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...

Full description

Saved in:

Bibliographic Details
Published in:	Polymers Vol. 17; no. 7; p. 976
Main Authors:	Ma, Qiang, Li, Meng, Shu, Hang, Xi, Lei
Format:	Journal Article
Language:	English
Published:	Switzerland MDPI AG 03.04.2025 MDPI
Subjects:	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 Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Be the first to leave a comment!