Modeling of temperature-frequency correlation using long-term monitoring data: Methods and comparison

Yiqing Ni, H. F. Zhou, J. M. Ko

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

Abstract

A good understanding of environmental effects on structural modal properties is essential for reliable performance of vibration-based damage diagnosis methods. In this paper, the temperature-frequency correlation models are developed by means of the neural network (NN) technique and the combined principal component analysis and neural network (PCA-NN) technique. Then a comparative study of these two techniques for reproducing and predicting temperature-caused variability of modal frequencies is conducted with the use of long-term monitoring data from the cable-stayed Ting Kau Bridge. It is shown that perceptron neural networks with single hidden layer are sufficient for modeling the correlation and an appropriate number of hidden nodes are crucial to achieve good prediction performance. Using principal components of the measured temperatures as input to neural networks can achieve almost same simulation and prediction capabilities and also ensure stable regression estimates.
Original languageEnglish
Title of host publicationStructural Health Monitoring and Intelligent Infrastructure - Proceedings of the 2nd International Conference on Structural Health Monitoring of Intelligent Infrastructure, SHMII 2005
Pages335-344
Number of pages10
Volume1
Publication statusPublished - 1 Dec 2006
Event2nd International Conference on Structural Health Monitoring of Intelligent Infrastructure, SHMII 2005 - Shenzhen, China
Duration: 16 Nov 200518 Nov 2005

Conference

Conference2nd International Conference on Structural Health Monitoring of Intelligent Infrastructure, SHMII 2005
CountryChina
CityShenzhen
Period16/11/0518/11/05

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Building and Construction

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