Prediction of standard penetration test (SPT) value in Izmir, Turkey using radial basis neural network

Abstract

Site exploration, characterization and prediction of soil properties by in-situ test are key parts of a geotechnical preliminary process. In-situ testing is progressively essential in geotechnical engineering to recognize soil characteristics alongside. In this study, radial basis neural network ( RBNN ) model was developed for estimating standard penetration resistance ( SPT-N ) value. In order to develop the RBNN model, 121 SPT-N values collected from 13 boreholes spread over an area of 17 km 2 of Izmir was used. While developing the model, borehole location coordinates and soil component percentages were used as input parameters. The results obtained from the model were compared with those obtained from the field tests. To examine the accuracy of the RBNN model constructed, several performance indices, such as determination coefficient, relative root mean square error, and scaled percent error were calculated. The obtained indices make it clear that the RBNN model has a high prediction capacity to estimate SPT-N .