Predicting Compressive Strength of Concrete Containing Recycled Aggregate Using Modified ANN with Different Optimization Algorithms

Using recycled aggregate in concrete is one of the best ways to reduce construction pollution and prevent exploitation natural resources provide needed aggregate. However, aggregates affect mechanical properties concrete, but existing information on subject less than what industry needs. Compressive strength, other hand, most important property concrete. Therefore, having predictive models required can be helpful convince increase use In this research, three different optimization algorithms including genetic algorithm (GA), salp swarm (SSA), grasshopper (GOA) are employed hybridized with artificial neural network (ANN) separately predict compressive strength containing aggregate, a M5P tree model used test efficiency ANNs. The results study show superior modified ANN SSA when compared models. statistical indicators hybrid ANNs SSA, GA, GOA so close each other.