Fire load: Survey data, recent standards, and probabilistic models for office buildings

Negar Elhami Khorasani, Maria Eugenia Moreyra Garlock, Paolo Gardoni

Research output: Contribution to journalArticle

19 Scopus citations

Abstract

To enable a probabilistic performance-based approach to fire design, probabilistic models to represent the fire load are needed. Such probabilistic models are presented in this paper for office buildings. First, a literature review of recent fire load density surveys is presented. These surveys indicate a large range of fire load density values, and strong correlation between fire load density, compartment area, and use. However, current codes and standards (such as Eurocode and a recent publication of NFPA 557) that are used to estimate fire load density do not account for these variables and specify constant values. Based on survey data, a Bayesian probability approach is used to develop probabilistic models to predict the fire load density in office buildings (one for light-weight use and one for heavy-weight use). The models consider the size of the compartment and the office room use (general office, library, storage, etc.). The proposed models correlate well to the data and have a better fit than that obtained, using the Eurocode and NFPA 557. The proposed models for fire load density are then used to develop probabilistic models for the maximum fire temperature in a given compartment. Several scenarios with different floor areas and openings are defined and the fire load models developed in this paper are used to investigate the range of possible maximum fire temperatures and their corresponding probabilities. It is found that the proposed maximum temperature model results in a range of temperatures that correlates well with the test data and the Refined Tanaka Method proposed by a recent SFPE standard. It is shown that both the fire load density and the maximum temperature probabilistic models are well suited for application in a probabilistic performance-based approach to fire design.

Original languageEnglish (US)
Pages (from-to)152-165
Number of pages14
JournalEngineering Structures
Volume58
DOIs
StatePublished - Jan 1 2014

All Science Journal Classification (ASJC) codes

  • Civil and Structural Engineering

Keywords

  • Bayesian
  • Compartment fire
  • Fire load
  • Fire load density
  • Fire temperature
  • Office
  • Performance-based design
  • Probabilistic model
  • Survey

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