Advanced machine learning techniques for predicting compressive strength and ultrasonic pulse velocity of concrete incorporating industrial by-products
The incorporation of fly ash, ground granulated blast furnace slag (GGBFS), and other pozzolanic materials into cement formulations presents a sustainable solution for utilizing industrial waste. This study introduces a novel, dual-stage machine learning framework that uniquely integrates both physi...
Saved in:
| Main Authors: | Ehsan Mohsennia, Alireza Javid, Vahab Toufigh |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-07-01
|
| Series: | Case Studies in Construction Materials |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214509525005996 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Investigating the correlation between ultrasonic pulse velocity and compressive strength in polyurethane foam concrete
by: R. Roobankumar, et al.
Published: (2025-07-01) -
In-situ Compressive Strength Assessment of Reinforced Concrete Structures
by: Al-Neshawy Fahim, et al.
Published: (2025-06-01) -
ANALYSIS OF THE INFLUENCE OF TRANSDUCER FREQUENCY ON THE ULTRASONIC MEASUREMENT OF CONCRETE HOMOGENEITY
by: Dalibor Kocáb, et al.
Published: (2019-07-01) -
Ultrasonic Pulse Velocity for Real-Time Filament Quality Monitoring in 3D Concrete Printing Construction
by: Luis de la Flor Juncal, et al.
Published: (2025-07-01) -
Analysis of concrete taken from a loaded column by determining the modulus of elasticity
by: Kristýna Hrabová, et al.
Published: (2024-05-01)