Correlation of Mechanical Properties of Weathered Basaltic Terrain for Strength Characterization of Foundation using ANN
|
10.5120/4054-5820 |
Saklecha P.P., Katpatal Y.B., Rathore S.S. and Agarawal D.K.. Article: Correlation of Mechanical Properties of Weathered Basaltic Terrain for Strength Characterization of Foundation using ANN. International Journal of Computer Applications 33(10):7-12, November 2011. Full text available. BibTeX
@article{key:article,
author = {Saklecha P.P. and Katpatal Y.B. and Rathore S.S. and Agarawal D.K.},
title = {Article: Correlation of Mechanical Properties of Weathered Basaltic Terrain for Strength Characterization of Foundation using ANN},
journal = {International Journal of Computer Applications},
year = {2011},
volume = {33},
number = {10},
pages = {7-12},
month = {November},
note = {Full text available}
}
Abstract
This paper presents the application of artificial neural network (ANN) to study the strength characterization of a foundation soils in a basaltic terrain. The prediction models were developed for foundation strength characteristic California Bearing Ratio (CBR) using correlations with mechanical properties of foundation soil viz. optimum moisture content (OMC), maximum dry density (MDD), liquid limit (LL), plastic limit (PL), and plasticity index (PI). For this study, 387 laboratory test data sets were collected for different locations in Wardha district in the state of Maharashtra, India. It has been shown that ANN was able to learn the relations between strength characteristic CBR and mechanical properties of foundation soil. The results indicated a strong correlation (r = 0.87). The performance of the developed ANN model has been validated by actual laboratory tests and a good correlation r = 0.9971 was obtained.
Reference
- Abu Kiefa, M. A. (1998). “General regression neural networks for driven piles in cohesionless soils.” J. Geotech. & Geoenv. Engrg. ASCE, 124(12), 1177-1185.
- Lee, I. M., & Lee, J. H. (1996). “Prediction of pile bearing capacity using artificial neural networks.” Computer and Geotechnics, 18(3), 189–200.
- Ellis, G. W., Yao, C., Zhao, R. and Penumadu, D. (1995). “Stress-strain modeling of sands using artificial neural networks.” J. Geotech. Engrg. ASCE, 121(5), 429- 435.
- Zhou, Y., and Wu, X. (1994). “Use of neural networks in the analysis and interpretation of site investigation data.” Computer and Geotechnics, 16, 105-122.
- Goh, A. T. C. (1994). “Seismic liquefaction potential assessed by neural network.” J. Geotech. & Geoenv. Engrg. ASCE, 120(9), 1467-1480.
- Black, W.P.M. (1962). “A Method of Estimating the CBR of Cohesive Soils from Plasticity Data.” Geotechnique, 12, 271 - 272.
- de Graft - Johnson, J.W.S. and Bhatia, H.S. 1969). “The Engineering Characteristics of the Lateritic Gravels of Ghana.” Proceedings of 7th International Conference on Soil Mechanics and Foundation Engineering, Mexico. August 28-29. Bangkok: Asian Institute of Technology. vol.2, 13 - 43.
- Agarwal, K. B. and Ghanekar, K.D. (1970). “Prediction of CBR from Plasticity Characteristics of Soil”. Proceeding of 2nd South-east Asian Conference on Soil Engineering, Singapore. June 11-15, 1970. Bangkok: Asian Institute of Technology, 571-576.
- National Cooperative Highway Research Program (2001). “Correlation of CBR Values with Soil Index Properties.” Guide for Mechanistic and Empirical Design for New and Rehabilitated Pavement Structures, Appendix CC-1, Final Document, West University Avenue Champaign, Illinois: Ara, Inc.
- Kin, M. W. (2006). “California bearing ratio correlation with soil index properties.” Master degree Project, Faculty of Civil Engineering, University Technology Malaysia.
- T. Taskiran (2010).”Prediction of California bearing ratio (CBR) of fine grained soils by AI methods.” journal of Advances in Engineering Software, 41, 886–892.
- B. Yildirim, O. Gunaydin (2011). “Estimation of California bearing ratio by using soft computing systems.” journal of Expert Systems with Applications 38, 6381–6391.