Bridge condition modelling and prediction using dynamic Bayesian belief networks

Rafiq, MI; Chryssanthopoulos, Marios K.; Sathananthan, Saenthan

doi:10.1080/15732479.2013.879319

Cited by 57 publications

(48 citation statements)

References 12 publications

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“…A more recent method of probabilistic approach is the causal probabilistic networks also known as Bayesian Networks (BN). BN can be used to determine system reliability by considering the associated uncertainties [10] and are computationally efficient in updating performance when new knowledge becomes available [9], [11]. Moreover, the extensions of BNs, known as Dynamic Bayesian Network (DBN) can be used to represent the discrete-time stochastic process.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Reliability Analysis of Corroded Pipeline Using Bayesian Network

Sulaiman¹,

Tan²

2018

IJET

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Maintenance and integrity management of hydrocarbons pipelines face the challenges from uncertainties in the data available. This paper demonstrates a way for pipeline remaining service life prediction that integrates structural reliability analysis, accumulated corrosion knowledge, and inspection data on a sound mathematical foundation. Pipeline defects depth grows with time according to an empirical corrosion power law, and this is checked for leakage and rupture probability. The pipeline operating pressure is checked with the degraded failure pressure given by ASME B31G code for rupture likelihood. As corrosion process evolves with time, Dynamic Bayesian Network (DBN) is employed to model the stochastic corrosion deterioration process. From the results obtained, the proposed DBN model for pipeline reliability is advanced compared with other traditional structural reliability method whereby the updating ability brings in more accurate prediction results of structural reliability. The comparisons show that the DBN model can achieve a realistic result similar to the conventional method, Monte Carlo Simulation with very minor discrepancy.

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Section: Introductionmentioning

confidence: 99%

Dynamic Reliability Analysis of Corroded Pipeline Using Bayesian Network

Sulaiman¹,

Tan²

2018

IJET

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“…In addition, some fusion diagnosis methods in other engineering fields are also worth learning. Rafiq et al [19] proposed Bayesian networks to diagnose the bridge behavior according to expert evaluations. Masoumi et al [20] constructed finite element model of steel structure and introduced the optimization algorithm to improve diagnostic precision about the modulus of elasticity.…”

Section: Introductionmentioning

confidence: 99%

Method of Fusion Diagnosis for Dam Service Status Based on Joint Distribution Function of Multiple Points

Jiang

2016

Mathematical Problems in Engineering

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The traditional methods of diagnosing dam service status are always suitable for single measuring point. These methods also reflect the local status of dams without merging multisource data effectively, which is not suitable for diagnosing overall service. This study proposes a new method involving multiple points to diagnose dam service status based on joint distribution function. The function, including monitoring data of multiple points, can be established with t-copula function. Therefore, the possibility, which is an important fusing value in different measuring combinations, can be calculated, and the corresponding diagnosing criterion is established with typical small probability theory. Engineering case study indicates that the fusion diagnosis method can be conducted in real time and the abnormal point can be detected, thereby providing a new early warning method for engineering safety.

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“…Байесовские сети, в том числе алгебраические байесовские сети (АБС) [2,3] и связанные с ними конструкции, являются одним из видов вероятностных графических моделей. Байесовские сети все шире используются в системах распознавания образов [4,5], системах моделирования поведения [6], системах моделирования рисков и обнаружения отказов [7][8][9], оценивания повреждений и мониторинга состояния здоровья [10][11][12][13], а также при анализе климата и окружающей среды [14,15].…”

Section: Introductionunclassified