Intelligent Building Fire Safety and Smart Firefighting

Traditionally, the fire service has improved by following a model based on experience [1]. Introduced by Brunacini, this is an iterative model that describes the experience-based approach to improvement and how fire service operations evolve over time. At the core of this approach are a department’s standard operating guidelines (SOGs) which drive how activities will be performed (i.e., strategy and tactics) at an emergency. Firefighters train to department SOGs and subsequently work to execute based on their training at emergencies. Post-incident, firefighters assess their training/SOGs based on the outcome. These assessments can lead to revisions to the SOGs and corresponding training.

Emerging innovative architectural designs and new material inventions continue challenging building fire safety and calling for new design approaches. This chapter reviews the conventional approaches in building fire safety design and the latest smart design driven by Artificial Intelligence (AI) technologies. Today, fire engineers play a core while iterative role during the whole design process, including drawing, auditing, reviewing, and approving. Although computer-aided tools help conduct smoke management and evacuation analysis, the current design process is very time-consuming with many repetitive works and inevitable human errors. By training with massive data and past designs, AI can interpret the rules, code and patterns of fire safety design, so make the whole design more automatic and reliable with minimum human intervention. By lowering the overall cost of fire safety design and quickly identifying the design limit, the architectural and structural designs have more flexibility. The authority having jurisdiction can also use the AI tool to accelerate the review and approval process. AI will lead a revolution in building fire safety design and analysis achieving safer and more cost-effective solutions.

Wildfire activity across the globe has increased in frequency and intensity in recent years. Alongside this increased fire activity has been rapid population growth bringing human settlements closer to wildlands. This has resulted in the growth of the wildland urban interface (WUI) where an increased risk to people and their property from fires exists. To better plan for increased WUI fire activity, various initiatives have focused on building and planning practices that decrease the chance of ignition and increase building survivability in the case of a fire. Crucial to understanding how to best protect the built environment during a WUI fire is a thorough understanding of the causes of WUI fire ignition and spread and the factors that lead to building survivability. In this chapter, we describe key factors leading to building ignition, namely ember ignition and direct flame contact, building features shown to enhance building survivability, land management and fuel treatment practices. We use these basic principles of WUI fire behavior to propose the adoption of artificial intelligence and machine learning to tackle the WUI fire safety problem.

Fire is a vital threat to both occupants inside the building and firemen during their firefighting and rescue operation. Once a fire occurs, building environment changes rapidly, showing high temperature, toxic gas composition, low luminance and visibility. The occupants in a confined fire environment need to evacuate before reaching untenable conditions. Specific fire safety design of buildings has been required for fire evacuation over the last decades. To design a safe fire evacuation, conventional approaches rely on prescriptive codes or performance-based design. On top of that, with the booming of emerging technologies and thorough understanding of human behavior, smart design is increasingly welcome and applied to evacuation such as artificial intelligence evacuation modelling and real-time guidance systems However, few design considerations are given to firefighters who enter fire scenes and are exposed to more dangerous environment. Considering the firefighting and rescuing of trapped occupants, building fire safety design should include firefighting facilities and exclusive paths following specific codes, and corresponding safe firefighting principles for firefighters’ operation according to principles for evacuation. Similarly, smart design should be applied in firefighting and rescue including automatic firefighting facilities, intelligent early warning systems. Thus, this chapter provides an overview of the fire safety design progress for evacuation, firefighting and rescue using both conventional and smart approaches. Specifically, it introduces smart fire safety design development and discusses their perspectives and challenges.

In science, the terminology “data” is used to describe a gathered body of facts, which represents the information obtained from observing and testing experiments.

Firefighters often face uncertain conditions when entering a building to attack a fire, and have to make decisions based solely on their experience.

In combustion reactions, organic fuels (containing Carbon compounds) generally release a combination of signatures of heat, light, gases, and soot particulates.

IoT and Digital Twin are emerging cutting-edge technologies in the building and construction industry. There is a considerable amount of research has been conducted on the applications of IoT in building fire safety. However, the research and applications of Digital Twin in building fire safety are quite limited, and its definition and scope are not well-defined. This chapter first clarifies the concepts of IoT and digital twin and highlights their unique features in fire safety and firefighting. Then, it systematically reviews the studies related to Digital Twin in fire safety, including those that do not contain the keyword of “digital twin” but meet its definition. Afterwards, the framework of Fire Digital Twin is proposed, and some case studies are presented to facilitate future research and development. The enabling technologies and tools for the fire digital twin will also be introduced and discussed. Finally, we discuss the main challenges and future research areas in fire digital twin.

Structural fire safety is crucial for building design and construction. Fires in buildings can affect strength and stability of structures and cause devastating consequences on human life and property.

In the period of 2015 to 2019, US fire departments responded to an estimated average of 346,800 home structure fires per year.

A wireless sensor network was created to measure water-flow rate in a fire hose. An integrated electronic piezoelectric (IEPE) accelerometer was chosen as the sensor to measure the flow rate based on the vibrations generated by water flowing through a fire hose. The flow apparatus, including the accelerometer, was lightweight, small, and easily attached and removed to any location along the fire hose, not obstructing the water’s flow path. A relationship between the dominant-frequency metric and the flow rate was applied with a custom graphical user interface for quick, real-time, visual referencing of flow rate in a fire hose by fire personnel. The wireless flow apparatus was used with realistic firefighting hose conditions (i.e., holding the hose nozzle, nozzle motion during simulated fire suppression, simulated hose dragging) to evaluate the apparatus function and determine the influence of the hose motion, accelerometer location, and nozzle spray on the dominant-frequency metric. While more research is needed, such as enhancing the robustness of the dominant frequency metric, and physically hardening the apparatus, this research shows the potential of a “smart” fire hose for improved situational awareness during fire suppression.

Firefighters are constantly exposed to the danger of hot, dark, and toxic fire environments during their firefighting operations and suffer injuries now and then. Today, firefighting robots are gradually deployed to support fire services and enter the explosive, toxic, and smoky fire scene for detection, mitigation, and rescue. Robotic firefighting aims to reduce the risk of casualties and improve disposal efficiency significantly. However, firefighting robots are still far from massive applications, because of the problematic remote control in complex fire incidents and their limited autonomy and small working range. There is still an urgent need for intelligent and autonomous firefighting robots, as well as research and development to expand their firefighting tasks and reliability. This chapter reviews the development of firefighting robots and UAVs over the last six decades, from early master-slave remote control to the latest sensor-driven semi-autonomous control. It also summarizes the classification of firefighting robots and their respective development directions. Finally, we point out the key technologies and challenges in the autonomous intelligent firefighting robots and predict the future application of firefighting robots and their interaction with fire services.