Hybrid Metaheuristic Optimized Random Forest Models for Predicting Compressive Strength of Alkali Activated Concrete

Hybrid Metaheuristic Optimized Random Forest Models for Predicting Compressive Strength of Alkali Activated Concrete

This study explores the application of hybrid machine learning models for predicting the compressive strength (CS) of alkali-activated concrete (AAC), a sustainable substitute for traditional Portland cement concrete. A random forest (RF) model was optimized using six metaheuristic algorithms: diffe...

Full description

Saved in:
Bibliographic Details
Main Authors: Mana Alyami, Muhammad Faisal Javed, Irfan Ullah, Hisham Alabduljabbar, Furqan Ahmad
Format: Article
Language:English
Published: Taylor & Francis Group 2025-12-01
Series:Journal of Natural Fibers
Subjects:
Alkali-activated concrete
hybrid machine learning
random forest
model interpretability
mix design optimization
compressive strength
Online Access:https://www.tandfonline.com/doi/10.1080/15440478.2025.2509123
Tags: Add Tag
No Tags, Be the first to tag this record!

Internet

https://www.tandfonline.com/doi/10.1080/15440478.2025.2509123

Similar Items