The impact of a PEER funded research project, “Caltrans-PEER Workshop on Characterizing Uncertainty in Bridge-Component Capacity Limit-States" is highlighted below. The project Principal Investigator (PI) is Sashi Kunnath, Professor, UC Davis. The research team includes Jin Zhou, Graduate Student Researcher, UC Davis.
The broad topic of seismic fragility-model development is an active research pursuit with contributions involving various methodologies, scales and applications. However, no current work or documented references is available which systematically capture community perspectives regarding optimal CCLS models or the uncertainty associated with differing perspectives. Thus, the proposed survey and workshop documentation is expected to serve as a central community resource. In terms of downstream application, there is growing adoption of the ShakeCast platform as a primary means for implementing organization-specific earthquake-damage alerting and loss estimation strategies for both live emergency situations and for pre-event planning. Multiple state DOT’s have already adopted ShakeCast and others are committing to a Transportation Pooled Fund project (http://www.pooledfund.org/Details/Solicitation/1406). Fragility models developed for state DOT’s may vary due to differences in the composition of the local bridge inventory, thus affecting the PSDM’s for local classes. However, establishing a benchmark framework for characterizing uncertainty in CCLS models will serve each of these model-development efforts.
The results of the workshop will have both immediate and long-term impact on practice. Immediately, the expert views and consensus results will be used as guidance for the specification of CCLS models being incorporated into Caltrans emerging fragility models to be deployed within ShakeCast. Over the longer term, workshop documentation will serve as a benchmark of expert views that can be referenced in comparable studies and used to characterize and communicate this critical source of fragility-model uncertainty.