Fire Technology最新文献

Using timber as a structural material in buildings is promising in reducing the carbon footprint of construction, although fire safety has been a long-standing challenge prohibiting its wider use. The current design practice usually complies with Eurocode 5, but it is largely prescribed only for standard fire scenario. While investigating the fire behavior in real compartments containing timber members, the fire action imposed on the surface of timber members can be hardly represented by the standard fire curve. Therefore, modelling the heat transfer of timber sections in these real fire scenarios requires adequate numerical models. In this paper, the heat transfer model equipped with temperature-variant thermal properties of timber as well as a heat generation model addressing timber combustion effect, has been developed and implemented in the open-source simulation platform OpenSees for fire. Advancing from the existing heat transfer models, the present model has been validated against test results of timber sections subjected to the prescribed non-standard fire actions supported by the e-controlled radiant panel system. The modelling results have shown good performance in predicting the temperature evolution at various depths of the timber sections subjected to non-standard heating conditions.

This study presents a systematic evaluation of fire suppression strategies for lithium-ion Battery Energy Storage Systems (BESS), specifically examining thermal runaway propagation in small domestic system (8 kWh). Five distinct suppression methods were evaluated: water mist, encapsulator agent (water mist with proprietary encapsulator), carbonate agent (water mist with ammonium bicarbonate), mixed agent (containing boron compounds and surfactants), and liquid nitrogen. Performed experiments revealed significant differences between suppression methods. Water mist and encapsulator agents demonstrated better performance, extending propagation delay times by 179% and 167%, respectively, compared to control tests without a suppression method. Registered maximum temperatures varied across methods from 780° to 890 °C. However, none of the tested methods prevented thermal runaway propagation entirely and were able to save the system from being destroyed. Critical safety concerns emerged regarding vapour cloud production, which correlated strongly with cooling effectiveness (r = 0.87) but increased explosion risks. Statistical analysis confirmed significant method-dependent differences (p < 0.001), with water mist and encapsulator agents reducing thermal runaway hazard ratios by over 70%. These results indicate that current suppression technologies can delay but not prevent thermal runaway propagation. Findings emphasize the need for integrated approaches combining efficient cooling with vapour management strategies, particularly for residential BESS installations.

Conventional single-tube concrete-filled steel tubular (CFST) columns are susceptible to fire-induced failure due to rapid steel tube degradation at elevated temperatures. While external fireproofing measures are commonly used to improve fire resistance, they often increase construction costs and maintenance complexity. Addressing this issue, the present research introduces an innovative double-tube CFST column design incorporating functionally graded concrete between concentric steel tubes. This configuration maintains equivalent steel consumption and ambient-temperature structural capacity compared to traditional single-tube systems. Utilizing finite element modeling, the fire performance of axially loaded short and slender columns—both single-tube and double-tube variants—is systematically evaluated. Key parameters influencing fire resistance, including cross-sectional dimensions, outer/inner concrete compressive strengths, steel tube yield strength, applied load ratio, inter-tube gap distance, and column slenderness, are analyzed. Results indicate that the double-tube CFST columns exhibit substantially superior fire resilience compared to their single-tube counterparts, particularly in scenarios involving larger cross-sections and reduced load ratios, which collectively prolong structural integrity under fire exposure. The study concludes that optimized double-tube configurations incorporating functionally graded concrete can achieve fire resistance requirements without supplementary protective measures, offering a cost-effective and maintenance-efficient alternative for fire-prone environments.

Steel members in open areas like parking lots and bridges are vulnerable to localized fire. The use of fire-protection measures may slow the rate of temperature rise in such cases. However, due to uncertainty in the mechanical and thermal properties of the members and fire-protective systems, quantitative assessment of the safety of the members considering these uncertainties is necessary. The present paper describes a load and resistance factor design (LRFD)-based framework to evaluate the safety of steel and composite beams exposed to localized fire. The reliability index of the beams is calibrated with the partial safety factors of parameters such as beam strength, fuel load and thermal properties of fire-protection material and correspondingly, empirical relations are developed to define their relationship. For composite beam, it was concluded that the reliability indices were marginally sensitive to the safety factors of material strength of concrete slab and majorly depended on the safety factors of material strength of steel beam.

CO₂ is a significant fire detection parameter. However, its effectiveness can be compromised by high background concentrations, leading to false alarms or inaccurate detection. To mitigate the effects of uncertain ambient background concentrations on the performance of CO₂ fire detectors, this study developed an environmentally adaptive multi-sensor fire detection model based on an ensemble learning approach. First, an ensemble learning model consisting of base classifiers of gated recurrent unit (GRU) and convolutional neural network (CNN) was designed to learn and classify CO₂ and smoke concentrations under fire and non-fire conditions. Second, the fire detection performance of the multi-sensor ensemble learning model was validated using accuracy, precision, recall, and F1 score metrics. Finally, its performance was compared with those of multi-sensor fire detection models constructed with a CNN and GRU network. The results indicate that the multi-sensor fire detection model based on ensemble learning outperformed the fire detection models based on CNN and GRU. Moreover, the proposed model became more self-adaptive to the environment of the installation space as the number of CO₂ background concentration signals used for training increased. The fire recognition accuracy of the multi-sensor fire detection model exceeded 99% with 150 sets of CO₂ background concentration signals supplemented to the training dataset.

This paper presents unpublished results and analysis of data collected in a survey with survivors of the Kiss nightclub fire. The goal is to better understand their behavior and the factors that influenced in the evacuation process on the night of the fire. The survey started in July 2019 and ended in March 2021, receiving valid responses from 162 participants. Responses, expressed as nominal or ordinal qualitative variables, were statistically processed using a public domain software and the same software, which was also used to check for dependence between pairs of variables using the chi-squared test. The answers to the discursive questions were analyzed using a generative artificial intelligence algorithm to identify the expression of feelings and actions presented in the survivors’ statements. This approach handles the informal typical language of daily conversations, reducing subjectivity inherent in the manual analysis of these responses. Based on the respondents’ information, it was concluded that people were slow to correctly identify that a fire was occurring and consequently, to decide to leave the building. Additionally, several survivors reported searching for others before initiating the evacuation. The absence of a fire alarm system or guidance from local staff significantly contributed to this delay. There was a sense of confusion and insecurity among people during the whole process, due to lack of communication. Many respondents stated they helped others to evacuate or were assisted when leaving, demonstrating altruism and solidarity. The rapid development of the fire and the generation of dense smoke, as well as the difficulty to move around inside the building due to the presence of physical obstacles, worsened the evacuation process. Most of the respondents suffered some kind of injury during the evacuation. The results emphasizes the importance of securing strategies to reduce the pre-evacuation time in this type of occupancy, such as improving communication and training local staff to deal with emergency situations.