Investigation of ANN Model Containing One Hidden Layer for Predicting Compressive Strength of Concrete with Blast-Furnace Slag and Fly Ash

Abstract

The prediction accuracy of concrete compressive strength is important and considered a challenging task, aiming at reducing costly and time-consuming experiments. Moreover, compressive strength prediction of concrete using blast-furnace slag (BFS) and fly ash (FA) is more difficult due to the complex mix design of a composition. In this investigation, an approach using the artificial neuron network (ANN), one of the most powerful machine learning algorithms, is applied to predict the compressive strength of concrete containing BFS and FA. The ANN models with one hidden layer containing 13 neuron number cases are proposed to determine the best ANN structure. Under the effect of random sampling strategies and the network structures selected, Monte Carlo simulations (MCS) are introduced to statistically investigate the convergence of results. Next, the evaluation of the model is concluded over 100 simulations for the convergence analysis. The results show that ANN is a highly efficient predictor of the compressive strength using BFS and FA, with maximum values of the coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE) of 0.9437, 3.9474, and 2.9074, respectively, on the training part and 0.9285, 4.4266, and 3.2971, respectively, for the testing part. The best-defined structure of ANN is [8-24-1], with 24 neurons in the hidden layer. Partial Dependence Plots (PDP) are also performed to investigate the dependence of the prediction results of input variables used in the ANN model. The age of sample and cement content are found to be the two most crucial factors that affect the compressive strength of concrete using BFS and FA. The ANN algorithm is practical for engineers to reduce costly experiments.