Investigation of Evolutionary Optimization Algorithms for Estimating Sandstone Compressive Strength
Publish place: The 5th National Geotechnical Conference and the 2nd International Conference on Earthquake Engineering and Seismic Geotechnics
Publish Year: 1402
نوع سند: مقاله کنفرانسی
زبان: English
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شناسه ملی سند علمی:
GEOTEC05_088
تاریخ نمایه سازی: 5 شهریور 1403
Abstract:
Directly determining rock compressive strength is both laborious and expensive. As a result, indirectestimation methods, such as artificial neural networks, are employed. Input parameters, such as quartzcontent, dry density, and Brazilian tensile strength, have been used to predict the compressive strength ofsandstone. genetic algorithm (GA) and particle swarm optimization (PSO) were selected to improvenetwork training using evolutionary optimization algorithms' effectiveness. The results demonstrate thatthe PSO model achieved the best estimation performance with an MSE of ۰.۰۲۱۴ and R of ۰.۹۵. Thelinear regression model exhibited inferior performance with an R of ۰.۸۷.
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Authors
Somaie Jolfaei
Department of Civil Engineering, Faculty of Engineering, University of Zanjan
Ali Lakirouhani
Department of Civil Engineering, Faculty of Engineering, University of Zanjan