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 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

References

1. [1] Erzin, Y. ; Tuskan, Y. Prediction of standard penetra-tion test (SPT) value in Izmir, Turkey using generalized regression neural network. International conference on agricultural, civil and environmental engineering (acee-16). 2016.
2. [2] Sarkar, G.; Siddiqua, S.; Banik, R.; Rokonuzzaman, Md. Prediction of soil type and standard penetration test (SPT) value in Khulna City, Bangladesh using general re-gression neural network”, Q. Journal of Engineering Geol-ogy and Hydrogeology. 2015; 48,190-203.
3. [3] Kayen, R.E; Mitchell, J.K; Seed, H.B.; Lodge, A; Nish-io, S; Coutinho, R. Evaluation of SPT, CPT, and shear wave-based methods for liquefaction potential assessment using Loma Prieta data. NCEER .1992; 1, 177-204.
4. [4] Youd, T.L; Idriss I.M. Liquefaction resistance of soils: Summary report from the 1996 NCEER and 1998 NCEER/NSF workshops on evaluation of liquefaction re-sistance of soils. Journal of Geotechnical and Geoenviron-mental engineering, ASCE. 2001; 127(10),817-833.
5. [5] Peck, R.B.; Hanson, W.E.; Thorburn, T.H. Foundation engineering, John Wiley & Sons, New York, 1974.
6. [6] Terzaghi, K. ; Peck, R.B. Soil Mechanics in Engineer-ing Practice. 2nd edn. Wiley, New York, 1968.
7. [7] Turkish Highways, Stability Problem-Geotechnical Report, Turkish Highways, Izmir. (Unpublished Work) 2015.
8. [8] McCulloch, W.S; Pitts, W. A Logical Calculus of the Ideas Immanent in Nervous Activity. Bulletin of Mathemat-ical Biophysics. 1943; 5,115-133.

9. [9] Haykin, S. Neural Networks: A Comprehensive Foundation. Macmillan College Publishing Company, New York, USA, 1994.
10. [10] Tasdemir, Y; Kolay, E; Kayabali, K. Comparison of three artificial neural network approaches for estimating of slake durability index, Environmental Earth Science 2013; 68, 23–31.
11. [11] Zhongzhi, S. Neural Network. Higher Education Press, Beijing, 2009.
12. [12] Xu, Y; Zheng, J. Identification of Network- Traffic Based on Radial Basis Function Neural Network. ICICIS 2011, Part I, CCIS. 2011; 134, 173–179.
13. [13] Shahlaei, M; Madadkar-Sobhani, A; Fassihi, A; Saghaie, L; Arkan, E. QSAR study of some CCR5 antago-nists as anti-HIV agents using radial basis function neural network and general regression neural network on the basis of principal components, Medical Chemistry Re-search. 2012; 21, 3246–3262.
14. [14] Moradkhani, H; Hsu, K; Gupta H.V.; Sorooshian, S. Improved stream flow forecasting using self-organizing radial basis function artificial neural networks, Journal of Hydrology. 2004; 295, 246–262.