Correlating modal properties with temperature using long-term monitoring data and support vector machine technique

Ni, Yiqing; Hua, Xugang; Fan, Ke; Ko, J. M.

doi:10.1016/j.engstruct.2005.02.020

Cited by 243 publications

(131 citation statements)

References 17 publications

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“…One of the main advantages of polynomial response surface models is their simplicity; they are easily fitted using least-squares methods, and they are very easy to interpret, as coefficient values can indicate the significance of a parameter (as long as input variables are normalised prior to use). Alternative approaches such as neural networks and support-vector machines have previously been explored in the literature for similar problems [12,13]. These methods are known to have powerful prediction capabilities, however, no knowledge of the physical system can be gained directly from these non-parametric approaches.…”

Section: Mathematical Models Of Modal Frequenciesmentioning

confidence: 99%

Long-term monitoring and data analysis of the Tamar Bridge

Cross

Koo

Brownjohn

et al. 2013

Mechanical Systems and Signal Processing

323

180

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A sound understanding of a structure's normal condition, including its response to normal environmental and operational variations is desirable for structural health monitoring and necessary for performance monitoring of civil structures. The current paper outlines the extensive monitoring campaign of the Tamar suspension bridge as well as analysis carried out in the attempt to understand the bridge's normal condition. Specifically the effects of temperature, traffic loading and wind speed on the structure's dynamic response are investigated. Finally, initial steps towards development of a structural health monitoring system for the Tamar Bridge are addressed.

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Section: Mathematical Models Of Modal Frequenciesmentioning

confidence: 99%

Long-term monitoring and data analysis of the Tamar Bridge

Cross

Koo

Brownjohn

et al. 2013

Mechanical Systems and Signal Processing

323

180

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“…Cross et al [11] investigated temperature effects on the dynamic response of Tamar bridge and showed that ambient temperature significantly influences seasonal alterations in the structure's fundamental frequency. Ni et al [12] and Hua et al [9] showed that the modal properties such as frequencies are highly correlated with ambient temperatures for the Ting Kau bridge. These studies highlight the importance of temperature effects on long-term structural behaviour.…”

Section: Introductionmentioning

confidence: 99%

Predicting thermal response of bridges using regression models derived from measurement histories

Kromanis

Kripakaran

2014

Computers & Structures

106

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This study investigates the application of novel computational techniques for structural performance monitoring of bridges that enable quantification of temperature-induced response during the measurement interpretation process. The goal is to support evaluation of bridge response to diurnal and seasonal changes in environmental conditions, which have widely been cited to produce significantly large deformations that exceed even the effects of live loads and damage. This paper proposes a regression-based methodology to generate numerical models, which capture the relationships between temperature distributions and structural response, from distributed measurements collected during a reference period. It compares the performance of various regression algorithms such as multiple linear regression (MLR), robust regression (RR) and support vector regression (SVR) for application within the proposed methodology. The methodology is successfully validated on measurements collected from two structures -a laboratory truss and a concrete footbridge. Results show that the methodology is capable of accurately predicting thermal response and can therefore help with interpreting measurements from continuous bridge monitoring.

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“…Other applications in SHM include structural integrity assessment [38] and structural system identification [39]. SVR has also been shown to effectively capture correlations between temperatures and modal frequencies [40]. However, previous studies have not examined the application of SVR for quasi-static measurements, the focus of this research.…”

Section: Introductionmentioning

confidence: 95%