Damping Effect of a Passing Vehicle for Indirectly Measuring Bridge Frequencies by EMD Technique

Yang, Judy P.; Lee, Wei Chun

doi:10.1142/s0219455418500086

Cited by 66 publications

(9 citation statements)

References 15 publications

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“…It is shown in previous studies [7,16,25,33] that the presence of a road profile introduces inaccuracies in indirect bridge monitoring methods. In this case, the vehicle frequencies are dominant in the responses measured on the passing vehicle, which might hide the bridge responses.…”

Section: Numerical Results Of the Acceleration-based Algorithmmentioning

confidence: 99%

A Machine Learning Approach to Bridge-Damage Detection Using Responses Measured on a Passing Vehicle

Malekjafarian

Golpayegani

Moloney

et al. 2019

Sensors

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This paper proposes a new two-stage machine learning approach for bridge damage detection using the responses measured on a passing vehicle. In the first stage, an artificial neural network (ANN) is trained using the vehicle responses measured from multiple passes (training data set) over a healthy bridge. The vehicle acceleration or Discrete Fourier Transform (DFT) spectrum of the acceleration is used. The vehicle response is predicted from its speed for multiple passes (monitoring data set) over the bridge. Root-mean-square error is used to calculate the prediction error, which indicates the differences between the predicted and measured responses for each passage. In the second stage of the proposed method, a damage indicator is defined using a Gaussian process that detects the changes in the distribution of the prediction errors. It is suggested that if the bridge condition is healthy, the distribution of the prediction errors will remain low. A recognizable change in the distribution might indicate a damage in the bridge. The performance of the proposed approach was evaluated using numerical case studies of vehicle–bridge interaction. It was demonstrated that the approach could successfully detect the damage in the presence of road roughness profile and measurement noise, even for low damage levels.

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Section: Numerical Results Of the Acceleration-based Algorithmmentioning

confidence: 99%

A Machine Learning Approach to Bridge-Damage Detection Using Responses Measured on a Passing Vehicle

Malekjafarian

Golpayegani

Moloney

et al. 2019

Sensors

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“…In this method, road roughness is added to the simulation to examine the robustness of the proposed method. Following the power spectral density (PSD) based procedure given in Yang and Lee, 56 three different road profiles are generated using G d ( n 0 ) of 0.01 × 10 −6 m 3 , 1 × 10 −6 m 3 , and 16 × 10 −6 m 3 , representing low, medium, and high road roughness, respectively 56 . The generated road profiles are presented in Figure 23.…”

Section: Discussionmentioning

confidence: 99%

Bridge mode shape identification using moving vehicles at traffic speeds through non‐parametric sparse matrix completion

Mei

Shirzad-Ghaleroudkhani

Gül

et al. 2021

Struct Control Health Monit

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Summary Advances in smart infrastructure produces a natural demand of system identification techniques for structural health and performance monitoring that can be scaled to regions and large asset inventories. Conventional approaches require sensors to be installed, often in long‐term deployments, on the monitored infrastructure systems, which is a costly undertaking when thousands of systems (e.g., bridges) need to be monitored. This paper presents a novel mode shape identification method for bridges that uses data collected from moving vehicles as input—a paradigm that can overcome limitations associated with conventional approaches. The method consists of two steps. First, the data collected from moving measurement points are mapped to virtual fixed points to generate a sparse matrix. Then, a “soft‐imputing” technique is employed to fill the sparse matrix. Finally, a singular value decomposition is applied to extract the mode shapes of the bridge. Experiments with synthetic, yet realistic, data are conducted to verify the method. The sensitivity of the proposed approach to different factors, including the number of vehicles, car speed, road roughness, and measurement errors, are also investigated. The results show that the proposed method is capable of identifying the mode shapes of the bridge accurately.

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“…Malekjafarian and OBrien [10] use instantaneous amplitude of the signal measured on a passing vehicle for the estimation of bridge mode shapes. Yang and Lee [11] use empirical mode decomposition (EMD) method to study the effect of vehicle damping on indirect identification of bridge frequency. They find that higher vehicle damping tends to suppress the vehicle frequency and make the bridge frequency more visible.…”

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 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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Damping Effect of a Passing Vehicle for Indirectly Measuring Bridge Frequencies by EMD Technique

Cited by 66 publications

References 15 publications

A Machine Learning Approach to Bridge-Damage Detection Using Responses Measured on a Passing Vehicle

A Machine Learning Approach to Bridge-Damage Detection Using Responses Measured on a Passing Vehicle

Bridge mode shape identification using moving vehicles at traffic speeds through non‐parametric sparse matrix completion

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

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