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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 국제구조공학회 Structural Engineering and Mechanics, An Int'l Journal Structural Engineering and Mechanics, An Int'l Journal Vol.71 No.6
- 발행연도
- 2019.1
- 수록면
- 739 - 749 (11page)
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This article deals with the application of reliability analysis for determining the safety of simply supported beam under the uniformly distributed load. The uncertainties of the existing methods were taken into account and hence reliability analysis has been adopted. To accomplish this aim, Generalized Regression Neural Network (GRNN), Extreme Learning Machine (ELM) and Gaussian Process Regression (GPR) models are developed. Reliability analysis is the probabilistic style to determine the possibility of failure free operation of a structure. The application of probabilistic mathematics into the quantitative aspects of a structure and improve the qualitative aspects of a structure. In order to construct the GRNN, ELM and GPR models, the dataset contains Modulus of Elasticity (E), Load intensity (w) and performance function () in which E and w are inputs and is the output. The achievement of the developed models was weighed by various statistical parameters; one among the most primitive parameter is Coefficient of Determination (R2) which has 0.998 for training and 0.989 for testing. The GRNN outperforms the other ELM and GPR models. Other different statistical computations have been carried out, which speaks out the errors and prediction performance in order to justify the capability of the developed models.
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참고문헌 (68)
참고문헌 신청
Aaron S. Campbell / 2013 / Investigating the GRNN Oracle as a Method for Combining Multiple Predictive Models of Colon Cancer Recurrence from Gene Microarrays / Procedia Computer Science 20:374~378
Abeer A. Al-Musawi / 2018 / Shear strength of SFRCB without stirrups simulation: implementation of hybrid artificial intelligence model / Engineering with Computers 36(1):1~11
Arvydas Beržonskis / 2016 / Reliability Analysis of Fatigue Fracture of Wind Turbine Drivetrain Components / Energy Procedia 94:146~154
Bartosz Krawczyk / 2016 / GPU-Accelerated Extreme Learning Machines for Imbalanced Data Streams with Concept Drift / Procedia Computer Science 80:1692~1701
Behrooz Keshtegar / 2016 / Chaotic conjugate stability transformation method for structural reliability analysis / Computer Methods in Applied Mechanics and Engineering 310:866~885
Abeer A. Al-Musawi / 2018 / Shear strength of SFRCB without stirrups simulation: implementation of hybrid artificial intelligence model / Engineering with Computers 36(1):1~11
Arvydas Beržonskis / 2016 / Reliability Analysis of Fatigue Fracture of Wind Turbine Drivetrain Components / Energy Procedia 94:146~154
Bartosz Krawczyk / 2016 / GPU-Accelerated Extreme Learning Machines for Imbalanced Data Streams with Concept Drift / Procedia Computer Science 80:1692~1701
Behrooz Keshtegar / 2016 / Chaotic conjugate stability transformation method for structural reliability analysis / Computer Methods in Applied Mechanics and Engineering 310:866~885
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