Stochastic characterization of wind field characteristics of an arch bridge instrumented with structural health monitoring system

Ye, X.W.; Xi, P.S.; Su, Yue; Chen, B.; Han, Jian

doi:10.1016/j.strusafe.2017.11.003

Cited by 23 publications

(7 citation statements)

References 23 publications

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“…This predictive analysis helps companies and researchers to determine the nature, standards, and bearing capacity of a structure against static and dynamic loads. Static loads occur because of displacement, acceleration, strain, stress, and temperature together with dynamic loads based on vibration, natural frequencies, model identification, time history analysis, and response spectrum, depending on the characteristics of the external and internal interactions and the type of structure [10,11]. Olivera Lopez et al [12] carried out real-time monitoring in a 14-floor building stationed in a coastal area of Chile subjected to dynamic loading.…”

Section: Literature Review 21 Process Of Shmmentioning

confidence: 99%

Practical Implementation of Structural Health Monitoring in Multi-Story Buildings

et al. 2021

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This study investigated operational and structural health monitoring (SHM) as well as damage evaluations for building structures. The study involved damage detection and the assessment of buildings by placing sensors and by assuming weak areas, and considered situations of assessment and self-monitoring. From this perspective, advanced sensor technology and data acquisition techniques can systematically monitor a building in real time. Furthermore, the structure’s response and behavior were observed and recorded to predict the damage to the building. In this paper, we discuss the real-time monitoring and response of buildings, which includes both static and dynamic analyses along with numerical simulation studies such as finite element analysis (FEA), and recommendations for the future research and development of SHM are made.

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Section: Literature Review 21 Process Of Shmmentioning

confidence: 99%

Practical Implementation of Structural Health Monitoring in Multi-Story Buildings

et al. 2021

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“…They analyzed the effects of environmental factors and traffic loads on the structural modal parameters using observation time series for two consecutive years [ 8 ]. Ye et al and Petersen et al extracted the vibration characteristics of arch bridges and suspension bridges under wind loads based on the bridge dynamic monitoring system data [ 9 , 10 ]. Concerning seismic damage research on bridge structures, Siringoringo et al analyzed the vibration response characteristics of bridge structures induced by seismic waves of different magnitudes, based on long-term observed displacement data from multiple sets of accelerometers deployed by the Hakucho Suspension Bridge health monitoring system [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Vibration Characteristics of Bridge Structures under Seismic Excitation

Li,

Huang

2024

Entropy

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Bridges may undergo structural vibration responses when exposed to seismic waves. An analysis of structural vibration characteristics is essential for evaluating the safety and stability of a bridge. In this paper, a signal time-frequency feature extraction method (NTFT-ESVD) integrating standard time-frequency transformation, singular value decomposition, and information entropy is proposed to analyze the vibration characteristics of structures under seismic excitation. First, the experiment simulates the response signal of the structure when exposed to seismic waves. The results of the time-frequency analysis indicate a maximum relative error of only 1% in frequency detection, and the maximum relative errors in amplitude and time parameters are 5.9% and 6%, respectively. These simulation results demonstrate the reliability of the NTFT-ESVD method in extracting the time-frequency characteristics of the signal and its suitability for analyzing the seismic response of the structure. Then, a real seismic wave event of the Su-Tong Yangtze River Bridge during the Hengchun earthquake in Taiwan (2006) is analyzed. The results show that the seismic waves only have a short-term impact on the bridge, with the maximum amplitude of the vibration response no greater than 1 cm, and the maximum vibration frequency no greater than 0.2 Hz in the three-dimensional direction, indicating that the earthquake in Hengchun will not have any serious impact on the stability and security of the Su-Tong Yangtze River Bridge. Additionally, the reliability of determining the arrival time of seismic waves by extracting the time-frequency information from structural vibration response signals is validated by comparing it with results from seismic stations (SSE/WHN/QZN) at similar epicenter distances published by the USGS. The results of the case study show that the combination of dynamic GNSS monitoring technology and time-frequency analysis can be used to analyze the impact of seismic waves on the bridge, which is of great help to the manager in assessing structural seismic damage.

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“…Li et al [24] employed the Gaussian mixture model for pattern modelling of the cable tension ratio with intent to assess the condition of stay cables. Ye et al [25] developed a genetic algorithmbased fnite mixture modelling approach to construct the joint distribution of wind speed and wind direction measured from a bridge SHM system. In the aforementioned studies, the mixture parameters (component parameters and mixing weights) were estimated based on the classical frequentist probability theory, with the use of inference techniques such as the expectation maximization algorithm, the hybrid estimation method, and the genetic algorithm.…”

Section: Introductionmentioning

confidence: 99%

A Nonparametric Bayesian Approach for Bridge Reliability Assessment Using Structural Health Monitoring Data

Chen

2023

Structural Control and Health Monitoring

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Integrating structural health monitoring (SHM) data into reliability assessment has increasingly been practiced in the condition evaluation of in-service bridges over the past decade. The selection of probability distribution models for load- and resistance-related random variables is a prerequisite for monitoring-based reliability assessment. However, the underlying probabilistic assumptions of the used models could be restrictive and unverifiable especially when dealing with real-world heterogeneous monitoring data, weakening the confidence on the estimated reliability index. This study aims to develop a nonparametric Bayesian model with the Dirichlet process prior for bridge reliability assessment, where the model order constraint can be released such that the complexity of the model adapts to the observed data. Reliability analysis via the nonparametric Bayesian model allows the aleatory uncertainty and the epistemic uncertainty arising from monitoring data to be concurrently accounted for in the formulated reliability index. A numerical example is presented to verify the effectiveness of the nonparametric Bayesian model for dealing with multimodal data. The feasibility of the proposed approach for reliability assessment is then demonstrated with one-year strain monitoring data acquired from a large-scale bridge instrumented with the SHM system.

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Stochastic characterization of wind field characteristics of an arch bridge instrumented with structural health monitoring system

Cited by 23 publications

References 23 publications

Practical Implementation of Structural Health Monitoring in Multi-Story Buildings

Practical Implementation of Structural Health Monitoring in Multi-Story Buildings

Analysis of Vibration Characteristics of Bridge Structures under Seismic Excitation

A Nonparametric Bayesian Approach for Bridge Reliability Assessment Using Structural Health Monitoring Data

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