In this study, an efficient implementation of machine learning models to predict compressive and tensile strengths of high-performance concrete (HPC) is presented. Four predictive algorithms including support vector regression (SVR), multilayer perceptron (MLP), gradient boosting regressor (GBR), and extreme gradient boosting (XGBoost) are employed. The process of hyperparameter tuning is based on random search that results in trained models with better predictive performances. In addition, the missing data is handled by filling with the mean of the available data which allows more information to be used in the training process. The results on two popular datasets of compressive and tensile strengths of high performance concrete show signif...
The innovation of geopolymer concrete (GPC) plays a vital role not only in reducing the environmenta...
A comparative analysis for the prediction of compressive strength of concrete at the ages of 28, 56,...
Machine learning (ML)-based prediction of non-linear composition-strength relationship in concretes ...
In this study, an efficient implementation of machine learning models to predict compressive and ten...
The current trend in modern research revolves around novel techniques that can predict the character...
This paper presents machine learning (ML) models for high fidelity prediction of compressive strengt...
Compressive strength is the most significant metric to evaluate the mechanical properties of concret...
In this research, we present an efficient implementation of machine learning (ML) models that foreca...
The prediction results of high-performance concrete compressive strength (HPCCS) based on machine le...
Strength of concrete is the major parameter in the design of structures and is represented by the 28...
Accurate prediction of the compressive strength of concrete is of great significance to construction...
Recently, research has centered on developing new approaches, such as supervised machine learning te...
This study introduces an improved artificial intelligence (AI) approach called intelligence optimize...
Despite previous efforts to relate concrete proportioning and strength, a robust knowledgebased mode...
The Ultra-High Performance Concrete (UHPC) as an efficient material in constructional projects needs...
The innovation of geopolymer concrete (GPC) plays a vital role not only in reducing the environmenta...
A comparative analysis for the prediction of compressive strength of concrete at the ages of 28, 56,...
Machine learning (ML)-based prediction of non-linear composition-strength relationship in concretes ...
In this study, an efficient implementation of machine learning models to predict compressive and ten...
The current trend in modern research revolves around novel techniques that can predict the character...
This paper presents machine learning (ML) models for high fidelity prediction of compressive strengt...
Compressive strength is the most significant metric to evaluate the mechanical properties of concret...
In this research, we present an efficient implementation of machine learning (ML) models that foreca...
The prediction results of high-performance concrete compressive strength (HPCCS) based on machine le...
Strength of concrete is the major parameter in the design of structures and is represented by the 28...
Accurate prediction of the compressive strength of concrete is of great significance to construction...
Recently, research has centered on developing new approaches, such as supervised machine learning te...
This study introduces an improved artificial intelligence (AI) approach called intelligence optimize...
Despite previous efforts to relate concrete proportioning and strength, a robust knowledgebased mode...
The Ultra-High Performance Concrete (UHPC) as an efficient material in constructional projects needs...
The innovation of geopolymer concrete (GPC) plays a vital role not only in reducing the environmenta...
A comparative analysis for the prediction of compressive strength of concrete at the ages of 28, 56,...
Machine learning (ML)-based prediction of non-linear composition-strength relationship in concretes ...