Displacement Prediction for Soil Nailing Based on ANN

Abstract:

Soil nailing has become an important excavation support system for its good performance and cost-effectiveness. It is complicated to predict deformation of soil nailing during excavating. The Artificial Neural Network (ANN) is developed very quickly these years, which can be applied in diverse applications such as complex non-linear function mapping, pattern recognition, image processing and so on, and has been widely used in many fields, including geotechnical engineering. In this paper, the artificial neural network is applied for deformation prediction for soil nailing in deep excavation. The time series neural networks-based model for predicting deformation is presented and used in an engineering project. The results predicted by the model and those observed in the field are compared. It is shown that the artificial neural network-based method is effective in predicting the displacement of soil nailing during excavation.

• Periodical: Applied Mechanics and Materials (Volumes 353-356)
• Pages: 614-618
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.353-356.614
• Online since: August 2013
• Authors: Lang Gao, Zhao Wen Tang, Quan Zhong Liu
• Keywords: Artificial Neural Network (ANN), Displacement Prediction, Soil Nailing

References

[1] Schlosser, F. 1982. Behavior and design of soil nailing. Proc. Symp. Recent Development in Ground Improvement Techniques, AIT, Bangkok: 389-397.

[2] Bruce, D. A., and Jewell, R. A. 1986. Soil nailing: application and practise-part 1. Ground Engineering. 19: 10-15.

[3] Gassler, G. 1990. In-situ techniques of reinforced soil. Proc. Int. Reinforced Soil Conference: 185-196.

[4] Zhaoyuan Chen, and Jinghao Cui 1998. Application of Soil Nailing in Foundation Excavation. Chinese Building Industry Publishing House, Beijing. (In Chinese).

[5] Ghaboussi, J. 1992. Potential application of neural-biological computational models in geotechnical engineering. Proc. 4th Int. Symp. on Numerical Models in Geomech., Swansea, U. K.: 138-149.

[6] Ghaboussi, J., Garrett, J. H. Jr., and Wu, X. 1991. Knowledge-based modeling of material behavior with neural networks. J. Engrg. Mech., ASCE, 117(1): 132-153.

DOI: 10.1061/(asce)0733-9399(1991)117:1(132)

[7] Ghaboussi, J and Sidarta, D. E. 1997. New method of material modeling using neural networks. Numerical models in Geomechanics, Pietruszczak & Pande Eds.: 393-400.

[8] Goh A.T.C. 1994. Seismic liquefaction potential assessed by neural networks. J. Geotech. Engrg. ASCE, 120(9): 1467-1480.

DOI: 10.1061/(asce)0733-9410(1994)120:9(1467)

[9] Goh A.T.C. 1996. Neural-networks modeling of CPT seismic liquefaction data. J. Geotech. Engrg. ASCE, 122(1): 70-73.

DOI: 10.1061/(asce)0733-9410(1996)122:1(70)

[10] Goh A.T.C., Wong K.S. and Broms B.B. 1995. Estimation of lateral wall movements in braced excavations using neural networks. Can. Geotech. J., 32: 1059-1064.

DOI: 10.1139/t95-103

[11] Chang, W. T., Chow, Y. K., and Liu, L. F. 1995. Neural network: an alternative to pile driving formulas. Comp. and Geotechnics, 17: 135-156.

DOI: 10.1016/0266-352x(95)93866-h

[12] 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.

DOI: 10.1061/(asce)0733-9410(1995)121:5(429)

[13] Basheer, I. A., and Najjar, Y. M. 1996. Predicting dynamic response of absorption columns with neural networks. J. Comp. in Civ. Engrg. ASCE, 10(1): 31-39.

[14] Teh C.I., Wong K. S. Goh A.T.C. and Jaritngam. 1997. Prediction of pile capacity using neural networks. J. Comp. in Civ. Engrg. ASCE, 11(2): 129-138.

DOI: 10.1061/(asce)0887-3801(1997)11:2(129)

[15] Shi J.S., Ortigao J.A.R. and Bai J. 1998. Modular neural networks for predicting settlements during tunneling. J. Geotech. Geoenviron. Engrg. ASCE, 124(5): 389-395.

DOI: 10.1061/(asce)1090-0241(1998)124:5(389)

[16] Mathew, A., Kumar, B., Sinha, B. P. and Pedreschi, R. F. 1999. Analysis of masonary panel under biaxial bending using ANNs and CBR. J. Comp. in Civ. Engrg. ASCE, 13(3): 170-177.

DOI: 10.1061/(asce)0887-3801(1999)13:3(170)

[17] Rumelhart D.E., Hinton G.E. and Williams R.J. 1986. Learning representation by back-propagation errors. Nature, 323(9): 533-536.

DOI: 10.1038/323533a0

[18] Karayiannis, N. B., and Venetsanopoulos, A. N. 1993. Artificial neural networks: learning algorithms, performance evaluation, and applications. Kluwer academic publishers, Boston.

[19] Flood, I., and Kartam, N. 1994. Neural networks in civil engineering. I: Principles and understanding. J. Comp. in Civ. Engrg., ASCE, 8(2): 131-148.

DOI: 10.1061/(asce)0887-3801(1994)8:2(131)