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학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
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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.26 No.6
- 발행연도
- 2020.1
- 수록면
- 533 - 546 (14page)
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초록· 키워드
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The prediction of spall response of reinforced concrete members like columns and slabs have been attempted by earlier researchers with analytical solutions, as well as with empirical models developed from data generated from physical or numerical experiments, with different degrees of success. In this article, compared to the empirical models, more versatile and accurate models are developed based on model-free approach of artificial neural network (ANN). Synthetic data extracted from the results of numerical experiments from literature have been utilized for the purpose of training and testing of the ANN models. For two concrete members, namely, slabs and columns, different sets of ANN models were developed, each of which proved to have definite advantages over the corresponding empirical model reported in literature. In case of slabs, for all three categories of spall, the ANN model results were superior to the empirical models as evaluated by the various performance metrics, such as correlation, root mean square error, mean absolute error, maximum overestimation and maximum underestimation. The ANN models for each category of column spall could handle three variables together: namely, depth, spacing of longitudinal and transverse reinforcement, as contrasted to the empirical models that handled one variable at a time, and at the same time yielded comparable performance. The application of the ANN models for spall prediction of concrete slabs and columns developed in this study has been discussed along with their limitations.
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참고문헌 (50)
참고문헌 신청
Satadru Das Adhikary / 2016 / Influence of cylindrical charge orientation on the blast response of high strength concrete panels / Engineering Structures 149:35~49
Sohaib Ahmad / 2018 / Bond strength prediction of steel bars in low strength concrete by using ANN / Computers and Concrete, An International Journal 22(2):249~259
Hafzullah Aksoy / 2018 / Markov chain-incorporated and synthetic data-supported conditional artificial neural network models for forecasting monthly precipitation in arid regions / Journal of Hydrology 562:758~779
Amir Hossein Alavi / 2011 / Prediction of principal ground-motion parameters using a hybrid method coupling artificial neural networks and simulated annealing / Computers & Structures 89(23-24):2176~2194
Danial Jahed Armaghani / 2015 / Prediction of blast-induced air overpressure: a hybrid AI-based predictive model / Environmental Monitoring and Assessment 187(11):666
Sohaib Ahmad / 2018 / Bond strength prediction of steel bars in low strength concrete by using ANN / Computers and Concrete, An International Journal 22(2):249~259
Hafzullah Aksoy / 2018 / Markov chain-incorporated and synthetic data-supported conditional artificial neural network models for forecasting monthly precipitation in arid regions / Journal of Hydrology 562:758~779
Amir Hossein Alavi / 2011 / Prediction of principal ground-motion parameters using a hybrid method coupling artificial neural networks and simulated annealing / Computers & Structures 89(23-24):2176~2194
Danial Jahed Armaghani / 2015 / Prediction of blast-induced air overpressure: a hybrid AI-based predictive model / Environmental Monitoring and Assessment 187(11):666
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