Probabilistic forecast of wind speed based on Bayesian emulator using monitoring data

Ye, Xiao-Wei; Ding, Yang; Wan, Hua‐Ping

doi:10.1002/stc.2650

Cited by 21 publications

(7 citation statements)

References 46 publications

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“…Long-span bridges such as the Tsing Ma Bridge in Hong Kong [4], the Great Belt Bridge in Denmark, the Messina Strait Bridge in Italy, and the Golden Ear Bridge in Canada have been put into use one after another. However, with the continuous extension of the service life of bridges, the external environmental conditions of bridges are also changing, such as wind load [5][6][7], ground temperature [8,9], temperature [10][11][12], humidity, and so on. Changes in these external environmental conditions will gradually reduce the durability and safety of bridge structures.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Threshold Cable-Stayed Bridge Health Monitoring System Based on Temperature Effect Correction

Tan,

Guo,

Luo

et al. 2023

Sensors

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The early health warning of a cable-stayed bridge is of great significance for discovering the abnormal condition of the structure, eliminating the risk factors, and ensuring the normal operation of the bridge in order to set a reasonable safety monitoring threshold to ensure the safety warning and condition assessment of the bridge structure. A method of dynamic early warning by considering the temperature effect is adopted in this paper on the basis of the benchmark threshold. Based on the long-term deflection monitoring data of a bridge in Wuhan, the generalized Pareto distribution (GPD) extreme value analysis theory is used to set the benchmark threshold. Then, by constructing the seasonal autoregressive integrated moving average (SARIMA) long-span bridge temperature effect prediction model, the reference threshold is dynamically adjusted. Finally, it is compared with the traditional fixed threshold monitoring system. The results show that the dynamic threshold has stronger adaptability to the monitoring of cable-stayed bridges and can also achieve effective monitoring of local mutations in other periods. Dynamic threshold early warning can reduce the shortcomings of traditional early warning methods such as underreporting and misreporting. At the same time, the GPD extreme value analysis theory overcomes the disadvantage that the extreme value information is not fully utilized. It has an important application value for bridge health monitoring.

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Section: Introductionmentioning

confidence: 99%

Dynamic Threshold Cable-Stayed Bridge Health Monitoring System Based on Temperature Effect Correction

Tan,

Guo,

Luo

et al. 2023

Sensors

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show abstract

“…The unknown parameter value can be expressed based on the error between the measured value and the theoretical value, that is, 43 𝜔 = 𝜔(𝜃) + 𝜀 (12) where 𝜔 is the measured value; 𝜔(𝜃) is the theoretical value; 𝜀 is the error, which followed by N(0, cov(Z)).…”

Section: P(𝜃|x) = P(𝜃)p(x|𝜃) ∫ P(𝜃)p(x|𝜃)d𝜃mentioning

confidence: 99%

Identification of bridge cable force damage based on Bayesian inference

Chen,

Zheng,

Chu

et al. 2023

Engineering Reports

Self Cite

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Bridge cable is an important force transmission component of bridge, but it is easy to be damaged during service, such as fatigue damage and corrosion damage, which seriously threatens the safety of bridge structure. Therefore, it is necessary to identify damage of cable force. Generally, the damage of cable force can be identified by the change of cable frequency. This paper establishes a cable force damage identification model based on Bayesian inference and uses Metropolis‐Hastings (MH) algorithm to solve the posterior probability function of unknown parameter. In the Bayesian inference model, the influence of the priori function of unknown parameters on the posterior probability distribution model is discussed. In the MH algorithm, the influence of different proposed distributions (Normal distribution, Gamma distribution and Weibull distribution) on the sampling results is discussed based on three numerical simulation studies, and the influence of burned sample proportion on the establishment of a posteriori distribution function is analyzed. Furthermore, the influence of monitoring noise data and missing data on cable force damage identification is considered, and the robustness of the proposed method is analyzed.

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“…5 (2) The model-free procedure does not need a mechanical model for forecasting future structural responses. It can predict structural behaviors on the basis of a sequence of measured response data through various algorithms, including autoregressive moving average model, 8–10 Bayesian dynamic linear model, 11–15 and Gaussian Process (GP), 14–17 etc. As one type of model-free methods, eigen-perturbation-based algorithms were also developed.…”

Section: Introductionmentioning

confidence: 99%

Towards high-precision data modeling of SHM measurements using an improved sparse Bayesian learning scheme with strong generalization ability

Wang

Yang

et al. 2023

Structural Health Monitoring

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Central to structural health monitoring (SHM) is data modeling, manipulation, and interpretation on the basis of a sophisticated SHM system. Despite continuous evolution of SHM technology, the precise modeling and forecasting of SHM measurements under various uncertainties to extract structural condition-relevant knowledge remains a challenge. Aiming to resolve this problem, a novel application of a fully probabilistic and high-precision data modeling method was proposed in the context of an improved Sparse Bayesian Learning (iSBL) scheme. The proposed iSBL data modeling framework features the following merits. It can remove the need to specify the number of terms in the data-fitting function, and automatize sparsity of the Bayesian model based on the features of SHM monitoring data, which will enhance the generalization ability and then improve the data prediction accuracy. Embedded in a Bayesian framework which exhibits built-in protection against over-fitting problems, the proposed iSBL scheme has high robustness to data noise, especially for data forecasting. The model is verified to be effective on SHM vibration field monitoring data collected from a real-world large-scale cable-stayed bridge. The recorded acceleration data with two different vibration patterns, that is, stationary ambient vibration data and non-stationary decay vibration data, are investigated, returning accurate probabilistic predictions in both the time and frequency domains.

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Probabilistic forecast of wind speed based on Bayesian emulator using monitoring data

Cited by 21 publications

References 46 publications

Dynamic Threshold Cable-Stayed Bridge Health Monitoring System Based on Temperature Effect Correction

Dynamic Threshold Cable-Stayed Bridge Health Monitoring System Based on Temperature Effect Correction

Identification of bridge cable force damage based on Bayesian inference

Towards high-precision data modeling of SHM measurements using an improved sparse Bayesian learning scheme with strong generalization ability

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