A Bayesian methodology for detection of railway ballast damage using the modified Ludwik nonlinear model

Keyphrases

• Tracking System 100%
• Nonlinear Model 100%
• Sleeper 100%
• Railway Ballast 100%
• Bayesian Methods 100%
• Impact Hammer Test 100%
• Proposed Methodology 100%
• Stress-strain 50%
• Nonlinearity 50%
• Modified Forms 50%
• Mechanical Behavior 50%
• Finite Element Model 50%
• Model Accuracy 50%
• Scale Factor 50%
• Sensor Configuration 50%
• Sparsity 50%
• Stopping Criterion 50%
• Timoshenko Beam Element 50%
• Vertical Vibration 50%
• Nonlinear Spring 50%
• Structural Damage Detection 50%
• Beam on Elastic Foundation 50%
• Model Updating 50%
• Correct Identification 50%
• In Situ Measurements 50%
• System Properties 50%
• Bayesian Framework 50%
• Underlay 50%
• Inherent Uncertainty 50%
• Markov Chain Monte Carlo 50%
• Track Segments 50%
• Impact Location 50%
• Bayesian Model Updating 50%
• Concrete Sleeper 50%
• Ballast Layer 50%
• Track Models 50%
• Impact Sensor 50%
• Ballasted Track 50%
• Impact Configuration 50%
• Elastoplasticity 50%

Engineering

• Railway 100%
• Nonlinear Model 100%
• Bayesian Methodology 100%
• Damage Detection 50%
• Structural Damage 50%
• Full Scale 50%
• Time Domain 50%
• Beam Element 50%
• Scaling Factor 50%
• Situ Measurement 50%
• Stopping Criterion 50%
• Concrete Sleeper 50%
• Ballast Layer 50%
• Ballasted Track 50%
• Finite Element Modeling 50%
• Sparsity 50%
• Bayesian Model 50%
• Nonlinearity 50%