Computers and Concrete

  • 제32권3호
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  • Pages.327-337
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  • 2023
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  • 1598-8198(pISSN)
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  • 1598-818X(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Deep learning method for compressive strength prediction for lightweight concrete

  • Yaser A. Nanehkaran (School of Information Engineering, Yancheng Teachers University) ;
  • Mohammad Azarafza (Department of Civil Engineering, University of Tabriz) ;
  • Tolga Pusatli (Department of Management Information Systems, Cankaya University) ;
  • Masoud Hajialilue Bonab (Department of Civil Engineering, University of Tabriz) ;
  • Arash Esmatkhah Irani (Department of Civil Engineering, Islamic Azad University) ;
  • Mehdi Kouhdarag (Department of Civil Engineering, Malekan Branch, Islamic Azad University) ;
  • Junde Chen (Department of Electronic Commerce, Xiangtan University) ;
  • Reza Derakhshani (Department of Earth Sciences, Utrecht University)
  • 투고 : 2023.02.25
  • 심사 : 2023.06.20
  • 발행 : 2023.09.25
⟨ 이전 논문 다음 논문 ⟩

초록

Concrete is the most widely used building material, with various types including high- and ultra-high-strength, reinforced, normal, and lightweight concretes. However, accurately predicting concrete properties is challenging due to the geotechnical design code's requirement for specific characteristics. To overcome this issue, researchers have turned to new technologies like machine learning to develop proper methodologies for concrete specification. In this study, we propose a highly accurate deep learning-based predictive model to investigate the compressive strength (UCS) of lightweight concrete with natural aggregates (pumice). Our model was implemented on a database containing 249 experimental records and revealed that water, cement, water-cement ratio, fine-coarse aggregate, aggregate substitution rate, fine aggregate replacement, and superplasticizer are the most influential covariates on UCS. To validate our model, we trained and tested it on random subsets of the database, and its performance was evaluated using a confusion matrix and receiver operating characteristic (ROC) overall accuracy. The proposed model was compared with widely known machine learning methods such as MLP, SVM, and DT classifiers to assess its capability. In addition, the model was tested on 25 laboratory UCS tests to evaluate its predictability. Our findings showed that the proposed model achieved the highest accuracy (accuracy=0.97, precision=0.97) and the lowest error rate with a high learning rate (R2=0.914), as confirmed by ROC (AUC=0.971), which is higher than other classifiers. Therefore, the proposed method demonstrates a high level of performance and capability for UCS predictions.

키워드

과제정보

The authors would like to thank the anonymous reviewers for providing invaluable review comments and recommendations for improving the scientific level of the article. This research was funded by the National Nature Science Foundation of China (Grant ID: 42250410321).

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