Prediction of Later-Age Concrete Compressive Strength Using Feedforward Neural Network

Abstract

Accurate prediction of the concrete compressive strength is an important task that helps to avoid costly and time-consuming experiments. Notably, the determination of the later-age concrete compressive strength is more difficult due to the time required to perform experiments. Therefore, predicting the compressive strength of later-age concrete is crucial in specific applications. In this investigation, an approach using a feedforward neural network (FNN) machine learning algorithm was proposed to predict the compressive strength of later-age concrete. The proposed model was fully evaluated in terms of performance and prediction capability over statistical results of 1000 simulations under a random sampling effect. The results showed that the proposed algorithm was an excellent predictor and might be useful for engineers to avoid time-consuming experiments with the statistical performance indicators, namely, the Pearson correlation coefficient (R), root-mean-squared error (RMSE), and mean squared error (MAE) for the training and testing parts of 0.9861, 2.1501, 1.5650 and 0.9792, 2.8510, 2.1361, respectively. The results also indicated that the FNN model was superior to classical machine learning algorithms such as random forest and Gaussian process regression, as well as empirical formulations proposed in the literature.