Reliability Analysis of Circular Footing by Using GP and MPMR

Circular footings are designed to bear a load of super structures. Studies have been done on the influence of soil properties on bearing capacity of shallow foundations. The use of circular foundation is practical in geotechnical engineering. During the design of circular footing, bearing capacity of soil is taken into consideration, and cohesion (c), unit weight (γ), and angle of internal friction (ϕ) are the most variable parameters. Reliability analysis is used frequently for the design of circular footing. Most of the authors have used first order second moment methods (FOSM). However, FOSM is a time-consuming method. Drawbacks of FOSM have been overcome by genetic programming (GP), minimax probability machine regression (MPMR). This article gives a distinct analysis between the developed MPMR based FOSM and GP-based FOSM.

Determination of Spatial Variability of Rock Depth of Chennai

This study adopts four modeling techniques Ordinary Kriging(OK), Generalized Regression Neural Network (GRNN), Genetic Programming(GP) and Minimax Probability Machine Regression(MPMR) for prediction of rock depth(d) at Chennai(India). Latitude (Lx) and Longitude(Ly) have been used as inputs of the models. A semivariogram has been constructed for developing the OK model. The developed GP gives equation for prediction of d at any point in Chennai. A comparison of four modeling techniques has been carried out. The performance of MPMR is slightly better than the other models. The developed models give the spatial variability of rock depth at Chennai.

Determination of Spatial Variability of Rock Depth of Chennai

This study adopts four modeling techniques Ordinary Kriging(OK), Generalized Regression Neural Network (GRNN), Genetic Programming(GP) and Minimax Probability Machine Regression(MPMR) for prediction of rock depth(d) at Chennai(India). Latitude (Lx) and Longitude(Ly) have been used as inputs of the models. A semivariogram has been constructed for developing the OK model. The developed GP gives equation for prediction of d at any point in Chennai. A comparison of four modeling techniques has been carried out. The performance of MPMR is slightly better than the other models. The developed models give the spatial variability of rock depth at Chennai.