Advances in materials Research

  • 제11권1호
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  • Pages.75-90
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  • 2022
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  • 2234-0912(pISSN)
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  • 2234-179X(eISSN)
테크노프레스 (Techno-Press)
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DOI QR코드

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Comparison of machine learning algorithms to evaluate strength of concrete with marble powder

  • Sharma, Nitisha (Department of Civil Engineering, Shoolini University) ;
  • Upadhya, Ankita (Department of Civil Engineering, Shoolini University) ;
  • Thakur, Mohindra S. (Department of Civil Engineering, Shoolini University) ;
  • Sihag, Parveen (Department of Civil Engineering, Chandigarh University)
  • 투고 : 2021.06.18
  • 심사 : 2021.12.02
  • 발행 : 2022.03.25
⟨ 이전 논문 다음 논문 ⟩

초록

In this paper, functionality of soft computing algorithms such as Group method of data handling (GMDH), Random forest (RF), Random tree (RT), Linear regression (LR), M5P, and artificial neural network (ANN) have been looked out to predict the compressive strength of concrete mixed with marble powder. Assessment of result suggests that, the overall performance of ANN based model gives preferable results over the different applied algorithms for the estimate of compressive strength of concrete. The results of coefficient of correlation were maximum in ANN model (0.9139) accompanied through RT with coefficient of correlation (CC) value 0.8241 and minimum root mean square error (RMSE) value of ANN (4.5611) followed by RT with RMSE (5.4246). Similarly, other evaluating parameters like, Willmott's index and Nash-sutcliffe coefficient value of ANN was 0.9458 and 0.7502 followed by RT model (0.8763 and 0.6628). The end result showed that, for both subsets i.e., training and testing subset, ANN has the potential to estimate the compressive strength of concrete. Also, the results of sensitivity suggest that the water-cement ratio has a massive impact in estimating the compressive strength of concrete with marble powder with ANN based model in evaluation with the different parameters for this data set.

키워드

과제정보

We, the authors, would like to acknowledge the researchers whose research findings we have referred to in this paper.

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