A BIM-based simulation framework for fire safety management and investigation of the critical factors affecting human evacuation performance

Abstract

Fire hazards are a big threat to human life and property safety. The U.S. fire statistics reveal that, in 2017 alone, 1,319,500 fires caused 3400 deaths and 14,670 injuries, which resulted in a loss of $23 billion [1]. Effective evacuation planning in densely occupied buildings should be primarily put in place if both the number of injuries/fatalities and the level of property loss are to be minimized. However, it is not realistic, and is unethical to study human evacuation performance under a burning building. For this reason, computational tools tend to be the best approach for simulating fire growth as well as human response to fire hazards. This study aims to develop a BIM-based simulation framework that implements the Fire Dynamic Simulator (FDS) and agent-based modeling (ABM) for simulating fire growth and evacuation performance for different building layout scenarios. An experimental implementation is conducted to validate the proposed framework, which verified the benefits of (1) using BIM to offer a platform for conducting simulation design and visualizing the simulation results of (a) hazardous fire zones and (b) effective escape routes; (2) simulating fire growth using the FDS tool; (3) developing an agent-based model that accounts for the critical factors affecting evacuation performance; and (4) applying a statistical analysis for investigating the effects of influential parameters from the proposed model. As a result, the simulation outputs can be used to optimize the building design and to investigate the influential factors on human evacuation efficiency. The proposed framework contributes to building fire safety management by enabling to minimize both injuries/fatalities and property loss.

Introduction

The U.S. Fire Administration (USFA) has reported that, over the past decade, the fatalities and injuries, as well as the level of property loss due to fire hazards have increased. In 2017 alone, there were 3400 deaths, 14,670 injuries, and a loss of $23 billion in property caused by 1,319,500 fires [1], which indicate an urgent need for improvement in current fire safety management practices. In addition, different from fires in other types of buildings, densely occupied buildings have a number of unique features, such as high-density pedestrian flows, rapid oxygen consumption, limited space for movement, and fixed exits. For this reason, effective evacuation planning in densely occupied buildings should be primarily put in place if both the number of injuries and fatalities and the level of property loss are to be minimized. Thus, appropriate building design is key to improving evacuation efficiency as well as maximizing the usable space within a building. However, it is unethical and not realistic to study human evacuation performance during a fire. Therefore, computational tools are widely accepted as the best approach for simulating fire growth and human response to fire hazards. The simulation results can be used to predict fire safety performance and to assist in fire evacuation planning.

To date, advanced computational modeling concepts, such as building information modeling (BIM), Fire Dynamics Simulator (FDS), and agent-based modeling (ABM), have all been used to simulate the fire safety performance of buildings. Given the variety of modeling tools that are available on the market, it is arguably fortunate to take advantage of their respective strengths and implement them in fire safety management.

BIM is an advanced 3D modeling-based process that can be implemented to support the decision-making process with regard to a building or other built asset. Young et al. [2] reported that BIM have contributed to lean construction since mid-2000s. In fact, it has helped contractors to maximize project efficiency while also reducing the overall risks associated with a given project. As building fires are directly related to both casualties and building property losses, BIM implementation by design professionals have increased tremendously within the last decade due to the attempts to deliver better designs in terms of fire safety. Rüppel et al. [3], for example, designed a BIM-based serious game to determine human behavior during an emergency for evacuation planning. Similarly, Wang et al. [4] developed a BIM-based virtual environment to determine the evacuation flow given different building design options to optimize evacuation efficiency. In addition, Choi et al. [5] developed a BIM-based evaluation system to check the building regulations in place for fire safety assessment. Likewise, Eftekharirad et al. [6] integrated industry foundation classes (IFC) BIM and human behavior into a real-time fire emergency management system for fire safety control. Furthermore, Wang et al. [7] integrated BIM with a fire dynamics simulator to improve fire safety management practice, including evacuation assessment, egress planning, fire safety education, and fire equipment maintenance.

However, the full potential of such approaches has not yet been achieved. There are several obstacles including data interoperability as well as the technical limitations of the currently available BIM software, which cannot simultaneously model the fire-driven fluid flow and the occupant evacuation process. Therefore, this study aims to develop a comprehensive BIM-based simulation framework that is capable of implementing both FDS and ABM for fire safety management.

FDS is a computational fluid dynamics (CFD) modeling tool that is used to simulate fire-driven fluid flows. FDS software is interoperable with architectural BIM software, i.e. architectural BIM file can be input into FDS software to simulate fire growth and display the results through a visualization software known as Smokeview (SMV) [8]. To date, FDS has helped to achieve various fire simulation goals, such as post-accident investigations [9], [10], fire safety assessments of existing buildings [11], and the analysis of factors that influence fire growth [12]. In an effort to integrate FDS with evacuation simulation tools (e.g., EVAC [13]), Tingyong et al. [14] developed a continuous model of FDS + EVAC to simulate the effects of fire on evacuation performance. However, they noted the difficulty of incorporating human behavior into an evacuation scenario design.

ABM technique enables to integrate human behavior into evacuation scenario design as it is a powerful modeling technique that is capable of facilitating both agent-to-agent interactions and agent-to-environment interactions. In ABM environment, agents can be defined as any type of individual that simulates behaviors in mathematical, theoretical, and logical ways [15]; it has been therefore applied across a wide range of research areas. For example, the prior applications of ABM in relation to evacuation scenarios have covered a variety of hazards, including earthquakes [16], [17], tsunamis [18], wildfires [19], hurricanes [20], and volcanic activity [21] among others. Additionally, ABM can be applied during design, construction, or maintenance phases in order to assist in the decision-making process regarding a given building. Papadopoulos et al. [22], for example, integrated ABM with the building performance simulation (BPS) technique to simulate energy consumption and to deliver an energy system design. Similarly, Azar et al. [23], [24] implemented ABM to investigate the effects of various occupancy parameters on energy usage in buildings during their maintenance phase. Therefore, it can be concluded that it is feasible to simulate indoor occupant responses using an ABM evacuation scenario design.

To date, a number of studies have been conducted with regard to the simulation of various emergency scenarios. Tang et al. [25] and Shi et al. [26], for example, designed ABM scenarios to simulate occupant performance in public building fires, while Peizhong et al. [27] designed an ABM simulation to investigate the evacuation outcomes in a given subway station. In [28], Joo et al. developed an affordance-based model to simulate agent reactions to different exit routes. Additionally, Boguslawski et al. [29] proposed a dynamic approach using agent-based models to determine optimal exit routes in hazardous environments. Nevertheless, their studies implied the difficulties of simulating human response to disasters and predicting the evacuation performance.

In addition to the human behavior (both individual and social behavior patterns), evacuation performance depends on physical properties of buildings (e.g. building design (building layout and materials) and occupant capacity) as well as the fire characteristics (e.g. changes in temperature, toxicity, and smoke layer height) [30]. Therefore, in order to deliver moderate predictions of evacuation outcomes, the human response during an emergency, more specifically the critical factors affecting human evacuation performance, should be further investigated.

The primary aim of this paper is to present a computational simulation framework for fire safety management. To that end, BIM serves as the platform for conducting the simulation and visualizing the simulation results—more specifically, simulating fire growth via the FDS tool and accounting for the critical factors in an ABM evacuation design. In order to determine its effectiveness, the proposed framework is tested on a set of data obtained from a night club fire. The simulation outputs are used to evaluate the fire resistance level and to predict evacuation outcomes for the studied building. Additionally, in terms of the statistical analysis of the simulation outputs, a linear regression model is used to optimize the building design; as well as two-sample t- tests are conducted to evaluate the proposed framework.

The remainder of the paper is organized as follows. Section 2 provides a comprehensive review of the relevant literature. Section 3 details the simulation framework and methodology. The experimental implementation and the results are then evaluated and discussed in Section 4. The final section draws conclusions based on the results and offers recommendations for future research.