Predictive Modeling of Shear Strength of Enzyme-Induced Calcium Carbonate Precipitation (EICP)-Solidified Rubber–Clay Mixtures Using Machine Learning Algorithms

The development of reliable predictive models for soil behavior represents a crucial advancement in geotechnical engineering, particularly for optimizing material compositions and reducing experimental uncertainties. Traditional experimental approaches for determining the optimal rubber particle siz...

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Vydané v:	Polymers Ročník 17; číslo 7; s. 976
Hlavní autori:	Ma, Qiang, Li, Meng, Shu, Hang, Xi, Lei
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Switzerland MDPI AG 03.04.2025 MDPI
Predmet:	Algorithms Artificial neural networks Calcium carbonate Civil engineering Clay Data mining Defense mechanisms Enzymes Geotechnical engineering Geotechnology Machine learning Mechanical properties Microorganisms Neural networks Optimization Particle size Performance evaluation Prediction models Rubber Shear strength China shear strength enzyme-induced carbonate precipitation predictive modeling rubber soil solidification machine learning rubber–clay mixtures
ISSN:	2073-4360, 2073-4360
On-line prístup:	Získať plný text
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