Fine-tuning of neural computing using whale optimization algorithm for predicting compressive strength of concrete

Due to the important role of concrete in construction sector, a novel metaheuristic method, namely whale optimization algorithm (WOA), is employed for simulating 28-day compressive strength of concrete (CSC). To this end, the WOA is coupled with a neural network (NN) to optimize its computational parameters. Also, dragonfly algorithm (DA) and ant colony optimization (ACO) techniques are considered as the benchmark methods. The CSC influential parameters are cement, slag, water, fly ash, superplasticizer (SP), fine aggregate (FA), and coarse aggregate (CA). First, a population-based sensitivity analysis is carried out to achieve the most efficient structure of the proposed model. In this sense, the WOA-NN with the population size of 400 and five hidden nodes constructed the best-fitted network. The results revealed that the WOA-NN (Error = 2.0746 and Correlation = 0.8976) presents the most reliable prediction of the CSC, followed by the DA-NN (Error = 2.5138 and Correlation = 0.8209) and ACO-NN (Error = 2.8843 and Correlation = 0.8000) benchmark models. The findings showed that utilizing the WOA optimization technique, along with typical neural network, results in developing a promising tool for modeling the CSC.