Predicting compressive strength of manufactured-sand concrete using conventional and metaheuristic-tuned artificial neural network

Abstract

Compressive strength (CS) is the maximum resistance of concrete against axial compressive loading in standard conditions. Estimation of this parameter is essential for the proper design of concrete mixture. Considering the complexity of this task as a burden for traditional approaches, machine learning models like artificial neural network (ANN) have been successfully used for analyzing the nonlinear relationship between the CS and concrete ingredients. This study implements two ANN-based scenarios to approximate the uniaxial CS of manufactured-sand concrete. First, the ANN is trained by nine regular algorithms, and the best one is selected to represent the conventional ANN (CNN). For the second scenario, two improved ANNs are created with metaheuristic algorithms, namely biogeography-based optimization (BBO) and multi-tracker optimization algorithm (MTOA). The first scenario revealed that Levenberg-Marquardt is the strongest regular trainer. Comparing the performance of the CNN with hybrid models showed that both BBO and MTOA can construct a more accurate ANN. In this sense, root mean square error of the CNN experienced 8.77 and 8.84% reduction in the training phase, and more effectively, 13.05 and 11.46% in the testing phase by applying the BBO and MTOA, respectively. Hence, the suggested hybrids can act as promising alternatives to traditional models for predicting the CS of concrete. Two explicit formulas optimized by the MTOA and BBO are derived for practical applications. Also, importance analysis revealed the high contribution of curing age and water to binder ratio to the compressive behavior of the concrete.