Interpretable machine learning methods for clarification of load-displacement effects on cable-stayed bridge

Keyphrases

• Cable-stayed Bridge 100%
• Load-displacement 100%
• Interpretable Machine Learning 100%
• Displacement Effect 100%
• Girder 60%
• Pylon 60%
• Structural Displacement 40%
• Ensemble Learning Model 40%
• Shapley Additive Explanations 40%
• Automobile 20%
• Wind Effects 20%
• Machine Learning 20%
• Performance Prediction 20%
• Pedestrian 20%
• Effective Management 20%
• Learning Algorithm 20%
• Wind Load 20%
• Temperature Effect 20%
• Traffic Load 20%
• Monitoring Data 20%
• Bridge Girder 20%
• Transportation System 20%
• Displacement Response 20%
• Input-output Relation 20%
• Bridge Management 20%
• Bridge Maintenance 20%
• Bridge Displacement 20%
• Tuning Approach 20%
• Hyperparameters 20%
• Effective Maintenance 20%
• Load Response 20%
• Displacement Prediction 20%
• Model Tuning 20%
• Explanatory Analysis 20%
• Critical Displacement 20%
• Bridge Pylon 20%
• Global Interpretation 20%
• Proposed Methodology 20%
• Extreme Gradient Boosting(XGBoost) 20%

Engineering

• Cable Stayed Bridges 100%
• Machine Learning Method 100%
• Beams and Girders 60%
• Conventional Method 20%
• Prediction Performance 20%
• Bridge Girder 20%
• Displacement Response 20%
• Bridge Management 20%
• Bridge Maintenance 20%
• Monitoring Data 20%
• Learning Algorithm 20%
• Critical Load 20%