Probabilistic fire demand model for steel pipe-racks exposed to localised fires

Abstract

This paper proposes a new method to build a probabilistic fire demand model (PFDM) to investigate the structural behaviour of a steel pipe-rack located within an industrial installation and exposed to a localised fire. The PFDM will serve to develop fire fragility functions to be used either in a fire risk assessment or in a fully probabilistic structural fire engineering (PSFE) framework. The cloud analysis (CA) was exploited to build a PFDM based on different engineering demand parameters (EDP) -intensity measures (IM) pairs. In particular, the analysis was applied to a prototype steel pipe-rack integrating an industrial plant in Italy. In order to cover a wide range of plausible fire scenarios and to introduce uncertainties in the fire model, 539 fire scenarios were examined by varying the fire diameter, the fire-structure distance and the fuel. The selection of the fire diameters was based on parametric analyses quantifying liquid flow through orifices and pipes. The thermal impact of the pool fires on the structure was analysed using the LOCAFI localised fire model and the thermo-mechanical response of the pipe rack was evaluated by means of finite element analysis. Based on the structural analysis outcomes, it was found that the interstorey drift ratio (ISDR) -average heat flux impinging the structure (HFavg) EDP-IM pair was the most efficient and was also characterised the highest relative sufficiency among the other pairs. Moreover, it has to be noted that for this type of case study, the CA revealed to be a suitable and versatile tool to build a PFDM.

Fire fragility curves for steel pipe-racks exposed to localised fires

“…For brevity here only the 2 best IM candidates, i.e. HFavg and L/D, will be considered and for a more comprehensive analysis refer to Randaxhe et al 26 .…”

“…It was decided to define fire scenarios by varying three parameters: the pool fire diameter, the fuel and the fire-structure distance. As analysed by Randaxhe et al 26 , in petrochemical plants, pool fires can result from the ignition of a fuel contained in a cylindrical tank or from the ignition of a leaking fuel. Resulting fires are likely to present a diameter varying between 5m and 30m.…”

Development of an Analytical Model to Determine the Heat Fluxes to a Structural Element Due to a Travelling Fire

“…These correlations detailed above were derived for the following range of application: the diameter of the fire is limited by D ≤ 10 m and the rate of heat release of the fire by Q ≤ 50 MW, but these limitations were disregarded, in the lack of more precise equations. It is nevertheless worth to point out that the virtual solid flame model (project LOCAFI [8]) was compared against experimental data of kerosene pool fires as large as 50 m and characterised by RHR in the order of 3000 MW (Randaxhe [19]). McCaffrey [20] has reviewed effects on flame height of placing fire sources next to a wall or in a corner but these effects were reported to be small, and no such consideration is made in the present analytical procedure.…”

“…It corresponds to zones 1, 2, 5, 6 when the flame does not impact the ceiling (see Figure 4), and to zone 1, 5 when the flame impacts the ceiling (see Figure 5). If the point of interest is within the solid flame (i.e., in zone 3 or zone 4), Equation ( 18) applies, while if it is in the horizontal layer underneath the ceiling when the flame impacts the ceiling (i.e., in zone 2, 4, 6 in Figure 5), Equation (19), as described in EN1991-1-2 [13], applies. These equations, commonly referred as the Hasemi model, are based on experimental tests [23][24][25][26].…”

Retrofit of existing steel structures against progressive collapse through roof-truss

“…often cause local damage to building structures and pose a serious threat when one or more vertical load-bearing components fail, leading to the progressive collapse of the entire structure or a large part of it. Since the beginning of the 21 st century, there has been growing interest in the risks associated with extreme events [e.g., 1,2,3,4,5]. Indeed, despite being characterized by low probability of occurrence, the consequences can be very high.…”