IF 1 Q3 MULTIDISCIPLINARY SCIENCES
Ryan Paulik , Shaun Williams , Misaeli Funaki , Richard Turner
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引用次数: 0

摘要

热带气旋造成的极端风力为评估建筑结构的物理损坏提供了一个独特的机会。2016 年 2 月 20 日,5 级热带气旋温斯顿(TC Winston)登陆斐济,造成 3 万多栋建筑物受损。本文介绍了在 TC Winston 后期从现场损害评估中收集的斐济风灾建筑物损害经验数据集。该数据集代表了 2016 年 3 月为期四天的调查中记录的 700 多条特定建筑物的灾害、建筑物和损坏变量记录。数据集介绍了对建筑物结构、内含物、库存、设备和厂房造成的物理损坏,以及对住宅建筑居住性和非住宅建筑服务造成的破坏。该数据集提供了关于温斯顿热带气旋极端风造成的建筑物损坏的宝贵记录,可与数值风模型危害强度输出一起用于制定建筑物特定风脆弱性模型,以预测未来极端风事件的损坏情况。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Wind damage dataset for buildings from 2016 tropical cyclone Winston in Fiji
Extreme winds caused by tropical cyclones offer a unique opportunity to evaluate physical damage to building structures. On 20 February 2016, Category 5 Tropical Cyclone Winston (TC Winston) made landfall in Fiji, causing damage to over 30, 000 buildings. This article presents an empirical wind building damage dataset for Fiji collected from onsite damage assessments in the TC Winston aftermath. The dataset represents over 700 building-specific records of hazard, building and damage variables recorded during a four-day survey in March 2016. Physical damage to building structures, contents, stock, equipment and plant are presented, along with disruption to residential building habitability and non-residential building services. The dataset provides a valuable record of building damage caused by TC Winston extreme winds that can be used with numerical wind model hazard intensity outputs to formulate building-specific wind vulnerability models for damage prediction in future extreme wind events.
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来源期刊
Data in Brief
Data in Brief MULTIDISCIPLINARY SCIENCES-
CiteScore
3.10
自引率
0.00%
发文量
996
审稿时长
70 days
期刊介绍: Data in Brief provides a way for researchers to easily share and reuse each other''s datasets by publishing data articles that: -Thoroughly describe your data, facilitating reproducibility. -Make your data, which is often buried in supplementary material, easier to find. -Increase traffic towards associated research articles and data, leading to more citations. -Open up doors for new collaborations. Because you never know what data will be useful to someone else, Data in Brief welcomes submissions that describe data from all research areas.
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