Abstract
The objective of this work was to examine the compressive strength behavior of ground bottom ash (GBA) concrete by using an artificial neural network. Four input parameters, specifically, the water-to-binder ratio (WB), percentage replacement of GBA (PR), median particle size of GBA (PS), and age of concrete (AC), were considered for this prediction. The results indicated that all four considered parameters affect the strength development of concrete, and GBA with a high fineness can act as a good pozzolanic material. The optimal ANN model had an architecture with two hidden layers, with six neurons in the first hidden layer and one neuron in the second hidden layer. The proposed ANN-based explicit equation represented a highly accurate predictive model, for which the statistical values of R2 were higher than 0.996. Moreover, the compressive strength behavior determined using the optimal ANN model closely followed the trend lines and surface plots of the experimental results.
Highlights
Artificial neural networks (ANNs) have been widely applied owing to the excellent performance of the associated highaccuracy predictive model in learning and analyzing the effects of the input and output variables
The graphs of the 3D plots are not sufficiently smooth owing to the use of insufficient data. e use of more information can further smoothen the graph. ese findings indicate that the ANN model can be used to predict the compressive strength and better clarify the relationships among all the factor parameters
It was observed that, at the same water-to-binder ratio (WB) and replacement of ground bottom ash (GBA), the concrete mixed with the coarser GBA exhibited a lower compressive strength than that of the concrete with the finer GBA
Summary
Concrete and Computer Research Unit, Civil Engineering, Faculty of Engineering, Mahasarakham University, Kantharawichai, Mahasarakham 44150, ailand. E objective of this work was to examine the compressive strength behavior of ground bottom ash (GBA) concrete by using an artificial neural network. E results indicated that all four considered parameters affect the strength development of concrete, and GBA with a high fineness can act as a good pozzolanic material. E optimal ANN model had an architecture with two hidden layers, with six neurons in the first hidden layer and one neuron in the second hidden layer. E proposed ANN-based explicit equation represented a highly accurate predictive model, for which the statistical values of R2 were higher than 0.996. The compressive strength behavior determined using the optimal ANN model closely followed the trend lines and surface plots of the experimental results
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
