Dynamic Bayesian network-based system-level evaluation on fatigue reliability of orthotropic steel decks

Heng, Junlin; Zheng, Ke; Kaewunruen, Sakdirat; Zhu, Jin; Baniotopoulos, Charalampos

doi:10.1016/j.engfailanal.2019.06.092

Cited by 32 publications

(17 citation statements)

References 37 publications

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“…In the reliability analysis, the major task is to solve the possibility that the resistance is lower than the load effect, i.e., the limit state function (LSF) reaches zero or less. In the case of fatigue issue, the LSF can be established, as shown in Equation (7) [3,24,26].…”

Section: Analysis Methods Of Fatigue Reliabilitymentioning

confidence: 99%

“…During fabrication, a considerable amount of welding works is applied to make up the complicated structural details in OSDs. As a result, fatigue cracking of welded joints becomes a very prominent issue in OSDs, especially when the truck traffic volume is high [2,3]. Among the different types of welded joints, fatigue cracks are frequently observed in the rib-to-deck (RD) joint located between the U-rib and deck plate [4].…”

Section: Introductionmentioning

confidence: 99%

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Fatigue Reliability Analysis of Rib-To-Deck Joints Using Test Data and In-Situ Measurements

et al. 2019

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In this paper, the fatigue performance of rib-to-deck joints in orthotropic steel decks (OSDs) using thickened edge U-ribs (TEUs) and the OSD using conventional U-ribs (CUs) are investigated based on the fatigue test result and in-situ monitoring data. Firstly, comparative fatigue tests were carried out with full-scale rib-to-deck specimens. Probability–stress–life (P–S–N) curves were derived through the test data measured from a total of 18 specimens, including 7 CU specimens and 11 TEU specimens. According to the results, the TEU can lead to a notable enhancement in the fatigue strength of rib-to-deck joints, i.e., 21.4% in terms of nominal stress and 21.1% in terms of hot spot stress. After that, a typical OSD steel bridge was selected as the prototype to investigate the fatigue performance of rib-to-decks in the OSD using TEUs under actual applications. In the analysis, the uncertainty in both fatigue strength, and vehicle loads were considered. A multi-scale finite element model of the prototype bridge was established, and numerical analysis conducted to derive the vehicle-induced stress spectra of the rib-to-deck joints in critical positions. In the derivation, a stochastic traffic model was employed, through which the in-situ measurement was incorporated. Finally, fatigue reliability analysis was carried out for the prototype bridge based on the above works. The result showed that a notably higher fatigue reliability can be expected in the rib-to-deck joint in OSDs using TEUs when compared with the joints in OSDs using CUs, which in turn can lead to notable improvement in fatigue life. For instance, under the target reliability of 2.3, the fatigue life of the two critical rib-to-deck joints were, respectively, increased by 153% and 155% when using TEUs.

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Section: Analysis Methods Of Fatigue Reliabilitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fatigue Reliability Analysis of Rib-To-Deck Joints Using Test Data and In-Situ Measurements

et al. 2019

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“…e observed data are the basis of inference. Observation data can be divided into discrete observation data and continuous Gaussian observation data, and hidden variables can also be divided into discrete data [21,22] and continuous Gaussian data [12]. In this paper, the observed data and the hidden variables are not Gauss state; therefore, the M-Hs sampling in Monte Carlo simulations is used to discretize them first, and then the reliability probability of the whole system is calculated, MATLAB R2018a programming language combined with FullBNT toolbox to achieve automatic solution.…”

Section: Inference For Dbnmentioning

confidence: 99%

Reliability Analysis of Gasifier Lock Bucket Valve System Based on DBN Method

Ming

Duo

et al. 2021

Mathematical Problems in Engineering

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In order to solve the problem of zero-failure data and dynamic failure in gasification system, a dynamic Bayesian network (DBN) combined with Monte Carlo simulations is proposed to analyze the reliability of the gasifier lock bucket valve system. On the basis of studying the structure of the gasifier lock bucket valve system, the reliability model of the system is built based on DBN, and the structure learning is realized. The Monte Carlo simulation is used for the timed ending test in Bayesian estimation, which effectively solves the problem of zero-failure data and realizes the parameter learning. Through the Metropolis-Hastings (M-Hs) algorithm, the prior distribution of dynamic node is randomly sampled to obtain the target distribution, which makes the reliability predictive inference for DBN of the gasifier lock bucket valve system faster and more accurate. The obtained reliability prediction is a curve varying with time. The results show that the valve frequent switch node of DBN of the gasifier lock bucket valve system is identified as the weak link by the powerful reverse inference for DBN, which needs to be paid more attention to. This method can effectively improve the maintenance level of the gasifier lock bucket valve system and can effectively reduce the possibility of accidents.

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“…Aiming at the above problems, the dynamic Bayesian network (DBN) model has certain advantages in system reliability assessment. [13][14][15] DBN model is based on Bayesian probability theory and is a powerful tool to deal with uncertain problems. At the same time, it also has a more intuitive knowledge representation, which can well show the hierarchical relationship between faults.…”

Section: Introductionmentioning

confidence: 99%

Reliability evaluation of concrete‐filled steel tube arch bridge based on DBN‐PSOSA hybrid algorithm

Hong

et al. 2021

Structural Concrete

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When traditional response surface method is used to evaluate the reliability of concrete-filled steel tubular arch bridge, due to its complex structure and highly nonlinear implicit function, the response surface fitting accuracy is not high, and the reliability accuracy is difficult to meet the requirements of design specifications. In order to solve the above problems, this paper chooses dynamic Bayesian networks (DBN) which is suitable for solving the problem of multiple state unit or system uncertainty to build implicit function of the response surface function. And this paper combines DBN and particle swarm optimization algorithm based on simulated annealing algorithm (PSOSA) to improve efficiency of model parameter optimization. It can make the construction of implicit function simulate the real structure of the limit state function. Then this paper verifies the suitability for this kind of complex structure reliability assessment of DBN-PSOSA hybrid algorithm. A numerical example is given to demonstrate the effectiveness of the proposed method, and the reliability of a concrete filled steel tube arch bridge project is evaluated. The results show that this method improves the calculation accuracy and efficiency.

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Dynamic Bayesian network-based system-level evaluation on fatigue reliability of orthotropic steel decks

Cited by 32 publications

References 37 publications

Fatigue Reliability Analysis of Rib-To-Deck Joints Using Test Data and In-Situ Measurements

Fatigue Reliability Analysis of Rib-To-Deck Joints Using Test Data and In-Situ Measurements

Reliability Analysis of Gasifier Lock Bucket Valve System Based on DBN Method

Reliability evaluation of concrete‐filled steel tube arch bridge based on DBN‐PSOSA hybrid algorithm

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