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

Year 2017, , 433 - 439, 30.06.2017

Yusuf Erzin , Yeşim Tuskan

https://doi.org/10.18466/cbayarfbe.319912

Cited By: 4

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 km² 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.

Keywords

Generalized regression neural network, In-situ test, Radial basis neural network

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