Predicting geotechnical parameters of sands from CPT measurements using neural networks

C. Hsein Juang, Ping C. Lu, Caroline J. Chen

Research output: Contribution to journalArticlepeer-review

15 Scopus citations


Predicting sand parameters such as Dr, Ko, and OCR from CPT measurements is an important and challenging task for the geotechnical engineer. In the present study, a system of neutral 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, Ko, 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.

Original languageEnglish
Pages (from-to)31-42
Number of pages12
JournalComputer-Aided Civil and Infrastructure Engineering
Issue number1
StatePublished - Jan 2002


Dive into the research topics of 'Predicting geotechnical parameters of sands from CPT measurements using neural networks'. Together they form a unique fingerprint.

Cite this