지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 국제구조공학회 Smart Structures and Systems, An International Journal Smart Structures and Systems, An International Journal Vol.22 No.5
- 발행연도
- 2018.1
- 수록면
- 561 - 574 (14page)
이용수
초록· 키워드
오류제보하기
In this paper, for efficiently reducing the computational cost of the model updating during the optimization process of damage detection, the structural response is evaluated using properly trained surrogate model. Furthermore, in practice uncertainties in the FE model parameters and modelling errors are inevitable. Hence, an efficient approach based on Monte Carlo simulation is proposed to take into account the effect of uncertainties in developing a surrogate model. The probability of damage existence (PDE) is calculated based on the probability density function of the existence of undamaged and damaged states. The current work builds a framework for Probability Based Damage Detection (PBDD) of structures based on the best combination of metaheuristic optimization algorithm and surrogate models. To reach this goal, three popular metamodeling techniques including Cascade Feed Forward Neural Network (CFNN), Least Square Support Vector Machines (LS-SVMs) and Kriging are constructed, trained and tested in order to inspect features and faults of each algorithm. Furthermore, three well-known optimization algorithms including Ideal Gas Molecular Movement (IGMM), Particle Swarm Optimization (PSO) and Bat Algorithm (BA) are utilized and the comparative results are presented accordingly. Furthermore, efficient schemes are implemented on these algorithms to improve their performance in handling problems with a large number of variables. By considering various indices for measuring the accuracy and computational time of PBDD process, the results indicate that combination of LS-SVM surrogate model by IGMM optimization algorithm have better performance in predicting the of damage compared with other methods.
목차
등록된 정보가 없습니다.
참고문헌 (31)
참고문헌 신청
AI-jarrah.OY / 2015 / Efficient machine learming for big data : A review / Big DaTa Res 2(3):87~93
Bakhary, N / 2007 / Damage detection using artificial neural network with consideration of uncertainties / Eng. Struct 29(11):2806~2815
Cortes, C / 1995 / Support-vector networks / Mach. Learn 20(3):273~297
Dizangian, B / 2016 / A fast decoupled reliability-based design optimization of structures using B-spline interpolation curves / J. Braz. Soc. Mech. Sci. Eng 38(6):1817~1829
Dubourg, V / 2011 / Reliability-based design optimization using kriging surrogates and subset simulation / Struct. Multidiscip. O 44(5):673~690
Bakhary, N / 2007 / Damage detection using artificial neural network with consideration of uncertainties / Eng. Struct 29(11):2806~2815
Cortes, C / 1995 / Support-vector networks / Mach. Learn 20(3):273~297
Dizangian, B / 2016 / A fast decoupled reliability-based design optimization of structures using B-spline interpolation curves / J. Braz. Soc. Mech. Sci. Eng 38(6):1817~1829
Dubourg, V / 2011 / Reliability-based design optimization using kriging surrogates and subset simulation / Struct. Multidiscip. O 44(5):673~690
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