Structural Engineering and Mechanics

  • 제46권6호
  • /
  • Pages.853-868
  • /
  • 2013
  • /
  • 1225-4568(pISSN)
  • /
  • 1598-6217(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Identification of a suitable ANN architecture in predicting strain in tie section of concrete deep beams

  • 투고 : 2013.01.18
  • 심사 : 2013.06.16
  • 발행 : 2013.06.25
⟨ 이전 논문 다음 논문 ⟩

초록

The comparison of the effectiveness of artificial neural network (ANN) and linear regression (LR) in the prediction of strain in tie section using experimental data from eight high-strength-self-compact-concrete (HSSCC) deep beams are presented here. Prior to the aforementioned, a suitable ANN architecture was identified. The format of the network architecture was ten input parameters, two hidden layers, and one output. The feed forward back propagation neural network of eleven and ten neurons in first and second TRAINLM training function was highly accurate and generated more precise tie strain diagrams compared to classical LR. The ANN's MSE values are 90 times smaller than the LR's. The correlation coefficient value from ANN is 0.9995 which is indicative of a high level of confidence.

키워드

과제정보

연구 과제 주관 기관 : University of Malaya

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