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

Authors:
Chang Xu;Dongjie Yue;Chengfa Deng
Affiliations:
School of Earth Sciences and Engineering, Hohai University, Nanjing 210098, PR China and Zhejiang Water Conservancy and Hydropower College, Hangzhou 310018, PR China;School of Earth Sciences and Engineering, Hohai University, Nanjing 210098, PR China;Zhejiang Institute of Hydraulic & Estuary, Hangzhou 310020, PR China
Venue:
Engineering Applications of Artificial Intelligence
Year:
2012

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Abstract

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.