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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Veröffentlicht in:	Polymers Jg. 17; H. 7; S. 976
Hauptverfasser:	Ma, Qiang, Li, Meng, Shu, Hang, Xi, Lei
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Switzerland MDPI AG 03.04.2025 MDPI
Schlagworte:	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
Online-Zugang:	Volltext
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