Bridge Structural Identification Using Moving Vehicle Acceleration Measurements

Eshkevari, Soheil Sadeghi; Pakzad, Shamim N.

doi:10.1007/978-3-319-74421-6_34

Cited by 9 publications

(4 citation statements)

References 25 publications

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“…When the recorded sensor data are amalgamated with the vibration from other sources mentioned above, conventional SHM methods cannot be directly applied as they are designed to work with pure structural vibration responses [ 46 ]. To circumvent this problem, the existing literature addresses primary approaches: (1) controlling the sensing conditions like vehicle speed and road roughness intensity so that the recorded response mainly contains the bridge vibration [ 14 , 27 ], (2) modeling the vehicle–bridge interaction in a closed form to eliminate the uncertainties due to vibration sources other than the bridge itself [ 47 , 48 ], (3) to use blind source separation (BSS) techniques to extract the different sources of the recorded response [ 35 ]. The BSS technique is capable of estimating pure bridge vibration response.…”

Section: Proposed Framework and Its Significance In Bridge Shmmentioning

confidence: 99%

“…Matarazzo et al [ 33 , 34 ] introduced the “structural identification using expectation maximization (STRIDE)” method for mode shape identification from mobile sensors. Eshkevari et al [ 35 , 36 , 37 ] formulated mobile sensing data as a sparse matrix with missing values. They employed alternating least-square (ALS) for matrix completion, followed by principal component analysis (PCA) and structured optimization analysis (SOA) for modal identification.…”

Section: Introductionmentioning

confidence: 99%

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Full-Field Vibration Response Estimation from Sparse Multi-Agent Automatic Mobile Sensors Using Formation Control Algorithm

Jana,

Nagarajaiah

2023

Sensors

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In structural vibration response sensing, mobile sensors offer outstanding benefits as they are not dedicated to a certain structure; they also possess the ability to acquire dense spatial information. Currently, most of the existing literature concerning mobile sensing involves human drivers manually driving through the bridges multiple times. While self-driving automated vehicles could serve for such studies, they might entail substantial costs when applied to structural health monitoring tasks. Therefore, in order to tackle this challenge, we introduce a formation control framework that facilitates automatic multi-agent mobile sensing. Notably, our findings demonstrate that the proposed formation control algorithm can effectively control the behavior of the multi-agent systems for structural response sensing purposes based on user choice. We leverage vibration data collected by these mobile sensors to estimate the full-field vibration response of the structure, utilizing a compressive sensing algorithm in the spatial domain. The task of estimating the full-field response can be represented as a spatiotemporal response matrix completion task, wherein the suite of multi-agent mobile sensors sparsely populates some of the matrix’s elements. Subsequently, we deploy the compressive sensing technique to obtain the dense full-field vibration complete response of the structure and estimate the reconstruction accuracy. Results obtained from two different formations on a simply supported bridge are presented in this paper, and the high level of accuracy in reconstruction underscores the efficacy of our proposed framework. This multi-agent mobile sensing approach showcases the significant potential for automated structural response measurement, directly applicable to health monitoring and resilience assessment objectives.

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Section: Proposed Framework and Its Significance In Bridge Shmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Full-Field Vibration Response Estimation from Sparse Multi-Agent Automatic Mobile Sensors Using Formation Control Algorithm

Jana,

Nagarajaiah

2023

Sensors

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“…Several field tests have demonstrated the feasibility and reliability of using the VSM to extract bridge frequencies indirectly [ 12 , 13 ]. In recent years, the VSM has gained popularity for identifying frequencies of bridge systems [ 14 , 15 , 16 , 17 , 18 ]. Many scholars all around the world have carried out research based on the VSM, and an array of advanced filtering techniques have been used to extract the bridge frequencies from the vehicle signals, such as the empirical modal decomposition (EMD) [ 19 ], the variational modal decomposition (VMD) [ 20 ], and the stochastic subspace (SSI) methods [ 21 ].…”

Section: Introductionmentioning

confidence: 99%

Subspace Identification of Bridge Frequencies Based on the Dimensionless Response of a Two-Axle Vehicle

Quan,

Zeng,

Jin

et al. 2024

Sensors

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As an essential reference to bridge dynamic characteristics, the identification of bridge frequencies has far-reaching consequences for the health monitoring and damage evaluation of bridges. This study proposes a uniform scheme to identify bridge frequencies with two different subspace-based methodologies, i.e., an improved Short-Time Stochastic Subspace Identification (ST-SSI) method and an improved Multivariable Output Error State Space (MOESP) method, by simply adjusting the signal inputs. One of the key features of the proposed scheme is the dimensionless description of the vehicle–bridge interaction system and the employment of the dimensionless response of a two-axle vehicle as the state input, which enhances the robustness of the vehicle properties and speed. Additionally, it establishes the equation of the vehicle biaxial response difference considering the time shift between the front and the rear wheels, theoretically eliminating the road roughness information in the state equation and output signal effectively. The numerical examples discuss the effects of vehicle speeds, road roughness conditions, and ongoing traffic on the bridge identification. According to the dimensionless speed parameter Sv1 of the vehicle, the ST-SSI (Sv1 < 0.1) or MOESP (Sv1 ≥ 0.1) algorithm is applied to extract the frequencies of a simply supported bridge from the dimensionless response of a two-axle vehicle on a single passage. In addition, the proposed methodology is applied to two types of long-span complex bridges. The results show that the proposed approaches exhibit good performance in identifying multi-order frequencies of the bridges, even considering high vehicle speeds, high levels of road surface roughness, and random traffic flows.

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“…The feasible characteristic index of frequency information is the frequency band of small distortion (FBSD), which is within ±5% or ±10% amplitude deviation due to the extreme conditions of dynamic measurement [3]. In [4][5][6][7], the assumed second-order model is adopted to depict the nonlinear error characteristics of accelerometer. Also, in [8][9], the state-space transfer function assumes the accelerometer model to be an ordinary second-order difference function.…”

Section: Introductionmentioning

confidence: 99%

Dynamic characterization method of accelerometers based on the Hopkinson bar calibration system

Yang

Wang

et al. 2019

Sensors and Actuators A: Physical

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Bridge Structural Identification Using Moving Vehicle Acceleration Measurements

Cited by 9 publications

References 25 publications

Full-Field Vibration Response Estimation from Sparse Multi-Agent Automatic Mobile Sensors Using Formation Control Algorithm

Full-Field Vibration Response Estimation from Sparse Multi-Agent Automatic Mobile Sensors Using Formation Control Algorithm

Subspace Identification of Bridge Frequencies Based on the Dimensionless Response of a Two-Axle Vehicle

Dynamic characterization method of accelerometers based on the Hopkinson bar calibration system

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