Machine Learning–Based Seismic Reliability Assessment of Bridge Networks
Mengdie Chen, Sujith Mangalathu, Jong‐Su Jeon
Abstract
Transportation networks are critical components of lifeline systems. They can experience disruptions due to seismic hazards that could lead to severe emergency response and recovery problems. Finding an efficient and effective method to evaluate the seismic reliability of bridge networks is crucial for risk managers. This study proposes a method that can compute the seismic reliability of bridge networks using machine learning techniques. The proposed method is computationally less expensive than existing methods and can be implemented easily in emergency risk management systems. Moreover, it includes information on ranking bridges and prioritizing retrofit plans.
Topics & Concepts
Bridge (graph theory)Reliability (semiconductor)Computer scienceRanking (information retrieval)Reliability engineeringSeismic riskEmergency managementEngineeringMachine learningCivil engineeringLawMedicinePolitical scienceQuantum mechanicsPower (physics)Internal medicinePhysicsInfrastructure Maintenance and MonitoringConcrete Corrosion and DurabilityStructural Health Monitoring Techniques