Risk-based Method Research Articles

Risk factors affecting the food safety risk in food business operations for risk-based inspection: A systematic review.

Foodborne illnesses result in a high disease burden worldwide, making food safety control of food business operations (FBOs) an urgent issue. With public agencies and FBOs facing challenges in monitoring the complex food supply chain with limited resources, scientific and objective insights into those factors that are related to food safety at FBOs are needed. These factors can be used as input for risk-based inspection. We conducted a systematic review to identify and analyze risk factors affecting the FBOs' food safety risk. We used a set of predefined search strings in Scopus and Web of Science to search for scientific manuscripts published in the English language between January 1 2003 and February 1 2023. The review identified 53 relevant studies and 43 risk factors. The presence of certified personnel turned out to be the most cited factor. Nearly half of the extracted factors had only been investigated in one study. Additional challenges were identified for developing a universal ready-to-use list of factors for the building of a risk-based inspection method, such as the limitation in the applicability of identified factors in different types of FBOs, and the variability in conclusions between publications for certain factors (e.g., FBO location and inspection history), stressing the need for additional research. Future studies should also prioritize standardizing definitions and measurements, particularly regarding compliance factors. In general, the current list of factors brought forward in our review lays the groundwork for building a transparent, objective, and risk-based method for food safety inspections of FBOs.

Risk-based method for substantiating reduction of pressure of hydraulic strength tests and its application on steam line of nuclear power plant with WWER (PWR) reactor plant

Hydraulic tests are used in various sectors of the national economy to check the strength and tightness of vessels and pipelines, their parts and assembly units loaded with pressure. The hydraulic test pressure is a calculated value and is determined on the basis of regulatory documentation for the relevant control objects. Analysis of current trends in the field of hydraulic testing both in the Russian Federation and abroad has shown a trend to reduce the damageability of test objects by choosing the most optimal hydraulic test pressure. The main purpose of the research was to reduce the operational damage to equipment and pipelines of nuclear power plants by reducing the pressure of hydraulic strength tests during maintenance. The research is based on probability theory, mathematical statistics, strength physics and fracture mechanics, taking into account the possibility of occasional discontinuities or defects that might be observed in operation. The method is based on a risk-based approach, which makes it possible to determine the permissible risk variation based on recommendations on the safety of nuclear power plants in the Russian Federation. The objects of research are the equipment and pipelines of nuclear power plants. The result of the research is the development of a method substantiating the reduction of the pressure of hydraulic strength tests for equipment and pipelines of nuclear power plants based on a risk-based approach. The developed method can be used by organizations involved in the production of electricity (at nuclear or thermal power plants), transportation of oil, gas or other substances, at facilities where pipelines are an essential part of their structures, affecting the reliability and efficiency of operation of these facilities.

Risk-Based Approach for Defining Retest Dates for Active Pharmaceutical Ingredients and Excipients.

Drug substances and excipients must be stored in recommended storage conditions and should comply with their specifications during the retest period for their use in the manufacture of drug products. The ICH (International Council for Harmonization of Technical Requirements for Pharmaceuticals for Human Use) and WHO (World Health Organization) regulatory guidelines mandate that after the retest period, the drug substances must be retested for compliance with the specification and then used immediately in the manufacture of the finished product. Although these substances can be retested multiple times, an emphasis is placed on immediate use following a retest and compliance with standards. The phrase "used immediately" is ambiguous and is left for interpretation. In this article, we will look at the various processes that must be completed to determine the retest date. In addition, we present a risk-based method for establishing retest dates and the time during which material can be used.

Identifying the dominant influencing factors of secondary lining cracking risk in an operating mountain tunnel

PurposeA risk-based method is proposed to identify the dominant influencing factors of secondary lining cracking in an operating mountain tunnel with weak surrounding rock.Design/methodology/approachBased on the inspection data from a mountain tunnel in Southwest China, a lognormal proportional hazard model is established to describe the statistical distribution of secondary lining cracks. Then, the model parameters are obtained by using the Bayesian regression method, and the importance of influencing factors can be sorted based on the absolute values of the parameters.FindingsThe results show that the order of importance of the influencing factors of secondary lining cracks is as follows: location of the crack on the tunnel profile, rock mass grade of the surrounding rock, time to completion of the secondary lining, and void behind the secondary lining. Accordingly, the location of the crack on the tunnel profile and rock mass grade of the surrounding rock are the two most important influencing factors of secondary lining cracks in the investigated mountain tunnel, and appropriate maintenance measures should be focused on these two aspects.Originality/valueThis study provides a general and effective reference for identifying the dominant influencing factors of secondary lining cracks to guide the targeted maintenance in mountain tunnels.

Design and Certification for Façade Access Systems: An Alternative Risk-Based Method

Design and Certification for Façade Access Systems: An Alternative Risk-Based Method

A New Risk-Based Method in Decision Making to Create Dust Sources Maps: A Case Study of Saudi Arabia

Dust storms are one of the major causes of the destruction of natural ecosystems and human infrastructure worldwide. Therefore, the identification and mapping of susceptible regions to dust storm formation (SRDSFs) is of great importance. Determining SRDSFs by considering the concept of risk in the decision-making process and the kind of manager’s attitude and planning can be very valuable in dedicating financial resources and time to identifying and controlling the negative impacts of SRDSFs. The purpose of this study was to present a new risk-based method in decision making to create SRDSF maps of pessimistic and optimistic scenarios. To achieve the purpose of this research, effective criteria obtained from various sources were used, including simulated surface data, satellite products, and soil data of Saudi Arabia. These effective criteria included vegetation cover, soil moisture, soil erodibility, wind speed, precipitation, and absolute air humidity. For this purpose, the ordered weighted averaging (OWA) model was employed to generate existing SRDSF maps in different scenarios. The results showed that the wind speed and precipitation criteria had the highest and lowest impact in identifying dust centers, respectively. The areas identified as SRDSFs in very pessimistic, pessimistic, neutral, optimistic, and very optimistic scenarios were 85,950, 168,275, 255,225, 410,000, and 596,500 km2, respectively. The overall accuracy of very pessimistic, pessimistic, neutral, optimistic, and very optimistic scenarios were 84.1, 83.3, 81.6, 78.2, and 73.2%, respectively. The very pessimistic scenario can identify the SRDSFs in the study area with higher accuracy. The overall accuracy of the results of these scenarios compared to the dust sources obtained from the previous studies were 92.7, 94.2, 95.1, 88.4, and 79.7% respectively. The dust sources identified in the previous studies have a higher agreement with the results of the neutral scenario. The proposed method has high flexibility in producing a wide range of SRDSF maps in very pessimistic to very optimistic scenarios. The results of the pessimistic scenarios are suitable for risk-averse managers with limited financial resources and time, and the results of the optimistic scenarios are suitable for risk-taking managers with sufficient financial resources and time.

Sliding Window-Based Machine Learning for Environmental Inspection Resource Allocation.

Environmental regulation is pivotal in mitigating environmental risks and promoting sustainable development, yet regulators frequently encounter resource constraints when inspecting enterprises. To address this limitation, we employed four sliding window-based machine learning techniques to enhance effective environmental inspections. Utilizing feature-engineered time-series data of characteristic and compliance records from 16,777 chemical enterprises in Jiangsu between 2010 and 2021, the four models were used to establish the predictive models that link enterprise characteristics to the likelihood of inspection failure. The results indicated that the models were comparable with the widely utilized deep learning sequence model, the Long Short-Term Memory model, achieving areas under the ROC (receiver operating characteristic) curves exceeding 0.83. Past violation significantly influences future violations, with a more recent violation history exerting stronger impacts. Besides, using predicted failure rates, we proposed seven resource allocation scenarios, considering different regulation intensities to target high-risk enterprises. Notably, the provincial-level risk-based method yielded a significant inspection failure rate increase (detecting violation), surpassing 8-fold the baseline. Overall, our study offers regulators an optimized inspection resource allocation method, thereby enhancing the regulatory effectiveness. Moreover, this study demonstrates the potential of window-based machine learning techniques in environmental regulation and highlights the importance of data-driven decision-making for promoting sustainable development.

Evaluating fare evasion risk in bus transit networks

In Proof-of-Payment Transit Systems (POP-TSs), fare evasion is a crucial issue for Transit Agencies (TAs) and/or Public Transport Companies (PTCs) worldwide. The related research background is based on the standard ratio between evaders and inspected passengers, whereas no research quantified the risk of fare evasion in POP-TSs. The objective of this study is the introduction of a framework covering this gap: it integrates fare evasion factors, prediction models, a risk-based method and returns the risk value on (parts of) routes as a function of the frequency of fare evasion, the related severity and exposure terms. Next, routes are ranked according to the risk value and classified by a 5-level scale, to show the (parts of) routes with the highest risk of evasion. Results show the capability of this framework on about 20,000 real-world data records gathered by a mid-sized Italian bus company through fare inspection logs and passenger surveys. To conclude, this framework is a support tool for TAs/PTCs to improve fare compliance and can be incorporated into any transit managerial system.

An incremental fuzzy approach to risk-based evaluation of process equipment isolation schemes

Any time that process equipment needs to be out of service for inspection, maintenance, cleaning, or repair, sufficient isolation should be applied to isolate the equipment from the hazardous energies. Opting and implementing a proper scheme to isolate such equipment is critical to prevent accidents in process plants.Conventional risk-based methods to determine the isolation schemes are not so effective in dealing with subjective errors and uncertainties. Therefore, in this study we have developed an incremental fuzzy system to cope with the aforesaid limitations of conventional risk-based methods. In the proposed model, a numerical technique is applied for developing membership functions to reduce the uncertainty. In the incremental fuzzy model, three fuzzy inference systems in three levels are generated to calculate a release factor, outcome factor, and isolation level.Applying the proposed model to a three-phase separator, the results of the proposed model were compared with those of a conventional risk-based method, demonstrating that the proposed model can effectively handle the uncertainty while keeping the simplicity of the conventional approaches. The results are more transparent, detailed, and thus of greater value in identifying isolation schemes. Therefore, the proposed model can be an effective alternative to the conventional methods currently used in process plants for determination of isolation schemes.

Self-Organized Neural Network Method to Identify Crash Hotspots

Crash hotspot identification (HSID) is an essential component of traffic management authorities’ efforts to improve safety and allocate limited resources. This paper presents a method for identifying hotspots using self-organizing maps (SOM). The SOM method was used to identify high-risk areas based on five commonly used HSID methods: crash frequency, equivalent property damage only, crash rate, empirical Bayes, and the societal risk-based method. Crashes on a major road in Iran were examined using the proposed method. Based on these criteria, high-risk locations were grouped into six clusters, which provided appropriate criteria for each location depending on the importance of the cluster. The findings show that the identification of crash hotspots tends to focus on areas with more crashes and deaths, demonstrating that the research methodology was appropriate.

Pavement maintenance planning using a risk-based approach and fault tree analysis

ABSTRACT Optimal planning of Maintenance and Rehabilitation (M&R) treatments due to limited resources and funding is vital. A pavement performance function formula is needed to model the M&R optimisation problem. By considering important features, a pavement deterioration function was developed with an R2 of 0.897. The essence of pavement deterioration is uncertain. Hence, disregarding the uncertainty may result in sub-optimal solutions. Therefore, two approaches were employed to optimise M&R treatments in a large-scale pavement network, including deterministic and risk-based. Both approaches aimed to minimise the required budget in the planning period constrained to acceptable pavement condition. A novel and robust metaheuristic algorithm was adjusted to solve the M&R planning problem deterministically. In the risk-based method, appropriate probability distribution functions were fitted to historical data of uncertain parameters. Monte Carlo simulation was performed to generate probability distributions of the required budget and condition of pavement sections. By using a risk-based strategy instead of the deterministic one, the required budget was reduced by 30%. Fault tree analysis was performed on the solutions obtained from the risk-based method to increase the pavement network’s reliability. The results of the mentioned analysis represented that only by spending 2.5% more budget network’s reliability could be significantly increased.

Risk-Based Method for Determining Separation Minima in Unmanned Aircraft Systems

Risk assessment is a key issue in enabling the use of unmanned aircraft systems (UASs) in nonsegregated areas, especially in very low-level airspace and urban areas. The specific operations risk assessment (SORA) methodology represents an important milestone in performing the risk assessments required by aviation safety agencies to UAS operators in specific operations. However, the SORA is a qualitative method used for UASs operating inside a well-bounded operational volume. This paper proposes a quantitative method that can not only be used in a closed volume but also in an airspace shared by several UAS missions and even general aviation. The basis for this is providing a separation volume (a “bubble”) to prevent collisions that is calculated using a risk-based approach. The method consists of setting a target level of safety, which is accomplished using a tradeoff between strategic and tactical mitigations. A probabilistic methodology for performing quantitative risk assessment of strategic and tactical mitigations is provided, and the dependence of the separation distance is carefully analyzed. All factors affecting the separation distance are identified, and their contributions to collision risk are probabilistically estimated. The method takes into account specific factors relating to UASs such as trajectories, separation methods, and performance. As a result, the method allows numerical determination of a separation distance for a given target level of safety and an operational scenario.

Net effect of air pollution controls on health risk in the Beijing–Tianjin–Hebei region during the 2022 winter Olympics and Paralympics

Due to the non-linearity in ozone (O3) formation, reducing the emission of nitrogen oxides (NOx) may increase O3 concentration. Given the counteractive O3 response to NOx reduction, overall impact of air pollution controls can be ambiguous when the assessments focus on the changes in pollutant concentrations. In this study, a risk-based method was used to gauge the net effect of air pollution controls on mortality risk in the Beijing–Tianjin–Hebei (BTH) region during the 2022 Winter Olympics and Paralympics (WOP). This mega-event presents a unique opportunity to investigate the efficacy of deep cuts in pollutant emissions. Results show that O3 concentrations greatly increased as nitrogen dioxide (NO2) concentrations decreased in the BTH. Due to the active photochemical formations, O3 became the dominant pollutant that affected human health during the WOP. Despite the substantial O3 increases, the health benefits of NO2 reductions overwhelmed the adverse health effects of O3 increases in most regions of the BTH (at 81 out of 112 stations). After considering the impacts of particulate matter, the integrated health risk of air pollution mixtures declined almost everywhere in the BTH. Our results underscore the great necessity of changing the assessment paradigm of pollution control from using concentration-based methods to using risk-based methods. Together with the carbon neutrality policy, stringent control of NOx emission from combustion sources is a promising way to achieve synergistic control solutions for air pollution and climate change.

Mitigating subway construction collapse risk using Bayesian network modeling

This paper develops a Bayesian network-based approach for risk modeling of collapses and derivative accidents to systematically avoid subway construction collapse events and mitigating the loss from derivatives. Given it was not feasible for risk quantification based upon current safety standards, a risk-based method was proposed for numerical computation of severity values from the perspective of workers' safety. In order for dimensionality reduction, each node's importance in the network was estimated through the variation of Gini index in random forest algorithm. The Bayesian network of subway construction collapse risk (SCCR-BN) contained two sub-graphs. Sensitivity analyses were performed to determine critical causal factor events for each collapse or non-collapse event. Countermeasures can be tailored for better response to these causal factors for risk controlling. Three representative scenarios were selected for case studies with the objective of demonstrating the applicability of SCCR-BN for dynamic investigation and prediction of subway construction collapse risks.

Risk tradeoffs between nitrogen dioxide and ozone pollution during the COVID-19 lockdowns in the Greater Bay area of China

Photochemical regime for ozone (O3) formation is complicated in the sense that reducing emission of nitrogen oxides (NOx) may increase O3 concentration. The lockdown due to COVID-19 pandemic affords a unique opportunity to use real observations to explore the O3 formation regime and the effectiveness of NOx emission control strategies. In this study, observations from ground networks during the lockdowns were used to assess spatial disparity of the Ratio of Ozone Formation (ROF) for nitrogen dioxide (NO2) reduction in the Greater Bay Area (GBA) of China. The health risk model from Air Quality Health Index (AQHI) system in Hong Kong was adopted to evaluate the risk tradeoffs between NO2 and O3. Results show that the levels of O3 increase and NO2 reduction were comparable due to high ROF values in urban areas of central GBA. The ozone reactivity to NO2 reduction gradually declined outwards from central GBA. Despite the O3 increases, the NOx emission controls reduced the Integrated Health Risk (IHR) of NO2 and O3 in most regions of the GBA. When risk coefficients from the AQHI in Canada or the global review were adopted in the risk analyses, the results are extremely encouraging because the controls of NOx emission reduced the IHR of NO2 and O3 almost everywhere in the GBA. Our results underscore the importance of using a risk-based method to assess the effectiveness of emission control measures and the overall health benefit from NOx emission controls in the GBA.

Decision-Making with Risk under Interval Uncertainty Based on Area Metrics

From the perspective of D-S evidence theory and area measurement, a risk-based comprehensive decision-making method that considers both the expected utility and the uncertainty of the scheme is proposed under the interval uncertainty environment of attribute values. The upper and lower bounds of the synthetic probability distribution of attributes values in different natural states are constructed based on the belief measure and plausibility measure. Based on the area measurement, a method for calculating the expected utility of each scheme is proposed. To reflect the influence of the uncertainty in the evaluation value of each scheme attribute on the final decision result, two indexes are defined: the evaluation uncertainty of attributes (EUA) and the uncertainty of the expected utility of scheme (UEU). Finally, considering the expected value of the expected utility and its uncertainty, three decision methods, namely, risk-neutral, risk-averse, and risk-preference, are constructed. An example is considered to show that the proposed method is effective and practical, and the uncertainty of the expected utility has a significant impact on the result of risky decisions. The new method can solve the problems of existing methods that overlook the impact of epistemic uncertainty on the decision-making process.

IGDT-based dynamic programming of smart distribution network expansion planning against cyber-attack

Power supply security is the vital driver in advent and development of the smart distribution networks (SDNs). Traditionally, SDN expansion planning (SDNEP) is used to identify the size, site, and time of the installation of new equipment or replacement of the pre-existent facilities to optimally satisfy electricity load demand that is increasing every year appropriately and cost-effectively while dealing with all the technical and operational constraints and needs. In SDNs, communication has a crucial role, but, cyber-physical security of the communication system is exceedingly vulnerable to cyber-attacks (CAs), especially in the consumer's sector. So, this paper aims to propose an approach for designing cyber-secure SDN (CSDN) by considering the problem as a dynamic model of tri-level cyber-secure SDNEP (CSDNEP) program. The procedure includes the design of SDN as ameshed topology equipped by multi-type switching devices, utilization of multi-type distributed generations, and demand response. The chaotic status of renewable resources, load and its market price, and ZIP modeling well incorporate into the problem. Also, for evaluation of different CSDNEP strategies, a risk-based method pivoting on information gap decision theory (IGDT) is applied. To size up the efficiency of CSDNEP, computer simulation is carried out on a large scale SDN and the results are touched upon.

A risk-based method to prioritize cumulative impacts assessment on marine biodiversity and research policy for offshore wind farms in France

This study developed the “ECUME” risk-based approach to identify and prioritize critical impact pathways to be considered in cumulative impact assessment of offshore windfarms, and for future research. The prioritization framework has been tested on two offshore windfarms projects located in the French part of the English Channel off the coast of Normandy, those of Fécamp and Courseulles-sur-Mer. The approach is based on a complete inventory of impact pathways, prioritizing those for which an impact assessment will be carried out. The aim was to avoid a “quantification bias” and elaborate a systemic vision. The novelty of the study is to apply a combination of expert judgement, consensus building, and a scoring system, to prioritize the pairs of pressures and receptors of the marine environment to work on. The scoring system is based on the ecological importance of receptors, the degree of knowledge on the effect of a pressure on a receptor and the sensitivity of each receptor to pressures. Priorities for research were also determined during the same process. Bringing together a large set of specialized marine environnement scientists, the initial challenge was to build a common vocabulary, and a shared understanding of the risk-based prioritization approach. This required significant time and effort but secured foundations for further work. This study confirms the increasingly shared view that adopting a risk-based approach considering adverse effects on receptors is an efficient way to assess cumulative impacts, to focus on critical impact pathways, and manage the scientific complexity and the significant uncertainties.

A Seismic Pounding Risk-based Method for Determination of Minimum Separation Distance between Nonlinear Adjacent Buildings

ABSTRACT This paper presents a seismic pounding risk-based method to determine the minimum separation distance (MSD) between nonlinear adjacent buildings. In this method, subset simulation is extended to assess seismic pounding fragility and subsequent risk. The MSD is iteratively solved corresponding to a target pounding risk. First, the proposed method is applied to both linear and nonlinear multi-degree-of-freedom systems with substantially different dynamic properties for various structural parameters. Then, the MSDs between three combinations of adjacent frame structures with unequal total heights in nonlinear finite element models are investigated using the proposed method, which are compared with different code specifications.

Risk-Based InspectionTechnique and Critical Success Factors in its Implementation to Improve Risk and Process Safety-Based Management Systems: A review study

Introduction: Risk-based inspection is a powerful tool provided by the American Petroleum Institute in order to respond to the needs of the process industries. The main focus of this method is on inspection, integrity management, maintenance, as well as reduction of the probability of failure. The purpose of this study was to conduct the risk-based inspection technique and critical success factors in its implementation to improve risk and process safety based management systems.
 Methods: In this review study, we first listed the keywords based on the research title including Critical Success Factors, risk assessment, process safety, corrosion management, inspection Plan, failure mechanism, Risk Based Inspection, Risk Based Maintenance, Risk-based Integrity Management, and Asset Integrity Management. Later, each term was searched specifically and without time limit in the internal and external databases of Science Direct, PubMed, Google Scholar, SID, Google Search, Magiran, Civilica, and Scopus.
 Result: A systematic review of various studies showed that critical success factors provided the necessary context for the implementation of a risk-based inspection process in the process industries and were essential factors for the effective implementation of the RBI method.
 Conclusion: By analyzing the results of the studies we found that identifying CSFs can improve the effective implementation of RBI in industries and guarantee the benefits mentioned for this method. Moreover, the engineering and accurate implementation of risk-based inspection method will lead to the best implementation of management systems based on the risk and process safety and its improvement.