Temperature-induced variations of measured modal frequencies of steel box girder for a long-span suspension bridge

Abstract

This paper addresses the temperature-induced variations of measured modal frequencies of steel box girder for a suspension bridge using long-tem monitoring data. The output-only modal frequency identification of the bridge is effectively carried out using the Iterative Windowed Curve-fitting Method (IWCM) in the frequency-domain. The daily and seasonal correlations of frequency-temperature are investigated in detail and the analysis results reveal that: (i) the identified modal frequencies using IWCM provide an effective indication for changes of the bridge due to the ambient temperature variations; (ii) temperature is the critical source causing modal variability, and there is an overall decrease in modal frequency with temperature for all the identified modes; (iii) the random variations in measured modal frequencies mainly arise from the identification algorithm due to the nonstationary loadings, which can be effectively eliminated using multi-sample averaging technique; (iv) the daily averaged modal frequencies of vibration modes have remarkable seasonal correlations with the daily averaged temperature and the seasonal correlation models of frequency-temperature are suitable for structural damage warning if future seasonal correlation models deviate from these normal models.

A State of the Art Review of Modal-Based Damage Detection in Bridges: Development, Challenges, and Solutions

“…More complex regression models may incorporate a polynomial curved fit as per Ding and Li [101], whose polynomial regression model exhibited an excellent capability in regard to mapping daily averaged output-only modal frequency variability using daily averaged temperature fluctuations in a long-span suspension bridge. The authors used daily averaged values to reduce the effect of random modal variations that arise from the modal identification algorithm being subjected to non-stationary loading.…”

Modeling of Temperature Effect on Modal Frequency of Concrete Beam Based on Field Monitoring Data

“…Moser and Moaveni [28] utilize several models (a static linear model, an ARX model, a bilinear model, and polynomials with various orders) to represent the relationship between the modal frequencies and measured temperatures. Ding and Li [29] propose a polynomial regression model to describe the frequency-temperature seasonal correlations of the Runyang Suspension Bridge. Ni et al [30,31] apply the support vector machine (SVM) and backpropagation neural network (BPNN) techniques to formulate regression models that quantified the temperature effect on modal frequencies of the cable-stayed Ting Kau Bridge.…”

Vibration Analysis of Reinforced Concrete Simply Supported Beam versus Variation Temperature

“…Based on the Autoregressive and Exogenous model, Peeters and De Roeck [24] proposed a methodology to distinguish these temperature effects from real damage events on the Z24 bridge. Ding and Li [29] investigated the daily and seasonal correlations of frequency-temperature using the output-only modal frequency identification obtained by the Iterative Windowed Curve-fitting Method (IWCM) for the Runyang Suspension Bridge. Zhou et al [30] formulated a correlation model with the backpropagation neural network (BPNN) technique to eliminate the temperature effect.…”