Reliability of sprinkler system in Australian shopping centres –A fault tree analysis

Moinuddin, Khalid; Innocent, Jasmine; Keshavarz, Koroush

doi:10.1016/j.firesaf.2019.03.006

Cited by 17 publications

(42 citation statements)

References 4 publications

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“…The HOE basic events that significantly contribute to the fire accidents' occurrence have been identified and listed in Table A1. Their occurrence probabilities are estimated based on statistical data obtained from the literature [14,[42][43][44][45][46][47][48] as shown in the following table. BNs allow for the combination of previous probability assignments with the newly available statistical data.…”

Section: Appendix A3 Step 3a-identification Of Human and Organizational Errorsmentioning

confidence: 99%

Sensitivity and Uncertainty Analyses of Human and Organizational Risks in Fire Safety Systems for High-Rise Residential Buildings with Probabilistic T-H-O-Risk Methodology

et al. 2021

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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.

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Section: Appendix A3 Step 3a-identification Of Human and Organizational Errorsmentioning

confidence: 99%

Sensitivity and Uncertainty Analyses of Human and Organizational Risks in Fire Safety Systems for High-Rise Residential Buildings with Probabilistic T-H-O-Risk Methodology

et al. 2021

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“…In practice, extensive use of industry average data is necessary to carry out the quantitative analysis. The accuracy of results obtained from the model depends upon the assigned probability values of the HOE variables and equipment reliability data obtained from [34][35][36][37][38][39]. The applicability of the data from these sources to the building sector should be carefully considered.…”

Section: Collection and Probability Analysis Of Hoesmentioning

confidence: 99%

Impact of Technical, Human, and Organizational Risks on Reliability of Fire Safety Systems in High-Rise Residential Buildings—Applications of an Integrated Probabilistic Risk Assessment Model

et al. 2020

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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.

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“…As communities often cover large areas and include complex geometries (e.g. car parks and shopping centers), there is a significant increase in the number of expected fire hazards [12][13]. Typically, it will be both expensive and impractical to apply traditional fire hazard identification methods within communities, because too many resources (including workers and devices) will be needed.…”

Section: (See Table Captionmentioning

confidence: 99%

Identifying Community Fire Hazards from Citizen Communication by Applying Transfer Learning and Machine Learning Techniques

Liu

Jomaas

2020

Fire Technol

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A cross-region transfer learning method is proposed to identify community (e.g. car parks, public spaces and shopping centers) fire hazards based on text input provided by community members. The key component of the method, which also accounts for data imbalance, is an improved transfer component analysis (TCA) that is embedded with a local discriminant analysis (LDA) to transfer non-local rich knowledge to the fire hazard identification of local communities with an insufficient number of samples. In addition, a fire hazard knowledge map is established and applied to supplement the missing key features for fire hazard identification, and ontology modeling is applied to standardize the text features and reduce the effect of semantic ambiguity brought by crossregion knowledge transfer. The proposed method is verified based on the text data of nine fire hazard classes from Lanzhou and Beidaihe in China. Machine learning experiments show that fire hazard identification performance of all nine classes were improved with the overall accuracy, precision, recall, F1 score and AUC increased by 12%, 15%, 16%, 15% and 15%, respectively. Under data imbalance scenarios, the proposed method outperforms the state of the art methods, such as sampling-based methods, FastText and ULMFiT. The results also show that the proposed method can achieve desired performance with only half of the training samples. These findings illustrate that the proposed method can assist regions by improving fire identification results significantly through knowledge transfer. The proposed approach can be followed to build smart systems for community fire risk management with reasonable performance and high efficiency.

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Reliability of sprinkler system in Australian shopping centres –A fault tree analysis

Cited by 17 publications

References 4 publications

Sensitivity and Uncertainty Analyses of Human and Organizational Risks in Fire Safety Systems for High-Rise Residential Buildings with Probabilistic T-H-O-Risk Methodology

Sensitivity and Uncertainty Analyses of Human and Organizational Risks in Fire Safety Systems for High-Rise Residential Buildings with Probabilistic T-H-O-Risk Methodology

Impact of Technical, Human, and Organizational Risks on Reliability of Fire Safety Systems in High-Rise Residential Buildings—Applications of an Integrated Probabilistic Risk Assessment Model

Identifying Community Fire Hazards from Citizen Communication by Applying Transfer Learning and Machine Learning Techniques

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