The Next Generation of Performance-Based Fire Safety Engineering in New Zealand
The introduction of a new Building Act in New Zealand in 2004 prompted a comprehensive review of the Building Code to ensure that its provisions were both consistent with the new Act, and contained sufficient quantification of performance requirements. The review resulted in new Protection from Fire code clauses, Acceptable Solutions (prescriptive, nonmandatory deemed-to-satisfy provisions), and a Verification Method (based on the structural design process where design loads and performance criteria are specified) being introduced in 2012. This new generation of fire safety regulation is expected to substantially reduce the level of inconsistency and inefficiency that previously existed. The next paradigm shift in the New Zealand building regulatory environment is expected to introduce a risk-informed regime, where probabilistic provisions will be incorporated to address the inherent risk and uncertainty associated with performance-based fire safety engineering design. With this scenario in mind, a collaborative research project involving BRANZ Ltd and the University of Canterbury has recently developed a new fire safety engineering design tool called B-RISK, forming part of the essential underlying research necessary to underpin any such future code change. This paper describes the development of the B-RISK tool and its application to the practice of performance-based fire safety engineering design. Instead of doing calculations in the traditional deterministic manner, B-RISK improves the designer’s riskinformed decision-making by using a physics-based model in conjunction with the probabilistic functionality of Monte-Carlo sampling techniques in an iterative fashion. A design fire generator that populates a room with items and predicts item to item fire spread provides probabilistic families of fire growth curves. User-defined distributions for key input parameters are included, as well as distributions for the reliability of fire protection systems, from which B-RISK samples for each iteration. The resulting model results are presented in the form of cumulative density functions of probability, whereby a risk-informed design decision can be made.