Computers and Concrete

  • 제27권4호
  • /
  • Pages.333-341
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  • 2021
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  • 1598-8198(pISSN)
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  • 1598-818X(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

A new formulation for strength characteristics of steel slag aggregate concrete using an artificial intelligence-based approach

  • Awoyera, Paul O. (Department of Civil Engineering, Covenant University) ;
  • Mansouri, Iman (Department of Civil Engineering, Birjand University of Technology) ;
  • Abraham, Ajith (Machine Intelligence Research Labs) ;
  • Viloria, Amelec (Universidad de la Costa)
  • 투고 : 2020.06.13
  • 심사 : 2021.03.04
  • 발행 : 2021.04.25
⟨ 이전 논문 다음 논문 ⟩

초록

Steel slag, an industrial reject from the steel rolling process, has been identified as one of the suitable, environmentally friendly materials for concrete production. Given that the coarse aggregate portion represents about 70% of concrete constituents, other economic approaches have been found in the use of alternative materials such as steel slag in concrete. Unfortunately, a standard framework for its application is still lacking. Therefore, this study proposed functional model equations for the determination of strength properties (compression and splitting tensile) of steel slag aggregate concrete (SSAC), using gene expression programming (GEP). The study, in the experimental phase, utilized steel slag as a partial replacement of crushed rock, in steps 20%, 40%, 60%, 80%, and 100%, respectively. The predictor variables included in the analysis were cement, sand, granite, steel slag, water/cement ratio, and curing regime (age). For the model development, 60-75% of the dataset was used as the training set, while the remaining data was used for testing the model. Empirical results illustrate that steel aggregate could be used up to 100% replacement of conventional aggregate, while also yielding comparable results as the latter. The GEP-based functional relations were tested statistically. The minimum absolute percentage error (MAPE), and root mean square error (RMSE) for compressive strength are 6.9 and 1.4, and 12.52 and 0.91 for the train and test datasets, respectively. With the consistency of both the training and testing datasets, the model has shown a strong capacity to predict the strength properties of SSAC. The results showed that the proposed model equations are reliably suitable for estimating SSAC strength properties. The GEP-based formula is relatively simple and useful for pre-design applications.

키워드

참고문헌

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