A novel hybrid extreme learning machine–grey wolf optimizer (ELM-GWO) model to predict compressive strength of concrete with partial replacements for cement

Compressive strength of concrete is one of the most determinant parameters in the design of engineering structures. This parameter is generally determined by conducting several tests at different ages of concrete in spite of the fact that such tests are not only costly but also time-consuming. As an...

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Bibliographic Details
Published in:	Engineering with computers 2022-02, Vol.38 (1), p.757-779
Main Authors:	Shariati, Mahdi, Mafipour, Mohammad Saeed, Ghahremani, Behzad, Azarhomayun, Fazel, Ahmadi, Masoud, Trung, Nguyen Thoi, Shariati, Ali
Format:	Article
Language:	eng
Subjects:	Adaptive systems Algorithms Artificial neural networks CAE) and Design Calculus of Variations and Optimal Control Optimization Classical Mechanics Compressive strength Computer Science Computer-Aided Engineering (CAD Concrete Control Design parameters Fuzzy logic Heuristic methods Kernel functions Learning theory Machine learning Math. Applications in Chemistry Mathematical and Computational Engineering Mathematical models Original Article Performance enhancement Performance evaluation Performance indices Polynomials Radial basis function Support vector machines Systems Theory
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Summary:	Compressive strength of concrete is one of the most determinant parameters in the design of engineering structures. This parameter is generally determined by conducting several tests at different ages of concrete in spite of the fact that such tests are not only costly but also time-consuming. As an alternative to these tests, machine learning (ML) techniques can be used to estimate experimental results. However, the dependence of compressive strength on different parameters in the fabrication of concrete makes the prediction problem challenging, especially in the case of concrete with partial replacements for cement. In this investigation, an extreme learning machine (ELM) is combined with a metaheuristic algorithm known as grey wolf optimizer (GWO) and a novel hybrid ELM-GWO model is proposed to predict the compressive strength of concrete with partial replacements for cement. To evaluate the performance of the ELM-GWO model, five of the most well-known ML models including an artificial neural network (ANN), an adaptive neuro-fuzzy inference system (ANFIS), an extreme learning machine, a support vector regression with radial basis function (RBF) kernel (SVR-RBF), and another SVR with a polynomial function (Poly) kernel (SVR-Poly) are developed. Finally, the performance of the models is compared with each other. The results of the paper show that combining the ELM model with GWO can efficiently improve the performance of this model. Also, it is deducted that the ELM-GWO model is capable of reaching superior performance indices in comparison with those of the other models.
ISSN:	0177-0667 1435-5663