Post-earthquake building services downtime distribution: a case study of the 2016 Kumamoto, Japan, earthquake

Tomoaki Nishino
{"title":"Post-earthquake building services downtime distribution: a case study of the 2016 Kumamoto, Japan, earthquake","authors":"Tomoaki Nishino","doi":"10.1007/s44150-024-00113-3","DOIUrl":null,"url":null,"abstract":"<div><p>Seismic damage to building services systems, that is, mechanical, electrical, and plumbing systems in buildings related to energy and indoor environments, affects the functionality of buildings. Assessing post-earthquake functionality is useful for enhancing the seismic resilience of buildings via improved design. Such assessments require a model for predicting the time required to restore building services. This study analyzes the downtime data for 250 instances of damage to building services components caused by the 2016 Kumamoto earthquake in Japan, presumably obtained from buildings with minor or no structural damage. The objectives of this study are (1) to determine the empirical downtime distribution of building services components and (2) to assess the dependence of the downtime on explanatory variables. A survival analysis, which is a statistical technique for analyzing time-to-event data, reveals that (1) the median downtime of building services components was 90 days and, 7 months after the earthquake, the empirical non-restoration probability was approximately 32%, (2) the services type and the building use are explanatory variables having a statistically significant effect on the downtime of building services components, (3) the log-logistic regression model reasonably captures the trend of the restoration of building services components, (4) medical and welfare facilities and hotels restored building services components relatively quickly, and (5) the 7-month restoration probability was observed to be highest for electrical systems, followed by sanitary systems, then heating, ventilation, and air conditioning systems, and finally life safety systems. These results provide useful information to support the resilience-based seismic design of buildings.</p></div>","PeriodicalId":100117,"journal":{"name":"Architecture, Structures and Construction","volume":"4 2-4","pages":"227 - 240"},"PeriodicalIF":0.0000,"publicationDate":"2024-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s44150-024-00113-3.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Architecture, Structures and Construction","FirstCategoryId":"1085","ListUrlMain":"https://link.springer.com/article/10.1007/s44150-024-00113-3","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Seismic damage to building services systems, that is, mechanical, electrical, and plumbing systems in buildings related to energy and indoor environments, affects the functionality of buildings. Assessing post-earthquake functionality is useful for enhancing the seismic resilience of buildings via improved design. Such assessments require a model for predicting the time required to restore building services. This study analyzes the downtime data for 250 instances of damage to building services components caused by the 2016 Kumamoto earthquake in Japan, presumably obtained from buildings with minor or no structural damage. The objectives of this study are (1) to determine the empirical downtime distribution of building services components and (2) to assess the dependence of the downtime on explanatory variables. A survival analysis, which is a statistical technique for analyzing time-to-event data, reveals that (1) the median downtime of building services components was 90 days and, 7 months after the earthquake, the empirical non-restoration probability was approximately 32%, (2) the services type and the building use are explanatory variables having a statistically significant effect on the downtime of building services components, (3) the log-logistic regression model reasonably captures the trend of the restoration of building services components, (4) medical and welfare facilities and hotels restored building services components relatively quickly, and (5) the 7-month restoration probability was observed to be highest for electrical systems, followed by sanitary systems, then heating, ventilation, and air conditioning systems, and finally life safety systems. These results provide useful information to support the resilience-based seismic design of buildings.

震后建筑服务停工时间分布:2016 年日本熊本地震案例研究
地震对建筑物服务系统(即建筑物中与能源和室内环境有关的机械、电气和管道系统)造成的破坏会影响建筑物的功能。评估震后功能有助于通过改进设计提高建筑物的抗震能力。此类评估需要一个模型来预测恢复建筑服务所需的时间。本研究分析了 2016 年日本熊本地震造成的 250 例建筑服务组件损坏的停机时间数据,这些数据可能来自结构损坏轻微或没有损坏的建筑物。本研究的目标是:(1)确定建筑服务组件的经验停机时间分布;(2)评估停机时间对解释变量的依赖性。生存分析是一种用于分析从时间到事件数据的统计技术,该分析表明:(1) 建筑设备部件停机时间的中位数为 90 天,地震发生 7 个月后,无法修复的概率约为 32%;(2) 服务类型和建筑用途是对建筑设备部件停机时间有显著影响的解释变量、(3) 对数-逻辑回归模型合理地反映了建筑服务组件的恢复趋势, (4) 医疗和福利设施以及酒店的建筑服务组件恢复相对较快, (5) 7 个月恢复概率最高的是电气系统,其次是卫生系统,然后是供暖、通风和空调系统,最后是生命安全系统。这些结果为基于抗震能力的建筑设计提供了有用的信息。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信