Research Article Prediction of Compressive Strength Behavior of Ground Bottom Ash Concrete by an Artificial Neural Network Kraiwut Tuntisukrarom and Raungrut Cheerarot Concrete and Computer Research Unit, Civil Engineering, Faculty of Engineering, Mahasarakham University, Kantharawichai, Mahasarakham 44150, ailand Correspondence should be addressed to Raungrut Cheerarot; raungrut@hotmail.com Received 2 December 2019; Revised 19 February 2020; Accepted 6 May 2020; Published 1 June 2020 Academic Editor: Fuat Kara Copyright © 2020 Kraiwut Tuntisukrarom and Raungrut Cheerarot. is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. e 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. 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 R 2 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. 1.Introduction Artificial neural networks (ANNs) have been widely applied owing to the excellent performance of the associated high- accuracy predictive model in learning and analyzing the effects of the input and output variables. In particular, ANN models are valuable tools for predicting experimental re- sults. In the field of concrete engineering, for the experi- mental datasets of concrete properties, an ANN model was proposed to predict the compressive strength of concrete containing pozzolanic materials such as fly ash, silica fume, metakaolin, and ground granulated blast furnace slag [1–3]. In addition, the ANN model has been extended for pre- dicting the properties of concrete, for instance, workability, corrosion currents, split tensile strength, water permeability, and chloride permeability [4]. Many studies have reported that ANN models lead to a more accurate and precise prediction than that obtained using linear and nonlinear regression techniques, and it can be easily expanded to new additional databases, enabling the retraining of the network [2–4]. Bottom ash (BA) is a byproduct of the combustion of powder coal in an electric power plant. In particular, fly ash (FA) is melted at high temperatures, and it agglomerates to form BA at low temperatures. BA is similar to FA; however, BA has a considerably larger particle size, and thus it cannot be used as a cementitious material in concrete. Ghafoori and Cai [5, 6] studied BA as a fine aggregate and reported the behavior of laboratory-made roller-compacted concrete (RCC) containing BA. e RCC containing BA exhibited excellent strength, stiffness, durability, and deformation properties. Furthermore, Ghafoori and Bucholc [7, 8] used dry BA as a fine aggregate in concrete and indicated that the amount of required mixing water increased rapidly with the increase in the BA content. Other researchers used BA with low density as an aggregate in lightweight concrete to de- crease the concrete density. Compressive strength is one of the most important properties of mortar or concrete. Many researchers [9, 10] studied the mechanism analysis of hydration and pozzolanic reaction which found that when cement and pozzolanic material were mixed with water, cement clinker minerals Hindawi Advances in Materials Science and Engineering Volume 2020, Article ID 2608231, 16 pages https://doi.org/10.1155/2020/2608231