A Dynamic, Probabilistic Fire Risk Model incorporating Technical, Human and Organizational Risks for High-rise Residential Buildings

Tan, Samson (2021) A Dynamic, Probabilistic Fire Risk Model incorporating Technical, Human and Organizational Risks for High-rise Residential Buildings. PhD thesis, Victoria University.


Fire events in high-rise residential buildings pose threats to both property and human life and upon investigation it is frequently revealed that the cause of a fire event is not simply due to technical errors. Often these investigations uncover human and organizational errors (HOEs) that contribute to fire risk and fire events. Many human factors identified in fire risk environments can be minimized through employee training and development while organizational factors, such as safety culture, can be changed over time through transformational interventions that shift existing mindsets. Probabilistic risk analysis (PRA) methods are modeling tools that allow fire risk professionals to estimate risk by computing several scenarios of what can go wrong, the likelihood of events occurring, and the consequences of the events. PRA often takes a fixed value of events occurring likelihood over the building design period, whereas it may change due to aging of a fire safety measure. PRA is an explicit methodology for complying with performance requirements of building codes, but existing PRA methods may underestimate safety risk levels by ignoring HOEs while focusing solely on technical risks and errors as well as not taking into account reliability changes over the time. In this work, a systematic review identifies HOEs that can potentially affect risk estimates in fire safety modelling of high-rise buildings. The importance and uniqueness of high-rise buildings is mainly due to the special nature of buildings where fire-fighting techniques require different safety measures than in other industries. In addition, the height of high-rise buildings and the increased number of occupants result in longer evacuation times than other types of buildings or industrial plants. Evacuation times are increased further when the number of stairways in these buildings is limited. A wide range of HOEs have been identified as impacting risk in various industries such as offshore oil production and nuclear plants, but not all these identified HOEs will be appropriate for high-rise buildings. Important factors are those that emerge consistently from different published sources supported by quantitative case studies of events such as the Grenfell Tower fire in London and the fire in the Lacrosse building fire in Melbourne. The linking of published HOEs with errors identified from high-rise building fire case studies uncover HOEs likely to influence risk estimates. Quantifications of the impact of HOEs on risk estimates in other industries indeed justify additional research and inclusion of HOEs for risk estimates in high-rise buildings. This work uniquely connects HOEs from various industries to likely HOEs associated with risks in high-rise buildings to address an important gap in the literature. The research provides empirical quantitative studies, theoretical framework, and guidelines demonstrating how HOEs risks can be distilled to improve PRAs of fires in high-rise buildings. To further address the gap, this work proposes a comprehensive Technical- Human-Organizational Risk (T-H-O-Risk) methodology to enhance existing PRA approaches by quantifying human and organizational risks. The methodology incorporates Bayesian Network (BN) analysis of HOEs and System Dynamics (SD) modeling for dynamic characterization of risk variations over time in high- rise residential buildings. Most current approaches assume that the relationships among HOEs are independent and current methods do not explain the interactions among these variables. An integrated T-H-O-Risk model overcomes this limitation by measuring causal relationships among variables and quantifying HOEs such as staff training, fire drill practices, safety culture and building maintenance. The model addresses the underestimation of risk resulting from not following the proper practices and regulations. Issues of selecting fire safety measures needed to reduce risk to an acceptable level are examined while evaluating the efficacy of active systems that are sensitive to HOEs. The methodology utilizes the “as low as reasonably practicable” (ALARP) principle in comparing risk acceptance for different case studies demonstrating the model’s value related to risk reduction with respect to initial designs of high-rise residential buildings. By incorporating both BN and SD techniques, the T-H-O-Risk model developed in this research evaluates HOEs dynamically in an innovative and integrated quantitative risk framework. This is possible by incorporating factors that vary with time since event tree/fault tree (ET/FT) and BN alone cannot deal with dynamic characteristics of the process variables and HOEs. The model includes risk variation over time which is significantly better than contemporary methods that only provide static values of risks. Initially three case studies are conducted with limited number of scenarios for the purpose of validation to demonstrate the application of this comprehensive approach to the designs of various high-rise residential buildings ranging from 18 to 24 stories. Societal risks are represented in F-N curves. Results show that in general, fire safety designs that do not consider HOEs underestimate the overall risks significantly which can reach 40% in some extreme cases. Furthermore, risks over time due to HOEs vary by as much as 30% over 10 years. A sensitivity analysis indicates that deficient training, poor safety culture and ineffective emergency plans have significant impact on overall risk. Subsequently, the application of the T-H-O-Risk methodology was expanded to seven designs of high-rise residential buildings (including earlier three) with 16 different technical solutions to quantify the impact of HOEs on different fire safety systems. The active systems considered are sprinklers, building occupant warning systems, smoke detectors, and smoke control systems. The results indicate that HOEs impact risks in active systems by approximately 20%, however, HOEs have a limited impact on passive fire protection systems. Large variations are observed in the reliability of active systems due to HOEs over time. Finally, sensitivity and uncertainty analyses of HOEs were carried out on three selected buildings from the above seven. The sensitivity analysis again indicates that deficient training, poor safety culture and ineffective emergency plans have significant impact on overall risk. The model also identifies multiple cases where tenable conditions are breached. A detailed uncertainty analysis is carried out using a Monte Carlo approach to isolate critical parameters affecting the risk levels. This research has developed a novel approach to enhance fire risk assessment methods using a holistic quantification of technical, human, and organizational risks for high-rise residential buildings which ultimately benefits future risk assessments providing more precise estimates. A significant contribution of this research involves the systematic identification of HOEs and their associated risks for consideration in future PRAs. By studying various trial designs, the impact of HOEs on fire safety systems is analyzed while demonstrating the robustness of the T-H-O-Risk methodology for high-rise buildings. The research lays foundations for next-generation building codes and risk assessment methods.

Item type Thesis (PhD thesis)
URI https://vuir.vu.edu.au/id/eprint/42814
Subjects Current > FOR (2020) Classification > 4005 Civil engineering
Current > Division/Research > College of Science and Engineering
Current > Division/Research > Institute for Sustainable Industries and Liveable Cities
Keywords thesis by publication; fire; high-rise residential buildings; human and organizational errors; HOEs; fire risk; fire safety modelling; probabilistic risk analysis; high-rise buildings
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