Table of Contents Author Guidelines Submit a Manuscript
Mathematical Problems in Engineering
Volume 2015, Article ID 571594, 13 pages
http://dx.doi.org/10.1155/2015/571594
Research Article

Safety Monitoring of a Super-High Dam Using Optimal Kernel Partial Least Squares

Hao Huang,1,2 Bo Chen,1,2 and Chungao Liu1,2

1State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China
2National Engineering Research Center of Water Resources Efficient Utilization and Engineering Safety, Hohai University, Nanjing 210098, China

Received 10 August 2015; Accepted 30 November 2015

Academic Editor: Maurizio Brocchini

Copyright © 2015 Hao Huang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Considering the characteristics of complex nonlinear and multiple response variables of a super-high dam, kernel partial least squares (KPLS) method, as a strongly nonlinear multivariate analysis method, is introduced into the field of dam safety monitoring for the first time. A universal unified optimization algorithm is designed to select the key parameters of the KPLS method and obtain the optimal kernel partial least squares (OKPLS). Then, OKPLS is used to establish a strongly nonlinear multivariate safety monitoring model to identify the abnormal behavior of a super-high dam via model multivariate fusion diagnosis. An analysis of deformation monitoring data of a super-high arch dam was undertaken as a case study. Compared to the multiple linear regression (MLR), partial least squares (PLS), and KPLS models, the OKPLS model displayed the best fitting accuracy and forecast precision, and the model multivariate fusion diagnosis reduced the number of false alarms compared to the traditional univariate diagnosis. Thus, OKPLS is a promising method in the application of super-high dam safety monitoring.