Identification of sudden stiffness changes in the acceleration response of a bridge to moving loads using ensemble empirical mode decomposition

Aied, Hussein; González, Arturo; Cantero, Daniel

doi:10.1016/j.ymssp.2015.05.027

Cited by 59 publications

(40 citation statements)

References 43 publications

(67 reference statements)

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“…The ability of EEMD method to remove high frequency components from other frequency components makes it possible to mitigate the influence from measurement noise. It is shown in the literature that the EEMD can solve the mode mixing problem of the EMD method [17]. The main purpose of the current study is to improve the accuracy of the indirect identification of bridge natural frequencies, particularly indifferent vehicle speeds.…”

Section: Ensemble Empirical Mode Decomposition (Eemd) Methodsmentioning

confidence: 99%

“…The noise amplitude A n is a key parameter affecting the performance of the EEMD method on the mode separation. Too low of a noise amplitude would not cause enough changes of the decomposed signal and too high of one would create redundant IMFs [17]. defined as the mean of an ensemble number of trials that white noise is added to the signal.…”

Section: Ensemble Empirical Mode Decomposition (Eemd) Methodsmentioning

confidence: 99%

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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

Zhu

Malekjafarian

2019

Infrastructures

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In this paper, ensemble empirical mode decomposition (EEMD) and empirical mode decomposition (EMD) methods are used for the effective identification of bridge natural frequencies from drive-by measurements. A vehicle bridge interaction (VBI) model is created using the finite element (FE) method in Matlab. The EMD is employed to decompose the signals measured on the vehicle to their main components. It is shown that the bridge component of the response measured on the vehicle can be extracted using the EMD method. The influence of some factors, such as the road roughness profile and measurement noise, on the results are investigated. The results suggest that the EMD shows good performance under those conditions, but the accuracy of the results may still need to be improved. It is shown that in some cases, the EMD may not be able to decompose the signal effectively and includes mode mixing. This results in inaccuracies in the identification of bridge frequencies. The use of the ensemble empirical mode decomposition (EEMD) method is proposed to overcome the mode mixing problem. The influence of factors such as road profile, measurement noise and vehicle velocity are investigated. It is numerically demonstrated that employing the EEMD improves the results compared to the EMD.

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Section: Ensemble Empirical Mode Decomposition (Eemd) Methodsmentioning

confidence: 99%

Section: Ensemble Empirical Mode Decomposition (Eemd) Methodsmentioning

confidence: 99%

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

Zhu

Malekjafarian

2019

Infrastructures

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“…In fact, the utilization of the EMDs alone can yield identification of structural damage based on changes to its intrinsic mode functions' curvature [127]. Additionally, many SHM applications of the HHT have been conducted on data obtained from numerical models [128] and laboratory-based experiments [129].…”

Section: Advancements To Operational Specific Challengesmentioning

confidence: 99%

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

2017

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Abstract:Traditionally, damage identification techniques in bridges have focused on monitoring changes to modal-based Damage Sensitive Features (DSFs) due to their direct relationship with structural stiffness and their spatial information content. However, their progression to real-world applications has not been without its challenges and shortcomings, mainly stemming from: (1) environmental and operational variations; (2) inefficient utilization of machine learning algorithms for damage detection; and (3) a general over-reliance on modal-based DSFs alone. The present paper provides an in-depth review of the development of modal-based DSFs and a synopsis of the challenges they face. The paper then sets out to addresses the highlighted challenges in terms of published advancements and alternatives from recent literature.

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“…Civil engineering structures are subjected to continuous structural deterioration caused by aging, low-cycle fatigue loads from smaller earthquakes, and daily environmental loading. erefore, the severity of deterioration needs to be monitored periodically in order to ensure structural integrity and safety [1]. If damage is detected in the early stage, maintenance works will be carried out timely with low cost.…”

Section: Introductionmentioning

confidence: 99%

“…e first question that arises is how to characterize a nonlinear response as mentioned above. is has been addressed in a wide range of mechanical and civil engineering applications via signal processing methods like Hilbert-Huang transform-based [1,17], Wavelet transform-based [7], neural network-based [18,19], and independent component analysis-based methods [20]. Yang and Nagarajaiah [6] combined wavelet transform with independent component analysis (ICA) to detect linear structural instant, which had excellent robustness.…”

Section: Introductionmentioning

confidence: 99%

Identification of Sudden Stiffness Change in the Acceleration Response of a Nonlinear Hysteretic Structure

Jiang

et al. 2020

Shock and Vibration

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e integration of discrete wavelet transform and independent component analysis (DWT-ICA) method can directly identify time-varying changes in linear structures. However, better metrics of structural seismic damage and future performance after an event are related to structural permanent and total plastic deformations. is study proposes a two-stage technique based on DWT-FastICA and improved multiparticle swarm coevolution optimization (IMPSCO) using a baseline nonlinear Bouc-Wen structural model to directly identify changes in stiffness caused by damage as well as plastic or permanent deflections. In the first stage, the measured structural dynamic responses are preprocessed firstly by DWT, and then the Fast ICA is used to extract the feature components that contain the damage information for the purpose of initially locating damage. In the second stage, the structural responses are divided at the identified damage instant into segments that are used to identify the time-varying physical parameters by using the IMPSCO, and the location and extent of damage can accordingly be identified accurately. e efficiency of the proposed method in identifying stiffness changes is assessed under different ground motions using a suite of two different ground acceleration records. Meanwhile, the effect of noise level and damage extent on the proposed method is also analyzed. e results show that in a realistic scenario with fixed filter tuning parameters, the proposed approach identifies stiffness changes within 1.25% of true stiffness within 8.96 s; therefore, it can work in real time. Parameters are identified within 14% of the actual as-modeled value using noisy simulation-derived structural responses. is indicates that, in accordance with different demands, the proposed method can not only locate and quantify damage within a short time with a high precision but also has excellent noise tolerance, robustness, and practicality.

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Identification of sudden stiffness changes in the acceleration response of a bridge to moving loads using ensemble empirical mode decomposition

Abstract: Publication informationMechanical Systems and Signal Processing, 66-67 : 314-338 Publisher ElsevierItem record/more information http://hdl.handle.net/10197/7727 Publisher's statement þÿ T h i s i s t h e a u t h o r s v e r s i o n o f a w o r k t h a t

Cited by 59 publications

References 43 publications

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

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

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

Identification of Sudden Stiffness Change in the Acceleration Response of a Nonlinear Hysteretic Structure

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