Similar articles

Abstract: This study employs the least square support vector machine (LSSVM) for the prediction of pullout capacity of small ground anchor. LSSVM is firmly based on the theory of statistical learning and uses regression technique. In LSSVM, Vapnik and Lerner (1963)’s ε-insensitive loss function was replaced by a cost function which corresponded to a form of ridge regression. The input parameters of LSSVM were equivalent anchor diameter, anchor embedment depth, average cone tip resistance, average cone sleeve friction, and installation technique. Using 83 out the available 119 In-situ test datasets, an LSSVM regression model was developed. The goodness of the model was tested using the remaining 36 data points. The developed LSSVM also gave an error bar of predicted data. A sensitivity analysis was conducted to determine the effect of each input parameter. The results were compared with the artificial neural network (ANN) model. Overall, LSSVM was shown to perform well.

In this paper, the authors employed Least Support Vector Machine (LSSVM) for the prediction of pullout capacity of small ground anchor. They used 119 in-situ datasets while developing the LSSVM model. They compared the LSSVM results with the in-situ test results and used different performance indices (R, MAE, and RMSE) to check the performance of the LSSVM model. Additionally, they performed sensitivity analyses to determine the effect of each input parameter. Finally, they compared the LSSVM results with the results obtained from the ANN model developed by Shahin and Jaksa (2006) for testing samples.

基于最小二乘支持向量机算法的小地锚抗拔承载力研究

[1]Basudhar, P.K., Singh, D.N., 1994. A generalized procedure for predicting optimal lower bound break-out factors of strip anchors. Geotechnique, 44(2):307-318.

[2]Das, B.M., 1978. Model tests for uplift capacity of foundations in clay. Soils and Foundations, 18(2):17-24.

[3]Das, B.M., 1980. A procedure for estimation of ultimate uplift capacity of foundations in clay. Soils and Foundations, 20(1):77-82.

[4]Das, B.M., 1987. Developments in Geotechnical Engineering, Theoretical Foundation Engineering. Elsevier.

[5]Das, B.M., Seeley, G.R., 1975. Breakout resistance of horizontal anchors. Journal of Geotechnology Engineering Division, ASCE, 101(9):999-1003.

[6]Deng, S., Yeh, T.H., 2010. Applying least squares support vector machines to the airframe wing-box structural design cost estimation. Expert Systems with Applications, 37(12):8417-8423.

[7]Dickin, E.A., 1988. Uplift behaviour of horizontal anchor plates in sand. Journal of Geotechnology Engineering, ASCE, 114(11):1300-1317.

[8]Erzin, Y., Cetin, T., 2013. The prediction of the critical factor of safety of homogeneous finite slopes using neural networks and multiple regressions. Computer Geoscience, 51:305-313.

[9]Huang, Z., Luo, J., Li, X., et al., 2009. Prediction of effluent parameters of wastewater treatment plant based on improved least square support vector machine with PSO. 1st International Conference on Information Science and Engineering (ICISE), Nanjing, No. 5454606, p.4058-4061.

[10]Kecman, V., 2001. Learning and Soft Computing Support Vector Machines, Neural Networks, and Fuzzy Logic Models. The MIT Press, Cambridge.

[11]Koutsabeloulis, N.C., Griffiths, D.V., 1989. Numerical modeling of the trap door problem. Geotechnique, 39(1):77-89.

[12]Kurup, P.U., Dudani, N.K., 2002. Neural networks for profiling stress history of clays from PCPT data. Journal of Geotechnical and Geoenvironmental Engineering, 128(7):569-579.

[13]Liong, S.Y., Lim, W.H., Paudyal, G.N., 2000. River stage forecasting in Bangladesh: neural network approach. Journal of Computing in Civil Engineering, 14(1):1-8.

[14]Meyerhof, G.G., 1973. Uplift resistance of inclined anchors and piles. Proceedings of 8th International Conference on Soil Mechanics and Foundation Engineering, Moscow, USSR, p.167-172.

[15]Meyerhof, G.G., Adams, J.I., 1968. The ultimate uplift capacity of foundations. Canadian Geotechnical Journal, 5(4):225-244.

[16]Murray, E.J., Geddes, J.D., 1987. Uplift of anchor plates in sand. Journal of Geotechnology Engineering Division, ASCE, 113(3):202-215.

[17]Pahasa, J., Ngamroo, I., 2011. A heuristic training-based least squares support vector machines for power system stabilization by SMES. Expert Systems with Applications, 38(11):13987-13993.

[18]Pal, M., 2006. Support vector machines-based modelling of seismic liquefaction potential. International Journal for Numerical and Analytical Methods in Geomechanics, 30(10):983-996.

[19]Park, D., Rilett, L.R., 1999. Forecasting freeway link ravel times with a multi-layer feed forward neural network. Computer Aided Civil and Infrastructure Engineering, 14:358-367.

[20]Rao, K.S.S., Kumar, J., 1994. Vertical uplift capacity of horizontal anchors. Journal of Geotechnology Engineering, ASCE, 120(7):1134-1147.

[21]Rowe, R.K., Davis, E.H., 1982a. The behaviour of anchor plates in clay. Geotechnique, 32(1):9-23.

[22]Rowe, R.K., Davis, E.H., 1982b. The behaviour of anchor plates in sand. Geotechnique, 32(1):25-41.

[23]Shahin, M.A., Jaksa, M.B., 2005. Neural network prediction of pullout capacity of marquee ground anchors. Computers and Geotechnics, 32(3):153-163.

[24]Shahin, M.A., Jaksa, M.B., 2006. Pullout capacity of small ground anchors by direct cone penetration test methods and neural networks. Canadian Geotechnical Journal, 43(6):626-637.

[25]Smola, A., Scholkopf, B., 1998. On a kernel based method for pattern recognition, regression, approximation and operator inversion. Algorithmica, 22(1-2):211-231.

[26]Sutherland, H.B., 1988. Uplift resistance of soils. Geotechnique, 38(4):473-516.

[27]Suykens, J.A.K., Lukas, L., van Dooren, P., et al., 1999. Least squares support vector machine classifiers: a large scale algorithm. Proceedings of European Conference Circuit Theory and Design, Stresa, Italy, p.839-842.

[28]Suykens, J.A.K., de Barbanter, J., Lukas, L., et al., 2002. Weighted least squares support vector machines: robustness and sparse approximation. Neurocomputing, 48(1-4):85-105.

[29]Tang, Y., Zang, Y.Q., Huang, G., et al., 2005. Granular SVM-RFE gene selection algorithm for reliable prostate cancer classification on microarray expression data. Proceedings of the 5th IEEE Symposium on Bioinformatics and Bioengineering, p.290-293.

[30]Tao, B., Xu, W.J., Pang, G.B., et al., 2008. Prediction of bearing raceways superfinishing based on least squares support vector machines. Proceedings of the 4th International Conference on Natural Computation (ICNC), 2:125-129.

[31]Vapnik, V.N., 1998. Statistical Learning Theory. Wiley, New York.

[32]Vapnik, V.N., Lerner, A., 1963. Pattern recognition using generalized portrait method. Automation and Remote Control, 24:774-780.

[33]Vermeer, P.A., Sutjiadi, W., 1985. The uplift resistance of shallow embedded anchors. Proceedings of the 11th International Conference Soil Mechanics and Foundation Engineering, San Francisco, p.1635-1638.

[34]Vesic, A.S., 1971. Breakout resistance of objects embedded in ocean bottom. Journal of Soil Mechanics and Foundation Division, ASCE, 96(SM4):1311-1334.