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Title: PREDICTING COLLAPSE POTENTIAL OF SOILS WITH NEURAL NETWORKS
Accession Number: 00740651
Record Type: Component
Record URL: Availability: Transportation Research Board Business Office 500 Fifth Street, NW Find a library where document is available Abstract: Collapsible soils are known to experience a dramatic decrease in volume upon wetting. This can be very detrimental to structures founded on collapsible soils. Whereas field testing might be the most reliable way to determine collapse potential, the engineer often sees it as the last resort. Neural network models for predicting the collapse potential of soils on the basis of basic index properties are presented. Field data, consisting of index properties and collapse potential, are used to train and test neural networks. Various network architectures and training algorithms are examined and compared. The trained networks are shown to be able to identify the collapsible soils and predict the collapse potential.
Supplemental Notes: This paper appears in Transportation Research Record No. 1582, Centrifuge Modeling, Intelligent Geotechnical Systems, and Reliability-Based Design.
Language: English
Corporate Authors: Transportation Research Board 500 Fifth Street, NW Authors: Juang, C HElton, D JPagination: p. 22-28
Publication Date: 1997
Serial: ISBN: 0309061571
Features: Figures
(4)
; References
(21)
; Tables
(3)
TRT Terms: Old TRIS Terms: Subject Areas: Geotechnology; Highways; I41: General Soil Surveys
Files: TRIS, TRB, ATRI
Created Date: Sep 10 1997 12:00AM
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