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
- 한국계산역학회 Computers and Concrete, An International Journal Computers and Concrete, An International Journal Vol.12 No.2
- 발행연도
- 2013.9
- 수록면
- 229 - 241 (13page)
이용수
초록· 키워드
오류제보하기
The objective of this study is to reveal the sufficiency of neural networks (NN) as a securer, quicker, more robust and reliable method to be used in seismic assessment of existing reinforced concrete buildings. The NN based approach is applied as an alternative method to determine the seismic performance of each existing RC buildings, in terms of damage level. In the application of the NN, a multilayer perceptron (MLP) with a back-propagation (BP) algorithm is employed using a scaled conjugate gradient. NN based model wasd eveloped, trained and tested through a based MATLAB program. The database of this model was developed by using a statistical procedure called P25 method. The NN based model was also proved by verification set constituting of real existing RC buildings exposed to 2003 Bingol earthquake. It is demonstrated that the NN based approach is highly successful and can be used as an alternative method to determine the seismic performance of each existing RC buildings.
목차
등록된 정보가 없습니다.
참고문헌 (21)
참고문헌 신청
Yousef A. Al-Salloum / 2012 / Prediction of compressive strength of concrete using neural networks / Computers and Concrete, An International Journal 10(2):197~217
Arjun, C.R. / 2011 / Neural network estimation of duration of strong ground motion using Japanese earthquake records / Soil Dyn. Earthq. Eng 31(7):866~872
Arslan, M. H. / 2010 / An evaluation of effective design parameters on earthquake eperformance of RC buildings using neural networks / Eng. Struct 32(7):1888~1898
Askan, A. / 2010 / Probabilistic methods for the estimation of potential seismic damage:Application to reinforced concrete buildings in Turkey / Struct. Saf 32(4):262~271
M. Badaoui / 2012 / Evaluation of accidental eccentricity for buildings by artificial neural networks / Structural Engineering and Mechanics, An Int＇l Journal 41(4):527~538
Arjun, C.R. / 2011 / Neural network estimation of duration of strong ground motion using Japanese earthquake records / Soil Dyn. Earthq. Eng 31(7):866~872
Arslan, M. H. / 2010 / An evaluation of effective design parameters on earthquake eperformance of RC buildings using neural networks / Eng. Struct 32(7):1888~1898
Askan, A. / 2010 / Probabilistic methods for the estimation of potential seismic damage:Application to reinforced concrete buildings in Turkey / Struct. Saf 32(4):262~271
M. Badaoui / 2012 / Evaluation of accidental eccentricity for buildings by artificial neural networks / Structural Engineering and Mechanics, An Int＇l Journal 41(4):527~538
이 논문의 저자 정보
최근 본 자료
댓글(0)
0