Abstract
This study aims at applying a machine learning-based model to establish the relationshipbetween different input variables to the 28-day compressive strength of normal and High-PerformanceConcrete (HPC). An Artificial Neural Network (ANN) model was trained, validated, and tested using acomprehensive database consisted of 361 records gathered from the previously circulated source. Variousmodels with different learning algorithms and neuron numbers in the hidden layer were examined to attain thebest performance model. The examination results revealed that the ANN model using the “trainlm” learningalgorithm delivered the best prediction outcomes with the overall coefficient of determination (R2) of 0.9277.The influence of input parameters on the output was also examined by performing the sensitivity analysis. Itwas observed that the compressive strength of concrete at 28 days was more responsive to the changes in thecement parameter (CM) and the amount of water (WT). In contrast, the 28-day concrete compressive strengthwas found less sensitive to the variation of the fly ash (FL) parameter.
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