Smart Structures and Systems

  • 제30권2호
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  • Pages.195-207
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  • 2022
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  • 1738-1584(pISSN)
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  • 1738-1991(eISSN)
테크노프레스 (Techno-Press)
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DOI QR코드

DOI QR Code

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

  • Hu, Pan (School of Civil and Architectural Engineering, Technical University of Munich) ;
  • Moradi, Zohre (Faculty of Engineering and Technology, Department of Electrical Engineering, Imam Khomeini International University) ;
  • Ali, H. Elhosiny (Advanced Functional Materials & Optoelectronic Laboratory (AFMOL), Department of Physics, Faculty of Science, King Khalid University) ;
  • Foong, Loke Kok (Faculty of Civil Engineering, Duy Tan University)
  • 투고 : 2020.08.24
  • 심사 : 2022.06.18
  • 발행 : 2022.08.25
⟨ 이전 논문 다음 논문 ⟩

초록

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.

키워드

참고문헌

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