Steel and Composite Structures

  • 제44권5호
  • /
  • Pages.691-705
  • /
  • 2022
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  • 1229-9367(pISSN)
  • /
  • 1598-6233(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

JAYA-GBRT model for predicting the shear strength of RC slender beams without stirrups

  • Tran, Viet-Linh (Department of Civil Engineering, Seoul National University of Science and Technology) ;
  • Kim, Jin-Kook (Department of Civil Engineering, Seoul National University of Science and Technology)
  • 투고 : 2021.11.18
  • 심사 : 2022.09.07
  • 발행 : 2022.09.10
⟨ 이전 논문 다음 논문 ⟩

초록

Shear failure in reinforced concrete (RC) structures is very hazardous. This failure is rarely predicted and may occur without any prior signs. Accurate shear strength prediction of the RC members is challenging, and traditional methods have difficulty solving it. This study develops a JAYA-GBRT model based on the JAYA algorithm and the gradient boosting regression tree (GBRT) to predict the shear strength of RC slender beams without stirrups. Firstly, 484 tests are carefully collected and divided into training and test sets. Then, the hyperparameters of the GBRT model are determined using the JAYA algorithm and 10-fold cross-validation. The performance of the JAYA-GBRT model is compared with five well-known empirical models. The comparative results show that the JAYA-GBRT model (R2 = 0.982, RMSE = 9.466 kN, MAE = 6.299 kN, µ = 1.018, and Cov = 0.116) outperforms the other models. Moreover, the predictions of the JAYA-GBRT model are globally and locally explained using the Shapley Additive exPlanation (SHAP) method. The effective depth is determined as the most crucial parameter influencing the shear strength through the SHAP method. Finally, a Graphic User Interface (GUI) tool and a web application (WA) are developed to apply the JAYA-GBRT model for rapidly predicting the shear strength of RC slender beams without stirrups.

키워드

과제정보

This work is supported by the Korea Agency for Infrastructure Technology Advancement(KAIA) grant funded by the Ministry of Land, Infrastructure and Transport (Grant 21CTAP-C163765-01).

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