Site Characterization Using GP, MARS and GPR

TLDR: The developed GP, MARS and GPR give the spatial variability of Nc values at Bangalore, which is to be approximated with which N value at any half space point in Bangalore can be determined.

Abstract: This article examines the capability of Genetic Programming (GP), Multivariate Adaptive Regression Spline (MARS) and Gaussian Process Regression (GPR) for developing site characterization model of Bangalore (India) based on corrected Standard Penetration Test (SPT) value (Nc). GP, MARS and GPR have been used as regression techniques. GP is developed based on genetic algorithm. MARS does not assume any functional relationship between input and output variables. GPR is a probabilistic, non-parametric model. In GPR, different kinds of prior knowledge can be applied. In three dimensional analysis, the function\( {\mathrm{N}}_{\mathrm{c}}=\mathrm{f}\left(\mathrm{X},\mathrm{Y},\mathrm{Z}\right) \) where X, Y and Z are the coordinates of a point corresponding to N value, is to be approximated with which N value at any half space point in Bangalore can be determined. A comparative study between the developed GP, MARS and GPR has been carried out in the proposed book chapter. The developed GP, MARS and GPR give the spatial variability of Nc values at Bangalore.