PROBABILISTIC-BASED HURRICANE RISK ASSESSMENT AND ...

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vulnerability function as a percentage of loss as a function of mean wind speed atgradient height. Unfortunately, the direct applicability of these functions is limited,because these models are not equipped to take into account how building codes,materials, etc. have changed over time. Furthermore, these models are limited toapplication within the location from where the insurance information was obtained tocreate the vulnerability model (e.g. Mitsuta et al. (1996) in Japan and Bhinderwala(1995) in Southern Florida).Holmes (1996) used a probability based simulation model to generate the vulnerabilitycurve for a fully engineered building, assuming that the resistance capacity of thebuilding is lognormally distributed; therefore, this model is solely applicable to fullyengineered structures not residential construction. Sill and Kozlowski (1997) estimateddamage as a function of various hurricane parameters (i.e. gradient wind speed, gustfactor), mean house value, and two empirically derived values that determine how therate of damage increase with wind speed. Because the empirically derived values werebased on the experience and judgment of the researchers and not defined explicitly, thisstudy is difficult to replicate.Huang et al. (2001) developed a hurricane damage model for single family housing unitsusing Southeastern U.S. insurance data from Hurricanes Hugo and Andrew. Thisparticular model predicts the damage ratio which is the total amount paid by the insurerversus the total insured value. To develop the vulnerability model, the damage ratio isrelated to the recorded average mean surface speed of the hurricane. This model islimited in that it was derived from only two hurricane events; a much larger data set isrequired to more accurately predict future losses expected due to hurricane events.Khanduri and Morrow (2003) broke the vulnerability curve into several curves torepresent individual building types, translating known vulnerabilities for one region toanother region by combining actual data and building inventory information for theregion. This model is relatively simple, but as it is based on claims information, it islimited to the type of construction the claims data pertains to.13
Hurricane vulnerability models developed from regressive curve-fitting methods oftenhave the capacity to estimate damage to only one specific type of construction (e.g.residential or timber), whereas the building inventory of most regions is comprised ofmany different types of construction. Hurricane damage prediction models should,ideally, be capable of accounting for potential changes within building inventory due to,for example, new codes, evolving building practices, and retrofit measures to reduce thevulnerability of existing structures. This has led to the development of component basedhurricane vulnerability models, in which the vulnerability of individual components arecombined.Stubbs and Perry (1996) set forth a model based on how different building componentsperform and their corresponding relative importance. Unanwa and McDonald (2000)developed a similar model that can be applied to an even larger area. Because currentmodels assume that vulnerability is constant over time, Davidson et al. (2003b) provide away to model how the hurricane wind vulnerability of the building inventory of a regionchanges, due to ageing, upgrading, construction, new technology and new building codes.Pinelli et al. (2004) set forth a component based hurricane damage prediction model forresidential structures, focusing on low-rise residential structures of different types that arecommon in Florida. The model uses Venn diagrams to illustrate the probability ofdamage for damage modes and sub-damage modes. Jain (2007) proposed a vulnerabilitymodel for wood-frame housing. This is an “over-time” model that focuses on thechanges that take place within the building inventory.The HAZUS-MH Hurricane Model was developed to estimate the damage a hurricaneevent can cause. The track and wind field of a hurricane is modeled using a hurricanehazard model, which has been validated with historic hurricane data (Lavelle et al. 2003,FEMA 2008). The wind speed is calculated as a function of central pressure, translationspeed, and surface roughness. The model classifies the building inventory of a region bythe characteristics of the building envelope and frame, and is capable of calculatingdamage loss to building interior and contents (FEMA 2008). This model is component-14
Page 6 and 7: 4.3 Design of Power Distribution Po
Page 8 and 9: 6.6 Conclusions and Future Work ...
Page 10 and 11: List of FiguresFigure 1.1:Simulatio
Page 12 and 13: Figure 4.10: Effects of Degradation
Page 14 and 15: List of TablesTable 2.1: Percentage
Page 16 and 17: PrefaceThe research presented in th
Page 18 and 19: AcknowledgementsI would like to sta
Page 20 and 21: AbstractStudies are suggesting that
Page 22 and 23: Chapter 1__________________________
Page 24 and 25: 1.1 Literature Review and Critical
Page 26 and 27: assumed, between now and 2080, it c
Page 28: due to climate change. Similar rese
Page 33: The "peaks-over-threshold" method i
Page 37 and 38: vulnerability of structures to dama
Page 39 and 40: purchase insurance to protect again
Page 41 and 42: 1.1.4 Hurricane Risk for Power Dist
Page 43 and 44: for both the potential impacts of c
Page 45 and 46: where Q is air density factor, k i
Page 47 and 48: degradation due to the ageing of ut
Page 49: aspects of climate change and may n
Page 52 and 53: • Climatic adaptation should be d
Page 54 and 55: surge height model, and hurricane v
Page 56 and 57: that assesses the cost-effectivenes
Page 58 and 59: Dagher, H.J., Lu, Q., and Peyrot, A
Page 60 and 61: Grigg, N.S, and Helweg, O.J. (1975)
Page 62 and 63: Knuston, T.R., McBride, J.L., Chan,
Page 64 and 65: Mitsuta, Y., Fujii, T., and Nagashi
Page 66 and 67: Rumpf, J., Weindl, H., Hoppe, P., R
Page 68 and 69: USACE (1970) Guidelines for Flood I
Page 70 and 71: Chapter 2__________________________
Page 72 and 73: devastating effect of Hurricane Kat
Page 74 and 75: There is a great uncertainty, both
Page 76 and 77: they can be incorporated into the f
Page 78 and 79: These are increases of 15.9% and 35
Page 80 and 81: 50% greater than the house replacem
Page 82 and 83: wind speed of 5% with COV of 0.50 i
Page 84 and 85:
different exposure sites, differenc
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damage costs can be reduced by arou
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3. Strengthening a percentage of co
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1.201.00Net Benefit, E b ($ billion
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2.001.50Net Benefit, E b ($ billion
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Figure 2.11 shows adaptation strate
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ecause of the sheer number of housi
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An increase of 5% and 10% in wind s
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the reduction factor (50% or 80%),
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[22] AGO. An Assessment of the Need
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[42] Pinelli JP, Torkian BB, Gurley
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3.1 AbstractThis paper presents a f
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over the last 100 years. For exampl
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these peaks have a Poisson arrival
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The parameters in Table 3.1 are imp
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3.5 Regional Loss Estimation Consid
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where j represents the exposure sit
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3.6.1 Annual Regional Loss Estimati
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wind or frequency) is 0.0260, 0.020
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increasing the frequency of hurrica
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Figure 3.3: Cumulative Damage Costs
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eplacement house value is $98,000,
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that the sea level rise may vary al
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Table 3.8: Combined Hurricane Damag
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Figure 3.6: Percentage Change in Da
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Hanover County, and $120 million pe
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Bjarnadottir, S., Li, Y. and Stewar
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Johnson, B. (2005) After the Disast
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National Cooperative Highway Resear
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Structural Engineers- Proc. Structu
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Chapter 4__________________________
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customers in New Orleans, Louisiana
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Southern pine is the most common ma
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type of load being designed for. Di
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Figure 4.2: Flowchart of the Design
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a(t) = π (D − 32 d(t))3 (4.6)whe
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3025Decay Depth (mm)201510500 5 10
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eliability of the pole, while the p
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f v (V, t) = α(t)u(t) ∙ Vu(t)
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Table 4.2: Design Variables and Val
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The wind load for the poles is dete
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parameters. Fragility curves are de
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4.6.3 Effect of Degradation on P fT
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2 and 3. This is attributed to the
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10.9Probability of Failure0.80.70.6
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Table 4.8: Annual P f for Design Ca
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0.250.20P f0.150.100.0550 years30 y
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Updated Annual P f1.00.90.80.70.60.
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4.7 ConclusionsThis paper proposes
Page 182 and 183:
Bhuyan, G., and Li, H. (2006) Achie
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Kwasinski, A., Weaver, W.W., Chapma
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Vanderbilt, M D., Criswell, M.E., F
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Chapter 5__________________________
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(Johnson 2005). Hurricane Andrew, i
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Figure 5.1: Typical Power Distribut
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where R(t) is the actual capacity o
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speeds than poles located further i
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eplacement in these exposure catego
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the larger pole and therefore a low
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that specified by the U.K. is used
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Table 5.2: Statistics for Wind Load
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Figure 5.2: Fragility Curves for Cl
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initial strength of Class 5 poles f
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decreased vulnerability of the pole
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2060. These results confirm the res
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Figure 5.7: Probability of Exceedan
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American Society of Civil Engineers
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Leicester, R.H., Wang, C.H., Minh,
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Viscusi, W.K. (2007) Rational Disco
Page 222 and 223:
6.1 AbstractThis paper presents the
Page 224 and 225:
social characteristics may have an
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(HDRI). The HDRI was calculated by
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The maximum annual wind speed for M
Page 230 and 231:
Using the Irish model and data from
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6.3.3 Coastal Community Social Vuln
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H surge (t) = X H2 (t)(6.7b)where H
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Queensland, Australia. A recent stu
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Table 6.2: Results of PCA, includin
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Table 6.3: Social Indicators for th
Page 242 and 243:
Lambert (2001). The second ratio (4
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height of 5%. From this comparison,
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their respective region to hurrican
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Figure 6.3: Distributions of CCSVI(
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including injury and death can be c
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GAO (2007) Climate Change: Financia
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Pryor, S.C., Barthelmie, R.J., and
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Chapter 7__________________________
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specifications, etc. Aging effects
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estimate system reliability indices
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esidential construction and then ad
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parameters, and the social characte
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Chapter 8__________________________
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the mitigation strategies, it was f
Page 270 and 271:
Baraneedaran, S., Gad, E.F., Flatle
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Climate Change Advisory Task Force.
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FEMA (1999). HAZUS-MH Hurricane Mod
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Gustavsen, B., and Rolfseng, L. (20
Page 278 and 279:
Klima, K., Lin, N., Emanuel, K., Mo
Page 280 and 281:
Li, Y. and Stewart, M.G. (2011) Cyc
Page 282 and 283:
National Research Council (NRC) (20
Page 284 and 285:
Pistrika A. and Jonkman S. (2010).
Page 286 and 287:
Scawthorn, C., Blais, N., Seligson,
Page 288 and 289:
Unanwa, C.O., and McDonald, J.R. (2
Page 290 and 291:
Wang, C.-h., Leicester, R.H., and N
Page 292 and 293:
Appendix I_________________________
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Table AI.2: The expected cumulative
Page 296 and 297:
Percentage Increase in Cumulative D
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Table AI.3: Comparing the net benef
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Figure AI.3 shows the various combi
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Appendix II________________________
Page 304 and 305:
Figure AII.1: Fragility Curve for C
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Appendix III_______________________
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AIII.2 ResultsA principal component
Page 310 and 311:
Appendix IV________________________
Page 312 and 313:
Expected completion dateEstimated s
Page 314 and 315:
This is documentation for Chapters
Page 316 and 317:
may use the publisher-supplied PDF
Page 318 and 319:
This is documentation for Chapter 6
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