On the Use of Ensemble Empirical Mode Decomposition for the Identification of Bridge Frequency from the Responses Measured in a Passing Vehicle

Zhu, Licheng; Malekjafarian, Abdollah

doi:10.3390/infrastructures4020032

Cited by 47 publications

(19 citation statements)

References 18 publications

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“…The ability of this method to overcome the mode mixing problem in the SHM field has been surpassed by other modern decomposition methods. 33 The evolution of methods that emerged from EMD describe a successive improvement. For example, the EEMD method defines the IMF component as a mean of ensemble of trails, having white noise as well as signal.…”

Section: Empirical Mode Decompositionmentioning

confidence: 99%

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Bridge damage detection via improved completed ensemble empirical mode decomposition with adaptive noise and machine learning algorithms

Delgadillo

Casas

2022

Structural Contr & Hlth

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Structural health monitoring field is growing in the use of more modern techniques and tools in order to identify damages in civil structures. The improvements in signal processing techniques and data mining have, recently, been employed due to their powerful computational ability to detect damage in bridges. Despite the majority of researchers have been studying laboratory-scale implementations and theoretical developments, the limited data to identify structural faults in real bridges are still a problem. The current study presents a novel approach for damage identification by using two improved methods such as decomposition techniques and machine learning algorithms. Since the data obtained from the traffic vibration in real bridges are non-linear and time varying, the Hilbert-Huang transform is used to process the vibration data.

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Section: Empirical Mode Decompositionmentioning

confidence: 99%

“…EMD uses all the extrema to construct the envelopes and the problem arises when the signal contains intermittent processes, since the phenomenon of mode mixing can occur. The ability of this method to overcome the mode mixing problem in the SHM field has been surpassed by other modern decomposition methods 33 …”

Section: Theoretical Backgroundmentioning

confidence: 99%

Bridge damage detection via improved completed ensemble empirical mode decomposition with adaptive noise and machine learning algorithms

Delgadillo

Casas

2022

Structural Contr & Hlth

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“…The IMFs extracted from vehicle response were used as damage indicators of bridge structure (Kildashti et al, 2020; O’Brien et al, 2017). EEMD method introduced by Wu and Huang (2009) to address the mode mixing problem of the EMD was used to identify bridge modal frequencies from vehicle response (Zhu and Malekjafarian, 2019). The results showed that EEMD method provided better performance on the decomposition of vehicle responses compared to EMD.…”

Section: Introductionmentioning

confidence: 99%

Bridge modal identification based on successive variational mode decomposition using a moving test vehicle

Zhu

Guo

2022

Advances in Structural Engineering

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Bridge modal identification using an instrumented test vehicle as a moving sensor is promising but challenging. A key factor is to extract bridge dynamic components from vehicle responses measured when the bridge is operating. A new method based on an advanced adaptive signal decomposition technique, the successive variational mode decomposition (SVMD), has been developed to estimate the bridge modal parameters from the dynamic responses of a passing test vehicle. When bridge-related dynamic components are extracted from the decomposition, the natural excitation technique and/or random-decrement technique based fitting methods are used to estimate the modal frequencies and damping ratios of the bridge. Effects of measurement noise, moving speed and vehicle properties on the decomposition are investigated numerically. The superiority of SVMD in the decomposition is verified by comparing to another adaptive decomposition technique, the singular spectrum decomposition. The results of the proposed method confirm that the bridge modal frequencies can be identified from bridge related components with high accuracy, while damping ratio is more sensitive to the random operational load. Finally, the feasibility of the proposed method for bridge monitoring using a moving test vehicle is further verified by an in-situ experimental test on a cable-stayed bridge. The components related to the bridge dynamic responses are successfully extracted from vehicle responses.

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“…For that problem, Wu and Huang (2009) propose a noise-assisted analysis approach called Ensemble Empirical Mode Decomposition (EEMD) [ 35 ] that can address the mode mixing problem of the EMD [ 35 , 49 ]. Zhu and Malekjafarian [ 50 ] have recently proposed an EEMD based drive-by approach for estimating bridge natural frequencies from vehicle responses. They suggest that EEMD is less sensitive to measurement noise and provides higher accuracy of bridge first natural frequency from the vehicle responses.…”

Section: Introductionmentioning

confidence: 99%

Indirect Monitoring of Frequencies of a Multiple Span Bridge Using Data Collected from an Instrumented Train: A Field Case Study

Malekjafarian

Khan

O’Brien

et al. 2022

Sensors

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In this paper, a field study is carried out to monitor the natural frequencies of Malahide viaduct bridge which is located in the north of Dublin. The bridge includes a series of simply supported spans, two of which collapsed in 2009 and were replaced. The replaced spans are stiffer than most of the others and these differences resulted in higher natural frequencies. An indirect bridge monitoring approach is employed in which acceleration responses from an instrumented train are used to estimate the natural frequencies of each span of the viaduct showing the locations of the two replaced spans with higher stiffness. For the indirect approach, an Ensemble Empirical Mode Decomposition (EEMD)-based Hilbert Huang Transform (HHT) technique is employed to identify the natural frequency of each span. This is carried out by analysing the Instantaneous Frequencies (IFs) from the calculated intrinsic mode functions. The average of the IFs calculated using 41 runs of the instrumented train (with varying carriage mass and speed for each run) are used to estimate the natural frequencies. To assess the feasibility of the indirect approach, a bespoke set of direct measurements was taken using accelerometers attached successively on each span of the viaduct. The free and forced vibrations from each span are used to estimate the first natural frequencies. The frequencies obtained from drive-by measurements are compared to those from direct measurements which confirms the effectiveness of indirect approaches. In addition, the instantaneous amplitudes of the drive-by signals are used to indicate the location of the stiffer spans. Finally, the accuracy and robustness of the indirect approaches for monitoring of multi span bridges are discussed.

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On the Use of Ensemble Empirical Mode Decomposition for the Identification of Bridge Frequency from the Responses Measured in a Passing Vehicle

Cited by 47 publications

References 18 publications

Bridge damage detection via improved completed ensemble empirical mode decomposition with adaptive noise and machine learning algorithms

Bridge damage detection via improved completed ensemble empirical mode decomposition with adaptive noise and machine learning algorithms

Bridge modal identification based on successive variational mode decomposition using a moving test vehicle

Indirect Monitoring of Frequencies of a Multiple Span Bridge Using Data Collected from an Instrumented Train: A Field Case Study

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