|Title||Development of probabilistic cost function for flood damage to residential structures|
|Author||McGrath, H; Abo
El Ezz, A; Nastev, M|
|Source||Canadian Society for Civil Engineering Annual Conferrence 2018, full schedule/Congrès annuel de la Société canadienne de génie civil 2018, horaire complet; 2018 p. DM19-1-DM19-8|
|Links||Online - En ligne (PDF, 464 KB)|
|Alt Series||Natural Resources Canada, Contribution Series 20170367|
|Publisher||Canadian Society of Civil Engineering|
|Meeting||Canadian Society for Civil Engineering Annual Conferrence 2018 / Congrès annuel de la Société canadienne de génie civil 2018; Fredericton, NB; CA; June 13-16, 2018|
|Subjects||hydrogeology; engineering geology; mathematical and computational geology; floods; flood plains; models; Risk management; Buildings|
|Program||Public Safety Geoscience Quantitative risk assessment project|
|Released||2018 06 01|
|Abstract||Risk models, which describe the relationship between hazard intensity and a damage ratio are increasingly used in flood risk management. Direct tangible damage resulting from flooding is typically
computed based on the internationally accepted method of depth-damage curves. Depth-damage curves relate absolute damage (in terms of currency) or relative loss (percentage of the estimated total replacement value of property) to a given flood depth.
Many depth-damage functions in use today are computed from synthetic data, where data are collected from a representative sample of buildings with similar properties in a floodplain during field surveys. A primary problem when assessing risk at an
object-based spatial resolution using depth-damage curves is that these damage functions represent an average structure in the study location. There is great variability across any given structural class as well as variation within individual
structures in a structural category (and across communities), for example, not all one-storey residences with basements are the same size, nor constructed of the same quality of materials and workmanship. The variability within a given class of
buildings and the resulting depth-damage curve are often not transparent to the end user, thus damage estimates for individual buildings may be over/under estimated. In this paper, synthetic depth-damage curve data from communities in southern
Ontario are used to develop probabilistic cost functions such that monetary damage estimates, as spent in Canadian dollars, and their likelihood of being exceeded at any given flood depth are more clearly expressed and communicated to end users.
|Summary||(Plain Language Summary, not published)|
Depth-damage curves are used as the basis to generate probabilistic cost functions which better represent the variability and variation in the estimated
cost of building damages with respect to a range of flood levels.The resulting probabilistic cost functions show the probability of exceeding a specific dollar amount (¿$ XX¿) across a range of flood levels.