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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 국제구조공학회 Steel and Composite Structures, An International Journal Steel and Composite Structures, An International Journal Vol.39 No.4
- 발행연도
- 2021.1
- 수록면
- 471 - 491 (21page)
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초록· 키워드
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In this study, we estimate the ultimate load of rectangular concrete-filled steel tubes (CFST) by developing a novel hybrid predictive model (ANN-BCMO) which is a combination of balancing composite motion optimization (BCMO) - a very new optimization technique and artificial neural network (ANN). For this aim, an experimental database consisting of 422 datasets is used for the development and validation of the ANN-BCMO model. Variables in the database are related with the geometrical characteristics of the structural members, and the mechanical properties of the constituent materials (steel and concrete). Validation of the hybrid ANN-BCMO model is carried out by applying standard statistical criteria such as root mean square error (RMSE), coefficient of determination (R2), and mean absolute error (MAE). In addition, the selection of appropriate values for parameters of the hybrid ANN-BCMO is conducted and its robustness is evaluated and compared with the conventional ANN techniques. The results reveal that the new hybrid ANN-BCMO model is a promising tool for prediction of the ultimate load of rectangular CFST, and prove the effective role of BCMO as a powerful algorithm in optimizing and improving the capability of the ANN predictor.
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참고문헌 (143)
참고문헌 신청
Miguel Abambres / 2019 / Neural Network-Based Formula for the Buckling Load Prediction of I-Section Cellular Steel Beams / Computers 8(1):2
Ahmadi, M. / 2017 / ANN model for predicting the compressive strength of circular steel confined concrete / Int. J. Civil Eng 15:213~221
Akbar, H. / 2011 / Training neural networks using Clonal Selection Algorithm and Particle Swarm Optimization: A comparisons for 3D object recognition / Proceedings of the 2011 11th International Conference on Hybrid Intelligent Systems (HIS)
Al-Khaleefi, A. M. / 2002 / Prediction of fire resistance of concrete filled tubular steel columns using neural networks / Fire Saf. J 37:339
Faris Ali / 2016 / Experimental and numerical study on the performance of hollow and concrete-filled elliptical steel columns subjected to severe fire / Fire and Materials 40(4):635~652
Ahmadi, M. / 2017 / ANN model for predicting the compressive strength of circular steel confined concrete / Int. J. Civil Eng 15:213~221
Akbar, H. / 2011 / Training neural networks using Clonal Selection Algorithm and Particle Swarm Optimization: A comparisons for 3D object recognition / Proceedings of the 2011 11th International Conference on Hybrid Intelligent Systems (HIS)
Al-Khaleefi, A. M. / 2002 / Prediction of fire resistance of concrete filled tubular steel columns using neural networks / Fire Saf. J 37:339
Faris Ali / 2016 / Experimental and numerical study on the performance of hollow and concrete-filled elliptical steel columns subjected to severe fire / Fire and Materials 40(4):635~652
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