The paper presents the potential of fuzzy logic (FL-I) and neural network techniques (ANN-I) for predicting the compressive strength, for SCC mixtures. Six input parameters that is contents of cement, sand, coarse aggregate, fly ash, superplasticizer percentage and water-to-binder ratio and an output parameter i.e. 28- day compressive strength for ANN-I and FL-I are used for modeling. The fuzzy logic model showed better performance than neural network model
Prediction of Compressive Strength of Concrete with Silica, Ash and Fiber Using Fuzzy Logic Method ...
Using soft computing methods could be of great interest in predicting the compressive strength of Ul...
developed for prediction of compressive strength of Ready Mix Concrete (RMC). Factors affecting stre...
In this study, an artificial neural network (ANN) and fuzzy logic (FL) study were developed to predi...
Self-compacting concrete (SCC) developed in Japan in the late 80s has enabled the construction indus...
In this study, three different models were developed to predict the compressive strength of SCC, inc...
This research examined machine learning (ML) techniques for predicting the compressive strength (CS)...
Self-Compacting Concrete (SCC) is a unique type of concrete that can flow and fill spaces without th...
In this study, an artificial neural networks study was carried out to predict the core compressive s...
High-strength concretes (HSC) were prepared with five different binder contents, each of which had s...
Artificial intelligence and machine learning are employed in creating functions for the prediction o...
Self-Compacting Concrete (SCC) is a relatively new type of concrete with high workability, high volu...
Ready mixed concrete (RMC) is an essential material in contemporary construction and engineering pro...
An artificial neural network (ANN) is presented to predict a 28-day compressive strength of a normal...
Concrete is the most vital composite construction material in industry of construction due to its pr...
Prediction of Compressive Strength of Concrete with Silica, Ash and Fiber Using Fuzzy Logic Method ...
Using soft computing methods could be of great interest in predicting the compressive strength of Ul...
developed for prediction of compressive strength of Ready Mix Concrete (RMC). Factors affecting stre...
In this study, an artificial neural network (ANN) and fuzzy logic (FL) study were developed to predi...
Self-compacting concrete (SCC) developed in Japan in the late 80s has enabled the construction indus...
In this study, three different models were developed to predict the compressive strength of SCC, inc...
This research examined machine learning (ML) techniques for predicting the compressive strength (CS)...
Self-Compacting Concrete (SCC) is a unique type of concrete that can flow and fill spaces without th...
In this study, an artificial neural networks study was carried out to predict the core compressive s...
High-strength concretes (HSC) were prepared with five different binder contents, each of which had s...
Artificial intelligence and machine learning are employed in creating functions for the prediction o...
Self-Compacting Concrete (SCC) is a relatively new type of concrete with high workability, high volu...
Ready mixed concrete (RMC) is an essential material in contemporary construction and engineering pro...
An artificial neural network (ANN) is presented to predict a 28-day compressive strength of a normal...
Concrete is the most vital composite construction material in industry of construction due to its pr...
Prediction of Compressive Strength of Concrete with Silica, Ash and Fiber Using Fuzzy Logic Method ...
Using soft computing methods could be of great interest in predicting the compressive strength of Ul...
developed for prediction of compressive strength of Ready Mix Concrete (RMC). Factors affecting stre...