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

Goi, Yoshinao; Kim, C.W.

doi:10.12989/sss.2016.17.3.391

Cited by 15 publications

(9 citation statements)

References 10 publications

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“…Only the first six modes are considered in this research, and the identification results are listed in Table 8. For better comparing the identification results, the modal parameters identified in similar studies 55,56 are also listed in Table 8 (second column). It can be found that the modal parameters identified via the two methods are close to those results, but there is some deviation for uncertainty quantification, even in the c.o.v.s of the frequencies (see SSI f 2 , as well as Bayesian f 1 , f 3 , and f 6 ).…”

Section: Comparison Of Tkb Identification Results From Ssi and Bayesian Methodsmentioning

confidence: 99%

Engineering performance of two analytical methodologies for estimating modal parameter uncertainty for structures

Huang

Zhang

et al. 2021

Struct Control Health Monit

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Stochastic subspace identification (SSI) and Bayesian methods are now the main representative approaches for high-performance system identification and uncertainty quantification. Both methods have been extensively used in engineering applications for the last 10 years despite them having quite different views in principle. However, no comparison has been carried out to inform which method is actually better for engineering applications in terms of modal identification accuracy, uncertainty quantification, and so forth. This paper therefore investigates the differences between these two quantifications of parameter uncertainty in system identification. Synthetic data for a six-degreeof-freedom spring-mass numerical system are first studied to compare their identification accuracy and applicable conditions. The investigation is then extended to field data from actual structures, which involves ambient modal testing of Heritage Court Tower in Canada, Canton Tower in China, and Ting Kau Bridge in Hong Kong. Results from the synthetic data show that, under white-noise excitation, identified modal parameters and quantified uncertainty for both methods are highly consistent with the values of frequentist statistics. However, when the excitation is not white noise, there may be some spurious modes identified via SSI, and the uncertainty quantified under colored-noise excitation via SSI is almost always larger than those under white noise, while the Bayesian method is not disturbed. Results for the structural field data indicate that in general applications under environmental excitation the identified modal parameters from both methods are almost identical; the quantified uncertainty of the SSI method is slightly larger than that of the Bayesian method, but they are of the same order of magnitude and can meet engineering requirements.

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Section: Comparison Of Tkb Identification Results From Ssi and Bayesian Methodsmentioning

confidence: 99%

Engineering performance of two analytical methodologies for estimating modal parameter uncertainty for structures

Huang

Zhang

et al. 2021

Struct Control Health Monit

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show abstract

“…During the impact test, 13 sensors were installed on the pier and connected girders to distinguish the pier-oriented mode with the girder modes. Stochastic subspace identification (SSI) [4] was utilized to identify modal parameters for the impact test, and the stabilization diagram (SD) [5] was used to decide steady vibration modes. Fig.…”

Section: Reference Natural Frequency Of the Target Piermentioning

confidence: 99%

Vibration monitoring of railway bridge pier and probability of scour occurrence

Kawabe

2023

Materials Research Proceedings

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Abstract. This study aims to propose a way to estimate probability of scour occurrence of a railway bridge pier by means of ambient vibration monitoring. A remote real-time scour monitoring scheme utilizing ambient vibrations of piers is considered as an alternative method for conventional impact test. A stochastic approach to deal with uncertainty of estimated frequencies caused by relatively poor signal to noise of the ambient vibration is discussed. The probability distribution of the identified frequencies in a normal condition is modeled with a stable distribution, and the probability of scour occurrence is estimated using a logistic curve. The validity of proposed method is verified using monitoring vibration data during swollen river water period in the past.

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“…Conventional methods, also known as direct methods , involve numerous sensors installed along the bridge to monitor the vibration response excited by traversing vehicles or other excitations. Over the past decades, scholars have carried out a large amount of research on these direct methods [ 6 , 7 , 8 , 9 ]. The limitation of the direct methods lies in the high cost of equipment installation and maintenance, together with the manual labor consumption and the interruption of the traffic flow [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

Subspace Identification of Bridge Frequencies Based on the Dimensionless Response of a Two-Axle Vehicle

Quan,

Zeng,

Jin

et al. 2024

Sensors

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As an essential reference to bridge dynamic characteristics, the identification of bridge frequencies has far-reaching consequences for the health monitoring and damage evaluation of bridges. This study proposes a uniform scheme to identify bridge frequencies with two different subspace-based methodologies, i.e., an improved Short-Time Stochastic Subspace Identification (ST-SSI) method and an improved Multivariable Output Error State Space (MOESP) method, by simply adjusting the signal inputs. One of the key features of the proposed scheme is the dimensionless description of the vehicle–bridge interaction system and the employment of the dimensionless response of a two-axle vehicle as the state input, which enhances the robustness of the vehicle properties and speed. Additionally, it establishes the equation of the vehicle biaxial response difference considering the time shift between the front and the rear wheels, theoretically eliminating the road roughness information in the state equation and output signal effectively. The numerical examples discuss the effects of vehicle speeds, road roughness conditions, and ongoing traffic on the bridge identification. According to the dimensionless speed parameter Sv1 of the vehicle, the ST-SSI (Sv1 < 0.1) or MOESP (Sv1 ≥ 0.1) algorithm is applied to extract the frequencies of a simply supported bridge from the dimensionless response of a two-axle vehicle on a single passage. In addition, the proposed methodology is applied to two types of long-span complex bridges. The results show that the proposed approaches exhibit good performance in identifying multi-order frequencies of the bridges, even considering high vehicle speeds, high levels of road surface roughness, and random traffic flows.

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

Cited by 15 publications

References 10 publications

Engineering performance of two analytical methodologies for estimating modal parameter uncertainty for structures

Engineering performance of two analytical methodologies for estimating modal parameter uncertainty for structures

Vibration monitoring of railway bridge pier and probability of scour occurrence

Subspace Identification of Bridge Frequencies Based on the Dimensionless Response of a Two-Axle Vehicle

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