Handling and updating uncertain information in bow-tie analysis

Bow-tie analysis is a fairly new concept in risk assessment that can describe the relationships among different risk control parameters, such as causes, hazards and consequences to mitigate the likelihood of occurrence of unwanted events in an industrial system. It also facilitates the performance of quantitative risk analysis for an unwanted event providing a detailed investigation starting from basic causes to final consequences. The credibility of quantitative evaluation of the bow-tie is still a major concern since uncertainty, due to limited or missing data, often restricts the performance of analysis. The utilization of expert knowledge often provides an alternative for such a situation. However, it comes at the cost of possible uncertainties related to incompleteness (partial ignorance), imprecision (subjectivity), and lack of consensus (if multiple expert judgments are used). Further, if the bow-tie analysis is not flexible enough to incorporate new knowledge or evidence, it may undermine the purpose of risk assessment.Fuzzy set and evidence theory are capable of characterizing the uncertainty associated with expert knowledge. To minimize the overall uncertainty, fusing the knowledge of multiple experts and updating prior knowledge with new evidence are equally important in addition to addressing the uncertainties in the knowledge. This paper proposes a methodology to characterize the uncertainties, aggregate knowledge and update prior knowledge or evidence, if new data become available for the bow-tie analysis. A case study comprising a bow-tie for a typical offshore process facility has also been developed to describe the utility of this methodology in an industrial environment.     

Fuzzy Application Procedure (FAP) for the risk assessment of occupational accidents

This paper concerns a procedure to quantify the risk of occupational accidents based on Fuzzy Logic approach, named Fuzzy Application Procedure (FAP). FAP was developed as a support tool, after the method based on fuzzy logic was developed and validated. The methodology and FAP support tool were found to be able to quantitatively assess the risk of occupational accidents for different industrial and site activities and to identify the most efficient intervention measures that can be taken to reduce risks. The application of FAP to two Italian industrial plant allowed the validation of the procedure. It can be seen how FAP should be considered easy to use for any type of company, with the only requirement of having a sufficient and homogeneous number of accidents recorded, so as to be able to correctly tune the reference parameters of the fuzzy methodology.     

建筑火灾风险评价技术初探

在分析建筑火灾发生原因的基础上,建立了建筑火灾风险评估因素集.并运用模糊评价法对建筑物的火灾安全进行了风险评价,通过评价得出建筑物所属的安全等级.该方法可用于保险公司对投保建筑的鉴别与筛选,同时可使管理者及时采取措施,以减少火灾危险性.     

基于模糊数学方法的门式起重机风险评估研究

本文采用模糊数学方法对门式起重机潜在风险因素（腐蚀、裂纹、强度及变形）导致的金属结构破坏进行风险评估。从主梁和支腿两个角度出发,建立了不同金属结构风险影响因素为子系统的多层次评判系统。该方法方便了门式起重机的综合安全风险评估,为提高其安全性能提供了方向。     

矿井安全评价中的几个问题的初步研究

研究了矿井灾害系统危险评价中的几个关键问题，分析了煤矿灾害系统的结构特点，针对矿井灾害系统这样一个过程性本质模糊的灾害发生环境，提出了危险程度的模糊风险度量理论和基于此理论的定量表示方法，以及对不同灾害事故后果相关性计算方法。还论述了全矿与其各组成部分的危险程度之间的相互关系。     

Risk and consequence analyses of hazardous chemicals in marshalling yards and warehouses at Ikonio/Piraeus harbour, Greece

The risk and the consequences of possible accidents at Ikonio (Piraeus harbour, Greece) are investigated and analyzed in this paper. At this harbour large cargoes of dangerous chemicals (toxic or flammable) are unloaded and stored in warehouses or in marshalling yards. Houses and a school are located near the plant and are directly exposed to danger in the case of an accident. The results were obtained by Breeze Hazard Professional software package, which contains several models for performing consequence modeling through quantitative risk assessment.     

Risk assessment of sour gas inter-phase onshore pipeline using ANN and fuzzy inference system – Case study: The south pars gas field

Nowadays, pipelines have been extensively used for transporting oil and gas for long distances. Therefore, their risk assessment could help to identify the associated hazards and take necessary actions to eliminate or reduce the risk. In the present research, an artificial neural network (ANN) and a fuzzy inference system (FIS) were used to prepare a new model for pipeline risk assessment with higher accuracy. To reach this objective, the Muhlbauer method, as a common method for oil and gas pipeline risk assessment, was used for determining important and influential factors in the pipeline performance. Mamdani fuzzy model was developed in Matlab software by considering expert knowledge. The outcomes of this model were used to develop an ANN. To verify the developed model, the inter-phase shore pipe of phase 9–10 refinery in the South Pars Gas field was considered as a case study. The results showed that the proposed model gives a higher level of accuracy, precision, and reliability in terms of pipe risk assessment.     

Test case based risk predictions using artificial neural network

INTRODUCTION: The traditional fuzzy-rule-based risk assessment technique has been applied in many industries due to the capability of combining different parameters to obtain an overall risk. However, a drawback occurs as the technique is applied in circumstances where there are multiple parameters to be evaluated that are described by multiple linguistic terms. METHOD: In this study, a risk prediction model incorporating fuzzy set theory and Artificial Neural Network (ANN) capable of resolving the problem encountered is proposed. An algorithm capable of converting the risk-related parameters and the overall risk level from the fuzzy property to the crisp-valued attribute is also developed. Its application is demonstrated by a test case evaluating the navigational safety within port areas. RESULTS: It is concluded that a risk predicting ANN model is capable of generating reliable results as long as the training data takes into account any potential circumstance that may be met. IMPACT ON INDUSTRY: This paper provides safety assessment practitioners with a novel and flexible framework of modelling risks using a fuzzy-rule-base technique. It is especially applicable in circumstances where there are multiple parameters to be considered. The proposed framework also enables the port industry to manage navigational safety in a rational manner.