Optimizing UCS Prediction Models through XAI-Based Feature Selection in Soil Stabilization

Mohammed, Ahmed Mohammed Awad, Husain, Omayma, Hamdan Mohamed, Mosab, Mohammed, Abdalmomen, Ansari, Abdullah, Badr, Atef, Elsafi, Abubakar and Siddig, Abubakr (2026) Optimizing UCS Prediction Models through XAI-Based Feature Selection in Soil Stabilization. Computer Modeling in Engineering & Sciences, 146 (2). ISSN 1526-1506

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Abstract

Unconfined Compressive Strength (UCS) is a key parameter for the assessment of the stability and performance of stabilized soils, yet traditional laboratory testing is both time and resource intensive. In this study, an interpretable machine learning approach to UCS prediction is presented, pairing five models (Random Forest (RF), Gradient Boosting (GB), Extreme Gradient Boosting (XGB), CatBoost, and K-Nearest Neighbors (KNN)) with SHapley Additive exPlanations (SHAP) for enhanced interpretability and to guide feature removal. A complete dataset of 12 geotechnical and chemical parameters, i.e., Atterberg limits, compaction properties, stabilizer chemistry, dosage, curing time, was used to train and test the models. R2, RMSE, MSE, and MAE were used to assess performance. Initial results with all 12 features indicated that boosting-based models (GB, XGB, CatBoost) exhibited the highest predictive accuracy (R2 = 0.93) with satisfactory generalization on test data, followed by RF and KNN. SHAP analysis consistently picked CaO content, curing time, stabilizer dosage, and compaction parameters as the most important features, aligning with established soil stabilization mechanisms. Models were then re-trained on the top 8 and top 5 SHAP-ranked features. Interestingly, GB, XGB, and CatBoost maintained comparable accuracy with reduced input sets, while RF was moderately sensitive and KNN was somewhat better owing to reduced dimensionality. The findings confirm that feature reduction through SHAP enables cost-effective UCS prediction through the reduction of laboratory test requirements without significant accuracy loss. The suggested hybrid approach offers an explainable, interpretable, and cost-effective tool for geotechnical engineering practice.

Item Type:	Article
Additional Information:	This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Uncontrolled Keywords:	Explainable AI; feature selection; machine learning; SHAP analysis soil stabilization; unconfined compressive strength
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning G Geography. Anthropology. Recreation > GE Environmental Sciences > Earth sciences > Geology > Physical geology > Sedimentation and deposition
Divisions:	School of Computing > Staff Research and Publications
Depositing User:	Tamara Malone
Date Deposited:	28 Mar 2026 15:44
Last Modified:	28 Mar 2026 15:44
URI:	https://norma.ncirl.ie/id/eprint/9238

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