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Fire protection measures are taken to prevent fires or to keep the resulting damage as low as possible. The statistical effectiveness of fire protection measures can be derived from a large number of fires that have already occurred. With the research paper presented here, such proof of effectiveness is rendered for certain specific fire protection measures, such as installed fire detection and fire alarm systems, fire extinguishing systems, smoke and heat exhaust systems, as well as according to the type of fire service. The investigation is based on a systematically collected database of 5,016 building fire interventions with 1,216 real fires by 29 fire services across Germany. The results can be used by applying engineering methods for quantitative risk analyses, within the scope of the risk-based performance level oriented planning of object-specific protection strategies. In this way, the performance level can be achieved effectively, flexibly and economically.

Modern fire alarm systems are essential for public safety, yet they often fail to exploit the wealth of historical alarm data and the complex spatiotemporal dependencies inherent in urban environments. Graph Neural Networks (GNNs) are currently among the most popular methods for handling complex spatiotemporal dependencies. While a range of dynamic GNN approaches have been proposed, many existing GNN-based predictors still rely on a static topology, which limits their ability to fully capture the evolving nature of risk propagation. Furthermore, even among dynamic graph methods, most focus on temporal link prediction or social interaction modeling, with limited exploration in safety-critical applications such as fire alarm prediction. DeaGAT dynamically updates inter-building edge weights through an attention mechanism, enabling the graph structure to evolve in response to shifting risk patterns. A margin-based contrastive learning objective further enhances the quality of node embeddings by distinguishing subtle differences in risk states. In addition, DeaGAT jointly models static building attributes and dynamic alarm sequences, effectively integrating long-term semantic context with short-term temporal dynamics. Extensive experiments on real-world datasets, including comparisons with state-of-the-art baselines and comprehensive ablation studies, demonstrate that DeaGAT achieves superior accuracy and F1-score, validating the effectiveness of dynamic graph updating and contrastive learning in enhancing proactive fire early-warning capabilities.

International Maritime Organization (IMO) has set regulations governing the installation of fire alarm systems on ships. The basic principle of the control system is to control the system output by comparing the actual output with the desired output. This study aims to determine the causes of false alarms on the fire alarm system on ships and to describe recommendations and solutions for failures in the fire alarm system on ships. This type of research uses a mix of methods by utilizing primary and secondary data. The data is processed by quantitative analysis and qualitative research. The results of this study indicate that the cause of the False Alarm on the Fire Alarm System is caused by the MFCA, Smoke Detector, and Cable Installation. The case that occurred in KL. 02 Sultan Hasanuddin there is no correlation between false alarms on the Fire Alarm System. The Smoke Detector correlation data with the False Alarm Fire Alarm System shows that there is a correlation with the Pearson Correlation of 0.857 > 0.4973.

The article presents a method of teaching the process of diagnosing the technical and functional condition of the fire alarm system (SSP). The fire alarm system’s laboratory model is a representation of a real fire alarm system. The lecturer has the opportunity to inflict several different independent damage. The aim of the laboratory exercise is to familiarize students with the methodology and structure of the fire alarm system diagnosing process.

With the rapid development of suburban rail transit, fire safety issues have received more and more attention. As the first line of the Shanghai Suburban Railway, the fire safety problem of the Shanghai Suburban Railway Airport Link Line is more prominent. In this paper, an automatic fire alarm system is designed and researched for the fire safety problem of the Shanghai Railway Airport Liaison Line, which adopts a hierarchical distributed structure and can realize accurate fire detection thorough immediately sending fire alarm information to the station control room and line operation control center and informing the location of the fire area. At the same time, the system also coordinates with the BAS system and ISCS system or independently realizes the linkage control of fire fighting equipment. The research results of this paper are of great significance for improving the fire safety level of Shanghai railway airport links.

The early detection of fire is of utmost importance since it is related to devastating threats regarding human lives and economic losses. Unfortunately, fire alarm sensory systems are known to be prone to failures and frequent false alarms, putting people and buildings at risk. In this sense, it is essential to guarantee smoke detectors’ correct functioning. Traditionally, these systems have been subject to periodic maintenance plans, which do not consider the state of the fire alarm sensors and are, therefore, sometimes carried out not when necessary but according to a predefined conservative schedule. Intending to contribute to designing a predictive maintenance plan, we propose an online data-driven anomaly detection of smoke sensors that model the behaviour of these systems over time and detect abnormal patterns that can indicate a potential failure. Our approach was applied to data collected from independent fire alarm sensory systems installed with four customers, from which about three years of data are available. For one of the customers, the obtained results were promising, with a precision score of 1 with no false positives for 3 out of 4 possible faults. Analysis of the remaining customers’ results highlighted possible reasons and potential improvements to address this problem better. These findings can provide valuable insights for future research in this area.

Fire, as a catastrophe that endangers the survival of human beings, is receiving more and more attention. People hope to find a way to detect fires in advance, so that they can detect and extinguish fires early, minimize the damage and protect people Safety of life and property. Fire occurs almost simultaneously with the use of fire, and with the development of society, material wealth increases, causing more and greater harm. In particular, cities have the characteristics of dense buildings, concentrated population, concentrated property, multiple sources of inflammable and explosive materials, large numbers of points, and wide coverage, which brings more fire and explosion hazards. Once fires and explosions occur, there will be heavy casualties and serious economic losses. So we must have the awareness of safety protection. Therefore, the fire alarm system based on the red-ultraviolet dual-checking platform is a more humane fire-fighting system, which allows people to detect fires early and can stifle the fire in the cradle, reducing people’s losses due to fire.

Security problem is the main factor restricting economic development of architecture. Fire accident, in particular, results in serious casualties and losses. As building structural layout is more diversified, there are more and more assembly forms of internal structures and facilities. Automatic fire alarm system is the key in building design and construction. The thesis first of all makes an analysis of the current construction fire accident, points out the application function of fire alarm system and analyzes automatic fire alarm system circuit junction, hoping to contribute to further studies.

The fire alarm system plays a very important role in life, but the system has problems such as false alarms and false alarms. Therefore, this paper proposes the application of fire detection based on GA-BP neural network. Firstly, the algorithm takes temperature, smoke concentration and CO concentration as the input of BP neural network, and the output is whether there is fire or not. Secondly, it combines the characteristics of genetic algorithm with strong global search ability and strong robustness. The algorithm has achieved 100% correct classification on the test set through simulation experiments. At the same time, the absolute error of the sample prediction is only 0.006, which proves that it has strong robustness, reliability and generalization ability. Finally, the model was transplanted to STM32 to prove its feasibility. This method provides a new method for intelligent identification of fire signals for early warning of fires and accurate identification of non-fire signals.

Fires in high-rise residential buildings can lead to human casualties and significant property damage. Therefore, ensuring fire safety in these buildings is an urgent task. The results of the Fire Protection System simulation of the development of a fire in a residential apartment showed that open windows and doors of the room, as well as the wind speed outside the window, affect the dynamics of fire development and the spread of non-hazardous fire factors. However, when the wind speed outside the window is 7.0 m/s, due to draft and cooling, the hazardous factors of the fire do not reach critical indicators for humans. This makes the evacuation process safer for people. The impact of the position of windows and doors, and the wind outside the window on the time of detection or failure of fire detectors was determined. It was determined that even if the current regulatory requirements for installing fire detectors are met, when the wind speed outside the window is 7.0 m/s, only the smoke detector located in close proximity to the source of the fire and having increased sensitivity is triggered. The results demonstrate the need for an adaptive approach to the placement of detectors in high-rise residential buildings.