Harnessing synergy of machine learning and nature-inspired optimization for enhanced compressive strength prediction in concrete
Concrete made with additives like slag and fly ash has revolutionized construction by reducing carbon emissions, minimizing waste, lowering labor costs, and enhancing durability and accuracy. Predicting the compressive strength (CS) is vital for achieving optimal performance. Given the nonlinear cha...
Saved in:
Main Authors: | Abba Bashir, Esar Ahmad, Shashivendra Dulawat, Sani I. Abba |
---|---|
Format: | Article |
Language: | English |
Published: |
Elsevier
2025-06-01
|
Series: | Hybrid Advances |
Subjects: | |
Online Access: | http://www.sciencedirect.com/science/article/pii/S2773207X25000284 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Enhancing performance of recycled aggregate concrete with supplementary cementitious materials
by: Abba Fatiha, et al.
Published: (2025-03-01) -
Estimation of the Compressive Strength of Self-Compacting Concrete (SCC) by a Machine Learning Technique Coupling with Novel Optimization Algorithms
by: Ling Chen, et al.
Published: (2023-03-01) -
An efficient bio-inspired algorithm based on humpback whale migration for constrained engineering optimization
by: Mojtaba Ghasemi, et al.
Published: (2025-03-01) -
Comparative use of different AI methods for the prediction of concrete compressive strength
by: Mouhamadou Amar
Published: (2025-03-01) -
Modeling Compressive Strength of Self-Compacting Concrete (SCC) Using Novel Optimization Algorithm of AOA
by: Francisca Blanco, et al.
Published: (2024-09-01)