مجله ژئوفیزیک ایران

مجله ژئوفیزیک ایران

Metaheuristic-optimized machine learning models for predicting the unconfined compressive strength of geopolymer-stabilized clay soils

نوع مقاله : مقاله پژوهشی‌

نویسندگان
Assistant Professor, Department of Civil Engineering, Faculty of Engineering, Ardakan University, Ardakan, Iran
چکیده
The low strength of clay soils is one of the main challenges in road construction projects. Geopolymerization using various additives such as fly ash, blast furnace slag, and alkaline activators is a common method to improve the mechanical properties of these soils. However, accurate and rapid evaluation of the effects of these materials on soil strength traditionally requires extensive and time-consuming laboratory tests. This study proposes an integrated machine-learning framework to predict the unconfined compressive strength (UCS) of clay soils stabilized with fly ash and blast‑furnace slag. A dataset comprising 283 experimental samples of lime/slag-stabilized clay activated with sodium hydroxide was utilized. Input variables include fly ash and slag percentages, alkaline activator molarity, alkaline‑to‑binder ratio, atomic Na/Al and Si/Al ratios, and the soil’s liquid and plastic limits.  To this end, several machine learning models including artificial neural networks (ANN), support vector machines (SVM), decision trees (DT), and extreme gradient boosting (XGBoost), were employed in combination with metaheuristic optimization algorithms such as ant colony optimization (ACO), particle swarm optimization (PSO), and genetic algorithm (GA). The findings revealed that the proposed ANN-PSO hybrid model demonstrated outstanding predictive accuracy, achieving an R² value of 0.98, confirming its effectiveness for reliable UCS estimation. Sensitivity analysis further indicated that blast furnace slag was the most influential factor in enhancing soil strength, while the type of clay also had a considerable effect on the final UCS.
 
کلیدواژه‌ها

عنوان مقاله English

Metaheuristic-optimized machine learning models for predicting the unconfined compressive strength of geopolymer-stabilized clay soils

نویسندگان English

Khadije Mahmoodi
Seyed Amir Banimahd
Assistant Professor, Department of Civil Engineering, Faculty of Engineering, Ardakan University, Ardakan, Iran
چکیده English

The low strength of clay soils is one of the main challenges in road construction projects. Geopolymerization using various additives such as fly ash, blast furnace slag, and alkaline activators is a common method to improve the mechanical properties of these soils. However, accurate and rapid evaluation of the effects of these materials on soil strength traditionally requires extensive and time-consuming laboratory tests. This study proposes an integrated machine-learning framework to predict the unconfined compressive strength (UCS) of clay soils stabilized with fly ash and blast‑furnace slag. A dataset comprising 283 experimental samples of lime/slag-stabilized clay activated with sodium hydroxide was utilized. Input variables include fly ash and slag percentages, alkaline activator molarity, alkaline‑to‑binder ratio, atomic Na/Al and Si/Al ratios, and the soil’s liquid and plastic limits.  To this end, several machine learning models including artificial neural networks (ANN), support vector machines (SVM), decision trees (DT), and extreme gradient boosting (XGBoost), were employed in combination with metaheuristic optimization algorithms such as ant colony optimization (ACO), particle swarm optimization (PSO), and genetic algorithm (GA). The findings revealed that the proposed ANN-PSO hybrid model demonstrated outstanding predictive accuracy, achieving an R² value of 0.98, confirming its effectiveness for reliable UCS estimation. Sensitivity analysis further indicated that blast furnace slag was the most influential factor in enhancing soil strength, while the type of clay also had a considerable effect on the final UCS.
 

کلیدواژه‌ها English

Blast furnace slag
geopolymer-stabilized clay soil
machine learning prediction
metaheuristic optimization algorithms
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