Estimation of the compressive strength of concretes containing ground granulated blast furnace slag using hybridized multi-objective ANN and salp swarm algorithm - Kandiri, Amirreza; Mohammadi Golafshani, Emadaldin; Behnood, Ali (Construction & building materials, 2020)
•Gathering a database for the compressive strength of concrete with GGBFS.•Modeling the compressive strength of concrete with GGBFS using machine learning.•Hybridization of ANN and multi-objective salp swarm algorithm.•Using M5P model tree to model the concrete compressive strength with GGBFS. The use of supplementary cementitious materials such as ground granulated blast furnace slag (GGBFS) in concrete mixtures provides many technical and economic benefits. The use of GGBFS as a partial replacement for cement in concrete mixtures can also decrease the energy consumption and reduce the greenhouse gas emissions. Developing an accurate model for estimating the compressive strength of the concretes containing GGBFS is a necessity since the value of the compressive strength is a required parameter in various design codes. Besides, a predictive model for the compressive strength instead of direct laboratory-based measurements can save in energy, cost, and time. Artificial neural network (ANN) algorithm was used in this research to develop a model for the estimation of the compressive strength of concretes containing GGBFS. To optimize the error and complexity of the developed ANN models, a multi-objective slap swarm algorithm (MOSSA), as a multi-objective optimization method, was proposed. The M5P model tree algorithm, as one of the most used classification techniques to solve engineering problems, was also used to develop predictive models of compressive strength. The efficiency of the proposed model developed based on the ANN algorithm was compared with that of the model developed based on the M5P model tree technique using various error measures. The findings from this research indicate that the M5P model tree and the proposed ANN model can successfully provide predictive tools for estimating the compressive strength of concretes containing GGBFS with 12.05% and 7.25% mean absolute percentage error (MAPE), respectively. These values indicate that the proposed model based on ANN algorithm has superior efficiency compared to the one developed using M5P model tree.