System Identification of a Six-Span Steel Railway Bridge Using Ambient Vibration Measurements at Different Temperature Conditions

Özçelik, Özgür; Yormaz, Doruk; Amaddeo, Carmen; Girgin, Ozgur; Kahraman, Serap

doi:10.1061/(asce)cf.1943-5509.0001260

Cited by 12 publications

(3 citation statements)

References 14 publications

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“…The study utilizes AVT for identification of the element present strength and reliability according to standards. There are various application of the AVT are available in the literature (Altunişik, Genç, Günaydin, Okur, & Karahasan, 2018;Aytulun, Soyoz, & Karcioglu, 2019;Bautista-De Castro, Sánchez-Aparicio, Carrasco-García, Ramos, & González-Aguilera, 2019;Di Lorenzo, Formisano, Krstevska, & Landolfo, 2019;Ozcelik, Yormaz, Amaddeo, Girgin, & Kahraman, 2019;Shimpi et al, 2019;Sun & Xu, 2020;Zonno, Aguilar, Boroschek, & Lourenço, 2019). In the presented study, the natural frequency and mode shape of Bridge No.…”

Section: Experimental Methodsmentioning

confidence: 99%

Serviceability analysis and increased axle load performance of heritage arch gallery railway bridge

Sivasubramanian¹,

Shimpi²,

Singh

2021

rdlc

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This investigation deals with the serviceability analysis and increased axle load performance of Bridge No. 541 of UNESCO recognised Kalka Shimla Mountain Railways, which is a multi-storey arch gallery stone masonry bridge situated in the state of Himachal Pradesh, India. For this purpose, a finite element (FE) model was developed based on the drawings and reports available in Shimla Division, Northern Railways, India. Further, the results obtained from ambient vibration test (AVT) and operational modal analysis (OMA) is utilized to model update the initial FE model by modifying the mechanical properties of stone masonry. The serviceability against present-day axle loading (H Class Loading, Indian Railways) and present speed of the train is assessed. At last, a parametric study is conducted to understand the feasibility to attain higher speeds (up to 40 kmph) of the present axle load and higher axle load with different speeds of the train over the bridge to check the serviceability conditions advised in the Indian Railways code and also International codes. Finally, the study concluded that Bridge No. 541 of Kalka Shimla Mountain Railways can sustain speeds up to 40 kmph for current axle loading satisfying serviceability conditions.

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Section: Experimental Methodsmentioning

confidence: 99%

Serviceability analysis and increased axle load performance of heritage arch gallery railway bridge

Sivasubramanian¹,

Shimpi²,

Singh

2021

rdlc

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“…In this case, the FDD method can be used, in which the auto- and cross-power spectral density (PSD) functions are calculated, to reduce the effect of white-noise excitation. For instance, the ambient vibration test data of a six-span railway bridge were respectively analyzed by data-driven stochastic subspace identification method and enhanced FDD method to track modal parameters of the bridge under different temperature conditions (Ozcelik et al, 2019). Apart from girder of the bridge, the FDD-based modal identification was employed to identify frequencies of cables (Yan et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

High-resolution frequency domain decomposition for modal analysis of bridges using train-induced free-vibrations

Chen,

Wang,

Yao

2024

Advances in Structural Engineering

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Modal parameters are structural inherent characteristics that can be applied for revealing performance of railway bridges. Free vibration signals generated by a passage of train are commonly utilized to estimate the modal parameters of railway bridges due to their higher signal-to-noise ratios compared to random vibrations caused by ambient loads. However, since free vibration signals rapidly decay over time, the available free-vibration data is typically short-time. When using the fast Fourier transform-based spectral estimation method for modal identification from short-time vibration data, a phenomenon known as spectral leakage occurs, leading to miss-identification of some structural modes. In this study, the classical frequency domain decomposition (FDD) is improved for modal identification of railway bridges, in which the higher resolution auto-power spectral density (PSD) and cross-PSD functions are calculated through the autoregressive (AR) model-based method. The AR model-based method improves both the smoothness and resolution of the PSD functions compared to the fast Fourier transform technique. These AR model-based PSD functions are then employed in the FDD process to facilitate frequency and mode shape identification while avoiding spurious noise modes. The proposed eigenvalue fitting technique is subsequently utilized to estimate damping ratios. Numerical simulation data as well as vibration data from an actual bridge are analyzed to validate the proposed method, with a comparison made to the Welch’s PSD-based method. The results demonstrate that the modified FDD approach enables more effective identification of structural modes, even in the presence of closely-spaced modes.

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“…Railway bridges can be analyzed through parametric methods, such as the stochastic subspace identification method (Reynders and Roeck, 2008), and non-parametric methods, such as the frequency domain decomposition technique (Brincker et al, 2001). Ozcelik et al (2019) used data-driven stochastic subspace identification and enhanced frequency domain decomposition methods to analyze the ambient vibrations of a steel railway bridge, and the results indicated that the frequencies decreased slightly with an increase in the temperature. Considering that vibration data are often nonstationary, He et al (2011) proposed an empirical mode decomposition-based random decrement technique to identify the modal parameters of the Nanjing Yangtze River Bridge.…”

Section: Introductionmentioning

confidence: 99%

Fully automated modal tracking for long-span high-speed railway bridges

Yang

et al. 2022

Advances in Structural Engineering

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Modal parameters are inherent structural characteristics that are valuable for model updating, condition assessment, and early warning of bridges. Operational modal tracking technology has been a popular research topic in bridge structural health monitoring (SHM) because of its output-only advantage; that is, only the vibration responses of the bridge are necessary for modal identification. The real-time objective of bridge SHM requires operational modal tracking to be fully automated. Because the loads acting on long-span high-speed railway bridges are various, the modal identification methods should be changed according to the excitation characteristics; otherwise, the results may be incorrect. However, there is no unified framework for simultaneously tracking the modal evolution of a bridge under different excitations. In this study, modal tracking strategies based on ambient loads, train loads and immediately a train moving past the bridge were developed to identify the operational modal parameters of the bridge. In addition, a unified tracking framework was established to automatically switch among the three-stage modal-tracking strategies, which utilizes the real-time positioning of the axle loads. Furthermore, the computational efficiency of the tracking strategies and the obstacles of operational modal analysis were analyzed to provide a reference for mode-based SHM of bridges, and the essential parameters in the tracking algorithms were suggested. The three-stage modal-tracking methods were validated through long-term monitoring data of a long-span high-speed railway bridge. The results indicated that the best tracking results were generated from free-vibration data, while the modal-tracking under ambient loads had best timeliness.

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System Identification of a Six-Span Steel Railway Bridge Using Ambient Vibration Measurements at Different Temperature Conditions

Cited by 12 publications

References 14 publications

Serviceability analysis and increased axle load performance of heritage arch gallery railway bridge

Serviceability analysis and increased axle load performance of heritage arch gallery railway bridge

High-resolution frequency domain decomposition for modal analysis of bridges using train-induced free-vibrations

Fully automated modal tracking for long-span high-speed railway bridges

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