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发布时间：2019-03-09

论文类型：期刊论文

发表时间：2010-08-01

发表刊物：STRUCTURAL CONTROL & HEALTH MONITORING

收录刊物：Scopus、EI、SCIE

卷号：17

期号：5

页面范围：495-512

ISSN号：1545-2255

关键字：modal identification; environmental effects; nonlinear principal component analysis; structural health monitoring; ANN models

摘要：Numerous investigations have indicated that structural modal parameters are significantly impacted by varying environmental and operational conditions. This phenomenon will cause confusion when conducting modal-based damage detection and model updating. This paper investigates the dependency of modal frequencies, mod I a. shapes and the associated clamping ratios on temperature and wind velocity. The nonlinear principal component analysis (NLPCA) is first employed as a signal pre-processing tool to distinguish temperature and wind effects on structural modal parameters from other environmental factors. The pre-processed dataset by NLPCA implies the relationship between modal parameters and temperature as well as wind velocity. Consequently, the artificial neural network (ANN) technique is employed to model the relationship between the pre-processed modal parameters and environmental factors. Numerical results indicate that pre-processed modal parameters by NLPCA can retain the most features of original signals. Furthermore, the pre-processed modal frequency and damping ratios are dramatically affected by temperature and wind velocity. The ANN regression models have good capacities for mapping relationship of environmental factors and modal frequency, damping ratios. However, environmental effects on the entire modal shapes are insignificant. Copyright (C) 2009 John Wiley & Sons, Ltd.