Optimized Visualization of Building Fire Risks through Mobile Application

One of the ways to control tropical peat fires is to use a combination of the Global Navigation Satellite System (GNSS) and smartphones for monitoring, reporting, and verification of the location of the fires. The latest smartphones have many sensors, such as a compass, accelerometer, GPS, and a camera, to collect forest activity data from a specific target area. The collected data were then transferred to a cloud server through global mobile communication. This paper discusses a mobile and web application-based approach to collect and analyze user-generated geographical information of human activity data. The paper aims is to promote law enforcement agencies, local government, and fire patrol to consider the use of this low-cost, easy-to-use technology in controlling and reducing the risk of tropical peatland fires. Our mobile application has an easy-to-use mapping technology that allows its users to locate addresses quickly and provides cartographic maps augmented with digital information and high-resolution aerial imagery. This study proposes online citizen reporting as a new approach for law enforcement by aiding local government and fire patrols to conduct monitoring, reporting, and verification to reduce the risk of peat forest fires.

Hydrogen is considered to be a clean, economical and safe renewable energy source that would be ideal to replace fossil fuels, because it is light, highly abundant and its oxidation product (water) is environmentally benign. However, hydrogen is easy to burn (the chemical energy per mass of hydrogen is at least three times larger than that of other chemical fuels), which has the risk of fire and explosion. The problems of transportation and storage restrict the application of hydrogen energy, which has become a key factor in the development and utilization of hydrogen energy. This gas adsorbs at solid surfaces depending on the applied pressure and temperature. For storage purposes in mobile applications, the adsorption of hydrogen has been studied mainly on carbon species, but light and reasonably cheap materials of high surface area should prove to be attractive as well. Porous material is a very promising hydrogen storage material, which stores the gas in the form of molecules at low temperatures and compresses hydrogen into the holes effectively. The purpose of this work was to develop a hybrid porous materials consisting of sulfonated polyetherimide matrix with aluminum nanoparticles and faujasite type zeolite. Dilute solutions were first prepared under stirring at room temperature and the solutions were dried under vacuum. The hybrids were analyzed by differential scanning calorimetry (DSC), thermogravimetric analysis (TGA), transmission electron microscopy (TEM) and hydrogen sorption measurements. The addition of aluminum decreased the glass transition temperature of the hybrids when compared to the sulfonated polymer and the TEM images showed that simply physically mixture occurred between polymer and metallic nanoparticles. Hydrogen sorption tests showed an increase in the amount of hydrogen in the presence of zeolite.

Building fire is a common disaster happening in our daily life that causes unfortunate casualties and deaths. Successfully escaping from fire depends on the design of evacuation route and time as most of the damage of fire is caused due to lack of evacuation equipments or poor design of the emergency route. In this research work, we designed a hybrid building fire evacuation system (HBFES) on a mobile phone using Radio Frequency Identification (RFID) techniques. The system will be implemented at Tamkang University on Lanyang campus where a central fire alarm system has been installed. Location Based Service (LBS) and several existing computer or mobile phone applications, namely Viewpoint Calculator, Path planner, and MobiX3D viewer will be used on the system to rapidly calculate the reliable evacuation routes when building fire takes place.

Building fire is a common disaster happening in our daily life that causes unfortunate casualties and deaths. Successfully escaping from fire depends on the design of evacuation route and time, as most of the damage of fire is caused due to lack of evacuation equipments or poor design of the emergency route. In this research work, we designed a hybrid building fire evacuation system (HBFES) on a mobile phone using Radio Frequency Identification (RFID) techniques and Cloud Computing. The system will be implemented at Tamkang University on Lanyang campus. Several existing computer or mobile phone applications, namely Viewpoint Calculator, Path planner, and MobiX3D viewer will be used on the system to rapidly calculate reliable evacuation routes when building fire takes place.

Fire spread scenarios associated with concealed cavity spaces have been relatively less discussed. The variation in studies with respect to geometry, influential parameters, and protection strategies has been an obstacle to deriving more generalized solutions in terms of cavity fire in buildings. A systematic literature review was conducted following the PRISMA method to identify the conclusive fire behaviour, safety risks, and protection strategies to enable future researchers to address cavity fire scenarios effectively, avoiding catastrophic disasters. This study identified that relative to open-fire scenarios, cavity fires could result in up to 10 times higher flame spread, up to 14 times higher heat exposure, and temperature conditions 13 times higher. Increased toxicity and smoke velocity are also found with cavity fires. Fire protection strategies and their efficiency were identified for a range of cavity geometries. Altogether, cavity spaces, especially narrow ones, cannot be neglected during fire safety, and proper risk identification is required to ensure the safety of the buildings and the occupants in a fire scenario.

Building occupants must know how to properly exit a building should the need ever arise. Being aware of appropriate evacuation procedures eliminates (or reduces) the risk of injury and death occurring during an existing catastrophe. Augmented reality (AR) is increasingly being sought after as a teaching and training tool because it offers a visualization and interaction capability that captures the learner’s attention and enhances the learner’s capacity to retain what was learned. Utilizing the visualization and interaction capability that AR offers and the need for emergency evacuation training, this paper explores mobile AR application (MARA) constructed to help users evacuate a building in the event of an emergency such as a building fire, active shooter, earthquake, and similar circumstances. The MARA was built for Android-based devices using Unity and Vuforia. Its features include the use of intelligent signs (i.e. visual cues to guide users to the exits) to help users evacuate a building. Inter alia, this paper discusses the MARA’s implementation and its evaluation through a user study utilizing the Technology Acceptance Model (TAM) and the System Usability Scale (SUS) frameworks. The results demonstrate the participants’ opinions that the MARA is both usable and effective in helping users evacuate a building.

Building fires are characterized by high uncertainty, so their fire risk assessment is a very challenging task. Many indexes and parameters related to building fires are ambiguous and uncertain; as a result, a flexible and robust method is needed to process quantitative or qualitative data and update existing information when new data are available. This paper presents a novel model to deal with the uncertainty of the residential building fire risk and systematically optimize its performance effectiveness. The model includes fuzzy theory, evidence reasoning theory, and expected utility methods. Fuzzy analysis hierarchy process is applied to analyze the residential building fire risk index system and determine the weights of the risk indexes, while the evidence reasoning operator is used to synthesize them. Three buildings were selected as a case study to illustrate the proposed fire risk model. The results show that the fire risk level of three buildings corresponds to “moderate” or below which is consistent with the previous study. These results also truly reflect the actual situation of fire safety in these residential buildings. The application of this model provides a powerful mathematical framework for cooperative modeling of the fire risk assessment system and allows data to be analyzed step by step in a systematic manner. It is expected that the proposed model could provide managers and researchers with flexible and transparent tools to effectively reduce the fire risk in the system.

This paper proposes a solution to the problem of software absence for the quantitative fire safety analysis of complex objects, which is suitable not only for highly qualified scientists and experts but also for fire safety engineers and inspectors and is compatible with mobile devices. Existing software solutions for quantitative fire safety analysis are desktop-based, and all require a high level of competence of users. We propose a solution by combining the formalism of typical probabilistic structural–logical (TPSL) models, the Software-as-a-Service (SaaS) concept, and end-to-end encryption. Using TPSL models allows the development of cloud-based applications with a simple request-based application programming interface and intuitively understandable user interface to assess the level of safety of complex objects. As a result, we could use such mobile device-ready applications both for developing mobile device applications and developing mobile-device-friendly website-based services to carry out safety control inspections or level of safety management.

The complex research method is used in the work, which includes: analysis and generalization of scientific achievements in the field of fire safety, application and processing of statistical data; application as analytical methods of research by collecting, generalizing and analyzing the current normative documents of the State Emergency Service of Ukraine and statistical methods of probability theory, geospatial, mathematical modeling, methods of system analysis. Research Object: The risk of death from fire in public buildings and structures. The purpose of the work is risk evaluation of death from fires in public buildings. Research methods. The complex method of researches is used in the work, which includes: analysis and application of statistical methods of data processing, verification of reliability of the obtained results, mathematical modeling and other analytical methods. The concept of the risk is described in the article and the main normative documents are outlined that treat them. The basic methods and methods of risk assessment for public buildings are analyzed. Fire risk assessment is the calculation of individual fire risk for residents, staff and visitors in a public building. The numerical expression of an individual fire risk is the frequency of exposure of hazardous fire factors to a person in a building or structure. The frequency of exposure to hazardous fire factors is determined for fire-hazardous situations, which are characterized by the greatest danger to the life and health of people in the building. The CFAST program simulated the occurrence of limit concentrations of hazardous factors during fires for two typical public buildings. It is also suggested how to evaluate the results on a specific color scale that allows you to create risk maps for visualization. The draft methodology proposes to consider the follow-up time of fire and rescue units when determining the evacuation time. The main methods and methodologies of risk assessment for buildings and public facilities have been analyzed. For two facilities, the risk of death from fire in buildings and facilities has been estimated. The results of evaluation have been suggested in a color scale, which allows creating maps to visualize the risks. The simulation of the limit concentrations of hazardous factors during the fires for two typical public facilities has been done in CFAST software. Mapping the risks of death from a fire in the appropriate group in the appropriate colors allows you to build a map of the risks of death from a fire and fire and rescue workers know the possible risks and dangers of the objects. The start time of the evacuation, in the absence of warning systems, is determined depending on the time the fire and rescue units follow to the fire site. The proposed calculation methodology and visualization tools allow the rescuer, who makes the decision, to comprehensively assess the situation during the design and to avoid the possible consequences of an emergency, which will increase the level of security.

This article presents building fire risk analysis model based on scenario clusters and its application in fire risk management of buildings. Building fire risk analysis is a process of understanding and characterizing the fire hazards, the unwanted outcomes that may result from the fire, and the probabilities of fire and unwanted outcomes occurring. The purpose is to evaluate and make a decision about the level of fire risk to determine whether to take appropriate risk management measures or not. Therefore, building fire risk analysis serves as a basis for fire risk management. In the paper, scenario clusters are constructed in the process of building fire risk analysis, and the number of deaths and directive property loss are selected as building fire risk indexes. Finally, the average fire risk of residential buildings is quantified in detail. With the types of detailed fire risk models developed here, fire risk management measures could be taken to improve the building fire safety grading and reduce fire risk levels and subsequent damage.

The integration of Internet of Things (IoT) technology in fire prediction and prevention is part of the “Fourth Industrial Revolution.” This study aims to mitigate fire risks in high-voltage cable joints by monitoring the real-time temperature and ambient temperature/humidity changes driven by variations in contact resistance. The limitations of onsite inspections are addressed using a real-time monitoring system that employs SMS alerts, mobile applications, and web-based interfaces. By enabling managers to assess cable conditions remotely, this system significantly reduces the labor, time, and costs associated with inspections. Furthermore, an IoT-based long-range (LoRa) communication network was used to establish a comprehensive sensor network to gather data on temperature, humidity, as well as the operational status of automatic gas-extinguishing systems. LTE-M communication facilitates big data collection, enabling the prediction of temperature anomalies caused by insulation failure in high-voltage cables. This approach not only prevents fires but also ensures the continuous monitoring of automatic gas-extinguishing systems before and after fire incidents.

The frequency of the forest fires that have occurred in the different parts of the world, In recent decades significant population problems and causing the death if the wild animals as the impact of these fires extend beyond the destruction of the natural habitats. The proliferation of the Internet of Things industry, resolutions for initial fire detection should be developed. The valuation of the fire risk of an area and communication of this realities to the population could reduce the amount of fires originated by accident or due to carelessness of the public user. This paper proposes a low-cost network based on NXP Rapid IOT kit and Long Range (Lora) technology to autonomously estimate the level of fire risk in the forest. The system comprises of NXP Rapid IOT kit which humidity, air quality and detection of the tree fall. The data from each node stored and processed in a in a web server or the mobile application that sendsthe recorded data to a web server for graphical conception of collected data.

Forest fires may cause considerable damages both in ecosystems and lives. This proposal describes the application of Internet of Things and wireless sensor networks jointly with multi-hop routing through a real time and dynamic monitoring system for forest fire prevention. It is based on gathering and analyzing information related to meteorological conditions, concentrations of polluting gases and oxygen level around particular interesting forest areas. Unusual measurements of these environmental variables may help to prevent wildfire incidents and make their detection more efficient. A forest fire risk controller based on fuzzy logic has been implemented in order to activate environmental risk alerts through a Web service and a mobile application. For this purpose, security mechanisms have been proposed for ensuring integrity and confidentiality in the transmission of measured environmental information. Lamport’s signature and a block cipher algorithm are used to achieve this objective.

Forest fire prevention is a critical aspect of protecting our natural resources and ensuring the safety of wildlife and communities. Traditional methods of fire prevention have limitations in detecting and predicting fires before they cause irreparable damage. In this report, we propose a novel approach using machine learning to enhance forest fire prevention efforts.By leveraging machine learning algorithms and analyzing relevant data such as oxygen levels, temperature, and humidity, we can develop a predictive model to assess the probability of a fire occurring in a particular area. This model can serve as a valuable tool for governments and authorities to allocate resources effectively and take proactive measures to prevent fires. The research focuses on creating a large dataset by collecting real-life examples of forest fires and their associated parameters. With this dataset, can train a machine learning model to accurately predict the likelihood of a fire outbreak based on the input parameters. By integrating this model into a web or mobile application, can provide real-time fire risk assessments and alerts to users, enabling them to take necessary precautions. The proposed system offers a comprehensive and efficient solution for forest fire prevention, with the potential to be applied on a global scale. By combining machine learning capabilities with preventive measures and community involvement, we can significantly reduce the negative impact of forest fires and safeguard the natural environment.

Huge losses and serious threats to ecosystems are common consequences of forest fires. This work describes a forest fire controller based on fuzzy logic and decision‐making methods aiming at enhancing forest fire prevention, detection, and fighting systems. In the proposal, the environmental monitoring of several dynamic risk factors is performed with wireless sensor networks and analysed with the proposed fuzzy‐based controller. With respect to this, meteorological variables, polluting gases and the oxygen level are measured in real time to estimate the existence of forest fire risks in the short‐term and to detect the recent occurrence of fire outbreaks over different forest areas. Besides, the Analytic Hierarchy Process method is used to determine the level of fire spread, and, when necessary, environmental alerts are sent by a Web service and received by a mobile application. For this purpose, integrity, confidentiality, and authenticity of environmental information and alerts are protected with implementations of Lamport’s authentication scheme, Diffie‐Lamport signature, and AES‐CBC block cipher.