Reliability analysis for the uncertainties in vehicle and high-speed railway bridge system based on an improved response surface method for nonlinear limit states

Cho, Taejun; Song, Myung-Kwan; Lee, Do Hyung

doi:10.1007/s11071-009-9521-0

Cited by 40 publications

(8 citation statements)

References 23 publications

(42 reference statements)

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“…In the present analysis each run of the Monte-Carlo simulation employs one sample of track irregularities generated from the power spectrum of the German low-interference track. In the existing research, Cho et al (2010) selected the bridge damping ratio, concrete elasticity modulus, and cross-sectional area as the random parameters for the calculation of bridge dynamic deflection. Rocha et al (2012) performed a variable sensitivity analysis without considering track irregularity to find out which one has impact on the bridge dynamic responses.…”

Section: Input and Output Variablesmentioning

confidence: 99%

A machine learning based approach for efficient safety evaluation of the high speed train and short span bridge system

Gang

Cui

2020

Lat. Am. j. solids struct.

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The dynamic responses of the high-speed railway bridge under the train passage can greatly affect the safety of the entire high-speed train and bridge system. Traditionally, these responses are obtained using either field measurement or numerical analysis. Both tools have their own limitations. For instance, the coupling dynamic train-bridge analysis is generally complicated and time-consuming. This paper proposes a novel machine learning based approach for efficient safety evaluation of the high speed train and short span bridge system. Artificial neural networks are established to map the complicated train-bridge system and to attain the critical bridge displacements. The proposed approach incorporates a complete numerical train-bridge system model to produce reliable data for the neural network training, considering multiple significant random features in the train-bridge system. Various neural network architectures are investigated and compared to find optimal ones that have considerable potentials in realizing online response prediction and safety evaluation. Although the proposed approach focuses on the high-speed train and short span bridge, the methodology is general and can also be applied to other scenarios associated with the vehicle-bridge systems.

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Section: Input and Output Variablesmentioning

confidence: 99%

A machine learning based approach for efficient safety evaluation of the high speed train and short span bridge system

Gang

Cui

2020

Lat. Am. j. solids struct.

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“…In order to reconstruct incompletely observed or missing data, imputation models are commonly selected, which include the regression method for monotone data, the non-parametric Propensity method [18], and the MCMC [16] for non-monotone cases. In this research, creating a predictive distribution has been applied based on predictive distributions, while the data are averaged over all possible parameter values for the maintenance data of a bridge structure.…”

Section: Hierarchical Bayesian Inference Model To Predict Degradationmentioning

confidence: 99%

“…For analyzing these correlations and the resulting deterioration, conventional stochastic modeling of structural components and systems has been applied mainly on the basis of Event Tree Analysis (ETA) [15], Fault Tree Analysis (FTA) [16], and regression simulation in terms of the Response Surface Method (RSM) or adaptive RSM [17,18]. ETA is a method for illustrating the sequence of outcomes that may arise after the occurrence of a selected initial failure event and for ranking accidents, considering that the order of events needs permutation-based calculations, which produce a large number of cases for predicting failure scenarios.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Correlation Prediction Model between Hydrogen-Induced Cracking in Structural Members

Cho

Delgado-Hernández

Lee

et al. 2017

Metals

Self Cite

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Background: A quantitative model was developed and applied for analyzing the correlation between hydrogen-induced corrosion cracking in both main cable wires and degraded stiffening of the girders of a cable suspension bridge, considering maintenance effects across time and space. Method: Bayesian inference is applied for predicting the correlations among the wires in the main cables owed to hydrogen-induced cracking (HIC) in the cable wires of a steel bridge, by using the improved hierarchical Bayesian models proposed here. Results: The simulated risk prediction under decreased strength of cable wires, due to the corrosion cracking, yields posterior distributions based on prior distributions and likelihoods. The Bayesian inference model can be applied to the design and maintenance of highly corroded and correlated components Data are updated through analyzed information from previous crack steps. A numerical example including not only reliability indices but also probabilities of failure for cable wires, damaged by HIC, is then presented. Compared with a conventional linear prediction model, the one herein developed provides highly improved convergence and closeness to the analyzed data. Conclusion: The proposed model can be used as a diagnostic or prognostic prediction tool for the performance of corroded bridge cable wires with crack propagation, allowing the development of maintenance plans for mechanical components and the overall structural system.Metals 2017, 7, 205 2 of 17 of appropriate rehabilitation methods, to the location of damaged members, and to the identification of maintenance tasks. Therefore, this piece of research introduces HIC and a method for quantifying the deterioration of steel members in structural systems.Corrosion reactions often result in the formation of hydrogen gas. Hydrogen atoms can either diffuse or be absorbed into the lattice of the metal, leading to the deterioration of material properties. This, combined with the stress applied to the metal, can result in crack propagation. Diffused hydrogen atoms can recombine to form hydrogen molecules, which can exert pressure on the surrounding steel, resulting in the crack propagation in wires under high tensile stress [3,4].Such corrosion can happen under exposure to carbonated and bicarbonated, caustic, nitrate, cyanide, anhydrous ammonia (liquid), mixture of nitric and sulfuric acid, mixture of magnesium chloride and sodium fluoride, or CO/CO 2 /H 2 O mixtures, and under hydrogen attack. Some mechanisms have been proposed in the literature for explaining the phenomenon; these can be classified as either anodic Stress Corrosion Cracking (SCC) mechanisms or mechanical fracture processes. Anodic SCC involves the rupture of the protective oxide layer at the crack tip, anodic dissolution of the base metal, and crack growth under constant stress. Crack growth in turn can be intergranular or transgranular [10].Another similar concept is that a film is formed on a metal surface, and brittle fracture follows due to dealloying or vacancy inj...

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“…Using the principle of virtual work, utilizing a linearized wheel-rail contact equation to avoid iterative solution at each time step of the integration, and applying rail irregularity as random excitations to the system, the probabilistic evaluation approach for nonlinear vehicle-bridge dynamic performances has been analysed by Jin et al [22]. By developing an improved response surface method for nonlinear limit states, Cho et al [23] evaluated the reliability of vehicle and highspeed railway bridge system.…”

Section: Introductionmentioning

confidence: 99%

Stochastic Analysis of Nonlinear Vehicle‐Track Coupled Dynamic System and Its Application in Vehicle Operation Safety Evaluation

Liu

Zeng

Wei-dong

et al. 2019

Shock and Vibration

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This paper presents a vehicle operation safety evaluation model; to this end, a nonlinear vehicle-track coupled dynamic system stochastic analysis model under random irregularity excitations based on probability density evolution method was developed. The nonlinear coupled vehicle-track dynamic system is used to accurately describe the wheel-rail contact state. The stochastic function-spectral representation is used to simulate the random track irregularity in the time domain for the first time; consequently, the frequency components in the irregularity are preserved and random variables are reduced. In the process of evaluating the safety of train operation, the probability evolution, reliability of evaluation indices for different limit values, and evaluation indices for different probability limits are calculated for more accurate evaluation. The dynamic model and safety evaluation method was verified using the Zhai-model and Monte Carlo method. The results show that, when the probability guarantee is increased, the running safety index of the vehicle increases more rapidly with running speed and the left/right wheel-rail derailment coefficient increases rapidly at running speeds above 400 km/h. The computational model provides a novel direction for vehicle operation safety evaluation.

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Reliability analysis for the uncertainties in vehicle and high-speed railway bridge system based on an improved response surface method for nonlinear limit states

Cited by 40 publications

References 23 publications

A machine learning based approach for efficient safety evaluation of the high speed train and short span bridge system

A machine learning based approach for efficient safety evaluation of the high speed train and short span bridge system

Bayesian Correlation Prediction Model between Hydrogen-Induced Cracking in Structural Members

Stochastic Analysis of Nonlinear Vehicle‐Track Coupled Dynamic System and Its Application in Vehicle Operation Safety Evaluation

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