The objective of this work was to examine the compressive strength behavior of ground bottom ash (GBA) concrete by using an\nartificial neural network. Four input parameters, specifically, the water-to-binder ratio (WB), percentage replacement of GBA\n(PR), median particle size of GBA (PS), and age of concrete (AC), were considered for this prediction. The results indicated that all\nfour considered parameters affect the strength development of concrete, and GBA with a high fineness can act as a good\npozzolanic material.The optimal ANN model had an architecture with two hidden layers, with six neurons in the first hidden layer\nand one neuron in the second hidden layer. The proposed ANN-based explicit equation represented a highly accurate predictive\nmodel, for which the statistical values of R2 were higher than 0.996. Moreover, the compressive strength behavior determined\nusing the optimal ANN model closely followed the trend lines and surface plots of the experimental results.
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