Prediction of Compressive Strength Behavior of Ground Bottom Ash Concrete by an Artificial Neural Network
Joint Authors
Cheerarot, Raungrut
Tuntisukrarom, Kraiwut
Source
Advances in Materials Science and Engineering
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-16, 16 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-06-01
Country of Publication
Egypt
No. of Pages
16
Abstract EN
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.
American Psychological Association (APA)
Tuntisukrarom, Kraiwut& Cheerarot, Raungrut. 2020. Prediction of Compressive Strength Behavior of Ground Bottom Ash Concrete by an Artificial Neural Network. Advances in Materials Science and Engineering،Vol. 2020, no. 2020, pp.1-16.
https://search.emarefa.net/detail/BIM-1128044
Modern Language Association (MLA)
Tuntisukrarom, Kraiwut& Cheerarot, Raungrut. Prediction of Compressive Strength Behavior of Ground Bottom Ash Concrete by an Artificial Neural Network. Advances in Materials Science and Engineering No. 2020 (2020), pp.1-16.
https://search.emarefa.net/detail/BIM-1128044
American Medical Association (AMA)
Tuntisukrarom, Kraiwut& Cheerarot, Raungrut. Prediction of Compressive Strength Behavior of Ground Bottom Ash Concrete by an Artificial Neural Network. Advances in Materials Science and Engineering. 2020. Vol. 2020, no. 2020, pp.1-16.
https://search.emarefa.net/detail/BIM-1128044
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1128044