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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 한국계산역학회 Computers and Concrete, An International Journal Computers and Concrete, An International Journal Vol.21 No.5
- 발행연도
- 2018.1
- 수록면
- 513 - 523 (11page)
이용수
초록· 키워드
오류제보하기
Time dependent strain due to creep is a significant factor in structural design. Multi-gene genetic programming (MGGP) and artificial neural network (ANN) are used to develop two models for prediction of creep compliance in concrete. The first model was developed by MGGP technique and the second model by hybridized MGGP-ANN. In the MGGP-ANN, the ANN is working in parallel with MGGP to predict errors in MGGP model. A total of 187 experimental data sets that contain 4242 data points are filtered from the NU-ITI database. These data are used in developing the MGGP and MGGP-ANN models. These models contain six input variables which are: average compressive strength at 28 days, relative humidity, volume to surface ratio, cement type, age at start of loading and age at the creep measurement. Practical equation based on MGGP was developed. A parametric study carried out with a group of hypothetical data generated among the range of data used to check the generalization ability of MGGP and MGGP-ANN models. To confirm validity of MGGP and MGGP-ANN models; two creep prediction code models (ACI209 and CEB), two empirical models (B3 and GL 2000) are used to compare their results with NUITI database.
목차
등록된 정보가 없습니다.
참고문헌 (33)
참고문헌 신청
Gholamreza Abdollahzadeh / 2016 / Predicting of compressive strength of recycled aggregate concrete by genetic programming / Computers and Concrete, An International Journal 18(2):155~163
Abed, M. / 2013 / Dynamic versus static artificial neural network model for masonry creep deformation / Proceedings of the Institution of Civil Engineers Structures and Buildings :355~366
Abed, M. / 2010 / Creep predicting model in masonry structure utilizing dynamic neural network / J. Comput. Sci. 6(5):597~605
2008 / ACI-Committee209, Guide for Modeling and Calculating Shrinkage and Creep in Hardened Concrete (ACI 209.2R-08)
Ao, S. / 2011 / Intelligent Control and Computer Engineering / Springer Netherlands
Abed, M. / 2013 / Dynamic versus static artificial neural network model for masonry creep deformation / Proceedings of the Institution of Civil Engineers Structures and Buildings :355~366
Abed, M. / 2010 / Creep predicting model in masonry structure utilizing dynamic neural network / J. Comput. Sci. 6(5):597~605
2008 / ACI-Committee209, Guide for Modeling and Calculating Shrinkage and Creep in Hardened Concrete (ACI 209.2R-08)
Ao, S. / 2011 / Intelligent Control and Computer Engineering / Springer Netherlands
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