Search Results (57)

This study presents a comparative analysis of six DFS implementations representing different maturity levels and investigates the systemic gap between technological capabilities and regulatory approaches. A structured narrative review with case-based analysis was conducted using the Scopus database (2015–2026) with six targeted queries. The case selection followed the PICo protocol. An original ten-criterion DFS maturity assessment rubric—grounded in the Technology Readiness Level (TRL), Integration Readiness Level (IRL), and Digital Twin Maturity Model frameworks—was applied to all six cases. Inter-rater validation yielded substantial agreement (κw = 0.797; unweighted κ = 0.674 [95% CI: 0.509, 0.839]). The results indicate a clear maturity gradient (Dimension X: 4–9 points; Dimension Y: 2–8 points). Benefits reported in the analysed primary studies include up to a 55 s reduction in evacuation time, a 72% improvement compared with static signage, and a 34-percentage-point increase in evacuation success rate under simulation-based conditions. Five normative recommendations are proposed to address the structural regulatory gap between current prescriptive frameworks and DFS deployment in Poland and the EU. This study argues that prescriptive rules should remain the baseline, whereas complex facilities may adopt performance-based DFS solutions, provided that equivalence to conventional protection levels is rigorously demonstrated. From a sustainability perspective, the study frames DFS as a dynamic safety layer that supports occupant protection, operational resilience, and lifecycle adaptability in complex buildings exposed to uncertain fire and crowd conditions. Full article

The rapid proliferation of single-phase non-linear loads, such as LED lighting and IT equipment, in modern Smart Buildings has introduced significant power quality challenges in low-voltage electrical installations. A critical but often underestimated consequence is the severe overloading of the neutral conductor caused by triplen harmonics (particularly the 3rd harmonic), which sum algebraically even in balanced three-phase systems. This paper analyzes the electrical and thermal impact of these distortions using a detailed MATLAB/Simulink model of a 400/230 V (3P + N) network. The simulation results demonstrate that under highly distorted conditions (Scenario S3), the neutral current can reach 180% of the nominal phase current (18 A vs. 10 A). Furthermore, the Joule losses analysis reveals a thermal stress more than three times higher on the neutral conductor (peak ~65 W) compared to the phase conductor (~20 W), challenging the traditional design practice of neutral undersizing. To address these safety issues, this study proposes a novel neutral-to-phase current ratio index (k_N) and a proactive decision matrix for Building Management Systems (BMS). Unlike traditional mitigation strategies that rely on static hardware oversizing, passive filters, or specialized transformers, the proposed approach offers a dynamic, cost-effective, and software-driven solution that can be easily integrated into the existing automation infrastructure of modern Smart Buildings. The model identifies a critical tipping point at a 3rd harmonic content of 35.3%, where k_N ≥ 1. By continuously monitoring the k_N parameter, the proposed algorithm enables a transition from passive protection to active power management, triggering automated responses to prevent insulation degradation and mitigate fire hazards. Full article

Taiwan’s rapid demographic shift toward a super-aged society has heightened demand for long-term care, yet limited staffing creates safety risks from fires; heating, ventilation, and air conditioning failures; and health incidents. To address this, we propose an IoT-based intelligent environmental monitoring and early-warning system designed for care facilities. The three-layer architecture integrates sensors for temperature, humidity, light, air quality, and noise; employs ESP-NOW and wireless fidelity mesh for reliable networking; and supports user interfaces with real-time anomaly alerts. Using PCA and Isolation Forest for efficient anomaly detection, the modular, node-based design enhances safety, reduces manpower burden, and enables scalable smart services. Full article

The thermally efficient and lightweight TRC/CLCi composite panels for functional and smart building envelopes, funded by the iclimabuilt project (Grant Agreement no. 952886), offer innovative solutions to sustainably address common failure risks in facade systems. This work specifically emphasizes strategies for mitigating structural, thermal, and fire-related failures through targeted material selection, advanced design methodologies, and rigorous validation protocols. To effectively mitigate structural failures, high-pressure concrete (HPC) reinforced with carbon fibers is utilized, significantly enhancing tensile strength, reducing susceptibility to cracking, and improving overall durability. To counteract thermal bridging—a critical failure mode compromising energy efficiency and structural integrity—the panels employ specially designed glass-fiber reinforced pins connecting HPC outer layers through the cellular lightweight concrete (CLC) insulation core that has a density of around 70 kg/m³ and a thermal conductivity in the range 35 mW/m∙K comparable to those of expanded polystyrene and Rockwool. These connectors ensure effective load transfer and maintain optimal thermal performance. A central focus of the failure mitigation strategy is robust fire behavior. The developed panels undergo rigorous standardized fire tests, achieving an exceptional reaction to fire classification of A2. This outcome confirms that HPC layers maintain structural stability and integrity even under prolonged fire exposure, effectively preventing catastrophic failures and ensuring occupant safety. In conclusion, this work highlights explicit failure mitigation strategies—reinforced concrete materials for structural stability, specialized glass-fiber connectors to prevent thermal bridging, rigorous fire behavior protocols, and comprehensive thermal performance validation—to produce a facade system that is robust, energy-efficient, fire-safe, and sustainable for modern buildings. Full article

Smart campuses employ advanced digital technologies and intelligent communication systems to enhance educational, operational, and living environments. This study investigates stakeholder perceptions of smart campus priorities in Saudi Arabia through a structured questionnaire administered to students and faculty. The study considered King Fahd University of Petroleum and Minerals (KFUPM) in Dhahran as a case study in this regard. The survey examined 22 smart campus aspects grouped into six domains: smart education, smart mobility, smart energy and waste management, smart buildings and work environment, smart safety and security, and smart open spaces. The results indicated strong consensus regarding the importance of all domains, with an overall mean rating of 4.3 out of 5.0 and Relative Importance Index (RII) values ranging from 0.77 to 0.91. The highest-ranked aspects included IoT-enabled cooling energy optimization, smart public transportation, smart lighting systems, smart workflow management, e-libraries, and fire prevention and detection systems, reflecting a pronounced emphasis on infrastructure quality, energy efficiency, and operational effectiveness. The findings suggest that smart campus development in Saudi Arabia should prioritize high-impact, user-valued initiatives that align with Vision 2030 objectives including digital transformation. Strategic early investments in smart buildings, energy management, and mobility systems can deliver measurable benefits in this regard. Further research is recommended to consider additional case studies in the Saudi context to ensure that smart campuses remain contextualized and responsive to user needs. Full article

The paper introduces a real-time digital-twin controller that manages evacuation routes while operating GEEM for emergency energy management during building fires. The system consists of three interconnected parts which include (i) a physics-based hazard surrogate for short-term smoke and temperature field prediction from sensor data (ii), a router system that manages path updates for individual users and controls exposure and network congestion (iii), and an energy management system that regulates the exchange between PV power and battery storage and diesel fuel and grid electricity to preserve vital life-safety operations while reducing both power usage and environmental carbon output. The system operates through independent modules that function autonomously to preserve operational stability when sensors face delays or communication failures, and it meets Industry 5.0 requirements through its implementation of auditable policy controls for hazard penalties, fairness weight, and battery reserve floor settings. We evaluate the controller in co-simulation across multiple building layouts and feeder constraints. The proposed method achieves superior performance to existing AI/RL baselines because it reduces near-worst-case egress time ($T_{95}$ and worst-case exposure) and decreases both event energy $E_{\mathrm{e}\mathrm{v}\mathrm{e}\mathrm{n}\mathrm{t}}$ and CO₂-equivalent ${{CO}_2}_{\mathrm{e}\mathrm{v}\mathrm{e}\mathrm{n}\mathrm{t}}$ while upholding all capacity, exposure cap, and grid import limit constraints. A high-VRE, tight-feeder stress test shows how reserve management, flexible-load shedding, and PV curtailment can achieve trade-offs between unserved critical load $U_{\mathrm{e}\mathrm{n}\mathrm{e}\mathrm{r}\mathrm{g}\mathrm{y}}$ and emissions. The team delivers implementation details together with reporting templates to assist researchers in reaching reproducibility goals. The research shows that emergency energy systems, which integrate evacuation systems, achieve better safety results and environmental advantages that enable smart-city integration through digital thread operations throughout design, commissioning, and operational stages. Full article

Developments in the textile industry occur both as a consequence of increased awareness among users and various requirements in terms of human and environmental safety. Modifications are aimed at improving performance parameters, using natural substances, moving away from synthetic materials, and improving ergonomics. In order to achieve this, various fibre-production techniques are used, as is the addition of substances, including nanosubstances, into the structure or onto the surface of a given material. In the case of fire-resistant fabrics, which primarily must meet thermal protection requirements, efforts are also being made to reduce weight and eliminate harmful chemicals (e.g., polycyclic aromatic hydrocarbons PAHs), and to create smart materials with sensors. However, it is necessary to further develop not only the materials themselves but also cleaning and decontamination techniques that will allow the fire resistance parameters that have been developed to be maintained. Full article

A utility tunnel is a complex underground facility that serves as a critical infrastructure integrating and operating systems such as electricity, telecommunications, and drainage within a city. However, various hazard events such as fire, flooding, condensation, damage, and intrusion can occur within utility tunnels, posing risks not only to the degradation of facility functions but also to potential human casualties and economic losses. Therefore, it is crucial to establish prompt and effective response measures for these hazard events. Unlike previous studies that focused on individual hazard types, this study proposes an integrated and standardized multihazard response framework for utility tunnels. Through case analysis, fire, flooding, condensation, damage, and intrusion were defined as representative hazard events, and the response stages for each were classified into attention, caution, warning, and critical levels. Appropriate response scenarios were developed for each stage, providing prompt and efficient response measures tailored to different situations. The proposed framework offers a unique contribution by presenting a unified structure that supports stage-based management and can be directly applied to smart monitoring and control systems in underground infrastructure. This study is expected to contribute to improving the disaster prevention and response systems of utility tunnels and enhancing overall facility safety. Full article

Fire detection systems play a critical role in ensuring safety, yet their reliance on external power sources limits their deployment in remote or energy-constrained environments. This study presents a novel system that transforms waste heat into electrical energy for fire detection. Using the See beck effect, the system harvests heat from power plant chimneys, vehicle exhausts, and direct fire sources to power a microcontroller, heat sensors, an OLED display, and an IoT module. The sensors monitor temperature fluctuations, identifying potential fire hazards. Data is displayed locally and sent to the cloud for remote monitoring and timely alerts. By repurposing waste heat, the system minimizes environmental impact, reduces energy waste, and eliminates dependence on external power sources. This approach combines energy recovery with smart safety features, offering a sustainable and cost-effective solution for fire detection while addressing global energy challenges. Full article

The movement towards low-emission and sustainable building practices has driven increased use of natural, carbon-based materials such as wood. While these materials offer significant environmental advantages, their inherent flammability introduces new challenges for timber building safety. Despite advancements in fire protection standards and building regulations, the risk of fire incidents—whether from technical failure, human error, or intentional acts—remains. The rapid detection of fire onset is crucial for safeguarding human life, animal welfare, and valuable assets. This study investigates the potential of monitoring fire precursor gases emitted inside building structures during pre-ignition and early combustion stages. The research also examines the sensitivity and effectiveness of commercial smoke detectors compared with custom sensor arrays in detecting these emissions. A representative structural sample was constructed and subjected to a controlled fire scenario in a laboratory setting, providing insights into the integration of gas sensing technologies for enhanced fire resilience in sustainable building systems. Full article

This study presents the design and validation of an integrated fire safety system that leverages edge AI, hybrid sensing, and precision suppression to overcome the latency and collateral limitations of conventional smoke detection and sprinkler systems. The proposed platform features a dual-mode sensor array for early fire recognition, motorized ventilation units for rapid smoke extraction, and a 360° directional nozzle for targeted agent discharge using a residue-free clean extinguishing agent. Experimental trials demonstrated an average fire detection time of 5.8 s and complete flame suppression within 13.2 s, with 90% smoke clearance achieved in under 95 s. No false positives were recorded during non-fire simulations, and the system remained fully functional under simulated cloud communication failure, confirming its edge-resilient architecture. A probabilistic risk analysis based on ISO 31000 and NFPA 551 frameworks showed risk reductions of 75.6% in life safety, 58.0% in property damage, and 67.1% in business disruption. The system achieved a composite risk reduction of approximately 73%, shifting the operational risk level into the ALARP region. These findings demonstrate the system’s capacity to provide proactive, energy-efficient, and spatially targeted fire response suitable for high-value infrastructure. The modular design and SmartThings Edge integration further support scalable deployment and real-time system intelligence, establishing a strong foundation for future adaptive fire protection frameworks. Full article

Despite recent advances in deep learning for fire detection, much of the current research prioritizes model-centric metrics over dataset fidelity, particularly from a fire safety engineering perspective. Commonly used datasets are often dominated by fully developed flames, mislabel smoke-only frames as non-fire, or lack intra-video diversity due to redundant frames from limited sources. Some works treat smoke detection alone as early-stage detection, even though many fires (e.g., electrical or chemical) begin with visible flames and no smoke. Additionally, attempts to improve model applicability through mixed-context datasets—combining indoor, outdoor, and wildland scenes—often overlook the unique false alarm sources and detection challenges specific to each environment. To address these limitations, we curated a new video dataset comprising 1108 annotated fire and non-fire clips captured via indoor surveillance cameras. Unlike existing datasets, ours emphasizes early-stage fire dynamics (pre-flashover) and includes varied fire sources (e.g., sofa, cupboard, and attic fires), realistic false alarm triggers (e.g., flame-colored objects, artificial lighting), and a wide range of spatial layouts and illumination conditions. This collection enables robust training and benchmarking for early indoor fire detection. Using this dataset, we developed a spatiotemporal fire detection model based on the mixed convolutions ResNets (MC3_18) architecture, augmented with Convolutional Block Attention Modules (CBAM). The proposed model achieved 86.11% accuracy, 88.76% precision, and 84.04% recall, along with low false positive (11.63%) and false negative (15.96%) rates. Compared to its CBAM-free baseline, the model exhibits notable improvements in F1-score and interpretability, as confirmed by Grad-CAM++ visualizations highlighting attention to semantically meaningful fire features. These results demonstrate that effective early fire detection is inseparable from high-quality, context-specific datasets. Our work introduces a scalable, safety-driven approach that advances the development of reliable, interpretable, and deployment-ready fire detection systems for residential environments. Full article

Early fire detection plays a crucial role in minimizing harm to human life, buildings, and the environment. Traditional fire detection systems struggle with detection in dynamic or complex situations due to slow response and false alarms. Conventional systems are based on smoke, heat, and gas sensors, which often trigger alarms when a fire is in full swing. In order to overcome this, a promising approach is the development of memristor-based gas sensors, known as gasistors, which offer a lightweight design, fast response/recovery, and efficient miniaturization. Recent studies on gasistor-based sensors have demonstrated ultrafast response times as low as 1–2 s, with detection limits reaching sub-ppm levels for gases such as CO, NH₃, and NO₂. Enhanced designs incorporating memristive switching and 2D materials have achieved a sensitivity exceeding 90% and stable operation across a wide temperature range (room temperature to 250 °C). This review highlights key factors in early fire detection, focusing on advanced sensors and their integration with IoT for faster, and more reliable alerts. Here, we introduce gasistor technology, which shows high sensitivity to fire-related gases and operates through conduction filament (CF) mechanisms, enabling its low power consumption, compact size, and rapid recovery. When integrated with machine learning and artificial intelligence, this technology offers a promising direction for future advancements in next-generation early fire detection systems. Full article

In an era where public safety hinges on real-time intelligence and rapid response, this paper delves into the pivotal role of location-based services (LBSs) in empowering law enforcement and fire rescue operations. GPS tracking systems have revolutionized situational awareness and resource management, yet they come with critical security and privacy challenges, including unauthorized access, real-time data interception, and insider threats. To address these vulnerabilities, this study introduces an innovative framework that combines blockchain, artificial intelligence (AI), and IoT technologies to redefine emergency management and public safety systems. Voice-command virtual assistants powered by AI enable hands-free operations, enhance hazard detection, and optimize resource allocation in real time, while blockchain’s decentralized and tamper-proof architecture ensures data integrity and security. By integrating these cutting-edge technologies, the research showcases a system design that not only secures sensitive information but also drives operational efficiency and resilience. With applications spanning smart cities, autonomous systems, and fire rescue operations, this study offers a transformative vision for public safety, emphasizing technology integration, digital innovation, and trust-building. These advancements promise not only to protect responders and communities but also to redefine the standards of security and efficiency in modern emergency management. Full article

Due to the development of power semiconductors and the increase in digital loads, DC microgrids are receiving attention, and their application scope is rapidly expanding. As the technological stability of high-voltage direct current (HVDC) continues to rise, the potential of low-voltage direct current (LVDC) distribution systems is becoming increasingly intriguing. Many researchers are actively conducting safety and efficiency research on DC distribution systems and power grids. In LVDC distribution systems, small-scale DC microgrids are formed by renewable energy sources supplying DC power. This paper analyzes the efficiency improvement that can be achieved by integrating a fire protection system into a DC microgrid. This research analyzed the changes when fire protection systems such as receivers, transmitters, fire alarms, emergency lighting, and evacuation guidance, which have traditionally used AC power, were converted to DC circuits. As a result, the power supply infrastructure within the DC microgrid can be simplified, energy loss can be reduced, and the stability of the power system can be improved. The research results of this paper suggest that DC circuit-based fire protection facilities can positively impact future smart grid and renewable energy goals. Full article