Modal Identification of Sutong Cable-Stayed Bridge during Typhoon Haikui Using Wavelet Transform Method

Wang, H.; Mao, Jian; Huang, J. H.; Li, A. Q.

doi:10.1061/(asce)cf.1943-5509.0000856

Cited by 28 publications

(13 citation statements)

References 18 publications

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“…The complex Morlet wavelet was set as the mother wavelet with the relevant parameters (frequency bandwidth and central frequency) tuned according to the minimisation of the Shannon wavelet entropy. [60][61][62] The instantaneous frequencies were extracted from the ridges of wavelet transform modulus using the modulus maxima method. [63]…”

Section: Monitoring Data Interpretationmentioning

confidence: 99%

A non-contact vision-based system for multipoint displacement monitoring in a cable-stayed footbridge

Brownjohn

Kong

2018

Struct Control Health Monit

172

106

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SummaryVision-based monitoring receives increased attention for measuring displacements of civil infrastructure such as towers and bridges. Currently, most field applications rely on artificial targets for video processing convenience, leading to high installation effort and focus on only single-point displacement measurement, for example, at mid-span of a bridge. This study proposes a low-cost and non-contact vision-based system for multipoint displacement measurement based on a consumer-grade camera for video acquisition and a custom-developed package for video processing. The system has been validated on a cablestayed footbridge for deck deformation and cable vibration measurement under pedestrian loading. The analysis results indicate that the system provides valuable information about bridge deformation of the order of a few centimetres induced, in this application, by pedestrian passing. The measured data enable accurate estimation of modal frequencies of either the bridge deck or the bridge cables and could be used to investigate variations of modal frequencies under varying pedestrian loads. KEYWORDSbridge displacement, cable vibration, cable-stayed bridge, pedestrian loads, vision-based system | INTRODUCTIONStructural health monitoring is aimed at providing valuable information about structural performance and characterisation of structural defects to the asset owners, especially for those civil infrastructures beyond the design life. Vibrationbased modal tests are a common way for structural condition and serviceability assessment, providing a direct view about the structural stiffness, mass properties, and their distributions. [1] As well as for validating designs of civil structures, modal parameters extracted from vibration data obtained in short-term or long-term measurements are widely believed to have potential for identifying changes in structural condition or "damage." [2] The sensitivity of these parameters to damage depends on the nature of the damage, for example, local deterioration of material, for example, due to corrosion, may not be detectable against background effects of environmental variability, whereas boundary conditions are known to have a relatively strong effect, for example, fixity of bridge supports.[3]This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Monitoring Data Interpretationmentioning

confidence: 99%

A non-contact vision-based system for multipoint displacement monitoring in a cable-stayed footbridge

Brownjohn

Kong

2018

Struct Control Health Monit

172

106

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“…Many scholars have also done a lot of research on identifying structural modal parameters by using the WT method [24][25][26][27]. Based on a long-span cable-stayed bridge, which was excited by typhoon Haikui, the model parameters including frequency and damping ratio were identified using the WT method [28].…”

Section: Introductionmentioning

confidence: 99%

Improved Continuous Wavelet Transform for Modal Parameter Identification of Long-Span Bridges

Zhang

Huang

et al. 2020

Shock and Vibration

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Accurate and timely identification of modal parameters of long-span bridges is important for bridge health monitoring and wind tunnel tests. Wavelet analysis is one of the most advantageous methods for identification because of its good localization characteristics in both time and frequency domain. In recent years, the wavelet method has been applied more frequently in parameter identification of linear and nonlinear systems. In this article, based on wavelet ridges and wavelet skeleton, the improved modal parameter identification method was studied. To find the appropriate time-frequency resolution, an optimal wavelet basis design principle based on minimum Shannon entropy was proposed. Aiming at endpoint effect in wavelet transform, a prediction continuation method based on support vector machine (SVM) was proposed, which can effectively suppress the endpoint effect of the extended samples. In view of the fact that the ridges of metric matrices obtained by the traditional crazy climber algorithm cannot fully reflect the distribution of ridges of modulus value matrices of wavelet coefficients, an improved high-precision crazy climber algorithm was put forward to accurately identify the position of the ridge of wavelet coefficients. Finally, taking a long-span cable-stayed bridge and a long-span suspension bridge as the engineering background, improved continuous wavelet transform (CWT) was applied to modal parameter identification of bridge under ambient excitation. The modal parameters such as modal frequency, damping ratio, and mode shape were obtained. Compared with the calculation value of the numerical simulation of long-span cable-stayed bridge and wind tunnel test of long-span suspension bridge, the reliability of CWT for modal parameter identification of long-span bridges under ambient excitation was verified.

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“…Structural modal parameters (i.e., modal frequencies, damping ratios, and vibration shapes) are fundamental and important parameters that SHM data can convey. [7][8][9] Knowledge of modal parameters could help the engineers and researchers to design and retrofit the vibration control devices, 10 calibrate the initial finite element model for dependent performance simulations, 11 and detect the structural damage for condition assessment. [12][13][14] For long-term SHM of the long-span bridge, automated modal parameter identification, that is, identifying the modal parameters from the raw monitoring data without any human manipulation, is required.…”

Section: Introductionmentioning

confidence: 99%

Automated modal identification using principal component and cluster analysis: Application to a long‐span cable‐stayed bridge

Mao

Wang

et al. 2019

Struct Control Health Monit

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Summary Obtaining timely information about the health of civil infrastructure is critical to ensuring safe and reliable operation. Structural health monitoring has been proposed as a means to provide such information; however, most structural health monitoring systems provide only raw data, rather than actionable information. It is necessary to develop automated modal analysis strategies that can provide near real‐time dynamic information regarding the in‐service state of a bridge, which is essential to vibration control, finite element model calibration, and damage detection for safety and serviceability condition assessment. This study presents an automated framework to extract structural modal parameters from the stabilization diagram using a parametric modal identification method such as stochastic subspace identification. The framework focuses on the automated modal analysis issues of an in‐service long‐span bridge with close‐frequency modes. The presented framework is validated using experimental tests of a 1.8‐m 18‐story laboratory model. Subsequently, data from Sutong Cable‐Stayed Bridge are employed to demonstrate its potential usage in the field. Finally, an application of the automated framework is presented to identify and track the modal parameters of the deck of Sutong Cable‐Stayed Bridge for 20 days. Results show that the presented framework can successfully extract the structural modal parameters with good accuracy and robustness, hence can provide a reliable technical support for in‐service monitoring of long‐span bridges.

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Modal Identification of Sutong Cable-Stayed Bridge during Typhoon Haikui Using Wavelet Transform Method

Cited by 28 publications

References 18 publications

A non-contact vision-based system for multipoint displacement monitoring in a cable-stayed footbridge

A non-contact vision-based system for multipoint displacement monitoring in a cable-stayed footbridge

Improved Continuous Wavelet Transform for Modal Parameter Identification of Long-Span Bridges

Automated modal identification using principal component and cluster analysis: Application to a long‐span cable‐stayed bridge

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