A Neural Network Model for Prediction of Pile Setup
The time-dependent increase in pile capacity after its driving may be significant. A reliable prediction of this increase (setup) may lead to a significant saving in pile design. A neural network model to predict pile setup was developed. A database derived from field tests reported in the literature showing setup of driven piles was compiled, with six variables selected as input parameters: soil type, pile type, pile diameter, pile length, time after pile installation, and initial effective stress at tip. Ultimate pile capacity at beginning of restrike (QBOR) is the sum of pile capacity (QEOD) at end of drive and increased in capacity (ΔQBOR) caused by setup, which in this study is predicted by a backpropagation neural network. The results demonstrate that the neural network model provides a better prediction than predictions by the available empirical methods. A neural network model can serve as a reliable tool for the prediction of pile setup, and further training with additional data will lead to additional improvement in the quality of prediction.
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- Summary URL:
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Availability:
- Find a library where document is available. Order URL: http://www.trb.org/Main/Public/Blurbs/159491.aspx
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Authors:
- Jeon, JongKoo
- Rahman, M Shamimur
- Publication Date: 2007
Language
- English
Media Info
- Media Type: Print
- Features: Figures; References; Tables;
- Pagination: pp 12-19
- Monograph Title: Soil Mechanics 2007
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Serial:
- Transportation Research Record: Journal of the Transportation Research Board
- Issue Number: 2004
- Publisher: Transportation Research Board
- ISSN: 0361-1981
Subject/Index Terms
- TRT Terms: Backpropagation; Bearing capacity; Mathematical prediction; Neural networks; Pile driving; Soil mechanics; Soil types; Support piles
- Uncontrolled Terms: Pile diameter; Pile length; Pile setup
- Subject Areas: Data and Information Technology; Geotechnology; Highways; I42: Soil Mechanics;
Filing Info
- Accession Number: 01044062
- Record Type: Publication
- ISBN: 9780309104302
- Files: TRIS, TRB
- Created Date: Mar 16 2007 10:55AM