Fire Technology最新文献

Abstract

The traditional storage method of fire accident cases is mainly in the form of text, and it is difficult to effectively conduct comprehensive analysis due to the limited ability to display key information and fire knowledge. In this paper, a structured storage form of building fire cases was proposed based on knowledge graph, which can comprehensively describe and visualize the fire causes, the dynamic fire development process and evacuation process. It enables readers to get information and knowledge from building fire cases intuitively, and supports the comprehensive analysis for building fire prevention strategies. The knowledge graphs are constructed for two common building types (residential and public buildings), and have the capacity to reflect the dynamic development law of fires from ignition to spread in different buildings. Meanwhile, as the occupants’ evacuation is the first concern when a fire occurs, the knowledge graphs also visualize the relationship among various conditions in the evacuation process. Different application scenarios are displayed in the paper, including case query, root-cause analysis and consequence forecasting, which shows the advantages and applicability of building fire knowledge graph.

The mechanical material behavior of mild steels is reversible in the cooling phase of natural fires, which is proven by experimental evidence. For the material behavior of high-strength steels during cooling, no results are yet available. The paper provides the first comprehensive test program on the constitutive material behavior of high-strength steels S690QL and S960QL as well as mild steel S355 J2 + N in the case of natural fires. It is elaborated that the mechanical material behavior of high-strength steels in the cooling phase differs from the behavior in the heating phase and is not reversible due to phase changes of the microstructure. A constitutive material model for structural fire design purposes is developed on the basis of experimental data and the soundness and reliability of the model are proven by a statistical study that systematically evaluates the deviation of the model prediction from the test data.

The fire resistance and the thermomechanical behaviour of Steel–Timber Composite (STC) beams are studied through 4 fire tests. 35 non-loaded reduced specimens and 2 mechanically loaded real-scale beams were tested considering standard fire conditions (ISO 834 temperature-time curve). The tested configurations consist of various steel profiles associated with timber elements in such a way that steel is fully or partially protected from fire. Timber is used as a fire-protective material since it has a low thermal conductivity and a predictable charring rate. The fire tests on non-loaded reduced specimens allowed us to investigate a wide variety of configurations and to identify key parameters. It is found that full timber protection is very efficient as steel remains below 250°C during 35 or 70 min when timber protection is respectively 30 or 50 mm thick. Moreover, timber moisture is found to have a beneficial impact on steel temperature, while hollow sections favour timber combustion and steel heating during the cooling phase. The full-scale mechanically loaded fire tests highlight the importance of assembly joints because deflection can open them, which accelerates the heating of steel. Finally, an 81 min fire resistance was measured for an STC beam with a 45 mm thick timber protection. These findings contribute to better understand the behaviour of steel–timber structural elements in fire situations. It appears that a judicious mixing of timber and steel can significantly improve the fire performance of these structural elements. The presented results can be used to improve the design of STC beams.

Fire testing enables an individual or an organisation to make a claim about how a material, product, or system will perform in operational use. This paper describes and analyses the various reaction-to-fire tests that have used over the last 100 years in the UK. By analysing the commonalities and differences between these tests we propose a ‘taxonomy of testing’. We suggest that tests may be classified by the degree to which users may unthinkingly apply the results—without leading to negative fire safety outcomes. We propose three categories: unrepresentative tests; model tests; and technological proof tests. Unrepresentative tests are those which do not mimic building fire scenarios, but have thresholds so conservative that users need not consider whether the test was applicable to their intended application. Model tests are those based on ‘models’ of expected fire scenarios—users must therefore be confident that the model is sufficiently similar to their application. Technological proof tests are those which provide a more realistic test of a real building system—users must carefully analyse the similarities between their test and the real building before applying the results. From this we conclude that where user competence is low, policymakers should cite only unrepresentative (and conservative tests) within their guidance. Conversely where user competence is high, policy makers may more safety cite model or technological proof tests. The kinds of tests that may be safely cited in guidance are therefore indelibly linked to the expertise of the user.

The effects of the slope and the trench inclination on the spread of wildfires with a homogeneous fuel bed in inclined trenches, were studied numerically by Fire Dynamics Simulator. This simulation is based on a solid–gas two-phase numerical model that incorporates the physicochemical combustion characteristics of Platycladus orientales leaves. The results show that the rate of fire spread accelerates with increasing slope and trench inclination. The flame front inclines until it attaches to the fuel bed for slope angles ranging from 20.9° to 35.2°, and it was found a critical angle for full attachment is about 35.2° for trench inclination of 45°. Increasing the trench inclination causes a decrease in the critical angle because the trench wall restricts air entrainment at the bottom flame, promoting the flame to adhere to obtain sufficient air. Flame radiation is the dominant heat transfer mechanism at low slopes, and as the slope increases, convective heat transfer starts to be relevant and significantly changes with the trench inclination. This study provides scientific insights and guidance for early prevention and fire fighting in inclined trenches of wildfires.

Concrete-filled hollow section (CFHS) columns with a solid steel core have gained popularity in the construction of tall buildings due to their robust load-bearing capacity, slender design, ease of prefabrication, and exceptional structural fire resistance. In this research paper, we introduce an innovative approach aimed at enhancing the structural performance of these columns. Our method involves replacing the solid steel core with high-strength bar bundles and substituting concrete with grout to achieve superior fire resistance. These modified columns are referred to as “bar-bundle columns.” The paper presents the results of extensive fire tests conducted on three bar-bundle columns, each with different bar-bundle sizes, quantities, and configurations. Additionally, we determine the temperature-dependent material properties of the high-strength steel used for reinforcing bars and the thermal properties of the grout used as a filler through standard experimental tests, which are crucial for numerical simulations. An advanced nonlinear finite element model is describe which is capable of predicting the fire behavior of bar-bundle columns. Finally, this numerical model is employed to conduct parametric analyses and propose a simplified design model for bar-bundle columns under fire conditions.Our findings indicate that the bar-bundle configuration and using grout as a filler significantly delays the heating of the steel core, resulting in enhanced fire resistance when compared to CFHS columns with a solid steel core. The simplified method proposed in this study can be used to estimate the fire resistance of slender bar bundles, but further experimental testing could further refine and improve its accuracy.

Large and downed woody fuels remaining behind a wildfire’s flame front tend to burn in a smoldering regime, producing large quantities of toxic gases and particulate emissions, which deteriorates air quality and compromises human health. Smoldering burning rates are affected by fuel type and size, the amount of oxygen reaching the surface, and heat losses to the surroundings. An external wind has the dual effects of bringing fresh oxidizer to the fuel surface and porous interior, while at the same time enhancing convective cooling. In this work, a series of experiments were conducted on single and adjacent poplar dowels to investigate the effect of fuel geometry and wind speed on smoldering of woody fuels, including its burning rate and combustion products. Dowels had variable thickness (19.1 and 25.4 mm), aspect ratios, and arrangement (number of dowels and spacing between them). Using measurement of mass loss, CO, and HC production as indicators of the smoldering intensity, the results indicate that the arrangement of smoldering objects significantly affects burning rates and emissions. Specifically, spacings of 1/8 and 1/4 of the dowel thickness enhanced the smoldering process. The smoldering intensity was also enhanced by increased external wind (ranging between 0.3 m/s and 1.5 m/s), but its effect was dependent upon the spacing between the dowels. The convective losses associated with the spacing were further investigated with a simplified computational model. The simulations show that the wind significantly increases convective losses from the smoldering surfaces, which in turn may offset the increase in smoldering intensity related to the higher oxygen flux at higher wind speeds.

This paper proposes a fully automated generative network (“SynFAGnet”) for automatically creating a realistic-looking synthetic fire image. SynFAGnet is used as a data augmentation technique to create diverse data for training models, thereby solving problems related to real data acquisition and data imbalances. SynFAGnet comprises two main parts: an object-scene placement net (OSPNet) and a local–global context-based generative adversarial network (LGC-GAN). The OSPNet identifies suitable positions and scales for fires corresponding to the background scene. The LGC-GAN enhances the realistic appearance of synthetic fire images created by a given fire object-background scene pair by assembling effects such as halos and reflections in the surrounding area in the background scene. A comparative analysis shows that SynFAGnet achieves better outcomes than previous studies for both the Fréchet inception distance and learned perceptual image patch similarity evaluation metrics (values of 17.232 and 0.077, respectively). In addition, SynFAGnet is verified as a practically applicable data augmentation technique for training datasets, as it improves the detection and instance segmentation performance.

The paper describes the outcomes of the analysis of a steel braced frame protected with spray-applied fire resistive material and subjected to fires following earthquake (FFE). Nonlinear time-history analyses were performed in order to evaluate the seismic response. Then, the post-earthquake fire ignitions within selected compartments were considered based on the damage suffered by the structure, which was estimated according to the inter-storey drift ratio and floor acceleration. Natural fire curves were determined by means of zone models. Thus, compartmentation and opening characteristics were included in the analysis. Finally, thermomechanical analyses were completed and failure criteria based on the column and beam displacement and rate of displacement were investigated. The results of the probabilistic analyses were used to produce fragility functions to evaluate the probability of exceeding a limit state conditioned on an intensity measure in the context of FFE.