@inproceedings{280d49ad8f5042b89075cc53733d3b69,
title = "Interpretation of in-situ test data using artificial neural networks",
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.",
author = "Juang, {C. H.} and Lin, {Pin Sien} and Tso, {Tien Hsiung}",
note = "Publisher Copyright: {\textcopyright} 1997 IEEE.; 1997 International Conference on Intelligent Information Systems, IIS 1997 ; Conference date: 08-12-1997 Through 10-12-1997",
year = "1997",
doi = "10.1109/IIS.1997.645211",
language = "???core.languages.en_GB???",
series = "Proceedings - Intelligent Information Systems, IIS 1997",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "168--172",
editor = "Hojjat Adeli",
booktitle = "Proceedings - Intelligent Information Systems, IIS 1997",
}