Interpretable machine learning models for evaluating strength of ternary geopolymers

Interpretable machine learning models for evaluating strength of ternary geopolymers

Ternary geopolymers incorporating multiple solid wastes such as steel slag (SS), fly ash (FA), and granulated blast furnace slag (GBFS) are considered environmentally friendly and exhibit enhanced performance. However, the mechanisms governing strength development and the design of optimal mixtures...

Full description

Saved in:
Bibliographic Details
Main Authors: Junfei Zhang, Huisheng Cheng, Ninghui Sun, Zehui Huo, Junlin Chen
Format: Article
Language:English
Published: KeAi Communications Co. Ltd. 2025-12-01
Series:Artificial Intelligence in Geosciences
Subjects:
Geopolymer
Solid waste
Mix proportion
Machine learning
Unconfined compressive strength
Online Access:http://www.sciencedirect.com/science/article/pii/S2666544125000243
Tags: Add Tag
No Tags, Be the first to tag this record!

Similar Items

Similar Items