While probabilistic risk assessment (PRA) is an explicit methodology for complying with the performance requirements of the Building Code of Australia (BCA) or similar codes, it traditionally focuses only on technical risks of fire safety systems in a building. There are growing concerns that performance-based fire engineering designs underestimate safety risk levels in high-rise residential buildings. Existing fire risk models account for failures of technical systems but ignore human and organizational errors (HOEs) and the complex interactions among these variables. Probabilistic models in other applications, such as offshore platforms and nuclear plants, demonstrate the importance of HOE inclusion in risk models and the resulting impacts on overall risk. This paper proposes a comprehensive technical-human-organizational risk (T-H-O-Risk) methodology to enhance the PRA approach by quantifying human and organizational risks in a probabilistic model using Bayesian Network (BN) analysis of HOEs and System Dynamics (SD) modelling for dynamic characterization of risk variations over time. While risk modelling itself is not novel, the current research develops unique and specific enhancements to existing risk approaches by integrating HOE risks with technical risks in a comprehensive dynamic and probabilistic model for high-rise residential buildings. Three case studies are conducted to demonstrate the application of this comprehensive approach to the designs of various high-rise residential buildings ranging from 18 to 24 storeys. Societal risks are represented in F-N curves. Results show that in general, fire safety designs that do not consider HOEs underestimate overall risks generally by20%-and can reach up to 42% in an extreme case. Furthermore, risks over time due to HOEs vary by as much as 30% over a 10-year period. A sensitivity analysis indicates that deficient training, poor safety culture and ineffective emergency plans have significant impact on overall risk.

This paper presents the differential mass scattering cross section [m 2 ·g -1 ·sr -1 ] of various non-flaming and flaming fire generated smoke aerosols as well as nuisance aerosols created in the Fire Emulator/Detector Evaluator. These measurements have been determined for two linear polarizations and the scattering angle range of 5° to 135° at a wavelength of 632.8 nm. Small diameter particles have been separated from large particles using the forward scattering information. Discrimination of soot generated by flaming fuels from both smoke aerosols generated by non-flaming fires and nuisance aerosols is demonstrated by the ratio of forward (45°) to backward scattering (135°), the polarization ratio, and dependence on scattering parameter,

The current paper presents an application of an alternative probabilistic risk assessment methodology that incorporates technical, human, and organizational risks (T-H-O-Risk) using Bayesian network (BN) and system dynamics (SD) modelling. Seven case studies demonstrate the application of this holistic approach to the designs of high-rise residential buildings. An incremental risk approach allows for quantification of the impact of human and organizational errors (HOEs) on different fire safety systems. The active systems considered are sprinklers, building occupant warning systems, smoke detectors, and smoke control systems. The paper presents detailed results from T-H-O-Risk modelling for HOEs and risk variations over time utilizing the SD modelling to compare risk acceptance in the seven case studies located in Australia, New Zealand, Hong Kong, Singapore, and UK. Results indicate that HOEs impact risks in active systems up to ~33%. Large variations are observed in the reliability of active systems due to HOEs over time. SD results indicate that a small behavioral change in ’risk perception’ of a building management team can lead to a very large risk to life variations over time through the self-reinforcing feedback loops. The quantification of difference in expected risk to life due to technical, human, and organizational risks for seven buildings for each of 16 trial designs is a novel aspect of this study. The research is an important contribution to the development of the next generation building codes and risk assessment methods.

Given that existing fire risk models often ignore human and organizational errors (HOEs) ultimately leading to underestimation of risks by as much as 80%, this study employs a technical-human-organizational risk (T-H-O-Risk) methodology to address knowledge gaps in current state-of-the-art probabilistic risk analysis (PRA) for high-rise residential buildings with the following goals: (1) Develop an improved PRA methodology to address concerns that deterministic, fire engineering approaches significantly underestimate safety levels that lead to inaccurate fire safety levels. (2) Enhance existing fire safety verification methods by incorporating probabilistic risk approach and HOEs for (i) a more inclusive view of risk, and (ii) to overcome the deterministic nature of current verification methods. (3) Perform comprehensive sensitivity and uncertainty analyses to address uncertainties in numerical estimates used in fault tree/event trees, Bayesian network and system dynamics and their propagation in a probabilistic model. (4) Quantification of human and organizational risks for high-rise residential buildings which contributes towards a policy agenda in the direction of a sustainable, risk-based regulatory regime. This research contributes to the development of the next-generation building codes and risk assessment methodologies.

No abstract