prediction the ultimate bearing capacity of shallow foundations on the cohesionless soils using m5p model tree

bearing capacity prediction of shallow foundation is one of the most important problems in geotechnical engineering practices, with a wide variety range of methods which have been introduced to forecast it accurately. recently, soft computing methods such as artificial neural networks (anns) and support vector machines (svms) have been used for prediction of the ultimate bearing capacity of shallow foundation. however, in these methods the modeling process is complex and are not as easy to use as the empirical equations. in this paper, m5p model tree as a new soft computing method has been used for prediction of the ultimate bearing capacity of shallow foundation. the main advantage of model tree is that, compared to ann and svm, they are easier to use and more importantly they represent understandable mathematical rules. laboratory experimental tests of shallow foundations on cohesionless soils were used with parameters of the internal friction angle, the unit weight of the soil, and the geometry of a foundation considers depth, width, and length to develop and test the model. the results achieved from the proposed model was compared with those obtained from the meyerhof, hansen and vesic computation formulas. the results indicate that m5p model tree perform better than the mentioned theoretical methods.