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

Mei, Qipei; Shirzad-Ghaleroudkhani, Nima; Gül, Mustafa; Ghahari, S. Farid; Taciroğlu, Ertuǧrul

doi:10.1002/stc.2747

Cited by 36 publications

(13 citation statements)

References 48 publications

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“…Although some researchers used time–frequency domain techniques [ 16 , 17 , 24 , 25 , 26 , 27 ], the majority of the most recent vehicle scanning methods in the literature apply two mode shape identification approaches to construct the bridge mode shapes: (i) using signal segmentation with an OMA tool [ 28 , 29 , 30 , 31 , 32 , 33 , 34 ], and (ii) using signal decomposition together with Hilbert transform [ 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 ]. This study involves three of the most recent VSM methods proposed in the literature for estimating mode shapes.…”

Section: Vehicle Scanning Methodsmentioning

confidence: 99%

“…Li et al [ 31 ] used acceleration responses measured on both a stationary (reference) and a moving car, employing stochastic subspace identification (SSI) for mode shape identification. Applications that are based on the same philosophy can be found in [ 32 , 33 , 34 ], which aim to estimate the mode shapes of the bridge utilizing a crowdsourcing-based methodology through a non-parametric sparse matrix completion.…”

Section: Introductionmentioning

confidence: 99%

“…However, most of the existing studies in this field have only utilized simplified car models simulated as a single sprung mass, which does not account for the pitching action of real vehicles. Only a few studies have employed two-axle vehicle models that consider the pitching action to scan the bridge mode shapes [ 24 , 30 , 32 , 40 , 41 , 42 ]. More importantly, most of the numerical studies on mode shape identification have tested the accuracy of their methods on simply supported bridge models.…”

Section: Introductionmentioning

confidence: 99%

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Efficacy of Vehicle Scanning Methods in Estimating the Mode Shapes of Bridges Seated on Elastic Supports

Demirlioglu

Gönen

Erduran

2023

Sensors

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This study systematically assesses the efficacy of the vehicle scanning methods (VSM) in accurately estimating the mode shapes of bridges seated on elastic supports. Three state-of-the-art VSM methods are employed to obtain the mode shapes of bridges using the vehicle data during its travel. Two of the evaluated methods use a signal decomposition technique to extract the modal components of the bridge from the contact point of the response while the third one relies on the segmentation of the measured signals along the bridge deck and applying an operational modal analysis tool to each segmented signal to estimate the mode shapes. Numerical analyses are conducted on one single- and one two-span bridge, considering smooth and rough road profiles, different vehicle speeds, and presence of lead vehicle. The accuracy of the numerical models used in developing and assessing vehicle scanning models is tested, and the results of the study demonstrate the method using a half-car vehicle model and signal decomposition technique shows robustness against increasing vehicle speeds and road roughness while the method applying the segmentation of the measured signals provides relatively accurate mode shape estimates at the bridge edges at low speed, although the three methods have their limitations. It is also observed that simplified bridge and vehicle models can hide potential challenges that arise from the complexity of actual vehicle and bridge systems. Considering that a significant number of bridges worldwide are built on elastic supports, the practical success of vehicle scanning methods depends on their ability to handle elastic boundary conditions with reliability. Therefore, the article provides valuable insights into the capabilities and limitations of the current vehicle scanning methods, paving the way for further advancements and refinements in these techniques.

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Section: Vehicle Scanning Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Efficacy of Vehicle Scanning Methods in Estimating the Mode Shapes of Bridges Seated on Elastic Supports

Demirlioglu

Gönen

Erduran

2023

Sensors

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“…Based on the system shown in Figure 4, relative displacement and acceleration data were determined, as commonly done with ABAQUS software, generating a set of virtual sensors [22,23], which were placed at the interface between each column drum, the upper drum and the capital, as well as between the capital and the epistyle. The position of each virtual sensor is indicated with the capital letter D, followed by a number, reported in the same figure.…”

Section: Output Analysismentioning

confidence: 99%

FEM-based models of real-time dynamic structural behavior in archaeology and monumental heritage: the case of ancient Greek colonnades

Casazza,

Barone

2023

Proceedings of the 2022 IMEKO TC4 International Conference on Metrology for Archaeology and Cultural Heritage

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The preservation of historical buildings and infrastructures requires a multidisciplinary approach to provide a real-time knowledge of their dynamical structural behavior. For this task, an effective procedure can be based on the integration between tailored dynamic models, optimized through an iterative synergic process, and an adaptive and modular distributed monitoring system. With respect to modelling, a Finite Element Model (FEM) is a valid solution. In fact, it is not only an effective reference dynamical model, but it can also be used to support the definition of the potential technical requirements for the sensors (e.g., typology, sensitivity, band and number) that will be installed to monitor the chosen structure. We applied this idea to the case of an ancient Greek colonnade, building a FEM validated through published experimental measures. The obtained results prove that this model, behind its traditional use to describe the dynamic structural behaviour of a monument, can serve as a the basis of a metrological tool, especially during the iterative optimization process, to define the technical characteristics of distributed structural monitoring systems.

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“…This interpolation results in a sparse response matrix, where each row corresponds to the response of a particular fixed node and each column corresponds to the response vector of the fixed nodes in a time stamp. This matrix contains numerous missing values (invalid regions) that require advanced statistical, mathematical, or machine learning techniques to predict or complete the response signals for the virtual fixed nodes [25,26].…”

Section: Introductionmentioning

confidence: 99%

A Mobile Sensing Framework for Bridge Modal Identification through an Inverse Problem Solution Procedure and Moving-Window Time Series Models

Talebi-Kalaleh,

Mei

2023

Sensors

Self Cite

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With the rise and development of smart infrastructures, there has been a great demand for installing automatic monitoring systems on bridges, which are key members of transportation networks. In this regard, utilizing the data collected by the sensors mounted on the vehicles passing over the bridge can reduce the costs of the monitoring systems, compared with the traditional systems where fixed sensors are mounted on the bridge. This paper presents an innovative framework for determining the response and for identifying modal characteristics of the bridge, utilizing only the accelerometer sensors on the moving vehicle passing over it. In the proposed approach, the acceleration and displacement response of some virtual fixed nodes on the bridge is first determined using the acceleration response of the vehicle axles as the input. An inverse problem solution approach based on a linear and a novel cubic spline shape function provides the preliminary estimations of the bridge’s displacement and acceleration responses, respectively. Since the inverse solution approach is only capable of determining the response signal of the nodes with high accuracy in the vicinity of the vehicle axles, a new moving-window signal prediction method based on auto-regressive with exogenous time series models (ARX) is proposed to complete the responses in the regions with large errors (invalid regions). The mode shapes and natural frequencies of the bridge are identified using a novel approach that integrates the results of singular value decomposition (SVD) on the predicted displacement responses and frequency domain decomposition (FDD) on the predicted acceleration responses. To evaluate the proposed framework, various numerical but realistic models for a single-span bridge under the effect of a moving mass are considered; the effects of different levels of ambient noise, the number of axles of the passing vehicle, and the effect of its speed on the accuracy of the method are investigated. The results show that the proposed method can identify the characteristics of the three main modes of the bridge with high accuracy.

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

Cited by 36 publications

References 48 publications

Efficacy of Vehicle Scanning Methods in Estimating the Mode Shapes of Bridges Seated on Elastic Supports

Efficacy of Vehicle Scanning Methods in Estimating the Mode Shapes of Bridges Seated on Elastic Supports

FEM-based models of real-time dynamic structural behavior in archaeology and monumental heritage: the case of ancient Greek colonnades

A Mobile Sensing Framework for Bridge Modal Identification through an Inverse Problem Solution Procedure and Moving-Window Time Series Models

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