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A platform for research: civil engineering, architecture and urbanism
A spatiotemporal feature fusion-based deep learning framework for synchronous prediction of excavation stability
Highlights A spatiotemporal feature fusion-based deep learning (FFDL) framework is developed. The FFDL framework is utilized to forecast synchronous excavation stability. It’s designed with feature fusion, deep learning, and global sensitivity analysis. Feature fusion-based indicators are generated for excavation database preparation. A case study is conducted to verify the applicability of the FFDL framework.
Abstract Under rapid urbanization, analyzing and perceiving synchronous excavation stability is essential to generate valuable and foresightful information in identifying and mitigating potential risks in deep excavations. Although the existing forecasting methods can achieve high accuracy in the safety state perception of deep excavations, it is prone to lack proper consideration of the monitoring item’s fusion and cause error and uncertainty via the single-parameter risk evaluation. To address it, this paper proposes a spatiotemporal feature fusion-based deep learning (FFDL) framework to implement the feature fusion-based data analysis and time-series forecasting on the factor of safety (FS) on behalf of excavation stability, and global sensitivity analysis. The fusion indicators and proposed FFDL framework are validated through a comprehensive case study encompassing 100 typical metro station constructions. The feature fusion-based data analysis results confirm the statistical significance of fusion indicators, contributing to establishing a high-quality spatiotemporal sequential database. As for the real-time prediction, the Bayesian optimization CNN-LSTM model provides a reliable prediction of excavation stability and the relative errors in the training and testing datasets do not exceed 1% and 3% in construction duration. It also enhances the predictive accuracy and outperforms the existing models LSTM, GRU, BiLSTM, and CNN-LSTM, reaching an average improvement of 38%, 32%, and 32% in RMSE, MAE, and MAPE, respectively. Furthermore, the proposed model has an excellent generalization performance in accurately predicting the excavation stability of the other ten excavation cases with an average relative error of 5.37%. In short, this work contributes to not only providing a new perspective in risk management by integrating feature fusion-based indicators and excavation stability but also verifying the applicability of the proposed framework.
A spatiotemporal feature fusion-based deep learning framework for synchronous prediction of excavation stability
Highlights A spatiotemporal feature fusion-based deep learning (FFDL) framework is developed. The FFDL framework is utilized to forecast synchronous excavation stability. It’s designed with feature fusion, deep learning, and global sensitivity analysis. Feature fusion-based indicators are generated for excavation database preparation. A case study is conducted to verify the applicability of the FFDL framework.
Abstract Under rapid urbanization, analyzing and perceiving synchronous excavation stability is essential to generate valuable and foresightful information in identifying and mitigating potential risks in deep excavations. Although the existing forecasting methods can achieve high accuracy in the safety state perception of deep excavations, it is prone to lack proper consideration of the monitoring item’s fusion and cause error and uncertainty via the single-parameter risk evaluation. To address it, this paper proposes a spatiotemporal feature fusion-based deep learning (FFDL) framework to implement the feature fusion-based data analysis and time-series forecasting on the factor of safety (FS) on behalf of excavation stability, and global sensitivity analysis. The fusion indicators and proposed FFDL framework are validated through a comprehensive case study encompassing 100 typical metro station constructions. The feature fusion-based data analysis results confirm the statistical significance of fusion indicators, contributing to establishing a high-quality spatiotemporal sequential database. As for the real-time prediction, the Bayesian optimization CNN-LSTM model provides a reliable prediction of excavation stability and the relative errors in the training and testing datasets do not exceed 1% and 3% in construction duration. It also enhances the predictive accuracy and outperforms the existing models LSTM, GRU, BiLSTM, and CNN-LSTM, reaching an average improvement of 38%, 32%, and 32% in RMSE, MAE, and MAPE, respectively. Furthermore, the proposed model has an excellent generalization performance in accurately predicting the excavation stability of the other ten excavation cases with an average relative error of 5.37%. In short, this work contributes to not only providing a new perspective in risk management by integrating feature fusion-based indicators and excavation stability but also verifying the applicability of the proposed framework.
A spatiotemporal feature fusion-based deep learning framework for synchronous prediction of excavation stability
Wang, Xiong (author) / Pan, Yue (author) / Chen, Jinjian (author) / Li, Mingguang (author)
2024-03-27
Article (Journal)
Electronic Resource
English
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