Proposed Risk Assessment Model Research Articles

Integrated driving risk surrogate model and car-following behavior for freeway risk assessment

Drivers’ risk perception plays a crucial role in understanding vehicle interactions and car-following behavior under complex conditions and physical appearances. Therefore, it is imperative to evaluate the variability of risks involved. With advancements in communication technology and computing power, real-time risk assessment has become feasible for enhancing traffic safety. In this study, a novel approach for evaluating driving interaction risk on freeways is presented. The approach involves the integration of an interaction risk perception model with car-following behavior. The proposed model, named the driving risk surrogate (DRS), is based on the potential field theory and incorporates a virtual energy attribute that considers vehicle size and velocity. Risk factors are quantified through sub-models, including an interactive vehicle risk surrogate, a restrictions risk surrogate, and a speed risk surrogate. The DRS model is applied to assess driving risk in a typical scenario on freeways, and car-following behavior. A sensitivity analysis is conducted on the effect of different parameters in the DRS on the stability of traffic dynamics in car-following behavior. This behavior is then calibrated using a naturalistic driving dataset, and then car-following predictions are made. It was found that the DRS-simulated car-following behavior has a more accurate trajectory prediction and velocity estimation than other car-following methods. The accuracy of the DRS risk assessments was verified by comparing its performance to that of traditional risk models, including TTC, DRAC, MTTC, and DRPFM, and the results show that the DRS model can more accurately estimate risk levels in free-flow and congested traffic states. Thus the proposed risk assessment model provides a better approach for describing vehicle interactions and behavior in the digital world for both researchers and practitioners.

Vessel intrusion interception utilising unmanned surface vehicles for offshore wind farm asset protection

Remotely located offshore wind farms (OWFs) are usually in unmanned operation, and are severely at risk from accidental or deliberate man-made damages, such as cable faults caused by vessel anchoring. In this work, a proactive alarming and interception method utilising unmanned surface vehicles (USV) is proposed to prevent suspect vessels from intruding OWF areas and damaging the assets. More specifically, the vessel intrusion interception system keeps monitoring the motion of surrounding vessels and assessing their potential intrusive risks. When a suspect intruder is identified, USV will be automatically deployed to proactively intercept the target vessel with an alarming and expelling system. In particular, a risk assessment model based on the analytic hierarchy process (AHP) and entropy weight method (EWM) is established to predict the potential intrusive risk, and an improved fast marching square (FMS) algorithm is designed for USV path planning to guarantee dynamic and reliable vessel interception. In order to verify the effectiveness of the proposed method, case studies have been conducted for the Zhejiang Jiaxing 2 OWF with authentic vessel traffic data. Results indicate that the proposed risk assessment model based on AHP-EMW is able to correctly predict the potential intrusion risk in various scenarios, and the improved FMS path planning algorithm with multi-objective optimisation is capable of providing both efficient and safe trajectories for USV interception.

An Integrated Risk Assessment Methodology of In-Service Hydrogen Storage Tanks Based on Connection Coefficient Algorithms and Quintuple Subtraction Set Pair Potential

At present, there have been a number of hydrogen storage tank explosions in hydrogen filling stations, causing casualties and property losses, and having a bad social impact. This has made people realize that the risk assessment and preventive maintenance of hydrogen storage tanks are crucial. Therefore, this paper innovatively proposes a comprehensive risk assessment model based on connection coefficient algorithms and quintuple subtractive set pair potential. First of all, the constructed index system contains five aspects of corrosion factors, material factors, environmental factors, institutional factors and human factors. Secondly, a combined weighting analysis method based on FAHP and CRITIC is proposed to determine the weight of each indicator. The basic indicators influencing hydrogen storage tanks are analyzed via the quintuple subtraction set pair potential and full partial connection coefficient. Finally, the risk level and development trend of hydrogen storage tanks in hydrogen filling stations are determined by a combination of the three-category connection coefficient algorithms and the risk level eigenvalue method. The results of our case analysis show that the proposed risk assessment model can identify the main weak indicators affecting the safety of hydrogen storage tanks, including installation quality, misoperation and material quality. At the same time, it is found that the risk of high-pressure hydrogen storage tanks is at the basic safety level, and the development trend of safety conditions holds a critical value. The evaluation results can help establish targeted countermeasures for the prevention and maintenance of hydrogen storage tanks.

Critical risk factors of public building green retrofit projects- an empirical study in Chongqing, China

ABSTRACT In order to discuss the critical risk factors of green retrofit project in existing public buildings, this study identifies the risk factors from the whole life cycle firstly, and the experts are invited to assess the probability of the occurrence, the degree of the severity, and the follow-up effect of the risk occurrence, respectively. In addition, a Choquet integral-based FMEA (Failure mode and effect analysis) model is introduced to assess and analyze the risks in which the descriptive language results are transformed into triangle fuzzy number to analyze the risk assessment results quantitatively, and the relative preference relations are used to rank the risk factors, empirical study is conducted focusing on Chongqing. Based on the proposed risk assessment model, the top 10 high-risk factors in green retrofit projects of public buildings are extracted, the possible potential causes are analyzed, and the corresponding risk response measures are proposed, which provide references for the improvement of risk management in urban renewal.

A Runway Overrun Risk Assessment Model for Civil Aircraft Based on Quick Access Recorder Data

The quick access recorder (QAR), as an airborne device used to monitor and record flight parameters, has been widely installed on various types of aircraft. Based on QAR data, research on runway overrun, a typical flight safety incident, has attracted widespread attention in recent years. However, existing runway overrun risk models generally suffer from oversimplified risk metrics or insufficient consideration of risk dynamics. In this paper, we propose a new dynamic runway overrun risk assessment model based on QAR data. We first consider the noise of aircraft trajectory data in the QAR parameters and present a landing trajectory correction method combining ground speed and runway position information. Second, to improve the accuracy of the risk assessment model, we design an algorithm to automatically recognize the aircraft autobrake level during the landing phase, based on which a new dynamic risk assessment model is developed. Finally, feature engineering is performed to extract the relevant contributing factors of runway overrun risk, based on which classification and regression models are applied to identify risky flights and predict the risk values. The proposed risk assessment model was evaluated using QAR data from an airline in China. The results show that the automatic deceleration rate, the way that the aircraft approaches the runway, the touchdown distance, and the kinetic energy at 50 ft are key factors in the risk of runway overrun during the landing phase.

RISK-ORIENTED MANAGEMENT OF PORTS IN THE IMPLEMENTATION OF CONCESSION PROJECTS

The subject matter of research in the article is the models and methods of risk management in the process of implementing concession projects within the port activities. The goal of the work is to develop a risk management model for the implementation of concession projects in the port to improve the efficiency of port activities and ensure the competitiveness of port services. The following tasks are solved in the article: analysis of international and national experience in reforming the port industry, consideration of the essence of the project-oriented approach in port management, determination of the relevance of the implementation of public-private partnership projects on the example of concession projects in the port, analysis of risks arising from the project management of ports within the concession, identification of concession project stakeholders, proposal of a classification model for risk distribution among project stakeholders, prioritization of risks based on the probability of their occurrence, identification and significance of negative consequences, development of a risk assessment model for port production in concession projects. The following methods are used: project management methods, failure mode and effects analysis method, risk management theory, quality management methods. The following results were obtained: based on analytical research of the world and Ukrainian experience in the operation of transport hubs, it was proved that an effective mechanism for increasing the competitiveness of ports is the implementation of reform principles based on project management methodology. It has been established that the most common type of port activity improvement is the implementation of concession projects. It is noted that in the process of managing a concession project, certain risks arise that require assessment. The stakeholders of the concession project are identified and a classification model for the distribution of risks between them is proposed. A model of risk-based port management in concession projects has been developed based on the failure mode and effects analysis method taking into account the determination of the risk priority rank. Conclusions. To increase the competitiveness of ports, it is proposed to develop and implement concession projects in port activities based on the project management methodology, the implementation of which can increase the efficiency of port services. There are risks in the management of port activities within a concession project. It is proposed to distribute them among stakeholders and determine the probabilities of occurrence, identifying risks, assessing the significance of their impact on the quality of port services. Based on the proposed risk assessment model, taking into account the determination of the priority rank, it is shown that the greatest risk is associated with the implementation of customs formalities and the danger of the port water area.

Novel risk assessment model of food quality and safety considering physical-chemical and pollutant indexes based on coefficient of variance integrating entropy weight

Food safety is important for sustainable social and economic development and people's health. The traditional single risk assessment model is one-sided to the weight distribution of food safety factors including physical-chemical and pollutant indexes, which cannot comprehensively assess food safety risks. Therefore, a novel food safety risk assessment model combining the coefficient of variation (CV) integrating the entropy weight (EWM) (CV-EWM) is proposed in this paper. The CV and the EWM are used to calculate the objective weight of each index with physical-chemical and pollutant indexes effecting food safety, respectively. Then, the weights determined by the EWM and the CV are coupled by the Lagrange multiplier method. The ratio of the square root of the product of two weights and the weighted sum of the square root of the product are regarded as the combined weight. Thus, the CV-EWM risk assessment model is constructed to comprehensively assess the food safety risk. Moreover, the Spearman rank correlation coefficient method is used to test the compatibility of the risk assessment model. Finally, the proposed risk assessment model is applied to evaluate the quality and safety risk of sterilized milk. By analyzing the attribute weight and comprehensive risk value of physical-chemical and pollutant indexes effecting the sterilized milk quality, the results show that this proposed model can scientifically obtain the weight of physical-chemical and pollutant indexes to objectively and reasonably evaluate the overall risk of food, which has certain practical value for discovering the influencing factors of risk occurrence to risk prevention and control of food quality and safety.

Risk identification and quantitative assessment method of offshore platform equipment

Equipment failure risk identification and risk assessment are essential part of offshore platform equipment integrity management. Realizing the risk identification and quantitative assessment of offshore platform equipment and failure modes will help to formulate more reasonable risk management measures. However, the previous methods are not enough to support precise risk assessment of offshore platform equipment. Therefore, this paper proposes a new quantitative risk assessment method based on fuzzy set theory, gray correlation analysis and fuzzy Borda method. This paper creatively proposed the approach, which integrates the solid expressive ability of the fuzzy set theory to vagueness and uncertainty information, the strong point of GRA method in studying the dependence of failure modes, and the advantage of FBM for aggregating experts’ diversity evaluations. Based on the above integrated approach, the proposed risk assessment model can favorably capture and aggregate diversity evaluations of FMECA team members. In addition, the effectiveness of this method is verified by the risk assessment of offshore platform equipment. The new method solves the problem existing in the quantitative risk assessment of multiple risks and multiple FMECA reports. This comprehensive quantitative risk assessment method can screen out high-risk failure modes and essential components in a system, and provide a reference for maintenance decision-making of complex industrial systems. This paper is conducive to promote the application of PHM, RCM and AIM in high-risk industries such as oil and gas production.

The risk assessment of manufacturing supply chains based on Bayesian networks with uncertainty of demand

The great changes in the external environment of the manufacturing supply chain make its demand more complex and difficult to control. This paper takes China as an example. According to questionnaire survey and principal component analysis, the risk indicators caused by uncertain demand are screened and classified to construct evaluation system and complete risk identification. The Bayesian network integrating fuzzy set theory and left and right fuzzy ranking is used to explore the relationship between risk indicators and supply chain to achieve risk evaluation. In view of the highest risk factors, an incentive mechanism model based on information sharing is put forward to prove theoretically that information sharing is an important strategy to reduce risk. The results are as follows: The uncertain demand will lead to a high level of risk in China’s manufacturing supply chain, in which the level of information technology is the biggest cause. Only when manufacturing enterprises are willing to share information and other node enterprises join the information sharing team, can demand uncertainty be fundamentally reduced. The proposed risk assessment model realizes the method innovation and theoretical innovation. It can practical and effectively help relevant enterprises to determine and control risks.

An Integrated Approach-Based FMECA for Risk Assessment: Application to Offshore Wind Turbine Pitch System

Failure mode, effects and criticality analysis (FMECA) is a well-known reliability analysis tool for recognizing, evaluating and prioritizing the known or potential failures in system, design, and process. In conventional FMECA, the failure modes are evaluated by using three risk factors, severity (S), occurrence (O) and detectability (D), and their risk priorities are determined by multiplying the crisp values of risk factors to obtain their risk priority numbers (RPNs). However, the conventional RPN has been considerably criticized due to its various shortcomings. Although significant efforts have been made to enhance the performance of traditional FMECA, some drawbacks still exist and need to be addressed in the real application. In this paper, a new FMECA model for risk analysis is proposed by using an integrated approach, which introduces Z-number, Rough number, the Decision-making trial and evaluation laboratory (DEMATEL) method and the VIsekriterijumska optimizacija i KOmpromisno Resenje (VIKOR) method to FMECA to overcome its deficiencies in real application. The novelty of this paper in theory is that the proposed approach integrates the strong expressive ability of Z-numbers to vagueness and uncertainty information, the strong point of DEMATEL method in studying the dependence among failure modes, the advantage of rough numbers for aggregating experts’ diversity evaluations, and the strength of VIKOR method to flexibly model multi-criteria decision-making problems. Based on the integrated approach, the proposed risk assessment model can favorably capture and aggregate FMECA team members’ diversity evaluations and prioritize failure modes under different types of uncertainties with considering the failure propagation. In terms of application, the proposed approach was applied to the risk analysis of failure modes in offshore wind turbine pitch system, and it can also be used in many industrial fields for risk assessment and safety analysis.

Risk assessment of unfavorable interorganizational relationships in CSR projects considering the logistic aspects

Purpose: The aim of this paper is to present a new model for risk assessment of unfavorable interorganizational relationships, among other things, in ventures classified as corporate social responsibility (CSR) projects. Design/methodology/approach: Scenario analysis, brainstorm sessions, literature study and own observations of interorganizational projects were used to develop a list of unwanted events and factors determining their occurrence. In the proposed risk assessment model, fault tree analysis and fuzzy logic were applied for qualitative and quantitative risk analysis. Thanks to applying the elements of fuzzy sets theory, it was possible to decrease the uncertainty and lack of precision in obtaining crisp values of the basic events’ probability. Findings: In this work 13 basic events and 41 risk factors determining occurrence of unfavorable interorganizational relationships in ventures were identified and described. The proposed model enabled to carry out qualitative and quantitative risk assessment of unfavorable interorganizational relationships projects. Its practical application was shown in an example of interorganizational CSR project concerning the organization of a mass event, considering its logistics aspects. Research limitations/implications: It is necessary to involve experts in risk assessment. This could be overcome by applying machine learning in future research. Practical implications: The application of the proposed model allows to effectively identify the critical risks, which should be of particular attention during the risk treatment stage. It aims to give a helping hand to all managers and practitioners who want to deliver attainable and successful interorganizational projects, supporting meeting the expectation of the engaged stakeholders. Social implications: Socially responsible activities contribute to solving and counteracting social problems. Originality/value: A novel risk assessment model of unfavorable interorganizational relationships in which 13 basic events and 41 risk determinants were considered. The model was presented at ventures classified as Corporate Social Responsibility projects. Keywords: risk assessment; interorganizational relationships; Corporate Social Responsibility; fuzzy logic; logistics for the organization of a mass event. Category of the paper: Research paper.

A new model for risk assessment in glass manufacturing using Risk Matrix based IVIF-TOPSIS method

The glass manufacturing includes operations, such as batch forming using raw materials, melting, forming, annealing, quality check and package. Due to risky processes in glass manufacturing, significant health hazards for workers are present in the glass industry. Risk assessment is effective way to prevent accidents and protect workers from serious accidents during glass manufacturing. To assess health hazards associated with glass manufacturing, in this study Risk Matrix and The Technique for Order Preferences by Similarity to an Ideal Solution (TOPSIS) method are integrated under Interval-Valued Intuitionistic Fuzzy (IVIF) environment to prioritize risk factors and suggest required preventive and protective measures. Suggested preventive and protective measures provide technical, economic and environmental challenges for glass manufacturing firms. Once the importance weight of risk parameters in Risk Matrix’ are determined, the risk factors are assessed by performing IVIF-TOPSIS method during glass manufacturing. In order to verify the validity and stability of the proposed risk assessment model, sensitivity and comparative analysis are accomplished at the end of the study.

Developing a Ship Collision Risk Index estimation model based on Dempster-Shafer theory

A reliable and accurate evaluation of the risk of a ship collision with other vessels is crucial for the avoidance of maritime accidents. The ship operators use the collision risk index (CRI) to detect the risk of a collision and take the necessary action. However, CRI can be assessed differently depending on various operating conditions or other vessels or encounter conditions, making it difficult to calculate such risk accurately and efficiently. In this study, a new method for calculating the CRI by combining machine learning with D-S theory is proposed to increase the efficiency of the computations while preserving the prediction accuracy of the CRI. Different machine learning models have been investigated and compared based on model accuracy and computational time, and the results showed that the gradient boosting regression (GBR) model efficiently estimates the collision risk and increases the calculation speed compared to the D-S theory. Further, the effectiveness of this approach was examined by collision avoidance simulation while simultaneously satisfying the COLREG rule and making early proper decisions to avoid collisions, which shows the advantages of the proposed risk assessment model in practical application.

Risk Assessment of Logistics Enterprises Using FMEA Under Free Double Hierarchy Hesitant Fuzzy Linguistic Environments

Failure Mode and Effects Analysis (FMEA) is an effective tool for prioritization of the failures by identifying and eliminating potential risks, free double hierarchy hesitant fuzzy linguistic term set (FDHHFLTS) can describe the hesitation more accurately and reasonably by adding the free second hierarchy hesitant fuzzy linguistic terms, and multiattribute decision-making (MADM) methods have advantages in ranking and selecting different alternatives. By considering the advantages of MADM methods and FMEA model, in this paper, we propose a novel model to evaluate the risk failure modes (FMs) under FDHHFLTS context. First, an extended Complex Proportional Assessment (COPRAS) method is proposed to address the risk assessment problems. The proposed risk assessment model is utilized to assess and rank the FMs to address the limitations of the traditional FMEA. Second, a weight determination model is constructed based on the Kemeny Median (KEM) method and Stepwise Weight Assessment Ratio Analysis (SWARA) method, which can make the weight more practical and effective under the situation of completely unknown weights. Finally, the proposed model is used to the case of logistics enterprises’ risk assessment. The application of the case shows the feasibility of the proposed model, and the comparisons with some existing methods illustrate the advantages of the proposed risk assessment model.

Model on Improved Variable Weight-Matter Element Theory for Risk Assessment of Water Inrush in Karst Tunnels

This paper presents an improved risk assessment model to evaluate the risk of water inrush in karst tunnels based on matter element theory and ideal point method. The 4 first-grade evaluation indexes and 13 s-grade factors are selected to establish the evaluation index system by considering the occurrence conditions of water inrush. All second-grade evaluation indexes are quantitatively divided into four risk grades. Based on improved Analytical Hierarchy Process and Triangular Fuzzy Number, the constant weights are obtained. The variable weights are determined according to the state variable weight theory and the values of evaluation indexes. The risk grade of water inrush is recognized by the closeness degree analysis. The proposed risk assessment model was applied to 3# inclined shaft of Yuelongmen tunnel, and the accuracy of the assessment result was verified by comparing with advance geological forecast and excavation. This proposed model provides a practical tool to evaluate the risk of water inrush in karst tunnels.

Flow-based seismic risk assessment of a water transmission network employing probabilistic seismic hazard analysis

In this study, a seismic risk assessment model was proposed to evaluate the seismic reliability of a water transmission network. The proposed risk assessment model involves earthquake generation and hydraulic analysis modules. To consider a comprehensive approach, the numerical simulation strategy includes probabilistic seismic hazard analysis, buried pipeline deterioration, numerical modeling of network facilities, and the interdependency between pumping plant and substation. For this purpose, a flow-based MATLAB code has been developed that enables iterative hydraulic analysis using EPANET software. For numerical simulation, the epicenter and earthquake magnitudes were determined based on probabilistic seismic hazard analysis, and a real water transmission network in South Korea was constructed. From the numerical results, two performance indicators (system serviceability and nodal serviceability) and four mean normal status ratios of facilities were adopted to evaluate the network performance. In addition, a component importance measure of facilities in a network system was calculated by introducing a reduction factor. The numerical results using the proposed flow-based model show that the system performance is affected by buried pipeline deterioration and network interdependency, as well as the location and magnitude of the input earthquake. It is thus concluded that the seismic risk assessment of a water transmission network should be performed using a model with a comprehensive approach.

DEVELOPING A COMPREHENSIVE RISK ASSESSMENT MODEL BASED ON FUZZY BAYESIAN BELIEF NETWORK (FBBN)

Reliable and efficient risk assessments are essential to deal effectively with potential risks in international construction projects. However, most conventional risk modeling methods are based on the hypothesis that risk factors are independent, which does not account adequately for the causal relationships among risk factors. In this study, a risk assessment model for international construction projects was developed to improve the efficacy of risk management by integrating fault tree analysis and fuzzy set theory with a Bayesian belief network. The risk rating of each risk factor, expressed as the product of risk occurrence probability and impact, was incorporated into the risk assessment model to evaluate degrees of risk. Therefore, risk factors were categorized into different risk levels taking into account their inherent causal relationships, which allowed the identification of critical risk factors. The applicability of the fuzzy Bayesian belief network-based risk assessment model was verified using a case study through a comparative analysis with the results from a fuzzy synthetic evaluation method. The comparison shows that the proposed risk assessment model is able to provide guidelines for an effective risk management process and ultimately to increase project performance in a complex environment such as international construction projects.

A Novel Maritime Risk Assessment Model of Waterway Transportation Based on Takagi-Sugeno Fuzzy Logic: Vietnam Case Study

In this paper, we analyzed the safety assessment getting from the opinions of captains, pilots and navigators in Ganh Rai Bay. The results showed that the highest risk was caused by unsafe fishing ships and the most unsafe situation was generated by the crossing ships, wind and currents. The influence of human psychology factors was one of the most important factors, which caused the risk of collision. Based on fuzzy logic theory, we proposed risk assessment model which was tested in simulation and compared with the evaluation results of the Experts in Vietnam. The results showed the effectiveness of the novel maritime risk assessment model.

Fuzzy Model for Risk Assessment of Machinery Failures

The main goal of this research was the development of an algorithm for the implementation of negative risk parameters in a synthesis model for a risk level assessment for a specific machine used in the mining industry. Fuzzy sets and fuzzy logic theory, in combination with statistical methods, were applied to analyze the time picture state of the observed machine. Fuzzy logic is presented through fuzzy proposition and a fuzzy composition module. Using these tools, the symmetric position of the fuzzy sets with regard to class was used, and the symmetric fuzzy inference approach was used in an outcome calculation. The main benefit of the proposed model is being able to use numerical and linguistic data in a risk assessment model. The proposed risk assessment model, using fuzzy logic conclusions and min–max composition, was used on a mobile crushing machine. The results indicated that the risk level of the mobile crushing machine was in the “high” category, which means that it is necessary to introduce maintenance policies based on this high risk. The proposed risk assessment model is useful for any engineering system.

Risk Assessment Model for UAV Cost-Effective Path Planning in Urban Environments

Increasing use of Unmanned Aerial Vehicle (UAV) in urban environments poses to an increased risk of fallen UAVs impacting people and vehicles on the ground, as well as colliding with manned aircraft in the vicinity of airports. Risk management of UAV flights for safe operations is essential. We proposed a comprehensive risk assessment model for UAV operation in urban environments. Three risk categories (people, vehicles, and manned aircraft) were considered and each risk cost was quantified using collision probability. We adjusted the risk costs in various magnitudes to a same scale and conducted a sensitivity analysis to determine the optimal coefficients of the three risk cost models. We then computed the total risk and generated a risk cost map for path planning. Modified path planning algorithms were used to produce a cost-effective path, and we compared their performances in terms of total risk cost and computational time. Lastly, we performed simulations to validate the feasibility and effectiveness of our proposed risk assessment model. The results show that the risk-cost-based path planning method can generate safer path for UAV operations than the traditional shortest-distance-based method. Our proposed model can be extended to complex urban environments by including more relevant parameters and data.