Hybrid GA/SIMPLS as alternative regression model in dam deformation analysis

Multicollinearity and difficulty of interpreting the coefficients of dam regression models pose two problems: (1) selection of informative variables for analysing dam deformation behaviour, and (2) mitigation of the multicollinearity among the variables. Resolving these two problems necessitates the application of genetic algorithm-based partial least square (GA-PLS) and statistically inspired modification of PLS algorithm (SIMPLS). A SIMPLS regression with the predictor variables selected by GA-PLS (hybrid GA/SIMPLS regression) is put forward to interpret the results obtained from periodic monitoring surveys of hydraulic structures. The hybrid model is employed for analysing the crack behaviour of an earth-rock dam in China. The results show the proposed model is superior to an ordinary SIMPLS and stepwise regression, especially when multicollinearity and influential outliers exist among the variables. Crown Copyright (C) 2011 Published by Elsevier Ltd. All rights reserved.