Use of Different Hyperparameter Optimization Algorithms in ANN for Predicting the Compressive Strength of Concrete Containing Calcined Clay

Abstract

Calcined clays as supplementary cementitious materials (SCMs) have the potential to answer the need for SCMs because they have many resources in comparison with other SCMs. Moreover, calcined clays can be combined with limestone, which makes limestone calcined clay cement (LC3). LC3 can reduce the clinker of cement, which leads to the reduction of carbon dioxide pollution. The application of calcined clays as SCMs can make concrete with lower cost and the same compressive strength in comparison with other SCMs such as fly ash. Therefore, in this research, a predictive model has been built by the application of an artificial neural network (ANN), which can encourage the industry to increase the clay application in concrete. On the other hand, for improving the performance of the ANN model, four different optimization algorithms, such as genetic algorithm (GA), Bayesian optimization with a Gaussian process (BO-GP), Bayesian optimization with tree parzen estimator (BO-TPE), and hyperband algorithm, have been applied to optimize the number of neurons, number of hidden layers, activation function, number of batches, and epochs to predict the compressive strength of concrete. The results performance of models showed the superiority of the hyperband algorithm in running time and accuracy.