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This study aims to improve the reliability of emergency safety barriers by using the subjective safety analysis based on evidential reasoning theory in order to develop on a framework for optimizing the reliability of emergency safety barriers.

The emergency event tree analysis is combined with an interval type-2 fuzzy-set and analytic hierarchy process (AHP) method. In order to the quantitative data is not available, this study based on interval type2 fuzzy set theory, trapezoidal fuzzy numbers describe the expert's imprecise uncertainty about the fuzzy failure probability of emergency safety barriers related to the liquefied petroleum gas storage prevent. Fuzzy fault tree analysis and fuzzy ordered weighted average aggregation are used to address uncertainties in emergency safety barrier reliability assessment. In addition, a critical analysis and some corrective actions are suggested to identify weak points in emergency safety barriers. Therefore, a framework decisions are proposed to optimize and improve safety barrier reliability. Decision-making in this framework uses evidential reasoning theory to identify corrective actions that can optimize reliability based on subjective safety analysis.

A real case study of a liquefied petroleum gas storage in Algeria is presented to demonstrate the effectiveness of the proposed methodology. The results show that the proposed methodology provides the possibility to evaluate the values of the fuzzy failure probability of emergency safety barriers. In addition, the fuzzy failure probabilities using the fuzzy type-2 AHP method are the most reliable and accurate. As a result, the improved fault tree analysis can estimate uncertain expert opinion weights, identify and evaluate failure probability values for critical basic event. Therefore, suggestions for corrective measures to reduce the failure probability of the fire-fighting system are provided. The obtained results show that of the ten proposed corrective actions, the corrective action “use of periodic maintenance tests” prioritizes reliability, optimization and improvement of safety procedures.

This study helps to determine the safest and most reliable corrective measures to improve the reliability of safety barriers. In addition, it also helps to protect people inside and outside the company from all kinds of major industrial accidents. Among the limitations of this study is that the cost of corrective actions is not taken into account.

Our contribution is to propose an integrated approach that uses interval type-2 fuzzy sets and AHP method and emergency event tree analysis to handle uncertainty in the failure probability assessment of emergency safety barriers. In addition, the integration of fault tree analysis and fuzzy ordered averaging aggregation helps to improve the reliability of the fire-fighting system and optimize the corrective actions that can improve the safety practices in liquefied petroleum gas storage tanks.

Mega construction projects (MCPs), which play an important role in the economy, society and environment of a country, have developed rapidly in recent years. However, due to frequent social conflicts caused by the negative social impact of MCPs, social risk control has become a major challenge. Exploring the relationship between social risk factors and social risk from the perspective of risk evolution and identifying key factors contribute to social risk control; but few studies have paid enough attention to this. Therefore, this study aims to systematically analyze the impact of social risk factors on social risk based on a social risk evolution path.

This study proposed a social risk evolution path for MCPs explaining how social risk occurs and develops with the impact of social risk factors. To further analyze the impact quantitatively, a social risk analysis model combining structural equation model (SEM) with Bayesian network (BN) was developed. SEM was used to verify the relationship in the social risk evolution path. BN was applied to identify key social risk factors and predict the probabilities of social risk, quantitatively. The feasibility of the proposed model was verified by the case of water conservancy projects.

The results show that negative impact on residents’ living standards, public opinion advantage and emergency management ability were key social risk factors through sensitivity analysis. Then, scenario analysis simulated the risk probability results with the impact of different states of these key factors to obtain management strategies.

This study creatively proposes a social risk evolution path describing the dynamic interaction of the social risk and first applies the hybrid SEM–BN method in the social risk analysis for MCPs to explore effective risk control strategies. This study can facilitate the understanding of social risk from the perspective of risk evolution and provide decision-making support for the government coping with social risk in the implementation of MCPs.

The purpose of this study is to introduce a relatively simple method of probabilistic analysis on the dimensions of gravity retaining walls which might lead to a more accurate understanding of failure. Considering the wall geometries in the case of allowable stress design, the probability of wall failure is not clearly defined. The available factor of safety may or may not be sufficient for the designed structure because of the inherent uncertainties in the geotechnical parameters. Moreover, two cases of correlated and uncorrelated geotechnical variables are considered to show how they affect the results.

This study is based on the failure and stability of gravity retaining walls which can be stated in three different modes of sliding, overturning and the foundation-bearing capacity failure. Each of these modes of failure might occur separately or simultaneously with a corresponding probability. Monte Carlo simulation and Taylor series method as two conventional methods of probability analysis are implemented, and the results of an assumed example are calculated and compared together.

The probability analysis of the failure in each mode is calculated separately and a global failure mode is introduced as the occurrence of three modes of sliding, overturning and foundation-bearing capacity failure. Results revealed that the global mode of failure can be used along with the allowable stress design to show the probability of the worst failure condition. Considering the performance and serviceability level of the retaining structure, the global failure mode can be used. Furthermore, the correlation of geotechnical variables seems to be relatively more dominant on the probability of global failure comparing to each mode of failure.

The introduced terminology of global mode of failure can be used to provide more information and confidence about the design of retaining structures. The resulted graphs maintain a thorough insight to choose the right dimensions based on the required level of safety.

Risk analysis is a critical investigation field for many sectors and organizations to maintain the information management reliable. Since mining is one of the riskiest sectors for both workers and management, comprehensive risk analysis should be carried out. The purpose of this paper is to explore comprehensively the undesired events that may occur during a particular process with their main reasons and to perform a risk analysis for these events, by developing a risk analysis methodology. For performing risk analysis, discovering and defining the potential accidents and incidents including their root causes are important contributions of the study as distinct from the related literature. The fuzzy approach is used substantially to obtain the important inferences about the hazardous process by identifying the critical risk points in the processes. In the scope of the study, the proposed methodology is applied to an underground chrome mine and obtaining significant findings of mining risky operations is targeted.

Fault tree analysis and fuzzy approach are used for performing the risk analysis. When determining the probability and the consequences of the events which are essential components for the risk analysis, expressions of the heterogeneous expert group are considered by means of the linguistic terms. Fault tree analysis and fuzzy approach present a quiet convenience solution together to specify the possible accidents and incidents in the particular process and determine the values for the basis risk components.

This study primarily presents a methodology for a comprehensive risk analysis. By implementing the proposed methodology to the underground loading and conveying processes of a chrome mine, 28 different undesired events that may occur during the processes are specified. By performing risk analysis for these events, it is established that the employee’s physical constraint while working with the shovel in the fore area, the falling of materials on employees from the chute and the scaling bar injuries are the riskiest undesired events in the underground loading and conveying process of the mine.

The proposed methodology provides a confidential and comprehensive method for risk analysis of the undesired events in a particular process. The capability of fault tree analysis for specifying the undesired events systematically and the applicability of fuzzy approach for converting the experts’ linguistic expressions to the mathematical values provide a significant advantage and convenience for the risk analysis.

The major contribution of this paper is to develop a methodology for the risk analysis of a variety of mining accidents and incidents. The proposed methodology can be applied to many production processes to investigate the dangerous operations comprehensively and find out the efficient management strategies. Before performing the risk analysis, determining the all possible accidents and incidents in the particular process using the fault tree analysis provides the effectiveness and the originality of the study. Also, using the fuzzy logic to find out the consequences of the events with experts’ linguistic expressions provides an efficient method for performing risk analysis.

The purpose of this paper is to improve risk assessment processes in airline flight operations by introducing a dynamic risk assessment method.

Fuzzy logic and Bayesian network are used together to form a dynamic structure in the analysis. One of the most challenging factors of the analyses in aviation is to get quantitative data. In this study, the fuzzy data quantification technique is used to perform dynamic risk assessment. Dynamic structure in the analysis is obtained by transforming the bow-tie model into a Bayesian network equivalent.

In this study, the probability of top-event from fault tree analysis is calculated as 1.51 × 10⁻⁶. Effectiveness of the model is measured by comparing the analysis with the safety performance indicator data that reflects past performance of the airlines. If two data are compared with each other, they are at the same order of value, with small difference (0.6 × 10⁻⁷).

This study proposes a dynamic model to be used in risk assessment processes in airline flight operations. A dynamic model for safety analysis provides real-time, autonomous and faster risk assessment. Moreover, it can help in the decision-making process and reduce airline response time to undesired states, which means that the proposed model can contribute to the efficiency of the risk management process in airline flight operations.

Stable approach concept has great importance for the safe operation of an airline during the approach and landing phases. The purpose of this study is to analyse the unstabilized approaches with bow-tie method and determine the threats that may cause risk in an unstable approach.

In this study, risk assessment of the unstabilized approaches is carried out by using fuzzy bow-tie method and Bayesian networks. Bow-tie method is the combination of event tree analysis and fault tree analysis. Bayesian network is used in the analysis to see interrelationship of basic and intermediate events as well as to update posterior probabilities. Finally, analysis results are verified by the safety performance indicator values.

In this study, the probabilistic values of the numerical model presented by the risk assessment system for risks were calculated using the fuzzy bow-tie method. Thus, the risk assessment system has been transformed into a structure that can be expressed in a probabilistic manner, and the relationship of the risks within the system has been examined and the effect of a possible change on the risk value has been found to be prevalent.

The bow-tie model is widely applied to assess the risks in aviation. Obtaining prior probabilities is not always possible in the risk assessment process. In this paper, innovative fuzzy bow-tie method is used to assess the risks to overcome the lack of prior probability problem in aviation operations.

Civil utility projects, both open-trench and trenchless, are subject to risk. These risks have both direct and indirect effect on project cost, schedule, quality and safety. It is therefore critical for the project management team to include risk management as an integral part of their project planning and execution. The purpose of this study is to identify the pipe-bursting construction risks and determine their probability of occurrence and cost impact and provide the appropriate responses to mitigate the identified risks.

This is an exploratory design using an industry-wide questionnaire survey to collect data on the probability of occurrence and impact of risks on cost of pipe-bursting projects. A probability-impact model was used to categorize the risks to determine their criticality and the appropriate risk responses.

The model revealed that majority of the analyzed risks have low impact-low probability of occurrence and high impact-low probability of occurrence. Undocumented repairs to host pipe was the only risk identified as having high probability of occurrence and high impact on cost. The risk responses suggest a combination of risk transfer, reduction and acceptance to be appropriately applied to mitigate the risks. A discussion on the good practices indicates that most pipe-bursting operations can be done safely and successfully if site and project conditions are known before bursting and the appropriate measures are taken to address those conditions.

Although the identified risks may apply to other utility construction methods, the focus of this research is limited to risks that occur during the construction phase of a pipe-bursting construction project.

Risk management is very critical to the success of any construction project. Identification and assessment of risks alone will not serve the purpose of risk management unless meaningful ways to mitigate those risks in a structured way are planned. The probability-impact model for the pipe-bursting construction risks with the mitigation strategies will help owners, engineers and contractors plan for and adequately respond to these risks. Additionally, a logical assessment of the risks will aid in effective decision-making regarding the management of the project.

Extensive literature review indicates that there is no existing literature on the probability of occurrence and impact on cost of risks in pipe-bursting projects. This paper presents the results of a wide-ranging analysis on construction risks in pipe-bursting projects. This is the first analysis incorporating the use of the probability-impact model to determine the criticality of various pipe-bursting construction risks.

The purpose of this paper is to develop a data system to assess failure probability in small to medium‐sized enterprise (SME) reorganization.

The data system is based on information from 83 reorganized Finnish SMEs. Information is divided into four types: pre‐filing non‐financial, pre‐filing financial, reorganization submission, and reorganization plan information. Partial least squares (PLS) analysis is used in data mining to factorize information for each type of information. Logistic regression analysis is applied to assess failure probability.

Useful data system can be developed on the basis of pre‐filing non‐financial information to support reorganization decision. Pre‐filing financial information only marginally improves quality of information. Submission and reorganization plan information improve quality in terms of fit but do not significantly improve classification accuracy.

The sample is small and should be expanded in further studies. The system is developed for Finnish reorganizing firms. It can be generalized to any similar reorganization process.

The data system is useful for managers, lending specialists, investors, reorganization lawyers, and judges. It warns a SME about reorganization failure before filing petition (passive use). It is also useful in developing successful reorganization plans (active use).

This paper builds an extensive data system for assessing reorganization failure risk. It makes use of many variables that have not been analyzed in reorganization studies earlier. It deals with SMEs that is rare in reorganization studies. The paper utilizes PLS in assessing failure probability. It includes new analytical results on PLS.

There are two ways in which the risk of a capital project can be described. This article outlines these two approaches: Sensitivity Analysis and Probability Analysis, and emphasises the connection between the two methods. The output of a computer model of the sensitivity of the project to underlying factors is used as input for a probability analysis. The methods are illustrated with a case study, the MM Co Ltd.

Assembly variation analysis generally demands probability distributions of variation sources. However, due to small production volume in aircraft manufacturing, especially prototype manufacturing, the probability distributions are hard to obtain, and only the small-sample data of variation sources can be consulted. Thus, this paper aims to propose a variation analysis method driven by small-sample data for compliant aero-structure assembly.

First, a hybrid assembly variation model, integrating rigid effects with flexibility, is constructed based on the homogeneous transformation and elasticity mechanics. Then, the bootstrap approach is introduced to estimate a variation source based on small-sample data. The influences of bootstrap parameters on the estimation accuracy are analyzed to select suitable parameters for acceptable estimation performance. Finally, the process of assembly variation analysis driven by small-sample data is demonstrated.

A variation analysis method driven by small-sample data, considering both rigid effects and flexibility, is proposed for aero-structure assembly. The method provides a good complement to traditional variation analysis methods based on probability distributions of variation sources.

With the proposed method, even if probability distribution information of variation sources cannot be obtained, accurate estimation of the assembly variation could be achieved. The method is well suited for aircraft assembly, especially in the stage of prototype manufacturing.

A variation analysis method driven by small-sample data is proposed for aero-structure assembly, which can be extended to deal with other similar applications.