Abstract: There are some factors that interact to influence the compressive strength of concrete. Traditional prediction methods rely on extensive experimental testing to optimize mix proportions.
Measuring the in-situ compressive strength of concrete in final structures is an essential but challenging process. Coring combined with non-destructive rebound hammer measurements provides the most ...
An end-to-end machine learning project for predicting the compressive strength of Compressed Earth Blocks (CEBs) using real laboratory-tested parameters and presenting results through an interactive ...
Abstract: This study investigates the prediction of concrete compressive strength using machine learning techniques. Based on 1,030 experimental samples, ensemble methods such as XGBoost and Gradient ...
This project demonstrates the application of feedforward neural networks to model the complex, nonlinear relationships between concrete mix components and compressive strength. The goal was to build a ...
A recent study has shown that machine learning models can accurately predict the compressive strength of carbonated recycled concrete, a discovery that could genuinely help us build better, faster, ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results