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
- 한국콘크리트학회 International Journal of Concrete Structures and Materials International Journal of Concrete Structures and Materials Vol.16 No.6
- 발행연도
- 2022.11
- 수록면
- 917 - 938 (22page)
이용수
초록· 키워드
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In this study, a modified Artificial Neural Network (ANN) and Support Vector Regression (SVR) with three different optimization algorithms (Genetic, Salp Swarm and Grasshopper) were used to establish an accurate and easy-to-use module to predict the lateral pressure exerted by fresh concrete on formwork based on three main inputs, namely mix proportions (cement content, w/c, coarse aggregates, fine aggregates and admixture agent), casting rate, and height of specimens. The data have been obtained from 30 previously piloted experimental studies (resulted 113 samples). Achieved results for the model including all the input data provide the most excellent prediction of the exerted lateral pressure. Additionally, having different magnitudes of powder volume, aggregate volume and fluid content in the mix exposes different rising and descending in the lateral pressure outcomes. The results indicate that each model has its own advantages and disadvantages; however, the root mean square error values of the SVR models are lower than that of the ANN model. Additionally, the proposed models have been validated and all of them can accurately predict the lateral pressure of fresh concrete on the panel of the formwork.
목차
Abstract
1 Introduction
2 Research Significance
3 Methodology
4 Results and Discussion
5 Conclusion
References
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