Novel Hybrid Radial-Based Neural Network Model for Predicting the Compressive Strength of Long-Term HPC Concrete
Additive usage like micro silica (MS) and fly ash (FA) through partial substitution of cohesive materials in concrete design has positive impacts on the concrete’s mechanical properties, reducing concrete production costs and declining environmental pollution. The concrete’s compressive strength is...
Saved in:
Main Authors: | Hanlie Cheng, Shiela Kitchen, Graciela Daniels |
---|---|
Format: | Article |
Language: | English |
Published: |
Bilijipub publisher
2022-07-01
|
Series: | Advances in Engineering and Intelligence Systems |
Subjects: | |
Online Access: | https://aeis.bilijipub.com/article_153129_d4e2491fefa5ff570721b73c1d7c7789.pdf |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Modeling Compressive Strength of Self-Compacting Concrete (SCC) Using Novel Optimization Algorithm of AOA
by: Francisca Blanco, et al.
Published: (2024-09-01) -
Radial Basis Function Coupling with Metaheuristic Algorithms for Estimating the Compressive Strength and Slump of High-Performance Concrete
by: Amir Reza Taghavi Khangah, et al.
Published: (2024-12-01) -
Estimation of Fresh and Hardened Properties of Self-Compacting Concrete by Optimized Radial Basis Function Methods
by: David Cadasse, et al.
Published: (2022-10-01) -
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) -
Investigating the Two Optimization Algorithms (GWO and ACO) Coupling with Radial Basis Neural Network to Estimate the Pile Settlement
by: Ehsanolah Assareh, et al.
Published: (2023-03-01)