数据驱动的高速铁路系统大风风险评估策略

IF 2.4 3区 工程技术 Q3 TRANSPORTATION
Guanyuan Zhao, Xiaoping Ma, Xuying Qiu, Hanqing Zhang, Zhiping Zhang
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引用次数: 0

摘要

摘要大风引起的铁路事故确实受多种因素的影响,是一个复杂的过程。然而,目前的研究和监测系统往往只关注风速,而忽略了其他因素的综合影响。本文提出了一种专门针对高速铁路的数据驱动评估策略。灾害的诱发因素、孕灾环境、承灾体、防灾减灾能力等都要考虑。此外,本文还探讨了这些因素之间的相互关系。为了验证上述机理,本文对中国高速铁路沿线大风风险的时空分布进行了研究。通过分析和解释这些数据,研究人员能够确定特别容易发生大风引发事故的地区和时间段。这些发现对于在高速铁路的背景下制定有效的防灾减灾战略至关重要。关键词:高铁灾害风险评估铁路安全披露声明作者未发现潜在利益冲突。国家自然科学基金(No. 61903023);轨道交通控制与安全国家重点实验室(No. 61903023)资助;RCS2022ZZ002)和中央高校基本科研业务费专项资金(2022JBXT009)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Data-driven gale-induced risk assessment strategy for the high-speed railway system
AbstractRailway accidents caused by gales are indeed influenced by multiple factors, making them a complex process. However, current research and monitoring systems often focus solely on wind speed, overlooking the combined effects of other factors. This paper proposes a data-driven assessment strategy specifically designed for high-speed railways. The disaster-inducing factor, disaster-pregnant environment, disaster-bearing body, and disaster prevention/mitigation capabilities are all taken into consideration. Moreover, it explores the interrelationships between these factors. To validate the proposed mechanism, the spatial-temporal distribution of gale-induced risks along China’s high-speed railways is studied in this paper. By analyzing and interpreting the data, the researchers are able to identify areas and time periods that are particularly prone to gale-induced accidents. These findings are crucial for the development of effective strategies for disaster prevention and mitigation in the context of high-speed railways.Keywords: high-speed railgale disasterrisk assessmentrailway safety Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work was supported by the National Natural Science Foundation of China (No. 61903023), State Key Laboratory of Rail Traffic Control and Safety (No. RCS2022ZZ002), and the Fundamental Research Funds for the Central Universities (No. 2022JBXT009).
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来源期刊
CiteScore
6.00
自引率
15.40%
发文量
38
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