An artificial neural network (ANN) is presented to predict a 28-day compressive strength of a normal and high strength self compacting concrete (SCC) and high performance concrete (HPC) with high volume fly ash. The ANN is trained by the data available in literature on normal volume fly ash because data on SCC with high volume fly ash is not available in sufficient quantity. Further, while predicting the strength of HPC the same data meant for SCC has been used to train in order to economise on computational effort. The compressive strengths of SCC and HPC as well as slump flow of SCC estimated by the proposed neural network are validated by experimental results
In this study, three different models were developed to predict the compressive strength of SCC, inc...
Abstract. Artificial Neural Networks of the backpropagation type was used to map the strength of Hig...
In this study, an artificial neural networks study was carried out to predict the core compressive s...
Self-compacting concrete (SCC) is a highly efficient concrete that can be compacted and formed under...
The paper presents a comparative performance of the models developed to predict 28 days compressive ...
A method to predict 28-day compressive strength of high strength concrete (HSC) by using MFNNs is pr...
High Strength Concrete (HSC) is defined as concrete that meets special combination of performance an...
Modeling is a very useful method for the performance prediction of concrete. Most of the models avai...
The objective of this work was to examine the compressive strength behavior of ground bottom ash (GB...
The compressive strength of normal weight concretes which include fly ash have been predicted by art...
Fly ash, a by-product procured from thermal power plants have been used alternatively in varying pro...
Artificial intelligence and machine learning are employed in creating functions for the prediction o...
Artificial neural networks (ANNs) are the result of academic investigations that use mathematical fo...
In the 21st century, numerous numerical calculation techniques have been discovered and used in seve...
Replacing a specified quantity of cement with Class F fly ash contributes to sustainable development...
In this study, three different models were developed to predict the compressive strength of SCC, inc...
Abstract. Artificial Neural Networks of the backpropagation type was used to map the strength of Hig...
In this study, an artificial neural networks study was carried out to predict the core compressive s...
Self-compacting concrete (SCC) is a highly efficient concrete that can be compacted and formed under...
The paper presents a comparative performance of the models developed to predict 28 days compressive ...
A method to predict 28-day compressive strength of high strength concrete (HSC) by using MFNNs is pr...
High Strength Concrete (HSC) is defined as concrete that meets special combination of performance an...
Modeling is a very useful method for the performance prediction of concrete. Most of the models avai...
The objective of this work was to examine the compressive strength behavior of ground bottom ash (GB...
The compressive strength of normal weight concretes which include fly ash have been predicted by art...
Fly ash, a by-product procured from thermal power plants have been used alternatively in varying pro...
Artificial intelligence and machine learning are employed in creating functions for the prediction o...
Artificial neural networks (ANNs) are the result of academic investigations that use mathematical fo...
In the 21st century, numerous numerical calculation techniques have been discovered and used in seve...
Replacing a specified quantity of cement with Class F fly ash contributes to sustainable development...
In this study, three different models were developed to predict the compressive strength of SCC, inc...
Abstract. Artificial Neural Networks of the backpropagation type was used to map the strength of Hig...
In this study, an artificial neural networks study was carried out to predict the core compressive s...