Year: 2025
Type: article
Abstract: Despite extensive research on slope seepage mechanisms, a reliable long‐term prediction method for slope deformation considering rainfall remains undeveloped, largely due to the complexity of rainfall‐induced slope instability. This study leverages a project in slope engineering to explore slope deformation under heavy rainfall using intelligent monitoring techniques and genetic algorithm (GA) optimization for neural network prediction. By analyzing slope deformation patterns under varied rainfall intensities, results reveal that limited rainfall has minimal impact on slope stability, whereas excessive rainfall disrupts internal seepage patterns, increasing pore water pressure and reducing soil shear strength, it thereby enhances the risk of slope instability and potential landslides, significantly impacting slope stability. The GA‐optimized network accurately captures abrupt slope deformation stages, avoids local optima, and provides a viable framework for early warning of slope instability.
Source: Advances in Civil Engineering
DOI: 10.1155/adce/6363972
Authors: Hong-ying Jiang, Kai-lian Du, He-peng Xia, Tao Yan, Qing-Lin Chen, Xin-ci Liu
Institutions: Hohai University, East China Jiaotong University
Language: English
Subfield: Safety, Risk, Reliability and Quality
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,760
OpenAlex ID: W4409559386