Fire analysis of steel frames with the use of artificial neural networks

The paper presents an alternative approach to the modelling of the mechanical behaviour of steel frame material when exposed to the high temperatures expected in fires. Based on a series of stress-strain curves obtained experimentally for various temperature levels, an artificial neural network (ANN) is employed in the material modelling of steel. Geometrically and materially, a non-linear analysis of plane frame structures subjected to fire is performed by FEM. The numerical results of a simply supported beam are compared with our measurements, and show a good agreement, although the temperature-displacement curves exhibit rather irregular shapes. It can be concluded that ANN is an efficient tool for modelling the material properties of steel frames in fire engineering design studies. (c) 2007 Elsevier Ltd. All rights reserved

Short-term fire front spread prediction using inverse modelling and airborne infrared images

A wildfire forecasting tool capable of estimating the fire perimeter position sufficiently in advance of the actual fire arrival will assist firefighting operations and optimise available resources. However, owing to limited knowledge of fire event characteristics (e.g. fuel distribution and characteristics, weather variability) and the short time available to deliver a forecast, most of the current models only provide a rough approximation of the forthcoming fire positions and dynamics. The problem can be tackled by coupling data assimilation and inverse modelling techniques. We present an inverse modelling-based algorithm that uses infrared airborne images to forecast short-term wildfire dynamics with a positive lead time. The algorithm is applied to two real-scale mallee-heath shrubland fire experiments, of 9 and 25 ha, successfully forecasting the fire perimeter shape and position in the short term. Forecast dependency on the assimilation windows is explored to prepare the system to meet real scenario constraints. It is envisaged the system will be applied at larger time and space scales.Peer ReviewedPostprint (author's final draft

Modelling the bond between concrete and reinforcing steel in a fire

This is the post-print version of the final paper published in Engineering Structures. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2010 Elsevier B.V.A non-linear procedure is presented for modelling the bond characteristic between concrete and reinforcing steel for reinforced concrete structures in a fire. The accuracy and reliability of the model are demonstrated by the analysis of one pull-out test and one beam test at ambient temperature and four full-scale beams tested under two fire conditions. The model is clearly capable of predicting the response of reinforced concrete members and structures in a fire with acceptable accuracy. The bond-link element has been found to have good computational stability and efficiency for 3D analysis of reinforced concrete structures in fires. It is shown that the bond condition between the concrete and reinforcing steel bar has an important influence on the fire resistance of reinforced concrete structures, especially when the temperature of the reinforcing steel bar is high (more than 500 °C). Hence, the current assumption of a perfect bond condition for analysis of reinforced concrete structures under fire conditions is unconservative

Prediction of smoke filling in large volumes by means of data assimilation-based numerical simulations

The concept of numerical simulations for real-time Numerical Fire Forecasting is illustrated for the case of natural smoke filling of a large-scale atrium in case of fire. The numerical simulations are performed within the Inverse Zone Modelling framework. The technique consists of assimilating collected data for a certain parameter, in casu the smoke layer height, into the zone model in order to estimate an unknown of the problem ('model invariant'), mainly the fire heat release rate. A forecast in terms of evolution of smoke level and temperature can then be produced. Because zone model calculations are very fast, positive lead times of several minutes are obtained. The developed model produces reliable forecasts for the cases considered. Equally important, the robustness of the technique is illustrated: the sensitivity of the results to the 'initial guess' of the model invariants is small (i.e. the method converges easily); one model invariant is sufficient to obtain reliable predictions for smoke layer height evolution; the data assimilation window length does not affect the results significantly. The method automatically provides a different value for the plume entrainment constant, depending on the position of the fire (in the middle of the atrium or in a corner)

Numerical modelling of two-way reinforced concrete slabs in fire

This paper describes numerical modelling of the fire behaviour of two-way reinforced concrete slabs using a special purpose non-linear finite-element program, SAFIR. Several two-way reinforced concrete and composite steel-concrete slabs are tested under exposure to the ISO standard fire in order to validate the shell finite element in the SAFIR program. The modelling results showed agreement with the fire tests and demonstrate that the SAFIR shell element can be used to predict tensile membrane behaviour of two-way reinforced concrete slabs in fire conditions. The analyses show that two-way slabs have excellent fire resistance if they deform in double curvature and develop tensile membrane action. (C) 2004 Elsevier Ltd. All rights reserved

Fire safety assessment of open wide gangway underground trains in tunnels using coupled fire and evacuation simulation

A new type of train configuration, known as Open Wide Gangway (OWG) is becoming popular, particularly in underground environments. Previous fire modelling analysis demonstrated that the OWG configuration was considered safe as or safer than conventional configurations as it reduced the likelihood of flashover. However, these studies have ignored the impact on evacuation of the spread of fire effluent to non-fire cars. Here we explore the fire safety offered by conventional and OWG configurations using coupled fire and evacuation modelling techniques. Two tunnel train situations are considered; one in which the car side doors are available for evacuation (train in a wide tunnel) and the other in which only the end cab doors are available (train in a narrow tunnel). Two population configurations are considered, fully and half loaded. Two ignition sources are also considered, one representing an accidental fire and the other an arson fire. The analysis demonstrates that while the OWG configuration may produce improved fire performance in the car of fire origin compared to the conventional configuration, if the interaction of the fire effluent with the evacuating passengers is considered, the OWG configuration results in a significantly greater number of casualties in virtually all the scenarios considered

Evaluation of graphical control flow management approaches for Event-B modelling

Integrating graphical representations with formal methods can help bridge the gap between requirements and formal modelling. In this paper, we compare and evaluate two graphical approaches aiming at describing control flows and refinement in Event-B, and we use a fire dispatch system case study to perform this evaluation. The fire dispatch system case study provides a good example of a complex workflow through which we try to identify a process that facilitates defining the structural and the behavioural parts of the Event-B model. In our case study, we focus on building the dynamic part of the model to evaluate the two diagrammatic notations: UML Activity Diagrams and Atomicity Decomposition Diagrams. Based on our evaluation, we try to identify the advantages and limitations of both approaches. Finally, we try to compare how both graphical notations can affect the Event-B formal modelling of our case study