2025
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Browsing 2025 by Author "Muhammad Faizan Jamil"
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Item Predicting the behaviour of geosynthetic-reinforced soil abutments using machine learning(UMT. Lahore, 2025) Rana Shabahat Qamar; Muhammad Faizan Jamil; Muhammad Arslan; Muhammad Saad; Abdul Salam ChandioGeosynthetic-reinforced soil (GRS) abutments are commonly used in bridge construction, retaining walls, and highway embankments because they are strong, flexible, and cost-effective. One of the most important things to consider when using these structures is how much they might settle, since too much movement can affect safety and how well they work. Traditional models for predicting settlement often make too many simplifying assumptions about how the soil interacts with the reinforcement, while more advanced methods like finite element modeling are accurate but need a lot of time, skill, and computing power. This study uses a machine learning approach called Gene Expression Programming (GEP) to predict how much GRS abutments settle. The researchers used a dataset with 354 experimental observations, where the goal was to predict settlement, and the inputs included factors like surcharge pressure, soil friction angle, reinforcement stiffness, vertical spacing, abutment shape, and the angle of the facing. The GEP model performed very well, with a testing score of 0.91, an RMSE of 5.53, and an MAE of 3.09, which shows it agrees closely with the actual experimental results. Additional checks using separate data confirmed the model's reliability. A sensitivity analysis also showed which factors had the biggest impact on settlement, giving useful guidance for future design improvements. This research shows that machine learning, especially GEP, can be a fast and dependable alternative to older methods for predicting settlement. It supports a more performance-based and data-driven approach in geotechnical engineering.