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Field-Test-Driven Sensitivity Analysis and Model Updating of Aging Railroad Bridge Structures Using Genetic Algorithm Optimization Approach

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Publicat a:Infrastructures vol. 10, no. 8 (2025), p. 195-216
Autor principal: Anand, Rahul
Altres autors: Tripathi Sachin, De Oliveira Celso Cruz, Malla, Ramesh B
Publicat:
MDPI AG
Matèries:
Amtrak
United States > US
Load
Finite element method
Dynamic response
Material properties
Sensitivity analysis
Railway bridges
Calibration
Laser doppler vibrometers
Data processing
Field study
Corrosion
Steel structures
Parameter identification
Genetic algorithms
Bridge maintenance
Lasers
Digital twins
Metal fatigue
Piers
Sensors
Optimization
Resonant frequencies
Design
Aging (metallurgy)
Fatigue life
Model updating
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Resum:Aging railroad bridges present complex challenges due to advancing deterioration and outdated design assumptions. This study develops a comprehensive analytical approach for assessing an aging steel truss railroad bridge through finite element (FE) modeling, sensitivity analysis, and model updating, supported by field testing. An initial FE model of the bridge was created based on original drawings and field observations. Field testing using a laser Doppler vibrometer captured the bridge’s dynamic response (vibrations and deflections) under regular train traffic. Key structural parameters (material properties, section properties, support conditions) were identified and varied in a sensitivity analysis to determine their influence on model outputs. A hybrid sensitivity analysis combining log-normal sampling and a genetic algorithm (GA) was employed to explore the parameter space and calibrate the model. The GA optimization tuned the FE model parameters to minimize discrepancies between simulated results and field measurements, focusing on vertical deflections and natural frequencies. The updated FE model showed significantly improved agreement with observed behavior; for example, vertical deflections under a representative train were matched within a few percent, and natural frequencies were accurately reproduced. This validated model provides a more reliable tool for predicting structural performance and fatigue life under various loading scenarios. The results demonstrate that integrating field data, sensitivity analysis, and model updating can greatly enhance the accuracy of structural assessments for aging railroad bridges, supporting more informed maintenance and management decisions.
ISSN:2412-3811
DOI:10.3390/infrastructures10080195
Font:Advanced Technologies & Aerospace Database