EMD-based random decrement technique for modal parameter identification of an existing railway bridge

He, Xuhui; Hua, Xugang; Chen, Z.Q.; Huang, Fang

doi:10.1016/j.engstruct.2011.01.012

Cited by 106 publications

(53 citation statements)

References 23 publications

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“…Although with a relatively short history of less than two decades, EMD has exponentially increased its popularity among the numerous techniques developed for signal processing and SHM. Simply taking civil and infrastructure engineering for example, extensive applications have been reported in structural system identification (Yang et al, 2003;Yu and Ren, 2005;Yang and Chang, 2009) and damage detection (Rezaei and Taheri, 2011;Bagherzadeh and Sabzehparvar, 2015), especially SHM of long-span bridges (Yu and Ren, 2005;Yang and Chang, 2009;He et al, 2011;Zhang et al, 2012). The long-span bridges typically include suspension bridges and cable-stayed bridges.…”

Section: Introductionmentioning

confidence: 99%

A Rapidly Convergent Empirical Mode Decomposition Method for Analyzing the Environmental Temperature Effects on Stay Cable Force

Chen

Jhou

et al. 2018

Computer aided Civil Eng

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The variation histories of cable force and effective temperature were recently processed by empirical mode decomposition (EMD) to discover that they can both be categorized into two main components including the daily and long‐term variations. A rapidly convergent EMD method aimed to more efficiently decompose these two types of signals is established in this study. To deal with the typically encountered problems of slow convergence, scale mixing, and end effect in EMD, an objective stoppage criterion is first proposed and efforts are also made to clarify the origin of scale mixing and slow divergence. Based on the above explorations, an extremum neglecting algorithm and a boundary modification technique with appropriate control points are further developed to complete the whole methodology. The superiority of this new method is verified by more concisely decomposing the previously processed data into fewer intrinsic mode functions with a significant reduction in computation time.

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Section: Introductionmentioning

confidence: 99%

A Rapidly Convergent Empirical Mode Decomposition Method for Analyzing the Environmental Temperature Effects on Stay Cable Force

Chen

Jhou

et al. 2018

Computer aided Civil Eng

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“…Identification of modal parameters is important in the theoretical research and engineering applications of health monitoring, damage assessment, and the active control of bridge structures [17,18,19]. Given the increasing span and complexity of modern bridge structures, gaining effective excitation signals through human motivation (such as shakers and drop weights) is difficult, and may cause unnecessary damage to bridge structures [20]. Therefore, traditional theories and techniques concerning the identification of structural modal parameters based on input and output signals are not applicable.…”

Section: Introductionmentioning

confidence: 99%

Operational Modal Analysis of Bridge Structures with Data from GNSS/Accelerometer Measurements

Xiong

Zhu

2017

Sensors

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Real-time dynamic displacement and acceleration responses of the main span section of the Tianjin Fumin Bridge in China under ambient excitation were tested using a Global Navigation Satellite System (GNSS) dynamic deformation monitoring system and an acceleration sensor vibration test system. Considering the close relationship between the GNSS multipath errors and measurement environment in combination with the noise reduction characteristics of different filtering algorithms, the researchers proposed an AFEC mixed filtering algorithm, which is an combination of autocorrelation function-based empirical mode decomposition (EMD) and Chebyshev mixed filtering to extract the real vibration displacement of the bridge structure after system error correction and filtering de-noising of signals collected by the GNSS. The proposed AFEC mixed filtering algorithm had high accuracy (1 mm) of real displacement at the elevation direction. Next, the traditional random decrement technique (used mainly for stationary random processes) was expanded to non-stationary random processes. Combining the expanded random decrement technique (RDT) and autoregressive moving average model (ARMA), the modal frequency of the bridge structural system was extracted using an expanded ARMA_RDT modal identification method, which was compared with the power spectrum analysis results of the acceleration signal and finite element analysis results. Identification results demonstrated that the proposed algorithm is applicable to analyze the dynamic displacement monitoring data of real bridge structures under ambient excitation and could identify the first five orders of the inherent frequencies of the structural system accurately. The identification error of the inherent frequency was smaller than 6%, indicating the high identification accuracy of the proposed algorithm. Furthermore, the GNSS dynamic deformation monitoring method can be used to monitor dynamic displacement and identify the modal parameters of bridge structures. The GNSS can monitor the working state of bridges effectively and accurately. Research results can provide references to evaluate the bearing capacity, safety performance, and durability of bridge structures during operation.

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“…However, these methods assume the structural response is stationary in order to simplify the signal processing, which is not true in many cases, and therefore the modal parameters cannot be adequately analyzed by these methods [4]. Besides, some methods need a large set of data to identify accurately the modal parameters and, moreover, the results may be affected in noisy conditions [16, 17].…”

Section: Introductionmentioning

confidence: 99%

EEMD-MUSIC-Based Analysis for Natural Frequencies Identification of Structures Using Artificial and Natural Excitations

Camarena-Martinez

Amezquita-Sanchez

Valtierra-Rodríguez

et al. 2014

The Scientific World Journal

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This paper presents a new EEMD-MUSIC- (ensemble empirical mode decomposition-multiple signal classification-) based methodology to identify modal frequencies in structures ranging from free and ambient vibration signals produced by artificial and natural excitations and also considering several factors as nonstationary effects, close modal frequencies, and noisy environments, which are common situations where several techniques reported in literature fail. The EEMD and MUSIC methods are used to decompose the vibration signal into a set of IMFs (intrinsic mode functions) and to identify the natural frequencies of a structure, respectively. The effectiveness of the proposed methodology has been validated and tested with synthetic signals and under real operating conditions. The experiments are focused on extracting the natural frequencies of a truss-type scaled structure and of a bridge used for both highway traffic and pedestrians. Results show the proposed methodology as a suitable solution for natural frequencies identification of structures from free and ambient vibration signals.

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EMD-based random decrement technique for modal parameter identification of an existing railway bridge

Cited by 106 publications

References 23 publications

A Rapidly Convergent Empirical Mode Decomposition Method for Analyzing the Environmental Temperature Effects on Stay Cable Force

A Rapidly Convergent Empirical Mode Decomposition Method for Analyzing the Environmental Temperature Effects on Stay Cable Force

Operational Modal Analysis of Bridge Structures with Data from GNSS/Accelerometer Measurements

EEMD-MUSIC-Based Analysis for Natural Frequencies Identification of Structures Using Artificial and Natural Excitations

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