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Evaluation of Bearing Capacity on PHC Auger-Drilled Piles Using Artificial Neural Network  

Lee, Song (서울시립대 토목공학과)
Jang, Joo-Won (서울시립대 토목공학과)
Publication Information
Journal of the Korea institute for structural maintenance and inspection / v.10, no.6, 2006 , pp. 213-223 More about this Journal
Abstract
In this study, artificial neural network is applied to the evaluation of bearing capacity of the PHC auger-drilled piles at sites of domestic decomposed granite soils. For the verification of applicability of error back propagation neural network, a total of 168 data of in-situ test results for PHC auger-drilled plies are used. The results show that the estimation of error back propagation neural network provide a good matching with pile test results by training and these results show the confidence of utilizing the neural networks for evaluation of the bearing capacity of piles.
Keywords
PHC auger-drilled piles; Artificial neural network; Error back propagation algorithm;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 박현일, 석정우, 황대진, 조천환 (2006), "항타말뚝의 지지력 예측을 위한 최적의 인공신경망모델에 관한 연구", 한국지반공학회논문집, 제22권, 제6호, pp15-26.   과학기술학회마을
2 Smith, E. A. L.(1960), "Pile Driving Analysis by the Wave Equation", Jounal of the Soil Mechanics and Foundation Engineering, ASCE, III(3), pp367-383.
3 김정수(2003), "화강풍화대 지반에 매입된 SIP말뚝의 지지력 평가에 관한 연구", 한양대학교, 박사학위 논문, pp7-19.
4 이송(2000), "인공신경망을 이용한 압밀거동 예측", 한국지반공학회 2000년 가을학술발표회, pp.673-680.
5 천병식, 조천환 (1999), "Set-up 효과를 반영한 타입말뚝의 파동이론해석", 한국지반공학회논문집, 제15권, 제2호, pp95-104.
6 도용태, 김일곤, 김종완, 박창현(2001), 인공지능, 사이텍미디어, 서울, pp.225-266.