Bayesian operational modal analysis of Jiangyin Yangtze River Bridge

Brownjohn, James; Au, Siu‐Kui; Zhu, Yichen; Sun, Zhen; Li, Binbin; Bassitt, James; Hudson, Emma; Sun, Hongbin

doi:10.1016/j.ymssp.2018.03.027

Cited by 61 publications

(24 citation statements)

References 33 publications

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“…Suppose that there are m categories, C1, C2, ..., Cm. Given a sample X, the classifier will predict that X belongs to the category having the highest posterior probability [29], conditioned on X. That is, the naive Bayesian classifier predicts that sample X belongs to the class Ci if and only if …”

Section: Methodsmentioning

confidence: 99%

Voxelwise Detection of Cerebral Microbleed in CADASIL Patients by Naive Bayesian Classifier

Bao¹,

Shi²,

Macdonald³

2018

Proceedings of the 2018 International Conference on Information Technology and Management Engineering (ICITME 2018)

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It is important to detect cerebral microbleed voxels from the brain image of cerebral autosomal-dominant arteriopathy with subcortical infarcts and Leukoencephalopathy (CADASIL) patients. Methods developed by other researchers before have a high variablity of intra-observer and inter-observer. In our study, we collect our dataset from the 20 brain volumetric images, 10 for CADASIL patients and 10 for healthy controls. And we used naive baysian classifier to get the results. We use cross validation to improve the performance of naive Baysian classifier. The results show that the average sensitivity is 74.53±0.96%, the average specificity is 74.51±1.05%, and the average accuracy is 74.52±1.00%.

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Section: Methodsmentioning

confidence: 99%

Voxelwise Detection of Cerebral Microbleed in CADASIL Patients by Naive Bayesian Classifier

Bao¹,

Shi²,

Macdonald³

2018

Proceedings of the 2018 International Conference on Information Technology and Management Engineering (ICITME 2018)

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“…The time synchronisation of better than 1 ms is achieved through 10-MHz oven-controlled crystal oscillators (OXCOs) [51]. Each case comprises the power source, cabling and a National Instruments (NI) CompactRIO cRIO-9064 to control the DAQ system.…”

Section: Daq and Sensorsmentioning

confidence: 99%

“…Time synchronisation is controlled through a blank NI-9977 C-Series module, while the local timing of each logger is controlled through an NI-9402 module at 120 MHz. The DAQ loggers have proven to be a dependable monitoring system having been successfully deployed for a number of projects, including modal analysis of bridges [51] and rock-based lighthouses [52].…”

Section: Daq and Sensorsmentioning

confidence: 99%

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Tracking bridge tilt behaviour using sensor fusion techniques

Faulkner

Wang

Huseynov

2020

J Civil Struct Health Monit

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The resilience of the built environment to extreme weather events is fundamental for the day-today operation of our transport network, with scour representing one of the biggest threats to bridges built over flowing water. Condition monitoring of the bridge using a structural health monitoring system enhances resilience by reducing the time needed to return the bridge to normal use by providing timely information on structural condition and safety. The work presented in this report discusses use of rotational measurements in structural health monitoring. Traditionally tiltmeters (which can be a form of DC accelerometer) are used to measure rotation but are known to be affected by dynamic movements, while gyroscopes react quickly to dynamic motion but drift over time. This review will introduce gyroscopes as a complementary sensor for accelerometer rotational measurements and use sensor fusion techniques to combine the measurements from both sensors to get an optimised rotational result. This method was trialled on a laboratory scaled model, before the system was installed on an in-service single-span skewed railway bridge. The rotational measurements were compared against rotation measurements obtained using a vision-based measurement system to confirm the validity of the results. An introduction to gyroscopes, field test measurement results with the sensors and their correlation with the vision-based measurement results are presented in this article.

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“…Magalhaes et al [24] presented an automated identification method of the modal parameters with the related bridge response under different wind conditions. Brownjohn [25] presented the first full modal survey of Jiangyin Yangtze River Bridge to identify the important features of the modal behavior. Guo et al [26] presented a damage detection method based on the modal strain energy equivalence index (MSEEI) to solve structural multi-damage identification problems.…”

Section: Introductionmentioning

confidence: 99%

Bridge Damage Identification Using Vehicle Bump Based on Additional Virtual Masses

Zhang

Hou

Jankowski

2020

Sensors

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Structural damage identification plays an important role in providing effective evidence for the health monitoring of bridges in service. Due to the limitations of measurement points and lack of valid structural response data, the accurate identification of structural damage, especially for large-scale structures, remains difficult. Based on additional virtual mass, this paper presents a damage identification method for bridges using a vehicle bump as the excitation. First, general equations of virtual modifications, including virtual mass, stiffness, and damping, are derived. A theoretical method for damage identification, which is based on additional virtual mass, is formulated. The vehicle bump is analyzed, and the bump-induced excitation is estimated via a detailed analysis in four periods: separation, free-fall, contact, and coupled vibrations. The precise estimation of bump-induced excitation is then applied to a bridge. This allows the additional virtual mass method to be used, which requires knowledge of the excitations and acceleration responses in order to construct the frequency responses of a virtual structure with an additional virtual mass. Via this method, a virtual mass with substantially more weight than a typical vehicle is added to the bridge, which provides a sufficient amount of modal information for accurate damage identification while avoiding the bridge overloading problem. A numerical example of a two-span continuous beam is used to verify the proposed method, where the damage can be identified even with 15% Gaussian random noise pollution using a 1-degree of freedom (DOF) car model and 4-DOF model.

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Bayesian operational modal analysis of Jiangyin Yangtze River Bridge

Cited by 61 publications

References 33 publications

Voxelwise Detection of Cerebral Microbleed in CADASIL Patients by Naive Bayesian Classifier

Voxelwise Detection of Cerebral Microbleed in CADASIL Patients by Naive Bayesian Classifier

Tracking bridge tilt behaviour using sensor fusion techniques

Bridge Damage Identification Using Vehicle Bump Based on Additional Virtual Masses

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