Determination of ultimate bearing capacity of shallow foundations using LSSVM algorithm
Abstract
Accurate determination of the ultimate bearing capacity (UBC) of shallow foundations is vital for the safety of structures and buildings. Due to the inherent spatial variability characteristics of soil properties, some new approaches are needed to accurately determine the UBC of shallow foundations. The objective of this study is to develop a hybrid least squares support vector machine (LSSVM) and an improved particle swarm optimization (IPSO) algorithm for determining the UBC of shallow foundations. To validate the hybrid IPSO-LSSVM model, a comparison of the predictions was carried out among different models and theoretical methods. Three statistical indexes, namely the root-mean-square error (RMSE), the mean absolute error (MAE) and the correlation coefficient (R) were employed to measure and evaluate the performance of these models. The results showed that the developed hybrid IPSO-LSSVM model can be used for determining the UBC of shallow foundations with high accuracy.
Keyword : ultimate bearing capacity, uncertainty, spatial variability, algorithm
How to Cite
Xue, X., & Chen, X. (2019). Determination of ultimate bearing capacity of shallow foundations using LSSVM algorithm. Journal of Civil Engineering and Management, 25(5), 451-459. https://doi.org/10.3846/jcem.2019.9875
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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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(5), 358-367. https://doi.org/10.1111/0885-9507.00154
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Ren, Y., & Bai, G. C. (2011). Colonial competitive algorithm assisted least squares support vector machines. Advanced Materials Research, 255-260, 2082-2086. https://doi.org/10.4028/www.scientific.net/AMR.255-260.2082
Sadrossadat, E., Soltani, F., Mousavi, S. M., Marandi, S. M., & Alavi, A. H. (2013). A new design equation for prediction of ultimate bearing capacity of shallow foundation on granular soils. Journal of Civil Engineering and Management, 19(Supplement 1), s78-s90. https://doi.org/10.3846/13923730.2013.801902
Sakthivel, V. P., Bhuvaneswari, R., & Subramanian, S. (2010). An improved particle swarm optimization for induction motor parameter determination. International Journal of Computer Applications, 1(2), 62-67. https://doi.org/10.5120/44-150
Shahnazari, H., & Tutunchian, M. A. (2012). Prediction of ultimate bearing capacity of shallow foundations on cohesionless soils: an evolutionary approach. KSCE Journal of Civil Engineering, 16(6), 950-957. https://doi.org/10.1007/s12205-012-1651-0
Shahin, M. A., Maier, H. R., & Jaksa, M. B. (2001). Artificial neural network applications in Geotechnical Engineering. Australian Geomechnics Journal, 36(1), 49-62.
Shoaei, M. D., Alkarni, A., Noorzaei, J., Jaafar, M. S., & Huat, B. B. K. (2012). Review of available approaches for ultimate bearing capacity of two-lyaered soils. Journal of Civil Engineering and Management, 18(4), 469-482. https://doi.org/10.3846/13923730.2012.699930
Suykens, J. A. K., Vandewalle, J., & De Moor, B. (2001). Optimal control by least squares support vector machines. Neural Networks, 14, 23-35. https://doi.org/10.1016/S0893-6080(00)00077-0
Terzaghi, K. (1943). Theoretical soil mechanics. New York: John Wiley & Sons. https://doi.org/10.1002/9780470172766
Vesic, A. S. (1973). Analysis of ultimate loads of shallow foundations. Journal of the Soil Mechanics and Foundations Division, 99(1), 45-73. https://doi.org/10.1016/0148-9062(74)90598-1
Xu, H. B., & Chen, G. H. (2013). An intelligent fault identification method of rolling bearings based on LSSVM optimized by improved PSO. Mechanical Systems and Signal Processing, 35, 167-175. https://doi.org/10.1016/j.ymssp.2012.09.005
Yamagami, Y., & Jiang, J. C. (1997). A search for the critical slip surface in three-dimensional slope stability analysis. Soils and Foundations, 37(3), 1-16. https://doi.org/10.3208/sandf.37.3_1
Yilmazkaya, E., Dagdelenler, G., Ozcelik, Y., & Sonmez, H. (2018). Prediction of mono-wire cutting machine performance parameters using artificial neural network and regression models. Engineeing Geology, 239, 96-108. https://doi.org/10.1016/j.enggeo.2018.03.009
Alkroosh, I., & Nikraz, H. (2014). Predicting pile dynamic capacity via application of an evolutionary algorithm. Soils and Foundations, 54(2), 233-242. https://doi.org/10.1016 /j.sandf.2014.02.013
Armaghani, D. J., Shoib, R. S. N. S. B. R., Faizi, K., & Rashid, A. S. A. (2017). Developing a hybrid PSO-ANN model for estimating the ultimate bearing capacity of rock-socketed piles. Neural Computing & Applications, 28, 391-405. https://doi.org/10.1007/s00521-015-2072-z
Baziar, M. H., Kashkooli, A., & Azizkandi, A. S. (2012). Prediction of pile shaft resistance using cone penetration tests (CPTs). Computers and Geotechnics, 45, 74-82. https://doi.org/10.1016/j.compgeo.2012.04.005
Cicek, E., & Guler, E. (2015). Bearing capacity of strip footing on reinforced layered granular soils. Journal of Civil Engineering and Management, 21(5), 605-614. https://doi.org/10.3846/13923730.2014.890651
Das, S. K., & Basudhar, P. K. (2006). Undrained lateral load capacity of piles in clay using artificial neural network. Computers and Geotechnics, 33, 454-463. https://doi.org/10.1016/j.compgeo.2006.08.006
Dibike, Y., Velickov, S., Solomatine, D., & Abbott, M. (2001). Model induction with support vector machines: Introduction and applications. Journal of Computing in Civil Engineering, 15(3), 208. https://doi.org/10.1061/(ASCE)0887-3801(2001)15:3(208)
Esamaldeen, A., Wu, G., & Abdelazim, I. (2014). Empirical relations between compressive strength and microfabric properties of amphibolites using multivariate regression, fuzzy inference and neural networks: A comparative study. Engineering Geology, 183, 230-240. https://doi.org/10.1016/j.enggeo.2014.08.026
Hansen, J. B. (1970). A revised and extended formula for bearing capacity (Bulletin No. 28). Danish Geotechnical Institute.
Kalinli, A., Acar, M. C., & Gunduz, Z. (2011). New approaches to determine the ultimate bearing capacity of shallow foundations based on artificial neural networks and ant colony optimization. Engineering Geology, 117, 29-38. https://doi.org/10.1016/j.enggeo.2010.10.002
Kennedy, J., & Eberhart, R. C. (1995). Particle swarm optimization. In Proceedings of IEEE International Conference on Neural Networks (pp. 1943-1948). Perth, Australia: IEEE. https://doi.org/10.1109/ICNN.1995.488968
Khanlari, G. R., Heidari, M., Momeni, A. A., & Abdilor, Y. (2012). Prediction of shear strength parameters of soils using artificial neural networks and multivariate regression methods. Engineering Geology, 131-132, 11-18. https://doi.org/10.1016/j.enggeo.2011.12.006
Li, X. Z., & Kong, J. M. (2014). Application of GA-SVM method with parameter optimization for landslide development prediction. Natural Hazards and Earth System Sciences, 14, 525-533. https://doi.org/10.519/nhess-14-525-2014
Lin, H.-M., Chang, S.-K., Wu, J.-H., & Juang, C. H. (2009). Neural network-based model for assessing failure potential of highway slopes in the Alishan, Taiwan Area: Pre- and post-earthquake investigation. Engineering Geology, 104, 280-289. https://doi.org/10.1016/j.enggeo.2008.11.007
Meyerhof, G. G. (1963). Some recent research on the bearing capacity of foundations. Canadian Geotechnical Journal, 1(1), 16-26. https://doi.org/10.1139/t63-003
Momeni, E., Nazir, R., Jahed Armaghani, D., & Maizir, H. (2014). Prediction of pile bearing capacity using a hybrid genetic algorithm-based ANN. Measurement, 57, 122-131. https://doi.org/10.1016/j.measurement.2014.08.007
Mustafa, M. R., Rezaur, R. B., Rahardjo, H., & Isa, M. H. (2012). Prediction of pore-water pressure using radial basis function neural network. Engineering Geology, 135-136, 40-47. https://doi.org/10.1016/j.enggeo.2012.02.008
Nejad, F. P., & Jaksa, M. B. (2017). Load-settlement behavior modeling of single piles using artificial neural networks and CPT data. Computers and Geotechnics, 89, 9-21. https://doi.org/10.1016/j.compgeo.2017.04.003
Neaupane, K. M., & Achet, S. H. (2004). Use of backpropagation neural network for landslide monitoring: a case study in the higher Himalaya. Engineering Geology, 74, 213-226. https://doi.org/10.1016/j.enggeo.2004.03.010
Ng, I. T., Yuen, K. V., & Lau, C. H. (2015). Predictive model for uniaxial compressive strength for Grade III granitic rocks from Macao. Engineering Geology, 199, 28-37. https://doi.org/10.1016/j.enggeo.2015.10.008
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(5), 358-367. https://doi.org/10.1111/0885-9507.00154
Pardo, M., & Sberveglieri, G. (2005). Classification of electronic nose data with support vector machines. Sensors & Actuatuators B: Chemical, 107(2), 730-737. https://doi.org/10.1016/j.snb.2004.12.005
Padmini, D., Ilamparuthi, K., & Sudheer, K. P. (2008). Ultimate bearing capacity prediction of shallow foundations on cohesionless soils using neurofuzzy models. Computers and Geotechnics, 35, 33-46. https://doi.org/10.1016/j.compgeo.2007.03.001
Ren, Y., & Bai, G. C. (2011). Colonial competitive algorithm assisted least squares support vector machines. Advanced Materials Research, 255-260, 2082-2086. https://doi.org/10.4028/www.scientific.net/AMR.255-260.2082
Sadrossadat, E., Soltani, F., Mousavi, S. M., Marandi, S. M., & Alavi, A. H. (2013). A new design equation for prediction of ultimate bearing capacity of shallow foundation on granular soils. Journal of Civil Engineering and Management, 19(Supplement 1), s78-s90. https://doi.org/10.3846/13923730.2013.801902
Sakthivel, V. P., Bhuvaneswari, R., & Subramanian, S. (2010). An improved particle swarm optimization for induction motor parameter determination. International Journal of Computer Applications, 1(2), 62-67. https://doi.org/10.5120/44-150
Shahnazari, H., & Tutunchian, M. A. (2012). Prediction of ultimate bearing capacity of shallow foundations on cohesionless soils: an evolutionary approach. KSCE Journal of Civil Engineering, 16(6), 950-957. https://doi.org/10.1007/s12205-012-1651-0
Shahin, M. A., Maier, H. R., & Jaksa, M. B. (2001). Artificial neural network applications in Geotechnical Engineering. Australian Geomechnics Journal, 36(1), 49-62.
Shoaei, M. D., Alkarni, A., Noorzaei, J., Jaafar, M. S., & Huat, B. B. K. (2012). Review of available approaches for ultimate bearing capacity of two-lyaered soils. Journal of Civil Engineering and Management, 18(4), 469-482. https://doi.org/10.3846/13923730.2012.699930
Suykens, J. A. K., Vandewalle, J., & De Moor, B. (2001). Optimal control by least squares support vector machines. Neural Networks, 14, 23-35. https://doi.org/10.1016/S0893-6080(00)00077-0
Terzaghi, K. (1943). Theoretical soil mechanics. New York: John Wiley & Sons. https://doi.org/10.1002/9780470172766
Vesic, A. S. (1973). Analysis of ultimate loads of shallow foundations. Journal of the Soil Mechanics and Foundations Division, 99(1), 45-73. https://doi.org/10.1016/0148-9062(74)90598-1
Xu, H. B., & Chen, G. H. (2013). An intelligent fault identification method of rolling bearings based on LSSVM optimized by improved PSO. Mechanical Systems and Signal Processing, 35, 167-175. https://doi.org/10.1016/j.ymssp.2012.09.005
Yamagami, Y., & Jiang, J. C. (1997). A search for the critical slip surface in three-dimensional slope stability analysis. Soils and Foundations, 37(3), 1-16. https://doi.org/10.3208/sandf.37.3_1
Yilmazkaya, E., Dagdelenler, G., Ozcelik, Y., & Sonmez, H. (2018). Prediction of mono-wire cutting machine performance parameters using artificial neural network and regression models. Engineeing Geology, 239, 96-108. https://doi.org/10.1016/j.enggeo.2018.03.009