AI-Driven predicting and optimizing lignocellulosic sisal fiber-reinforced lightweight foamed concrete: A machine learning and metaheuristic approach for sustainable construction

AI-Driven predicting and optimizing lignocellulosic sisal fiber-reinforced lightweight foamed concrete: A machine learning and metaheuristic approach for sustainable construction

This research investigates the application of machine learning (ML) and metaheuristic optimization to improve the mechanical properties of sisal fiber-reinforced foamed concrete. A Deep Neural Network (DNN) was developed, optimized using the Grey Wolf Optimizer (GWO) and the Slime Mould Algorithm (S...

Full description

Saved in:
Bibliographic Details
Main Authors: Mohamed Sahraoui, Aissa Laouissi, Yacine Karmi, Abderazek Hammoudi, Mostefa Hani, Yazid Chetbani, Ahmed Belaadi, Ibrahim M.H. Alshaikh, Djamel Ghernaout
Format: Article
Language:English
Published: Elsevier 2025-06-01
Series:Results in Engineering
Subjects:
Sisal fiber-reinforced concrete
Machine learning (ML)
Metaheuristic optimization
Deep neural networks (DNN)
Tensile strength prediction
Sustainable construction
Online Access:http://www.sciencedirect.com/science/article/pii/S2590123025016317
Tags: Add Tag
No Tags, Be the first to tag this record!

Internet

http://www.sciencedirect.com/science/article/pii/S2590123025016317

Similar Items