Interpretation of in-situ test data using artificial neural networks

C. H. Juang, Pin Sien Lin, Tien Hsiung Tso

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

Establishing a realistic working profile of soil properties has been, and is still, one of the most challenging problems facing geotechnical engineers. A neural network approach is used to tackle this problem. Source data of a series of standard penetration tests (SPT) performed at the Texas A&M University's National Geotechnical Experimental Site are used for training and testing artificial neural networks. The developed neural network is shown able to predict the SPT N-values of the site studied. Data are then generated for constructing the profiles of the N-values using the trained neural network. The study shows that the potential of neural networks in site characterization is significant.

Original languageEnglish
Title of host publicationProceedings - Intelligent Information Systems, IIS 1997
EditorsHojjat Adeli
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages168-172
Number of pages5
ISBN (Electronic)0818682183, 9780818682186
DOIs
StatePublished - 1997
Event1997 International Conference on Intelligent Information Systems, IIS 1997 - Grand Bahama Island, Bahamas
Duration: 8 Dec 199710 Dec 1997

Publication series

NameProceedings - Intelligent Information Systems, IIS 1997

Conference

Conference1997 International Conference on Intelligent Information Systems, IIS 1997
Country/TerritoryBahamas
CityGrand Bahama Island
Period8/12/9710/12/97

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