geotechnical uncertainties. This paper proposes an efficient Bayesian updating approach for excavation responses that considers the uncertainties of soil properties and the calculation model. To evaluate the depth-dependent characteristic of model uncertainty, the model factor is quantified by a constant part and a trending component. Bidirectional long short-term memory (BiLSTM) neural networks are constructed to act as a substitute for the finite-element method to achieve higher computational efficiency. An excavation project in Taipei, Taiwan, is used in this study to illustrate the proposed approach. The results demonstrate that the BiLSTM successfully learns the mapping between soil parameters and deflection responses. The uncertainties of key soil parameters and model factors are significantly reduced when observed deflections at multiple points are incorporated on a stage-by-stage basis. The trending component of the model factor plays an essential role in the early stages, but its impact decreases as the excavation progresses. The prediction intervals using the updated parameters generally cover the monitoring data. The proposed method can rapidly update and improve the predictions of subsequent responses once the monitoring data is obtained. This means early remedial actions can be taken and construction safety ensured.