Fire Technology最新文献

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.

With the increase of bridges and vehicles, bridge fire accidents are constantly reported, which seriously threaten the structural stability of bridges and traffic safety. A 1:8 large-size double-deck bridge fire experimental platform was constructed in this study. The effects of heat release rate, ambient wind and location on the heat flux of fire in the lower carriageway were investigated by 15 sets of fire experiments. In addition, the heat flux damage criterion was introduced to analyze the thermal threat of the vehicle fire. The main conclusions are as follows: (1) With the increase of heat release rate, the heat flux increases and the destructive ability increases. In a windless environment, when the heat release rate increases to 200 MW, the heat flux reaches 20 kW/m², at which time the steel structure of the bridge will be deformed, and the probability of death is as high as 98%, showing a strong destructive capability of thermal radiation. (2) The upstream heat flux decreases due to the increase of wind speed, while the downstream heat flux is opposite. When the wind speed increases to 4.4 m/s, the upstream heat flux of the 200 MW fire is 7.5 kW/m² and the downstream heat flux is 27 kW/m², which is close to four times of the heat flux in the upstream. (3) The effect of lanes on the heat flux in 100 MW and 200 MW fires is not significant, and the distance of lane change can be increased in subsequent experiments to examine the mechanism of lane effect on the heat flux. The experiments conducted in this paper will be useful for subsequent large-scale bridge fire tests.