Hurricane damage assessment for residential construction considering the non-stationarity in hurricane intensity and frequency

WANG Cao; LI Quanwang; PANG Long; ZOU Aming; ZHANG Long

doi:10.1007/s13131-016-0828-7

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Article Navigation > Acta Oceanologica Sinica > 2016 > 35(12): 110-118

WANG Cao, LI Quanwang, PANG Long, ZOU Aming, ZHANG Long. Hurricane damage assessment for residential construction considering the non-stationarity in hurricane intensity and frequency[J]. Acta Oceanologica Sinica, 2016, 35(12): 110-118. doi: 10.1007/s13131-016-0828-7

Citation:

WANG Cao, LI Quanwang, PANG Long, ZOU Aming, ZHANG Long. Hurricane damage assessment for residential construction considering the non-stationarity in hurricane intensity and frequency[J]. Acta Oceanologica Sinica, 2016, 35(12): 110-118. doi: 10.1007/s13131-016-0828-7

Citation:

WANG Cao, LI Quanwang, PANG Long, ZOU Aming, ZHANG Long. Hurricane damage assessment for residential construction considering the non-stationarity in hurricane intensity and frequency[J]. Acta Oceanologica Sinica, 2016, 35(12): 110-118. doi: 10.1007/s13131-016-0828-7

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Hurricane damage assessment for residential construction considering the non-stationarity in hurricane intensity and frequency

doi: 10.1007/s13131-016-0828-7

1.
Department of Civil Engineering, Tsinghua University, Beijing 100084, China
2.
CCCC Highway Consultants CO., Ltd., Beijing 100088, China

Received Date: 2015-09-28
Rev Recd Date: 2015-12-31

Abstract

Abstract

Natural hazards such as hurricanes may cause extensive economic losses and social disruption for civil structures and infrastructures in coastal areas, implying the importance of understanding the construction performance subjected to hurricanes and assessing the hurricane damages properly. The intensity and frequency of hurricanes have been reported to change with time due to the potential impact of climate change. In this paper, a probability-based model of hurricane damage assessment for coastal constructions is proposed taking into account the non-stationarity in hurricane intensity and frequency. The non-homogeneous Poisson process is employed to model the non-stationarity in hurricane occurrence while the non-stationarity in hurricane intensity is reflected by the time-variant statistical parameters (e.g., mean value and/or standard deviation), with which the mean value and variation of the cumulative hurricane damage are evaluated explicitly. The Miami-Dade County, Florida, USA, is chosen to illustrate the hurricane damage assessment method proposed in this paper. The role of non-stationarity in hurricane intensity and occurrence rate due to climate change in hurricane damage is investigated using some representative changing patterns of hurricane parameters.
- hurricane,
- damage assessment,
- intensity,
- frequency,
- non-stationarity,
- climate change

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References(32)

References

Australian Greenhouse Office (AGO). 2007. An assessment of the need to adopt buildings for the unavoidable consequences of climate change. Final report. Canberra, Australia:Commonwealth of Australia, Australian Greenhouse Office

Bjarnadottir S, Li Yue, Stewart M G. 2011. A probabilistic-based framework for impact and adaptation assessment of climate change on hurricane damage risks and costs. Structural Safety, 33(3):173-185

Blake E S, Gibney E J. 2011. The deadliest, costliest, and most intense United States tropical cyclones from 1851 to 2010 (and other frequently requested hurricane facts), NOAA technical memor-andum NWS NHC-6. Miami, Florida:National Hurricane Cen-ter (NHC)

Climate Change Science Program (CCSP). 2008. Weather and cli-mate extremes in a changing climate. Regions of focus:North America, Hawaii, Caribbean, and U.S. Pacific Islands. A report by the U.S. climate change science program and the subcom-mittee on global change research. Washington D C, USA:De-partment of Commerce, NOAA's National Climatic Data Center

Devore J L. 2000. Probability and Statistics for Engineering and the Sciences. 5th ed. Pacific Grove, CA:Duxbury Press

Ellingwood B R, Lee J Y. 2015. Life cycle performance goals for civil infrastructure:intergenerational risk-informed decisions. Structure and Infrastructure Engineering:Maintenance, Man-agement, Life-Cycle Design and Performance, doi: 10.1080/15732479.2015.1064966

Elsner J B, Bossak B H. 2001. Bayesian analysis of U.S. hurricane cli-mate. Journal of Climate, 14(23):4341-4350

Emanuel K. 2005. Increasing destructiveness of tropical cyclones over the past 30 years. Nature, 436(7051):686-688

Fitzpatrick P J. 2006. Hurricanes:A Reference Handbook. 2nd ed. Santa Barbara California, USA:ABC-CLIO Ltd

Hallegatte S. 2007. The use of synthetic hurricane tracks in risk ana-lysis and climate change damage assessment. Journal of Ap-plied Meteorology and Climatology, 46(11):1956-1966

Holland G J, Webster P J. 2007. Heightened tropical cyclone activity in the North Atlantic:natural variability or climate trend?. Philo-sophical Transactions of the Royal Society A:Mathematical, Physical and Engineering Sciences, 365(1860):2695-2716

Huang Zhigang, Rosowsky D V, Sparks P R. 2001. Long-term hur-ricane risk assessment and expected damage to residential structures. Reliability Engineering & System Safety, 74(3):239-249

Jain V K, Davidson R, Rosowsky D. 2005. Modeling changes in hur-ricane risk over time. Natural Hazards Review, 6(2):88-96

Katz R W. 2002. Stochastic modeling of hurricane damage. Journal of Applied Meteorology, 41(7):754-762

Knutson T R, McBride J L, Chan J, et al. 2010. Tropical cyclones and climate change. Nature Geoscience, 3(3):157-163

Landsea C W. 2007. Counting Atlantic tropical cyclones back to 1900. Eos, Transactions American Geophysical Union, 88(18):197-202, doi: 10.1029/2007EO180001

Landsea C W, Harper B A, Hoarau K, et al. 2006. Can we detect trends in extreme tropical cyclones?. Science, 313(5786):452-454

Li Yue, Ellingwood B R. 2006. Hurricane damage to residential con-struction in the US:Importance of uncertainty modeling in risk assessment. Engineering Structures, 28(7):1009-1018

Li Yue, Stewart M G. 2011. Cyclone damage risks caused by en-hanced greenhouse conditions and economic viability of strengthened residential construction. Natural Hazards Review, 12(1):9-18

Li Quanwang, Wang Cao, Ellingwood B R. 2015. Time-dependent re-liability of aging structures in the presence of non-stationary loads and degradation. Structural Safety, 52:131-142

Lin Ning, Emanuel K, Oppenheimer M, et al. 2012. Physically based assessment of hurricane surge threat under climate change. Nature Climate Change, 2(6):462-467

Liu Fangqian. 2012. Development and calibration of central pressure filling rate models for hurricane simulation[dissertation]. South Carolina, USA:Clemson University

Mudd L, Wang Yue, Letchford C, et al. 2014. Assessing climate change impact on the U.S. east coast hurricane hazard:temperature, frequency, and track. Natural Hazards Review, 15(3):doi: 10.1061/(ASCE)NH.1527-6996.0000128

Pinelli J P, Simiu E, Gurley K, et al. 2004. Hurricane damage predic-tion model for residential structures. Journal of Structural En-gineering, 130(11):1685-1691

Saunders M A, Lea A S. 2008. Large contribution of sea surface warm-ing to recent increase in Atlantic hurricane activity. Nature, 451(7178):557-560, doi: 10.1038/nature06422

Stewart M G, Rosowsky D V, Huang Zhigang. 2003. Hurricane risks and economic viability of strengthened construction. Natural Hazards Review, 4(1):12-19

Unanwa C O, McDonald J R. 2000. Building wind damage prediction and mitigation using damage bands. Natural Hazards Review, 1(4):197-203

Vickery P J, Masters F J, Powell M D, et al. 2009. Hurricane hazard modeling:the past, present, and future. Journal of Wind Engin-eering and Industrial Aerodynamics, 97(7-8):392-405

Vickery P J, Skerlj P F, Twisdale L A. 2000. Simulation of hurricane risk in the U.S. using empirical track model. Journal of Structural Engineering, 126(10):1222-1237

Vickery P J, Twisdale L A. 1995. Prediction of hurricane wind speeds in the United States. Journal of Structural Engineering, 121(11):1691-1699

Weiss N A. 2014. Introductory Statistics. 9th ed. Harlow, Essex:Pear-son

Xu Fumin, Bui Thi T D, Perrie W. 2014. The observed analysis on the wave spectra of Hurricane Juan (2003). Acta Oceanologica Sin-ica, 33(11):112-122

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