Prediction of Slope Safety Factor Based on Attention Mechanism-Enhanced CNN-GRU

Qi Da, Ying Chen (), Bing Dai, Danli Li and Longqiang Fan
Additional contact information
Qi Da: School of Resource Environment and Safety Engineering, University of South China, Hengyang 421001, China
Ying Chen: School of Resource Environment and Safety Engineering, University of South China, Hengyang 421001, China
Bing Dai: School of Resource Environment and Safety Engineering, University of South China, Hengyang 421001, China
Danli Li: School of Resource Environment and Safety Engineering, University of South China, Hengyang 421001, China
Longqiang Fan: School of Resource Environment and Safety Engineering, University of South China, Hengyang 421001, China

Sustainability, 2024, vol. 16, issue 15, 1-23

Abstract: This paper proposes a new method for predicting slope safety factors that combines convolutional neural networks (CNNs), gated recurrent units (GRUs), and attention mechanisms. This method can better capture long-term dependencies, enhance the ability to model sequential data, and reduce the dependence on noisy data, thereby reducing the risk of overfitting. The goal is to improve the accuracy of slope safety factor prediction, detect potential slope stability issues in a timely manner, and take corresponding preventive and control measures to ensure the long-term stability and safety of infrastructure and promote sustainable development. The Pearson correlation coefficient is used to analyze the relationship between the target safety factor and the collected parameters. A one-dimensional CNN layer is used to extract high-dimensional features from the input data, and then a GRU layer is used to capture the correlation between parameters in the sequence. Finally, an attention mechanism is introduced to optimize the weights of the GRU output, enhance the influence of key information, and optimize the overall prediction model. The performance of the proposed model is evaluated using metrics such as the mean absolute error (MAE), mean absolute percentage error (MAPE), mean squared error (MSE), root-mean-square error (RMSE), and R 2 . The results show that the CNN-GRU-SE model outperforms the GRU, CNN, and CNN-GRU models in terms of prediction accuracy for slope safety factors, with improvements of 4%, 2%, and 1%, respectively. Overall, the research in this paper makes valuable contributions to the field of slope safety factor prediction, and the proposed method also has the potential to be extended to other time-series prediction fields, providing support for a wide range of engineering applications and further promoting the realization of sustainable development.

Keywords: slope safety factor prediction; gated cycle unit; deep learning; CNN-GRU model; attention mechanism (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/16/15/6333/pdf (application/pdf)
https://www.mdpi.com/2071-1050/16/15/6333/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:16:y:2024:i:15:p:6333-:d:1441888

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().