Predicting geotechnical parameters of sands from CPT measurements using neural networks

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 back-propagation 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.