Stability Prediction of Highway Slope on Highly Plastic Clay Using Particle Swarm Optimization (PSO)-Based Neural Network
Publication: Geo-Congress 2024
ABSTRACT
It’s a critical task to ensure the safety of highway slopes constructed on highly plastic clay, given their significant economic, social, and safety impacts and the unique geotechnical challenges that they pose. The factor of safety (FS) for a slope is not constant, and it continues to evolve as a function of the weather condition, soil moisture content, and matric suction. Increasing heavy rainfall driven by climate change puts existing slopes at greater risk of failure. In this research, our goal is to create a particle swarm optimization (PSO)-based neural network (NN) (hybrid) model for predicting the long-term FS of highway slopes constructed on highly plastic clay considering the aforementioned factors. In order to obtain the necessary data for training the model, a series of 2D and 3D finite element analyses were performed, which were validated using a slope monitoring case study. In addition, sensitivity analyses were carried out to examine and assess the key factors that influence the FS of highway slopes. The developed PSO-based NN model can provide accurate FS predictions with a very low value of root mean square error.
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Information & Authors
Information
Published In
Geo-Congress 2024
Pages: 264 - 274
History
Published online: Feb 22, 2024
ASCE Technical Topics:
- Analysis (by type)
- Business management
- Clays
- Engineering fundamentals
- Engineering materials (by type)
- Geomechanics
- Geotechnical engineering
- Infrastructure
- Materials engineering
- Plastics
- Practice and Profession
- Public administration
- Public health and safety
- Safety
- Sensitivity analysis
- Slopes
- Soil mechanics
- Soils (by type)
- Synthetic materials
- Traffic engineering
- Traffic management
- Traffic signs
- Transportation engineering
- Transportation management
- Transportation networks
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