Prediction of compressive strength of self-compacting concrete using least square support vector machine and relevance vector machine

Bin, 2008, Prediction of bearing raceways superfinishing based on least squares support vector machines, Proceedings —; 4th International Conference on Natural Computation, 125

Deng, 2010, Applying least squares support vector machines to the airframe wing-box structural design cost estimation, Expert Systems with Applications, 37, 8417, 10.1016/j.eswa.2010.05.038

Guang, 2000, Prediction of compressive strength of concrete by neural networks, Cem Concr Res., 30, 1245, 10.1016/S0008-8846(00)00345-8

Huang, 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, 4058

Kecman, 2001

Kewalramani, 2006, Concrete compressive strength prediction using ultrasonic pulse velocity through articial neural networks, Automation in Construction, 15, 374, 10.1016/j.autcon.2005.07.003

Liong, 2000, River stage forecasting in Bangladesh: neural network approach, J. Computing in Civil Eng., 14, 1, 10.1061/(ASCE)0887-3801(2000)14:1(1)

Liying, 2010, Forecasting groundwater level based on Relevance Vector Machine, Advanced Materials Research, 121–122, 43

Noor, 2011, Pattern recognition method to predict recycling strategy for electronic equipments, Advanced Materials Research, 264–265, 949, 10.4028/www.scientific.net/AMR.264-265.949

Oztas, 2005, Predicting the compressive strength and slump of high strength concrete using neural network, Constr Build Mater., 20, 769, 10.1016/j.conbuildmat.2005.01.054

Pahasa, 2011, A heuristic training-based least squares support vector machines for power system stabilization by SMES, Expert Systems with Applications, 38, 13987

Park, 1999, Forecasting freeway link ravel times with a multi-layer feed forward neural network, Computer Aided Civil And Znfa Structure Engineering, 14, 358

Shen, 2008, Efficient multiple faces tracking based on Relevance Vector Machine and Boosting learning, Journal of Visual Communication and Image Representation, 19, 382, 10.1016/j.jvcir.2008.06.005

Siddique, 2011, Prediction of compressive strength of self-compacting concrete containing bottom ash using artificial neural networks, Advances in Engineering Software, 42, 780, 10.1016/j.advengsoft.2011.05.016

Smola, 1998, On a kernel based method for pattern recognition, regression, approximation and operator inversion, Algorithmica, 22, 211, 10.1007/PL00013831

Snell, 1989, Predicting early concrete strength, Concr. Int., 11, 43

Suykens, 1999, Least squares support vector machine classifiers, Neural Processing Letters, 9, 293, 10.1023/A:1018628609742

Tipping, 2000, The relevance vector machine, Advances in Neural Information Processing Systems, 12, 652

Topçu, 2008, Prediction of mechanical properties of recycled aggregate concretes containing silica fume using articial neural networks and fuzzy logic, Comp Mater Sci., 41, 74, 10.1016/j.commatsci.2007.06.011

Vapnik, 1998, Statistical learning theory

Wang, 1999, The application of automatic acquisition of knowledge to mix design of concrete, Cem Concr., 29, 1875, 10.1016/S0008-8846(99)00152-0

Zhang, 2009, Quantitative prediction of MHC-II peptide binding affinity using relevance vector machine, Applied Intelligence, 31, 180, 10.1007/s10489-008-0121-3