Windborne debris risk assessment

Ning Lin; Erik Vanmarcke

doi:10.1016/j.probengmech.2008.01.010

Windborne debris risk assessment

Ning Lin, Erik Vanmarcke

Research output: Contribution to journal › Article › peer-review

52 Scopus citations

Abstract

A probabilistic model, based on Poisson random measure theory, is developed for predicting windborne debris damage in residential areas. It is intended for incorporation into improved methodology to estimate economic losses due to hurricanes to individual houses or entire residential developments. A sample application to a coastal housing development is provided.

Original language	English (US)
Pages (from-to)	523-530
Number of pages	8
Journal	Probabilistic Engineering Mechanics
Volume	23
Issue number	4
DOIs	https://doi.org/10.1016/j.probengmech.2008.01.010
State	Published - Oct 2008

All Science Journal Classification (ASJC) codes

Statistical and Nonlinear Physics
Civil and Structural Engineering
Nuclear Energy and Engineering
Condensed Matter Physics
Aerospace Engineering
Ocean Engineering
Mechanical Engineering

Keywords

Hurricane risk
Poisson
Probability
Statistics
Wind tunnel
Windborne debris

Access to Document

10.1016/j.probengmech.2008.01.010

Cite this

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title = "Windborne debris risk assessment",

abstract = "A probabilistic model, based on Poisson random measure theory, is developed for predicting windborne debris damage in residential areas. It is intended for incorporation into improved methodology to estimate economic losses due to hurricanes to individual houses or entire residential developments. A sample application to a coastal housing development is provided.",

keywords = "Hurricane risk, Poisson, Probability, Statistics, Wind tunnel, Windborne debris",

author = "Ning Lin and Erik Vanmarcke",

note = "Funding Information: The research reported herein is supported by a grant from Baseline Management Corporation (BMC) to Princeton University. The first author gratefully acknowledges the instruction and advice on Poisson modeling from Professor Erhan Cinlar of the Department of Operations Research and Financial Engineering at Princeton University. Copyright: Copyright 2008 Elsevier B.V., All rights reserved.",

year = "2008",

month = oct,

doi = "10.1016/j.probengmech.2008.01.010",

language = "English (US)",

volume = "23",

pages = "523--530",

journal = "Probabilistic Engineering Mechanics",

issn = "0266-8920",

publisher = "Elsevier Ltd",

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TY - JOUR

T1 - Windborne debris risk assessment

AU - Lin, Ning

AU - Vanmarcke, Erik

N1 - Funding Information: The research reported herein is supported by a grant from Baseline Management Corporation (BMC) to Princeton University. The first author gratefully acknowledges the instruction and advice on Poisson modeling from Professor Erhan Cinlar of the Department of Operations Research and Financial Engineering at Princeton University. Copyright: Copyright 2008 Elsevier B.V., All rights reserved.

PY - 2008/10

Y1 - 2008/10

N2 - A probabilistic model, based on Poisson random measure theory, is developed for predicting windborne debris damage in residential areas. It is intended for incorporation into improved methodology to estimate economic losses due to hurricanes to individual houses or entire residential developments. A sample application to a coastal housing development is provided.

AB - A probabilistic model, based on Poisson random measure theory, is developed for predicting windborne debris damage in residential areas. It is intended for incorporation into improved methodology to estimate economic losses due to hurricanes to individual houses or entire residential developments. A sample application to a coastal housing development is provided.

KW - Hurricane risk

KW - Poisson

KW - Probability

KW - Statistics

KW - Wind tunnel

KW - Windborne debris

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U2 - 10.1016/j.probengmech.2008.01.010

DO - 10.1016/j.probengmech.2008.01.010

M3 - Article

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SP - 523

EP - 530

JO - Probabilistic Engineering Mechanics

JF - Probabilistic Engineering Mechanics

IS - 4

ER -

Windborne debris risk assessment

Abstract

All Science Journal Classification (ASJC) codes

Keywords

Access to Document

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