Temperature-based structural health monitoring baseline for long-span bridges

Yarnold, Matthew; Moon, Franklin

doi:10.1016/j.engstruct.2014.12.042

Cited by 111 publications

(49 citation statements)

References 19 publications

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“…Thermal gradients of concrete bridges were analyzed employing in-depth concrete temperatures and ambient temperatures [5], [6], thermal loadings have been included in finite element analysis [7], [8], effects of ambient temperature on the modal properties of bridges were reviewed [9]- [14] and recently, anomaly detection algorithms based on structure responses to temperature are being suggested [15]- [19]. [20] Although it has been shown that levels of stress caused by temperature far exceed any stress caused by recorded traffic [21]- [23], studying the thermal response of a structure and documenting the normal behavior of a structure due to environmental changes can be challenging; solar radiation and heat exchange of different parts of a structure with the surrounding environment are correlated with the orientation of the structure and hence the temperature profile is nonuniform and nonlinear [24], [25].…”

Section: Introductionmentioning

confidence: 99%

A study of thermal response of concrete towers employing linear regression

et al. 2016

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It has been shown that the variations of structural properties due to changing environmental conditions such as temperature can be as significant as those caused by structural damage and even liveload. Therefore, tracking changes that are correlated with environmental variations is a necessary step in order to detect and assess structural damage in addition to the normal structural response to traffic.In this paper, daily measurement data that is collected from the concrete towers of the Ironton-Russell Bridge will be presented and correlation of the collected measurement data and temperature will be overviewed. Variation of the daily thermal response of tower concrete walls will be compared with the daily thermal responses of the steel box within the tower and finally, thermal coefficient for compensating the thermal induced responses will be estimated.

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Section: Introductionmentioning

confidence: 99%

A study of thermal response of concrete towers employing linear regression

et al. 2016

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“…SHM using global technique namely vibration-based damage detection, is usually used to monitor modal properties such as monitors the changes in natural frequencies, damping loss factor and mode shapes. SHM techniques and models have been established and applied in aerospace structures [1], [2] and also civil structures such as building [3]- [5], and bridges [6]- [9]. The system's performance will be affected over a period of time or immediately depends on the size of the crack, the location of the crack and also the loading conditions applied to the system.…”

Section: Introductionmentioning

confidence: 99%

Operational Modal Analysis on a 3D Scaled Model of a 3-Storey Aluminium Structure

Shazmir

Safari

Anuar

et al. 2018

IJET

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Obtaining a good experimental modal data is essential in modal analysis in order to ensure accurate extraction of modal parameters. The parameters are compared with other extraction methods to ascertain its consistency and validity. This paper demonstrates the extraction of modal parameters using various identification algorithms in Operational Modal Analysis (OMA) on a 3D scaled model of a 3-storey aluminium structure. Algorithms such as Frequency Domain Decomposition (FDD), Enhanced Frequency Domain Decomposition (EFDD) and Stochastic Subspace Identification (SSI) are applied in this study to obtain modal parameters. The model test structure is fabricated of aluminium and assembled using bolts and nuts. Accelerometers were used to collect the responses and the commercial post processing software was used to obtain the modal parameters. The resulting natural frequencies and mode shapes using FDD method are then compared with other OMA parametric technique such as EFDD and SSI algorithm by comparing the natural frequencies and Modal Assurance Criterion (MAC). Comparison of these techniques will be shown to justify the validity of each technique used and hence confirming the accuracy of the measurement taken.

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“…As a result of this structural degradation, at some point, the structure may no longer be able to meet its performance requirements. This work contributes to the vast body of literature that develops diagnostics to detect such changes before they become safety or mission critical [7,8].…”

Section: Introductionmentioning

confidence: 99%

Guaranteeing robustness of structural condition monitoring to environmental variability

Buren

Reilly

Neal

et al. 2017

Journal of Sound and Vibration

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Advances in sensor deployment and computational modeling have allowed significant strides to be recently made in the field of Structural Health Monitoring (SHM). One widely used SHM strategy is to perform a vibration analysis where a model of the structure's pristine (undamaged) condition is compared with vibration response data collected from the physical structure. Discrepancies between model predictions and monitoring data can be interpreted as structural damage. Unfortunately, multiple sources of uncertainty must also be considered in the analysis, including environmental variability, unknown model functional forms, and unknown values of model parameters. Not accounting for these sources of uncertainty can lead to falsepositives or false-negatives in the structural condition assessment. To manage the uncertainty, we propose a robust SHM methodology that combines three technologies. A time series algorithm is trained using "baseline" data to predict the vibration response, compare predictions to actual measurements collected on a potentially damaged structure, and calculate a userdefined damage indicator. The second technology handles the uncertainty present in the problem. An analysis of robustness is performed to propagate this uncertainty through the time series algorithm and obtain the corresponding bounds of variation of the damage indicator. The uncertainty description and robustness analysis are both inspired by the theory of info-gap decision-making. Lastly, an appropriate "size" of the uncertainty space is determined through physical experiments performed in laboratory conditions. Our hypothesis is that examining how the uncertainty space changes throughout time might lead to superior diagnostics of structural damage as compared to only monitoring the damage indicator. This methodology is applied to a portal frame structure to assess if the strategy holds promise for robust SHM.

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Temperature-based structural health monitoring baseline for long-span bridges

Cited by 111 publications

References 19 publications

A study of thermal response of concrete towers employing linear regression

A study of thermal response of concrete towers employing linear regression

Operational Modal Analysis on a 3D Scaled Model of a 3-Storey Aluminium Structure

Guaranteeing robustness of structural condition monitoring to environmental variability

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