Modeling pre-evacuation delay by evacuees in World Trade Center Towers 1 and 2 on September 11, 2001: A revisit using regression analysis

We have tested a linear regression model to identify significant predictors of pre-evacuation delay in a sample of evacuees enrolled in the World Trade Center Evacuation Study. We have found that pre-evacuation delay was greater when there were more environmental cues, more seeking out of information, and more pre-evacuation actions. Additionally, higher perceived risk was predictive of shorter pre-evacuation delay times. These findings are compared and contrasted with an analysis of participants in the National Institute of Standards and Technology investigation of the World Trade Center disaster, recently reported by Kuligowski and Mileti (2009). Both studies reported factors associated with pre-evacuation delay that were similar to those associated with community evacuation. Additionally, we found that greater knowledge and greater emergency preparedness were associated with greater perception of risk. Greater emergency preparedness was negatively related to pre-evacuation delay within World Trade Center Tower I, but within World Trade Center Tower II, the relation between emergency preparedness and pre-evacuation delay was positive. These findings have implications for training of occupants of high-rise buildings.     

Predicting Human Behavior During Fires

Evacuation models, including engineering hand calculations and computational tools, are used to calculate the time it takes to evacuate a building, which can then be used in an engineering safety analysis. However, there is a lack of available data and theory on occupant behavior for use by evacuation models to estimate evacuation time results and their uncertainty. In lieu of data and theory, evacuation models (and users) make assumptions and simplifications about occupant behavior, which can inappropriately characterize the time it actually takes to evacuate a building. The purpose of this paper is to reevaluate current egress modeling techniques and advocate for the inclusion of a robust, comprehensive, and validated conceptual model of occupant behavior during building fires. This paper begins by describing the current state of evacuation modeling of human behavior in fires and identifying gaps in current behavioral prediction techniques. The second part of the paper outlines a model of occupant decision-making during emergencies, referred to as the protective action decision model (PADM); a theory that can serve as the basis for the development of a conceptual model of occupant decision-making and behavior during the pre-evacuation period of building fires. The PADM provides a framework that describes the decision-making steps that influence protective actions taken in response to natural and technological disasters—including perceiving information, paying attention to the information, comprehending the information, establishing the nature of the threat, personalizing the risk, searching for potential protective actions and choosing one of these, and then performing that action. The paper ends with a discussion of how to adapt and expand the PADM in order to develop a predictive conceptual model of the pre-evacuation period for use by computer evacuation models.     

The quantitative investigation on people's pre-evacuation behavior under fire

With the growth in urbanization process and activities in Hong Kong and many large cities in China, a great number of super high-rise buildings have been constructed in these years. The occurrences of many large fire tragedies, especially the US 9/11 terrorist attack, made people aware that super high-rise buildings may cause serious fatalities, and extremely they could collapse in a huge uncontrolled fire. Compared with people's evacuation behavior, little interests have been drawn to pre-movement behavior. In Hong Kong and some major cities in China, over 90% of people are living in multi-storey multi-compartment buildings. Their awareness and responses to fire incidents happening in the other parts of the same building have substantial influence on the whole evacuation process. Studies on pre-evacuation human behavior have been performed for many years, but the vast majority of the studies were qualitative-oriented. Accordingly, an attempt was made in this article to quantitatively investigate people's pre-evacuation behavior by using the Support Vector Machine (SVM) approach, which was trained by Hong Kong's post-fire field survey data.     

基于BIM技术的建筑火灾模拟人员疏散分析

如今高层建筑层出不穷,但也带来了诸多的安全隐患,例如人员的密集性,消防设施的老化等问题,都增大了火灾隐患,一旦疏忽发生火灾,都会造成不可估量的损失。同时建筑内部的复杂性,也为人员疏散造成了极大困难,所以了解建筑火灾危害,提前建立合理的疏散通道尤为重要。本文以天津大学高科技楼为研究对象,基于BIM技术与Pyrosim相结合,采用数值模拟与可视化分析的研究方法,建立火灾场景模型,添加所需的烟雾探测器、温度场及能见度切片视图等信息,对该建筑进行综合分析,得出火灾发生时烟气浓度、温度、CO含量和能见度对人员疏散的影响及合理的疏散时间和疏散通道,确定BIM技术在高层建筑火灾模型中的使用价值,为火灾现场应急指挥、人员疏散提供重要指导意义。     

高层建筑消防安全现状分析和火灾防控措施

近年来,我国经济水平大幅度提高,城市化进程日益加快,高层建筑日益增多,使我国消防部门不得不面对日益严峻的高层建筑火灾形势。提高高层建筑防火能力成为当前消防工作的主要课题。为了消除高层建筑内部所存在的火灾隐患,文章从当前高层建筑消防安全问题现状出发进行深入研究,并结合当前高层建筑火灾发生特点,为提高高层建筑防火管理工作水平、改变严峻的消防安全形势提出相关建议。笔者将对当前国家消防安全政策、高层建筑设计规范、科学使用消防设施和后期消防安全管理提出针对性建议,并从整体工作布局、疏散消防通道、自动报警系统等诸多方面出发,提出参考意见。     

高层建筑电梯楼梯协同疏散的仿真实验研究

近年来高层建筑逐渐增多,但高层建筑普遍存在逃生线路长,火灾严重性大的问题。为了缩短高层建筑在消防疏散中的疏散时间,降低高层建筑火灾发生后的事故严重程度,利用疏散软件Pathfinder分析了高层建筑中使用电梯楼梯协同疏散的可行性。结果表明:高层建筑电梯可以作为协同疏散的辅助工具,增加一部电梯比单纯增加电梯最大运行速度要更加有效;设置每层电梯都使用总人数的固定比例进行疏散的时候,楼梯资源不易被充分利用;当设置n层及n层以上每层总人数的不同比例使用电梯进行向下疏散,存在最佳疏散楼层N11及最佳疏散比例20%,最后一个通过电梯疏散完毕与最后一个通过楼梯疏散完毕的人员仅相差2.7s,此方法可以为相关高层建筑火灾疏散策略的决策提供一定的参考依据。实验结果说明了电梯疏散的教育和培训很重要,在高层建筑中合理使用电梯进行疏散可以很大程度减少总疏散时长。     

关于现代高层建筑防火及疏散设计问题的探究

本文从高层建筑火灾事故的发生特征着手,分析了高层建筑在设计环节存在的不足,并就当前高层建筑的防火及疏散状况加以分析。希望在满足国家政策的基础上,实现高层建筑防火与疏散设计的进一步优化。     

高层建筑现状分析与其火灾预防对策探讨

针对目前高层建筑存在的各种隐患和问题,从高层建筑存在的火灾特点和火灾危险性入手,分析讨论了制约高层建筑消防安全的因素,提出了高层建筑火灾的防控对策,以期指导实践,从源头上预防火灾。     

火灾中人员反应时间的分布对疏散时间影响的研究

建筑物火灾中人员的疏散时间主要包括反应时间（Pre-evacuation time）和在通道上的疏散时间（travel time）。利用国外最新的研究成果，对人员反应时闻的分布如何影响在通道上的的疏散时间和总的疏散时间进行了研究。结果表明：当反应时问很短时，行走（travelling）和排队等待（queuing）效应控制着整个疏散时间；当预反应时间较长时，行走和排队等待效应不重要了，而反应时间起主要作用。讨论了在人员高密度的条件下疏散模拟的结果．     

高层建筑的消防灭火救援有效措施研究

经济的飞速发展,使得建筑行业呈现出快速发展的势头,高层建筑的数量日渐增多,与之相关的消防火灾救援问题备受瞩目.文章重点概述了高层建筑火灾的具体成因,同时结合对高层建筑火灾基本特点详细的判断,对高层建筑消防灭火救援的难点重点进行了阐述,依照要点深入研究,提供建议和参考意见,希望对高层建筑消防灭火工作有所帮助.     

高层建筑消防隐患与防火监督工作探究

在我国现代化城市发展的进程中,加强高层建筑消防隐患和防火监督非常重要。在实际工作中,需要相关管理人员加强对高层建筑本身特点的了解和认识,融入先进的技术手段以及设备提升防火监督的效果以及水平,还要明确消防工作的核心任务,以维护高层建筑安全为主,更加高效地完成本职工作,从而使得安全隐患能够得以有效地消除,为人们日常居住和使用提供安全的空间。     

试论高层建筑消防安全疏散设计中存在的问题及对策

随着我国城市化进程的加快,高层建筑随之增加,安全问题也日益严重。高层建筑的特点给发生火灾时的安全疏散带来了不小的问题,因此必须加以重视。文章从高层建筑火灾的特点和消防安全疏散设计的重要性出发,分析了设计中存在的问题,以此为基础研究解决的对策。     

应急避难区对高层建筑安全疏散的作用初探

通过对建筑安全疏散现状以及建筑结构设计问题的分析,表明在高层建筑内设置应急避难区,对人员的安全疏散有着重要作用。本文就中高层建筑建设中存在的安全问题进行了分析,总结了应急避难区在高层建筑内设置的重要性。     

高层建筑消防隐患分析与防火监督策略研究

文章通过文献资料法进行分析,得出相应的结论:高层建筑火灾具有可燃物多、蔓延速度快、人员疏散困难等特点,存在消防设计与施工不规范、消防安全管理意识薄弱、消防安全检查工作落实不到位等消防隐患,需要围绕消防设计与施工管理、消防安全教育、消防安全检查制度、消防远程监管系统等方面工作,做好高层建筑防火监督工作。     

超高层城市综合体建筑消防设计分析

对城市综合体建筑消防设计进行了论述,从高层商业建筑、超高层办公建筑的人流疏散和首层、中庭消防处理等方面进行了分析,探讨了综合体中多种使用功能的集约问题及其适用性与经济性的关系,以供参考。     

Reconstruction of the Fires and Thermal Environment in World Trade Center Buildings 1, 2, and 7

The National Institute of Standards and Technology (NIST) conducted an extensive investigation of the collapse of the three tall World Trade Center (WTC) buildings. A central part of this investigation was the reconstruction and understanding of the initiation and spread of the fires. This paper describes the reconstruction of the fires, the thermal environment they created within the buildings, and the raising of the temperatures of the structural components. NIST analyzed thousands of documents, interviews, photographs, and videos to obtain information on the layout of the floors and the progress of the fires. Experiments provided information on the factors likely to have determined the fire growth. Simulations using the Fire Dynamics Simulator gave good agreement with the fire spread as observed at the windows. Imposition of the probable thermal environment on the structural steel produced maps of the probable temperature profile of the steel as the fires progressed. For WTC 1 and WTC 2, even in the vicinity of the fires, it was unlikely that the columns and floor trusses with intact insulation heated to temperatures where significant loss of strength occurred. This was in part due to the short time between aircraft impact and building collapse. There were regions of the towers in which the loss of structural strength of floors and columns, whose insulation had been damaged by aircraft impact, was likely. For WTC 7, even though the insulation was intact, the long periods of heating resulted in floor components whose temperatures exceeded 600°C, but columns did not exceed 300°C.     

浅析影响高层建筑的消防安全因素

本文针对现代高层建筑的实际问题,结合消防工作者的经验,简单介绍了高层建筑火灾的特点,简要分析了影响高层建筑的消防安全因素,提出了解决消防安全问题的相关对策。     

高层建筑消防隐患及防火监督对策研究

随着人们的生活水平和物质要求的不断提高,高层建筑中的可燃物数量也在不断地增加,这在一定程度上增加了高层建筑出现火灾的几率.在实际工作中需要加强对高层建筑消防隐患的全方位检查及研究,制定与之匹配的防火监督对策,减少高层建筑火灾发生的几率,从而推动我国建筑行业的持续性发展,为人们营造舒适、安心的居住环境.     

An Artificial Neural-network Based Predictive Model for Pre-evacuation Human Response in Domestic Building Fire

The post-1993 WTC attack study (Proulx and Fahy, In: Proceedings of ASIAFLAM’95—An International Conference on Fire Science and Engineering, Hong Kong, 1995, pp 199–210) revealed that occupants took 1–3 h to leave the 110-storey buildings, and the pre-movement reactions could account for over two-thirds of the overall evacuation time. This indicates that a thorough understanding of the pre-evacuation behavioral response of people under fire situations is of prime importance to fire safety design in buildings, especially for complex and ultra high-rise buildings. In view of the stochastic (the positions of the occupants) and fuzzy (uncertainty) nature of human behavior (Fraser-Mitchell, Fire Mater 23:349–355, 1999), conventional linear and polynomial predictive methods may not satisfactorily predict the people’s response. An alternative approach, Adaptive Network based Fuzzy Inference System (ANFIS), is proposed to predict the pre-evacuation behavior of peoples, which is an artificial neural network (ANN) based predictive model and integrates fuzzy logic (if-then rules) and neural network (based on back propagation learning procedures The ANFIS learning architecture can be trained by structured human behavioral data, and different fuzzy human decision rules. The applicability in simulating human behavior in fire is worth exploring.