Modeling to support acceleration of restoration of a residential building system in southeastern B.C. due to riverine flooding

• Main Author: Ivy, Afia Siddika
• Other Authors: Bristow, David N.
• Format: Others
• Language: English; en
• Published: 2020
• Subjects: Component level dependency; Depth-damage relation; Flood recovery; Graph model; Restoration prioritization
• Online Access: http://hdl.handle.net/1828/11528

Floods are among some of the most damaging natural disasters. They can cause major interruptions to buildings and infrastructure and can have lasting impacts. In the case of flood damage estimation to buildings, structural and non-structural damages are of interest to most flood risk research. Very few studies, conversely analyze the impact of the recovery timeline on losses. There is a challenge to clearly understand the cause of failures within an interconnected system such as a building, and the requirements for accelerating restoration to overcome the adverse results of flood in the most convenient way possible. This work seeks to map the various components involved in functional failures of flood damaged buildings to understand their recovery. A novel model of a residential building is constructed using the Graph Model for Operational Resilience (GMOR) to model the complex interaction among dependencies in building systems to understand the cascade of failure of restoration. A case study is performed to generate recovery model to simulate the restoration of a single residential building in a flood prone neighborhood of Surrey, BC, Canada. The depth-damage functions, along with construction and repair guides, are used to identify restoration dependencies and to formulate a unique sequence of flood recovery steps for several possible flood depths. This study demonstrates how restoration can be delayed and probable solutions to improve the resilience of the city through recovery planning of flooded buildings. The results provide insights that should be useful to help emergency managers and other decision makers to develop and implement resilience thinking while revealing the economic benefits associated with increased flood risk management. In future, the custom flood model can be adapted to other locations. === Graduate === 2020-12-04