Evaluating enhanced predictive modeling of foam concrete compressive strength using artificial intelligence algorithms
Artificial intelligence algorithms have recently demonstrated their efficacy in accurately predicting concrete properties by optimizing mixing proportions and overcoming design limitations. In this regard, foam concrete (FC) production presents a unique challenge, necessitating extensive experimenta...
Saved in:
Main Authors: | Abdellatief M., Wong L.S., Din N.M., Mo K.H., Ahmed A.N., El-Shafie A. |
---|---|
Other Authors: | 57855303900 |
Format: | Article |
Published: |
Elsevier Ltd
2025
|
Subjects: | |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Feasibility analysis for predicting the compressive and tensile strength of concrete using machine learning algorithms
by: Ziyad Sami B.H., et al.
Published: (2024) -
Equations for mix design of structural lightweight concrete
by: Abdullahi M., et al.
Published: (2023) -
The effect of the fibres length on flexural strength and compressive strength of reinforced foamed concrete
by: Mohammed A.A., et al.
Published: (2024) -
Development of robust procedures for partial least square regression with application to near infrared spectral data
by: Silalahi, Divo Dharma
Published: (2021) -
Predicting stroke using ant colony optimization algorithm / Azfaruddin Azri and Rizauddin Saian
by: Azri, Azfaruddin, et al.
Published: (2023)