Smart Structures and Systems

  • 제26권6호
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  • Pages.753-763
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  • 2020
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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

Employing TLBO and SCE for optimal prediction of the compressive strength of concrete

  • Zhao, Yinghao (Guangzhou Institute of Building Science Co., Ltd.) ;
  • Moayedi, Hossein (Institute of Research and Development, Duy Tan University) ;
  • Bahiraei, Mehdi (Faculty of Engineering, Razi University) ;
  • Foong, Loke Kok (Department for Management of Science and Technology Development, Ton Duc Thang University)
  • 투고 : 2020.03.22
  • 심사 : 2020.10.18
  • 발행 : 2020.12.25
⟨ 이전 논문 다음 논문 ⟩

초록

The early prediction of Compressive Strength of Concrete (CSC) is a significant task in the civil engineering construction projects. This study, therefore, is dedicated to introducing two novel hybrids of neural computing, namely Shuffled Complex Evolution (SCE) and Teaching-Learning-Based Optimization (TLBO) for predicting the CSC. The algorithms are applied to a Multi-Layer Perceptron (MLP) network to create the SCE-MLP and TLBO-MLP ensembles. The results revealed that, first, intelligent models can properly handle analyzing and generalizing the non-linear relationship between the CSC and its influential parameters. For example, the smallest and largest values of the CSC were 17.19 and 58.53 MPa, and the outputs of the MLP, SCE-MLP, and TLBO-MLP range in [17.61, 54.36], [17.69, 55.55] and [18.07, 53.83], respectively. Second, applying the SCE and TLBO optimizers resulted in increasing the correlation of the MLP products from 93.58 to 97.32 and 97.22%, respectively. The prediction error was also reduced by around 34 and 31% which indicates the high efficiency of these algorithms. Moreover, regarding the computation time needed to implement the SCE-MLP and TLBO-MLP models, the SCE is a considerably more time-efficient optimizer. Nevertheless, both suggested models can be promising substitutes for laboratory and destructive CSC evaluative models.

키워드

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