Examination of effects of indoor fires on building structures and people

Kuti, Rajmund; Zólyomi, Géza; Gabriella, László; Hajdú, C.; Környei, László; Hajdú, Flóra

doi:10.1016/j.heliyon.2022.e12720

Cited by 9 publications

(3 citation statements)

References 25 publications

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“…Pedestrian evacuation in large public buildings during fire incidents is complex and variable. Fires can result in severe injuries and property damage [1][2][3]. On the one hand, evacuating people from architectures with complex geometries poses challenges due to their unique characteristics.…”

Section: Introductionmentioning

confidence: 99%

Cellular Automaton Model for Pedestrian Evacuation Considering Impacts of Fire Products

Liu,

Li,

Sun

2023

Fire

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To accurately simulate realistic pedestrian evacuation from a fire, a cellular automaton model of the dynamic changes in pedestrian movement parameters is developed in conjunction with fire dynamics software. The fire dynamics software is used to simulate the spread of smoke within the scene to obtain visibility and CO concentration data within the scene. We imported the smoke data into the cellular automata and adjusted the pedestrian movement speed over time, resulting in simulation data that closely align with reality. The results show that for the single-room scenario, as pedestrian density increased from 0.1 to 0.5 persons per square meter (p/m2), the influence of the percentage of pedestrians familiar with their location on evacuation efficiency decreased from 44.93% to 24.52%. Conversely, in the multi-room scenario, it increased from 23.68% to 38.79%. The proportion of pedestrians less affected by smoke decreases and stabilizes as the CO yield increases. In the single-room scenario, when the CO yield is below 10%, the crowd with a low percentage of pedestrians familiar with the site is more affected by smoke than those with a high percentage. In the multi-room scenario, the victimization rate of the crowd follows an increasing-then-decreasing curve, ultimately stabilizing with changes in CO yield.

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Section: Introductionmentioning

confidence: 99%

Cellular Automaton Model for Pedestrian Evacuation Considering Impacts of Fire Products

Liu,

Li,

Sun

2023

Fire

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“…Due to their densely populated and complex structures, these large buildings harbor numerous fire hazards. The most common sources of indoor fire are faulty appliances or equipment, aging electrical systems, careless disposal of cigarettes or matches, gas leaks, improper handling or storage of flammable liquids, and deliberate arson [2]. Indoor fires can spread rapidly and produce toxic smoke and gases, which can impair the visibility and health of the occupants and the firefighters.…”

Section: Introductionmentioning

confidence: 99%

“…(1) We present an efficient deep learning semantic segmentation framework based on a dual attention mechanism, which involves position attention and channel attention and assigns pixels with object class and attribute labels. (2) We first simultaneously estimate the fire object and fire load in indoor scenes and explore a multi-task learning strategy to learn the correlations between fire burning degree and combustible material statistics. The segmentation accuracy levels of fire and combustible material can be significantly enhanced for detailed scene analysis.…”

Section: Introductionmentioning

confidence: 99%

Automatic Recognition of Indoor Fire and Combustible Material with Material-Auxiliary Fire Dataset

Hou,

Zhao,

Fan

2023

Mathematics

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Early and timely fire detection within enclosed spaces notably diminishes the response time for emergency aid. Previous methods have mostly focused on singularly detecting either fire or combustible materials, rarely integrating both aspects, leading to a lack of a comprehensive understanding of indoor fire scenarios. Moreover, traditional fire load assessment methods such as empirical formula-based assessment are time-consuming and face challenges in diverse scenarios. In this paper, we collected a novel dataset of fire and materials, the Material-Auxiliary Fire Dataset (MAFD), and combined this dataset with deep learning to achieve both fire and material recognition and segmentation in the indoor scene. A sophisticated deep learning model, Dual Attention Network (DANet), was specifically designed for image semantic segmentation to recognize fire and combustible material. The experimental analysis of our MAFD database demonstrated that our approach achieved an accuracy of 84.26% and outperformed the prevalent methods (e.g., PSPNet, CCNet, FCN, ISANet, OCRNet), making a significant contribution to fire safety technology and enhancing the capacity to identify potential hazards indoors.

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Influence of coatings on residual strength of geopolymer concrete columns subjected to fire exposure: An experimental investigation

Kanagaraj,

Anand,

Raj R

et al. 2024

Case Studies in Construction Materials

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Examination of effects of indoor fires on building structures and people

Cited by 9 publications

References 25 publications

Cellular Automaton Model for Pedestrian Evacuation Considering Impacts of Fire Products

Cellular Automaton Model for Pedestrian Evacuation Considering Impacts of Fire Products

Automatic Recognition of Indoor Fire and Combustible Material with Material-Auxiliary Fire Dataset

Influence of coatings on residual strength of geopolymer concrete columns subjected to fire exposure: An experimental investigation

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