b5c8ffc5-4fe2-458b-933e-884819d46cba urn:uuid eng UTF-8 nonGeographicDataset nonGeographicDataset 2026-01-08T12:53:47 creation ISO 19115 2016 An extension to the USGS ShakeCast for system-level impact assessment using a Bayesian approach b5c8ffc5-4fe2-458b-933e-884819d46cba The United States Geological Survey (USGS) ShakeCast provides near-real-time post-earthquake impact and inspection metrics for critical infrastructure facilities following an event. The system sends automatic notifications with information on inspection priorities based on damage estimates using a suite of fragility models and ShakeMap ground motion parameters. The current system provides only asset-level damage estimates, which can be further used for system-level impact assessment through a Bayesian approach. This paper describes a methodology for an application of a Bayesian approach using the product of the ShakeCast and demonstrates an example of its application for the road bridge network located in the Wheatbelt Region of southwestern Western Australia, inland from Perth. This region has been the subject of detailed collaborative research on infrastructure assets that has led to an operational piloting of ShakeCast in Geoscience Australia’s National Earthquake Alerts Centre. The bridge network in this research consists of approximately 100 bridges with earthquake vulnerability attributed. The performance of the selected routes is assessed assuming the bridges are the only vulnerable elements in the road network that can fail due to ground shaking. This proposed extension to ShakeCast will enable road transport asset managers to assess the likelihood of system operational status near-real-time following an event. Presented at 14th International Conference on Structural Safety and Reliability - ICOSSAR'25. {"type": "Polygon", "coordinates": [[[112.0, -44.0], [154.0, -44.0], [154.0, -9.0], [112.0, -9.0], [112.0, -44.0]]]} 112.0 154.0 -44.0 -9.0 Bayesian network Community Planning Earthquakes Published_External ShakeCast Link to paper documentDigital Link to paper HTML