Smart Structures and Systems

  • 제25권2호
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  • Pages.183-195
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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

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Prediction of concrete strength in presence of furnace slag and fly ash using Hybrid ANN-GA (Artificial Neural Network-Genetic Algorithm)

  • Shariati, Mahdi (Division of Computational Mathematics and Engineering, Institute for Computational Science, Ton Duc Thang University) ;
  • Mafipour, Mohammad Saeed (School of Civil Engineering, College of Engineering, University of Tehran) ;
  • Mehrabi, Peyman (Department of Civil Engineering, K.N. Toosi University of Technology) ;
  • Ahmadi, Masoud (Department of Civil Engineering, Ayatollah Boroujerdi University) ;
  • Wakil, Karzan (Research Center, Sulaimani Polytechnic University) ;
  • Trung, Nguyen Thoi (Division of Computational Mathematics and Engineering, Institute for Computational Science, Ton Duc Thang University) ;
  • Toghroli, Ali (Institute of Research and Development, Duy Tan University)
  • 투고 : 2019.03.24
  • 심사 : 2020.01.07
  • 발행 : 2020.02.25
⟨ 이전 논문 다음 논문 ⟩

초록

Mineral admixtures have been widely used to produce concrete. Pozzolans have been utilized as partially replacement for Portland cement or blended cement in concrete based on the materials' properties and the concrete's desired effects. Several environmental problems associated with producing cement have led to partial replacement of cement with other pozzolans. Furnace slag and fly ash are two of the pozzolans which can be appropriately used as partial replacements for cement in concrete. However, replacing cement with these materials results in significant changes in the mechanical properties of concrete, more specifically, compressive strength. This paper aims to intelligently predict the compressive strength of concretes incorporating furnace slag and fly ash as partial replacements for cement. For this purpose, a database containing 1030 data sets with nine inputs (concrete mix design and age of concrete) and one output (the compressive strength) was collected. Instead of absolute values of inputs, their proportions were used. A hybrid artificial neural network-genetic algorithm (ANN-GA) was employed as a novel approach to conducting the study. The performance of the ANN-GA model is evaluated by another artificial neural network (ANN), which was developed and tuned via a conventional backpropagation (BP) algorithm. Results showed that not only an ANN-GA model can be developed and appropriately used for the compressive strength prediction of concrete but also it can lead to superior results in comparison with an ANN-BP model.

키워드

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