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
- 한국지반공학회 International Journal of Geo-Engineering International Journal of Geo-Engineering 제4권 제2호
- 발행연도
- 2012.6
- 수록면
- 37 - 45 (9page)
이용수
초록· 키워드
오류제보하기
Artificial neural network (ANN) model is implemented for a reasonable estimation of measured-to-predicted unit shaft capacities of rock socketed drilled shafts using a database of bi-directional load tests. A database of the measured-to-predicted unit shaft capacities of drilled shafts socketed in weak rocks from bi-directional pile load test results is obtained from literature review, For the implementation of the ANN model, the pile diameter, the rock socketed length, and the unconfined compressive strength of rock are assumed to be the primary affecting factors on the measured-to-predicted unit shaft capacity. The database is randomly divided into two categories, and a larger group is used for training of an ANN and the other group is used for the verification of the accuracy of the trained-ANN. As a result, the predictions of measured-to-predicted unit shaft capacities closely matched with the original corresponding values. Uncertainty of the measured-to-predicted unit shaft capacity associated with its prediction from ANN is also assessed.
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UCI(KEPA) : I410-ECN-0101-2020-531-000198569