Event-driven modeling of elevator assisted evacuation in ultra high-rise buildings

In the past few decades, numerous ultra high-rise buildings have been erected in the metropolitan areas of many cities around the world. For the related building designers, building occupants and the governments, fire safety problem is certainly a major concern, especially after the collapse of World Trade Center in the 9/11 event. That disaster makes people reconsider ultra high-rise building evacuation strategies. Of the current strategies, using elevators in ultra high-rise buildings to assist evacuation seems to be promising in improving evacuation efficiency. To quantitatively evaluate elevator assisted evacuation process, an event-driven agent-based modeling approach is proposed in the present paper. This modeling approach could capture not only the movement characteristics of stair-using occupants but also the detailed elevator motion features. The combined effects of occupants’ and elevators’ parameters on the evacuation efficiency have been investigated. Results indicated that the model is helpful to reveal the dynamics of elevator assisted evacuation, and sometime, using elevators to move all occupants to ground safety point may not be an optimal solution.     

Behavioral,data-driven,agent-based evacuation simulation for building safety design using machine learning and discrete choice models

To improve occupant safety during building emergencies, evacuation simulations have been widely used for building safety design. Since occupant behavior is a determining factor for the outcome of building emergencies, accurately capturing how occupants make decisions and integrating occupants’ decision-making processes in evacuation simulations is important. In this study, based on the results of fire evacuation experiments in a virtual metro station, how different social (crowd flow) and environmental (visual access and vertical movement) factors would affect individuals’ wayfinding behavior was predicted using machine learning and discrete choice models. The trained models were further employed in agent-based evacuation simulations to examine crowd evacuation performance under different building design scenarios. Both the machine learning and discrete choice models could accurately predict individuals’ directional choices during emergency evacuations. Different building attributes could collectively influence occupant behavior, leading to distinct exit choices and evacuation times. While both the trained machine learning and discrete choice models generated similar results, the discrete choice model had better interpretability. Moreover, by comparing the trained models in this study with a model developed in a prior study, it was found that agents had significantly distinct responses to different building designs. Critical factors (e.g., type and size of buildings, occupants’ familiarity with the building) for the applicability of evacuation models were identified. Furthermore, recommendations were provided for future research that aims at employing evacuation simulations for building design evaluation and optimization.     

Prototyping virtual reality serious games for building earthquake preparedness: The Auckland City Hospital case study

Enhancing evacuee safety is a key factor in reducing the number of injuries and deaths that result from earthquakes. One way this can be achieved is by training occupants. Virtual Reality (VR) and Serious Games (SGs), represent novel techniques that may overcome the limitations of traditional training approaches. VR and SGs have been examined in the fire emergency context; however, their application to earthquake preparedness has not yet been extensively examined.We provide a theoretical discussion of the advantages and limitations of using VR SGs to investigate how building occupants behave during earthquake evacuations and to train building occupants to cope with such emergencies. We explore key design components for developing a VR SG framework: (a) what features constitute an earthquake event; (b) which building types can be selected and represented within the VR environment; (c) how damage to the building can be determined and represented; (d) how non-player characters (NPC) can be designed; and (e) what level of interaction there can be between NPC and the human participants. We illustrate the above by presenting the Auckland City Hospital, New Zealand as a case study, and propose a possible VR SG training tool to enhance earthquake preparedness in public buildings.     

Genetic algorithms optimized fuzzy controller for the indoor environmental management in buildings implemented using PLC and local operating networks

In this paper, an optimized fuzzy controller is presented for the control of the environmental parameters at the building zone level. The occupants’ preferences are monitored via a smart card unit. Genetic algorithm optimization techniques are applied to shift properly the membership functions of the fuzzy controller in order to satisfy the occupants’ preferences while minimizing energy consumption. The implementation of the system integrates a smart card unit, sensors, actuators, interfaces, a programmable logic controller (PLC), local operating network (LON) modules and devices, and a central PC which monitors the performance of the system. The communication of the PLC with the smart card unit is performed using an RS 485 port, while the PLC-PC communication is performed via the LON network. The integrated system is installed and tested in the building of the Laboratory of Electronics of the Technical University of Crete.     

Using footstep-induced vibrations for occupant detection and recognition in buildings

Occupant detection and recognition support functional goals such as security, healthcare, and energy management in buildings. Typical sensing approaches, such as smartphones and cameras, undermine the privacy of building occupants and inherently affect their behavior. To overcome these drawbacks, a non-intrusive technique using floor-vibration measurements, induced by human footsteps, is outlined. Detection of human-footstep impacts is an essential step to estimate the number of occupants, recognize their identities and provide an estimate of their probable locations. Detecting the presence of occupants on a floor is challenging due to ambient noise that may mask footstep-induced floor vibrations. Also, signals from multiple occupants walking simultaneously overlap, which may lead to inaccurate event separation. Signals corresponding to events, once extracted, can be used to identify the number of occupants and their locations. Spurious events such as door closing, chair dragging and falling objects may produce vibrations similar to footstep-impacts. Signals from such spurious events have to be discarded as outliers to prevent inaccurate interpretations of floor vibrations for occupant detection. Walking styles differ among occupants due to their anatomies, walking speed, shoe type, health and mood. Thus, footstep-impact vibrations from the same person may vary significantly, which adds uncertainty and complicates occupant recognition. In this paper, efficient strategies for event-detection and event-signal extraction have been described. These strategies are based on variations in standard deviations over time of measured signals (using a moving window) that have been filtered to contain only low-frequency components. Methods described in this paper for event detection and event-signal extraction perform better than existing threshold-based methods (fewer false positives and false negatives). Support vector machine classifiers are used successfully to distinguish footsteps from other events and to determine the number of occupants on a floor. Convolutional neural networks help recognize the identity of occupants using footstep-induced floor vibrations. The utility of these strategies for footstep-event detection, occupant counting, and recognition is validated successfully using two full-scale case studies.     

Crowd evacuation simulation using active route choice model based on human characteristics

Modern buildings have become larger in scale and function, and the complexity has also increased considerably. For these reasons, there are more difficulties in evacuation and rescue when an emergency occurs, so effective evacuation methods and risk should be predicted and applied to building design, safety training, and education. We have developed an active route choice model based on human body organs and characteristics that detects risks and route conditions, communicates with neighboring occupants, determines the bottleneck point, and selects evacuation routes according to each occupant's personal characteristics. In this study, we introduce the implementation process and characteristics of the active route choice model, and by applying the model to the occupants, we compared the evacuation times according to the route condition, number of occupants, and corridor width in a virtual environment. We believe that realistic and valid results can be obtained by applying the active route choice model in crowd evacuation simulation.     

一种面向建筑节能的强化学习自适应控制方法

针对建筑节能领域中传统控制方法对于建筑物相关设备控制存在收敛速度慢、不稳定等问题,结合强化学习中经典的Q学习方法,提出一种强化学习自适应控制方法--RLAC。该方法通过对建筑物内能耗交换机制进行建模,结合Q学习方法,求解最优值函数,进一步得出最优控制策略,确保在不降低建筑物人体舒适度的情况下,达到建筑节能的目的。将所提出的RLAC与On/Off以及Fuzzy-PD方法用于模拟建筑物能耗问题进行对比实验,实验结果表明,RLAC具有较快的收敛速度以及较好的收敛精度。     

Understanding building occupant activities at scale: An integrated knowledge-based and data-driven approach

Buildings are our homes and our workplaces. They directly affect our well-being, and they impact the natural global environment primarily through the energy they consume. Understanding the behavior of occupants in buildings has vital implications for improving the energy efficiency of building systems and for providing knowledge to designers about how occupants will utilize the spaces they create. However, current methods for inferring building occupant activity patterns are limited in two primary areas: First, they lack adaptability to new spaces and scalability to larger spaces due to the time and cost intensity of collecting ground truth data for training the embedded algorithms. Second, they do not incorporate explicit knowledge about occupant dynamics in their implementation, limiting their ability to uncover deep insights about activity patterns in the data. In this paper, we develop a methodology for classifying occupant activity patterns from plug load sensor data at the desk level. Our method makes us of a common unsupervised learning algorithm—the Gaussian mixture model—and, in addition, it incorporates explicit knowledge about occupant presence and absence in order to preserve adaptability and effectiveness. We validate our method using a pilot study in an academic office building and demonstrate its potential for scalability through a case study of an open-office building in San Francisco, CA. Our method offers key insights into spatially and temporally granular occupancy states and space utilization that could not otherwise be obtained.     

Towards a customizable immersive virtual reality serious game for earthquake emergency training

Earthquake emergencies require a variety of behavioral responses in order to ensure the safety of occupants, which is different from simply exiting a building in fire emergencies. This makes it more complex to train building occupants in order to acquire skills that align to best practices for immediate earthquake response and post-earthquake evacuation. In recent years, Immersive Virtual Reality (IVR) and Serious Games (SGs) have become popular as training tools for earthquake emergencies. IVR SGs have been introduced to train individuals for specific building layouts or settings with fixed training objectives. However, the lack of flexibility in existing IVR SGs makes it challenging to have widespread uptake as trainees require different training objectives, pedagogical strategies, context, and content. As a result, the effectiveness of IVR SGs training is jeopardized if the customization ability is limited. To overcome this limitation, this paper presents a customization framework for IVR SGs suited to earthquake emergency training, using the concept of adaptive game-based learning. Trainees can receive training in context by customizing virtual environments, storylines, and teaching methods. A case study was undertaken to validate the proposed framework. Results showed the potential to carry out the customization process with ease, to generate a customized training experience, and to deliver the customized training for optimum learning.     

Singular Perturbation Method for Smart Building Temperature Control Using Occupant Feedback

In this work we propose a framework for incorporating occupant feedback towards temperature control of multi‐occupant spaces, and analyze it using singular perturbation theory. Such a system would typically have to accommodate occupants with different temperature preferences, and incorporate that with thermal correlation among multiple zones to obtain optimal control for minimization of occupant discomfort and energy cost. In current practice, an acceptable temperature set point for the occupancy level of the zone is estimated, and the control law is designed to maintain temperature at the corresponding set point irrespective of the changes in occupancy and the preferences of multiple occupants. Proposed algorithm incorporates active occupant feedback to minimize aggregate user discomfort and total energy cost. Occupant binary feedback in the form of hot/cold or thermal comfort preference input is used by the control algorithm. The control algorithm also takes the energy cost into account, trading it off optimally with the aggregate occupant discomfort. For convergence to the optimal, sufficient separation between the occupant feedback frequency and the temperature dynamics of system is necessary; in absence of which, the occupant feedback provided do not correctly reflect the effect of current control input value on occupant discomfort. Under sufficient time scale separation, using singular perturbation theory, we establish the stability condition of the system and show convergence of the proposed solution to the desired temperature that minimizes energy cost plus occupant discomfort. The occupants are only assumed to be rational in that they choose their own comfort range to minimize individual thermal discomfort. Optimization for a multi‐zone building also takes into account the thermal correlation among different zones. Simulation study with parameters based on our test facility, and experimental study conducted in the same building demonstrates performance of the algorithm under different occupancy and ambient conditions.     

Axiomatic considerations of a rule based mechanism for the determination of a building’s egress capability

When a building is designed it possesses a spatial layout of routes and passageways all of which contribute to the building’s egress capability—a key consideration in the context of health and safety for occupants. In this paper we examine how such a spatial layout can be interpreted as a vital system of egress, which if processed exhaustively by a naïve agent will identify with a measure of egress complexity. We further examine how the established rule-based mechanism for the determination of egress complexity satisfies the generically accepted axioms of complexity and we illustrate some of the algorithms of egress complexity with examples.     

Computer simulations vs. building guidance to enhance evacuation performance of buildings during emergency events

Computer technologies can play an important role in the establishment of dynamic building information by introducing predictive modelling where behaviours of structures or groups of people can be simulated and observed. This way they can facilitate the design of the built environment to cope with emergency events. Modelling and simulation applications can be particularly useful at pre-planning, predicting possible damage, training responders, raising public awareness, and performance evaluation for reconstruction. They can be used for the development of virtual scenarios that include aspects of rescue operations, social behaviour of building occupants, and basic design requirements to test the current building codes and regulations. Within this context, the contribution of crowd simulation to improving the design of the built environment and guidelines is highlighted in this paper. Current building guidance for emergencies are summarised and the methodology developed to use crowd modelling to define design information associated with exit preferences of people during evacuations is explained. The results of the case studies underlined that there is a difference between the assumptions used for static information in current building guidance.     

Lessons from the evacuation of the world trade centre, 9/11 2001 for the development of computer-based simulations

This paper reviews the state-of-the-art in evacuation simulations. These interactive computer based tools have been developed to help the owners and designers of large public buildings to assess the risks that occupants might face during emergency egress. The development of the Glasgow Evacuation Simulator is used to illustrate the existing generation of tools. This system uses Monte Carlo techniques to control individual and group movements during an evacuation. The end-user can interactively open and block emergency exits at any point. It is also possible to alter the priorities that individuals associate with particular exit routes. A final benefit is that the tool can derive evacuation simulations directly from existing architects, models; this reduces the cost of simulations and creates a more prominent role for these tools in the iterative development of large-scale public buildings. Empirical studies have been used to validate the GES system as a tool to support evacuation training. The development of these tools has been informed by numerous human factors studies and by recent accident investigations. For example the 2003 fire in the Station nightclub in Rhode Island illustrated the way in which most building occupants retrace their steps to an entrance even when there are alternate fire exits. The second half of the paper uses this introduction to criticise the existing state-of-the-art in evacuation simulations. These criticisms are based on a detailed study of the recent findings from the 9/11 Commission (2004). Ten different lessons are identified. Some relate to the need to better understand the role of building management and security systems in controlling egress from public buildings. Others relate to the human factors involved in coordinating distributed groups of emergency personnel who may be physically exhausted by the demands of an evacuation. Arguably, the most important findings centre on the need to model the ingress and egress of emergency personnel from these structures. The previous focus of nearly all-existing simulation tools has been on the evacuation of building occupants rather than on the safety of first responders. Thanks are due to J. Appleby, P. Cooper, A. Foss, S. Hailey and B. Jenks who were responsible for the design and implementation of the GES application. They also drove the development of the Boyd Orr evacuation scenarios that are used to illustrate the opening sections of this paper.     

Green buildings need good ergonomics

A retrospective post-occupancy evaluation survey of 44 occupants in two Leadership in Energy and Environmental Design (LEED) Platinum buildings on a US college campus is reported. The Internet survey covered a range of indoor environment and ergonomics issues. Results show that working in these buildings were a generally positive experience for their health, performance and satisfaction. However, in one building there were persistent issues of variability in air temperature, air freshness, air quality and noise that affected the perceived health and performance of the occupants. Although the buildings were energy-efficient and sustainable structures, ergonomics design issues were identified. Implications for the role of ergonomics in green buildings and in the US LEED rating system are discussed.

Practitioner Summary: This survey identified a number of ergonomics design issues present in the LEED Platinum energy-efficient and sustainable buildings that were studied. These results highlight the importance of integrating ergonomics design into green buildings as a component in the US LEED rating system.     

WatchBot: A building maintenance and surveillance system based on autonomous robots

There is a growing interest in the use of intelligent technologies in new buildings. An intelligent system should be designed in a manner that allows minimum human intervention during daily operation. However, large buildings need substantial manpower for maintenance, management as well as surveillance to ensure a quality environment for the occupants. This paper describes a multi-robot system for building maintenance and surveillance applications over the Internet. Each robot can handle autonomously some daily maintenance and surveillance routine tasks, although remote control of the robots via the Internet or intranet is also possible. Apart from the user-started and scheduled tasks, the robots can also execute tasks to handle alarms triggered by the building automation system (BAS). Robots are connected to the central management office via a local area network. This paper shows the advantages of using mobile robots for building maintenance and surveillance tasks by improving efficiency and reducing manpower. The system was developed with the Robotics Integrated Development Environment (RIDE) and was tested intensively in different environments.     

Leveraging existing occupancy-related data for optimal control of commercial office buildings: A review

A primary strategy for the energy-efficient operation of commercial office buildings is to deliver building services, including lighting, heating, ventilating, and air conditioning (HVAC), only when and where they are needed, in the amount that they are needed. Since such building services are usually delivered to provide occupants with satisfactory indoor conditions, it is important to accurately determine the occupancy of building spaces in real time as an input to optimal control. This paper first discusses the concepts of building occupancy resolution and accuracy and briefly reviews conventional (explicit) occupancy detection approaches. The focus of this paper is to review and classify emerging, potentially low-cost approaches to leveraging existing data streams that may be related to occupancy, usually referred to as implicit/ambient/soft sensing approaches. Based on a review and a comparison of related projects/systems (in terms of occupancy sensing type, resolution, accuracy, ground truth data collection method, demonstration scale, data fusion and control strategies) the paper presents the state-of-the-art of leveraging existing occupancy-related data for optimal control of commercial office buildings. It also briefly discusses technology trends, challenges, and future research directions.     

Evaluating interface designs for user-system interaction media in buildings

Occupant control actions in a building (i.e., user interactions with environmental systems for heating, cooling, ventilation, lighting, etc.) can significantly affect both indoor climate in and the environmental performance of buildings. Nonetheless, relatively few systematic (long-term and high-resolution) efforts have been made to observe and analyze the means and patterns of such user-system interactions with building systems. Specifically, the necessary requirements for the design and testing of hardware and software systems for user-system interfaces have not been formulated in a rigorous and reliable manner. This paper includes an exploration of the requirements and functionalities of user interfaces for building systems in sentient buildings. We first compare a number of commercial user-interface products for building control systems. Thereby, we consider three dimensions (information types, control options, and hardware) and seven criteria (functional coverage, environmental information feedback, intuitiveness, mobility, network, input, and output). We then present the results of an experiment, in which 40 participants examined and evaluated a selected number of user interfaces for buildings’ control systems, mainly in view of their first impressions, user-interface layout design, and as well as ease of learning. The outcome of these studies are expected to serve as the starting point for developing a new generation of user-interface models to promote higher levels of connectivity between occupants and sentient environments.     

Modelling pedestrian merging in stair evacuation in multi-purpose buildings

Despite the total evacuation time of occupants in a multi-storey building not being affected by pedestrian merging on stairs, the calculation of evacuation times in each individual floor depends on the pedestrian merging ratio. In fact, different merging ratios may cause an evacuation to take place from the top to the bottom or vice versa. A simplified simulation model for the calculation of evacuation time at each floor is presented here. This model allows the investigation of the impact of two main variables affecting the evacuation time at each floor, namely 1) different initial numbers of pedestrians at each floor (i.e. occupant load), and 2) different merging ratios at each floor. This means that the model allows the calculation of the evacuation time at each floor considering a building with different occupancy types at different floors (e.g. office, residential, commercial, etc.) and different merging ratios which may be caused by a different configuration of the landing door at each floor. The model is presented in this paper using a case study of a hypothetical building. A detailed discussion on the model assumptions, advantages and limitations is also provided.     

Modeling evacuation dynamics on stairs by an extended optimal steps model

With more and more high-rise building springing into the cities, the movement of pedestrians on stairs is of great importance for the evacuation of these facilities; the topic of human performance on stairs has attracted a lot of researcher to study. However, there is still a lack of analysis of specific situations, such as mid-landing and merging behaviors. In this paper we employ a modified version of the Optimal Steps Model, modified by taking block-based floor field into consideration under open boundary conditions, to reproduce movement at mid-landing and study performance of occupants in stairwell. Movements on this kind of stairs are simulated and studied with the help of extended model: achieved results are in accord with previous research in respect of specific flow and fundamental diagram. Lane formations on stairs come into being before mid-landing area through simulations. Furthermore most people reach at the mid-landing by the outside stairs at higher density due to more space and less force from other agents, which is induced that outside of the stairs is good for evacuation. At last the model is implemented to study the effect of stairs geometry on merging behaviors, and it is found when corridor is connected to the landing opposite to the incoming stairs, this structure is biased in favor of occupants from stairs. The work in this paper is intended to better understand movement during stair evacuations and develop a technical foundation for codes and standards requirements as well as egress modeling techniques.     

不确定环境下智能大厦空调系统调度策略评估

近年来,智能大厦的概念在国内外受到了高度的关注.相比于传统的建筑,智能大厦更加节能、舒适、易维护,已成为未来建筑的发展趋势.作为智能大厦空调通风系统的关键部分,空调系统及其调度策略决定了大厦整体的节能效果以及大厦中用户的舒适度.然而由于智能大厦所处的环境具有许多不确定因素,这极大增加了空调系统调度策略设计与评估的复杂程度.因此如何设计与评估不确定环境下空调系统的调度策略成为了智能大厦设计者面临的一大挑战.已有的方法主要针对智能大厦空调系统进行能耗与性能等方面的分析,但尚未有方法针对调度策略本身进行分析与评估.提出了一种基于价格时间自动机的调度策略评估框架,支持对不确定环境下的智能大厦进行精确建模与定量评估.该框架使用UPPAAL-SMC作为属性查询引擎对模型进行随机模拟运行,根据模拟结果对不同调度策略下大厦的能耗及用户的舒适度进行定量分析.实验结果表明,该方法能有效地帮助设计者进行策略的制定和选取.