Investigation of mode identifiability of a cable-stayed bridge: comparison from ambient vibration responses and from typhoon-induced dynamic responses

Ni, Yiqing; Wang, Y. W.; Xia, Yunxia

doi:10.12989/sss.2015.15.2.447

Cited by 36 publications

(15 citation statements)

References 29 publications

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“…Therefore, they are expelled from the identification. The identified results from non-blind data are comparable to results reported in a previous study(Ni et al 2015), i.e. the second mode (0.23 Hz) is not identified under weak wind conditions.…”

supporting

confidence: 84%

“…Data from Sample 7 to Sample 10 were measured under typhoon conditions. Data from Sample 11 to Sample 13 were measured at wind speeds around the critical value (threshold) of 7.5 m/s, at which the deficient mode becomes identifiable (Ni et al 2015). Traffic was blocked during the typhoons.…”

Section: Benchmark Datamentioning

confidence: 99%

“…Such techniques have been applied to monitor real bridges (e.g. Peeters et al 2006, Kim and Chang 2014a, Ni et al 2015. Among many applications used for real bridge monitoring, Ni et al (2015) reported that the modal shape of the second mode (deficient mode) of a cable-stayed bridge named Ting Kau Bridge is not identifiable under weak wind conditions, although it can be identified clearly under typhoon conditions.…”

Section: Introductionmentioning

confidence: 99%

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Mode identifiability of a multi-span cable-stayed bridge utilizing stabilization diagram and singular values

Goi¹,

Kim²

2016

Smart Structures and Systems

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This study investigates the mode identifiability of a multi-span cable-stayed bridge in terms of a benchmark study using stabilization diagrams of a system model identified using stochastic subspace identification (SSI). Cumulative contribution ratios (CCRs) estimated from singular values of system models under different wind conditions were also considered. Observations revealed that wind speed might influence the mode identifiability of a specific mode of a cable-stayed bridge. Moreover the cumulative contribution ratio showed that the time histories monitored during strong winds, such as those of a typhoon, can be modeled with less system order than under weak winds. The blind data Acc 1 and Acc 2 were categorized as data obtained under a typhoon. Blind data Acc 3 and Acc 4 were categorized as data obtained under wind conditions of critical wind speeds around 7.5 m/s. Finally, blind data Acc 5 and Acc 6 were categorized as data measured under weak wind conditions.

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supporting

confidence: 84%

Section: Benchmark Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Mode identifiability of a multi-span cable-stayed bridge utilizing stabilization diagram and singular values

Goi¹,

Kim²

2016

Smart Structures and Systems

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“…Then, 8 longitudinal stabilizing cables used to diagonally connect the top of the central tower to the Ting Kau and Tsing Yi towers, whose length reaches 465m, are installed. Whereas, 64 cables are employed to strengthen the three towers in the lateral direction (Ni et al 2015). Within a long-term structural health monitoring system conceived by the Hong Kong SAR Government Highways Department, during the construction of the bridge and also after its completion in 1999 (Wong 2004;Ko and Ni 2005), more than 230 sensors have been placed on the TKB.…”

Section: Figure 2 General Layout Of the Ting Kau Bridgementioning

confidence: 99%

“…Moreover, when typhoon condition are considered, significant variations in the stay-cables can be observed. A benchmark problem on an existing cable-stayed bridge was recently proposed and it focused on the modal identification (Ni et al 2015;Casciati et al 2015). Particularly, during a typhoon, some frequencies that during service conditions are not detected, were excited.…”

Section: Introductionmentioning

confidence: 99%