Steel and Composite Structures

  • 제41권2호
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  • Pages.237-247
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  • 2021
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  • 1229-9367(pISSN)
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  • 1598-6233(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Reservoir bank slope stability prediction model based on BP neural network

  • Zhang, Guoqiang (State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, (Chengdu University of Technology)) ;
  • Feng, Wenkai (State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, (Chengdu University of Technology)) ;
  • Wu, Mingtang (Zhejiang Huadong Construction Engineering Corporation Limited) ;
  • Shao, Hai (State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, (Chengdu University of Technology)) ;
  • Ma, Feng (Zhejiang Huadong Construction Engineering Corporation Limited)
  • 투고 : 2020.02.25
  • 심사 : 2021.03.24
  • 발행 : 2021.10.25
⟨ 이전 논문 다음 논문 ⟩

초록

Safety monitoring and stability analysis of high slopes are essential for construction of concrete dam in precipitous gorges or mountainous areas. The estimate of slope stability is a difficult engineering shortcoming with a number of variables. Thereafter, a hybrid model of Support Vector Regression (SVR) and Teaching-learning-based optimization technique (TLBO) is proposed to develop the predicting function. TLBO was used in obtaining the best SRV factors to improve the prediction accuracy. Few essential factors, such as the installation height of instruments, classification of rock masses, modulus of elasticity, the complete measuring time cycle, the excavation height of slope, the start measuring time, and the actual excavation height after measurement are considered as the input parameter, but the slope displacement is regarded as output. The outcomes showed SRV-TLBO a reliable hybrid accurate prediction of slope stability, then it was effectively used to the left abutment slope of Jinping I hydropower station located in Yalongjiang concrete dam reservoir as a novel method for this purpose.

키워드

과제정보

This research was funded by the National Natural Science Foundation of China (Grant No. 41977252) and the State Key Laboratory of Geohazard Prevention and Geoenvironment Protection Independent Research Project (Grant No. SKLGP2020Z001).

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