Fire engineering properties in the IFC building product model and mapping to BRANZFIRE

The Industry Foundation Class (or IFC) Model is a standardised, object-oriented “building product model” that provides an electronic description of buildings. Entities are defined in the model to represent building elements with their associated properties. This paper reviews the latest release of the IFC Model considering entities and properties related to fire engineering. The paper goes on to examine how these entities and properties can be mapped to the input requirements of the BRANZFIRE fire simulation softwar

The New Zealand Building Act 2004 and the involvement of the New Zealand Fire Service

Prior to 1992, fire safety regulations in New Zealand operated under a prescriptive regime. Such prescriptive requirements provided direct guidance in specific terms for those designing buildings and in effect dictated design criteria. In December 1991 a new Building Act was passed in law, replacing the existing prescriptive fire safety code, NZ Standard 1900, Chapter 51. In doing so, it allowed for performance-based design to be carried out for the first time in New Zealand’s history and offered designers a less restrictive design environment. The changes implemented in 1991 also set out mandatory performance requirements in the Building Code2 that must be complied with by the designer. Those relating to fire safety are outlined in the C clauses and contain four categories: C1 Outbreak of fire, C2 Means of escape, C3 Spread of fire and C4 Structural stability during fire. Assessing compliance with these performance requirements and their enforcement lies with the Building Consent Authorities (BCA’s), formerly known as Territorial Authorities (TA’s). They must be satisfied on reasonable grounds that the provisions of the Building Code would be met if the building work was completed in accordance with the plans and specifications submitted with the building consent application. At the discretion of the BCA, a performance-based design can be passed to an independent fire engineer for peer review, prior to the BCA issuing consent

Querying a regulatory model for compliant building design audit

The ingredients for an effective automated audit of a building design include a BIM model containing the design information, an electronic regulatory knowledge model, and a practical method of processing these computerised representations. There have been numerous approaches to computer-aided compliance audit in the AEC/FM domain over the last four decades, but none has yet evolved into a practical solution. One reason is that they have all been isolated attempts that lack any form of standardisation. The current research project therefore focuses on using an open standard regulatory knowledge and BIM representations in conjunction with open standard executable compliant design workflows to automate the compliance audit process. This paper provides an overview of different approaches to access information from a regulatory model representation. The paper then describes the use of a purpose-built high-level domain specific query language to extract regulatory information as part of the effort to automate manual design procedures for compliance audit

Smoke management issues in buildings with large enclosures

Buildings that provide sporting, entertainment, and leisure facilities (e.g. sports arenas, exhibition halls, etc) can often contain large enclosed spaces or voids. In the event of a fire, these buildings often require the use of a smoke management system to provide conditions for safe means of escape for the building occupants. This paper raises a range of issues relating to smoke management in buildings with large enclosed spaces, including smoke management methods, design scenarios and some simple calculation methods. Experience of actual installed systems in real buildings has led to concerns on the efficacy of some smoke management systems, especially over the lifetime of a building. This paper discusses some of these concerns, real examples of sources of failure, and the importance of proper documentation, commissioning, maintenance and testing of these systems. As a way of addressing these concerns, a process validation methodology is presented to evaluate the design, the designer, the implementation of the design, and the long-term management, operation and maintenance of such systems

Scoping study on the significance of mesh resolution vs. scenario uncertainty in the CFD modelling of residential smoke control systems

Computational fluid dynamics (CFD) modelling is a commonly applied tool adopted to support the specification and design of common corridor ventilation systems in UK residential buildings. Inputs for the CFD modelling of common corridor ventilation systems are typically premised on a ‘reasonable worst case’, i.e. no specific uncertainty quantification process is undertaken to evaluate the safety level. As such, where the performance of a specific design sits on a probability spectrum is not defined. Furthermore, mesh cell sizes adopted are typically c. 100 – 200 mm. For a large eddy simulation (LES) based CFD code, this is considered coarse for this application and creates a further uncertainty in respect of capturing key behaviours in the CFD model. Both co-existing practices summarised above create uncertainty, either due to parameter choice or the (computational fire and smoke) model. What is not clear is the relative importance of these uncertainties. This paper summarises a scoping study that subjects the noted common corridor CFD application to a probabilistic risk assessment (PRA), using the MaxEnt method. The uncertainty associated with the performance of a reference design is considered at different grid scales (achieving different ‘a posteriori’ mesh quality indicators), with the aim of quantifying the relative importance of uncertainties associated with inputs and scenarios, vs. the fidelity of the CFD model. For the specific case considered herein, it is found that parameter uncertainty has a more significant impact on the confidence of a given design solution relative to that arising from grid resolution, for grid sizes of 100 mm or less. Above this grid resolution, it was found that uncertainty associated with the model dictates. Given the specific ventilation arrangement modelled in this work care should be undertaken in generalising such conclusions

Transformative forms of simulation in health care – the seven simulation-based ‘I’s: a concept taxonomy review of the literature

Simulation for non-pedagogical purposes has begun to emerge. Examples include quality improvement initiatives, testing and evaluating of new interventions, the co-designing of new models of care, the exploration of human and organizational behaviour, comparing of different sectors and the identification of latent safety threats. However, the literature related to these types of simulation is scattered across different disciplines and has many different associated terms, thus making it difficult to advance the field in both recognition and understanding. This paper, therefore, aims to enhance and formalize this growing field by generating a clear set of terms and definitions through a concept taxonomy of the literature. Due to the lack of alignment in terminology, a combination of pearl growing, snowballing and citation searching approach was taken. The search was conducted between November 2020 and March 2023. Data were extracted and coded from the included papers according to seven Simulation-Based I’s (SBIs; Innovation, Improvement, Intervention, Involvement, Identification, Inclusion and Influence). Eighty-three papers were identified from around the world, published from 2008 to 2023. Just over half were published in healthcare simulation journals. There were 68 different terms used to describe this form of simulation. Papers were categorized according to a primary and secondary Simulation-Based ‘I’. The most common primary SBI was Simulation-Based Identification. Selected categorized papers formed a descriptive narrative for each SBI. This review and taxonomy has revealed the breadth of an emerging and distinct field within healthcare simulation. It has identified the rate at which this field is growing, and how widespread it is geographically. It has highlighted confusion in terminology used to describe it, as well as a lack of consistency in how it is presented throughout the literature. This taxonomy has created a grounding and step change for this work which is embedded in the literature, providing a rich and varied resource of how it is being utilized globally