Predicting Geotechnical Parameters of Sands from CPT Measurements Using Neural Networks

Predicting sand parameters such as Dr, K0, 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 backpropagat...

Full description

Saved in:
Bibliographic Details
Published in:Computer-aided civil and infrastructure engineering 2002-01, Vol.17 (1), p.31-42
Main Authors: Hsein Juang, C., Lu, Ping C., Chen, Caroline J.
Format: Article
Language:English
Subjects:
Applied sciences
Buildings. Public works
Computation methods. Tables. Charts
Earth sciences
Earth, ocean, space
Engineering and environment geology. Geothermics
Engineering geology
Exact sciences and technology
Geotechnics
Soil investigations. Testing
Structural analysis. Stresses
Citations: Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Predicting sand parameters such as Dr, K0, 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 Dr, K0, 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.
ISSN:1093-9687
1467-8667
DOI:10.1111/1467-8667.00250