Metaheuristic-reinforced neural network for predicting the compressive strength of concrete

Pan Hu , Zohre Moradi , H. Elhosiny Ali , Loke Kok Foong

Abstract

Computational drawbacks associated with regular predictive models have motivated engineers to use hybrid techniques in dealing with complex engineering tasks like simulating the compressive strength of concrete (CSC). This study evaluates the efficiency of tree potential metaheuristic schemes, namely shuffled complex evolution (SCE), multi-verse optimizer (MVO), and beetle antennae search (BAS) for optimizing the performance of a multi-layer perceptron (MLP) system. The models are fed by the information of 1030 concrete specimens (where the amount of cement, blast furnace slag (BFS), fly ash (FA1), water, superplasticizer (SP), coarse aggregate (CA), and fine aggregate (FA2) are taken as independent factors). The results of the ensembles are compared to unreinforced MLP to examine improvements resulted from the incorporation of the SCE, MVO, and BAS. It was shown that these algorithms can considerably enhance the training and prediction accuracy of the MLP. Overall, the proposed models are capable of presenting an early, inexpensive, and reliable prediction of the CSC. Due to the higher accuracy of the BAS-based model, a predictive formula is extracted from this algorithm.

Key Words

artificial neural network; concrete compressive strength; hybrid metaheuristic algorithms

Address

(1) Pan Hu — School of Civil and Architectural Engineering, Technical University of Munich, Munich 80333, Germany
(2) Zohre Moradi — Faculty of Engineering and Technology, Department of Electrical Engineering, Imam Khomeini International University, 34149-16818 Qazvin, Iran
(3) Zohre Moradi — Department of Biomaterials, Saveetha Dental College and Hospital, Saveetha Institute of Medical and Technical Sciences, Chennai 600 077, India
(4) H. Elhosiny Ali — Advanced Functional Materials & Optoelectronic Laboratory (AFMOL), Department of Physics, Faculty of Science, King Khalid University, P.O. Box 9004, Abha, Saudi Arabia
(5) H. Elhosiny Ali — Research Center for Advanced Materials Science (RCAMS), King Khalid University, Abha 61413, P.O. Box 9004, Saudi Arabia
(6) H. Elhosiny Ali — Physics Department, Faculty of Science, Zagazig University, 44519 Zagazig, Egypt
(7) Loke Kok Foong — Faculty of Civil Engineering, Duy Tan University, Da Nang 550000, Vietnam.

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