Multiple regression model for compressive strength prediction of high performance concrete
By Muhammad Fauzi Mohd. Zain, Suhad M. Abd. 2009
187 citations recorded by the source corpus.
Subfields: Civil and Structural Engineering
View Paper: https://www.semanticscholar.org/paper/45922715cfe4245934cc5d88639d7222648fabe4
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Civil and Structural Engineering
- A. Pham
- Yu-Hsin Lu
- Chih-Fong Tsai
- Jui-Sheng Chou
- S. Londhe
- N. Deshpande
- S. Kulkarni
- Amir Hossein Alavi
Citations in the corpus
- Modeling compressive strength of recycled aggregate concrete by Artificial Neural Network, Model Tree and Non-linear Regression (2014, background): “This is called the multivariable power equation and in engineering, variables are often dependent on several independent variables, this functional dependency is best characterized by the equation mentioned earlier, and is said to give results that are more realistic too (Zain and Abd, 2009).”
- Machine learning in concrete strength simulations: Multi-nation data analytics (2014, methodology): “Zain and Abd, for instance, used multivariate power equations to predict the strength of high performance concrete [8].”