Probabilistic models and methods for risk assessment of lifeline networks and decision support

Abstract

Effective risk assessment of urban lifeline networks is essential in risk-informed decision support to reduce socio-economic losses. Risk assessment of lifeline networks is complex in nature due to a large number of network components, complex network topology and operation conditions, spatial correlation of hazards, and component/system interdependencies. This complexity often results in technical challenges that prevent efficient and accurate network risk assessment and effective decision-making. The papers in the proposed session will present recent developments of probabilistic models and methods for effective risk assessment of lifeline networks and decision support.

Organizers

Junho Song, University of Illinois, Urbana-Champaign, US, (junho@illinois.edu)
Leonardo Dueñas-Osorio, Rice University, US