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Predicting Geotechnical Parameters of Sands from CPT Measurements Using Neural Networks
Author(s) -
Hsein Juang C.,
Lu Ping C.,
Chen Caroline J.
Publication year - 2002
Publication title -
computer‐aided civil and infrastructure engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.773
H-Index - 82
eISSN - 1467-8667
pISSN - 1093-9687
DOI - 10.1111/1467-8667.00250
Subject(s) - artificial neural network , backpropagation , probabilistic logic , task (project management) , function (biology) , computer science , geotechnical engineering , artificial intelligence , machine learning , data mining , engineering , systems engineering , evolutionary biology , biology
Predicting sand parameters such as D r , K 0 , and OCR from CPT measurements is an important and challenging task for the geotechnical engineer. In the present study, a system of neural networks is developed for predicting these parameters based on CPT measurements. The proposed system uses backpropagation neural networks for function approximation and probabilistic neural networks for classification. By strategically combining both types of networks, the proposed system is able to predict accurately D r , K 0 , and OCR of sands from CPT measurements and other soil parameters. Details on the development of the proposed system are presented, along with comparisons of the results obtained by this system with existing methods.