Machine Learning-Based Modeling with Optimization Algorithm for Predicting Mechanical Properties of Sustainable Concrete
In this research, multiexpression programming (MEP) has been employed to model the compressive strength, splitting tensile strength, and flexural strength of waste sugarcane bagasse ash (SCBA) concrete. Particle swarm optimization (PSO) algorithm was used to fine-tune the hyperparameter of the propo...
Saved in:
| Main Authors: | Muhammad Izhar Shah, Shazim Ali Memon, Muhammad Sohaib Khan Niazi, Muhammad Nasir Amin, Fahid Aslam, Muhammad Faisal Javed |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
|
| Series: | Advances in Civil Engineering |
| Online Access: | http://dx.doi.org/10.1155/2021/6682283 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Soft-computing models for predicting plastic viscosity and interface yield stress of fresh concrete
by: Waleed Bin Inqiad, et al.
Published: (2025-03-01) -
Evaluating the Effect of Calcination and Grinding of Corn Stalk Ash on Pozzolanic Potential for Sustainable Cement-Based Materials
by: Shazim Ali Memon, et al.
Published: (2020-01-01) -
Evaluating the mechanical and durability properties of sustainable lightweight concrete incorporating the various proportions of waste pumice aggregate
by: Hafiz Muhammad Shahzad Aslam, et al.
Published: (2024-12-01) -
Experimental analysis and gene expression programming optimization of sustainable concrete containing mineral fillers
by: Ayesha Rauf, et al.
Published: (2024-11-01) -
Effects of Different Mineral Admixtures on the Properties of Fresh Concrete
by: Sadaqat Ullah Khan, et al.
Published: (2014-01-01)