Safety Risk Model Research Articles

Development of a flexible produce supply chain food safety risk model: Comparing tradeoffs between improved process controls and additional product testing for leafy greens as a test case

The produce industry needs a tool to evaluate food safety interventions and prioritize investments and future research. A model was developedin R for a generic produce supply chain and made accessible via Shiny. Microbial contamination events, increases, reductions, and testing can be modeled. The output for each lot was the risk of one, 300-gram sample testing positive, described by two industry-relevant risk metrics, the overall risk of a positive test (proxy for recall risk) and the number of lots with the highest risk (>1 in 10 chance) of testing positive (proxy for public health risk). A leafy green supply chain contaminated with Shiga-toxin-producing Escherichia coli was modeled with a mean of 1 pathogen cell per pound (µ=1 CFU/lb or -2.65 Log(CFU/g)) under high (σ=0.8 Log(CFU/g)) and low (σ=0.2 Log(CFU/g)) variability. Baseline risk of a positive test in the low-variability scenario (1 in 20,000) was lower than for high-variability (1 in 4,500), showing rare high-level contamination drives risk. To evaluate tradeoffs, we modeled two well-studied, frequently used interventions: additional product testing (8 of 375-gram tests/lot) and improved process controls (additional -0.87±0.32 Log(CFU/g) reduction). Improved process controls better reduced recall risk (to 1 in 115,000 and 1 in 26,000 for low- and high-variability, respectively), compared to additional product testing (to 1 in 21,000 and 1 in 11,000 for low- and high-variability, respectively). For low variability contamination, no highest risk lots existed. Under high variability contamination, both interventions removed all highest risk lots (about 0.05% of total). Yet, additional product testing rejected more lower-risk lots (about 1% of total), suggesting meaningful food waste tradeoffs. This model evaluates tradeoffs between interventions using industry-relevant risk metrics to support decision-making and can be adapted to assess other commodities, process stages, and less-studied interventions.

Research on Safety Risk Assessment of Xi'an Metro Operation Based on Structural Equation Model (SEM)-Matter-Element Extension Model (MEA)

To ensure the safety of Xi'an Metro operation and improve the level of operational safety risk management, a comprehensive evaluation model of operational safety risk of Xi'an Metro based on structural equation model (SEM)-matter-element extension model (MEA) was established based on 4M theory. Firstly, by analyzing both domestic and international Metro accident cases and literature, four risk factor perspectives were identified as personnel, equipment, management, and environment. Secondly, to accurately assess the safety risk of Xi'an Metro operation, a risk evaluation index system consisting of four primary indicators, eleven secondary indicators and forty-four observation points was established. Finally, the index weights were determined using structural equation modeling, and a comprehensive evaluation utilizing the Matter-element extension model was conducted to obtain precise safety evaluation results. The model was applied to Xi'an Metro Line 2, and the results indicated that the operational safety level of Xi'an Metro Line 2 is relatively secure. According to the evaluation results above, the findings align with the current conditions. Xi'an Metro operating company can utilize these results as a point of reference to propose measures that correspond to varying risks. This will effectively reduce safety risks and ensure the safe operation of Xi'an Metro.

A Risk Assessment Model for Navigation Safety of Maritime Aquaculture Platform Based on AIS Ship Trajectory

With the rapid increase of maritime aquaculture platform, how to reduce its impact on maritime traffic safety is of great significance to the rapid development of shipping. According to the characteristics of aquaculture platform and maritime environment, The aquaculture platform is superimposed on the ship AIS (Automatic identification systems) big data traffic flow analysis platform. The effects of the distance between the marine aquaculture platform and shipping routes, port facilities and cargo vessel traffic flow channels were studied. The effects between the platform and meteorological conditions, fishing vessel activity and passenger routes were studied. The mathematical assessment model of navigation safety risk of aquaculture platform based on MTLAB was studied. Taking the “Qiandong No. 1” semi-submersible wave energy farming platform as an example. The navigation safety risk assessment is carried out and the risk control measures are put forward. Experiments are carried out with actual data and the effectiveness of the proposed method is proved.

Construction Safety Risk Assessment of High-Pile Wharf: A Case Study in China

The complexity of the wharf components and the harshness of the offshore construction environment increase the safety risk of hazards, which has highlighted the importance and urgency of safety risk management in high-pile wharf constructions. This paper established a visualized digital construction safety risk model for high-pile wharf based on a so-called FAHP method (the combination of fuzzy comprehensive evaluation (FCE) and analytic hierarchy process (AHP) methods). The construction safety risk indicators were constructed as the target layer, the principle layer and the scheme layer, and then the corresponding safety risk assessment algorithm was established. The physical, functional and safety risk assessment parameters of the component in the BIM model were employed to the safety risk assessment algorithm, and the risk assessment level of each sub-process was subsequently classified. The case study indicated that the high-pile wharf construction project included five elements in principle layer and 15 risk indicators in the scheme layer. Moreover, it was demonstrated that the sub-processes with the highest construction risk level were steel pipe pile sinking in wharf construction and steel pipe pile, steel sheath-immersed pile sinking and embedded rock pile construction in approaches to bridge construction with a risk level of III. In this way, the quantitative visualization of the construction safety risk was effectively realized, which facilitates the safety risk management of construction sites and timely warning and response to unexpected safety accidents.

Research on Intelligent Control Model of Laboratory Safety Risks in Colleges and Universities Based on Digital Twins

This paper focuses on developing an intelligent management model for safety risks in university laboratories based on digital twin technology to improve safety management efficiency and accuracy. The virtual simulation environment of the laboratory is constructed by using digital twin technology, which is combined with the DEMATEL-ISM method for risk identification and analysis. Fault tree analysis (FTA) method was utilized to construct a laboratory safety accident fault tree to identify and assess potential risk factors. It was found that safety risks in laboratories can be effectively identified and controlled by digital twin technology. The risk assessment showed that unregulated drug storage, lack of monitoring and warning devices, insufficient safety awareness, inadequate systems and unreasonable layout of water, electricity and gas pipelines were the main risk factors. The empirical analysis of 11 university laboratories revealed that most of the laboratories were at a “relatively safe” level. The intelligent management model of safety risk in university laboratories based on digital twins can effectively identify and assess the safety risk, provide a scientific basis for the formulation of safety management measures, and thus improve the efficiency and accuracy of laboratory safety management.

A quantitative methodology for justification of platform edge protection systems on passenger rail networks

The implementation of appropriate platform edge protection systems (PEPSs) is a vital consideration in modern rail platform design to reduce injuries caused at the platform-train interface. This work presents a commercially tried and tested methodology for assessing the practicability of implementing PEPSs based on statistical analyses in combination with an As-Low-As-Reasonably-Practicable (ALARP) assessment, to assist in the justification of asset application viability. By extrapolating fatality-weighted injury data relating to the inclusion or non-inclusion of PEPSs, a safety risk model predicts changes in injury statistics to establish a quantified safety impact. The economic cost analysis identifies the operational expenditure for PEPSs throughout their lifecycle along with the capital expenditure and any impacts on the operational railway. The outcomes from these analyses are combined to produce a ratio of safety and operational benefits to capital and operational cost which is scrutinised against a designated disproportionality factor to establish suitability for use.

Dynamic evaluation method for Time-variant reliability of structural safety of Concrete-faced rockfill dam

The evaluation of the structural safety risk for concrete-faced rockfill dams (CFRDs) is susceptible to the time-varying parameters and the environments of topography, geology, and operations. Traditional evaluation methods, due to their involvement in numerical simulation, cannot meet the current requirements for intelligent monitoring in terms of timeliness and practicality. Therefore, a dynamic evaluation method of reliability in CFRDs is proposed in this paper. Due to the nonlinear mapping relationship between the time-variant reliability and the monitoring characterizations of CFRDs, the long and short-term memory neural network (LSTM) and support vector machine (SVM) models are introduced to construct the potential functional relationships between the reliability sequences and the monitoring characterizations, and the matching between the accuracy of the two models and the frequency of monitoring characterizations has been fully studied, forming an adaptive evaluation model of safety risk for the CFRDs according to the different monitoring frequencies of different monitoring items. The application of Sanbanxi CFRD shows an average relative error of less than 1% and 5% for dam slope stability and slab cracking simulations, respectively. These results demonstrate that the LSTM and SVM models with adaptive monitoring frequency exhibit high fitting and prediction accuracy and applicability, and possess theoretical and engineering application value.

Assessing the Risks Associated with the Canadian Railway System Using a Safety Risk Model Approach

Canada’s national rail network plays a vital role in moving goods and people, transferring $320 billion worth of goods and over 100 million passengers annually. Severe train occurrences are rare events. But they have the potential to cause fatalities and injuries, as well as environmental and property damage. Recent severe incidents, such as Burlington in 2012 and Lac-Mégantic in 2013, have shown that there is still a need for increased awareness and enhanced risk assessment. This work focuses on risk assessment on the Canadian railway system using the Safety Risk Model (SRM). The study applied a customized Canadian SRM (C-SRM) to two groups of hazardous events: main-track derailments and collisions with fatality and injury consequences, calibrated for data between 2007 and 2017. The model used Fault Tree Analysis (FTA) and Event Tree Analysis (ETA) to identify the risks of hazardous events. The individual risks of the hazardous events were then evaluated for three groups of people: passengers, employees, and members of the public (MOP). Finally, the effectiveness of introducing a new control measure, Enhanced Train Control (ETC), was assessed. The results of the study showed that the collective risk of main-track derailments is higher than main-track collisions. Moreover, the risk to MOP and employees form the most significant proportion of individual risk. Finally, risk reduction analysis of the ETC revealed that developing this system reduced the risk of main-track derailments and collisions. This new control measure thus has the potential to make Canadian railways safer.

Construction Safety Risk Model with Construction Accident Network: A Graph Convolutional Network Approach

Construction risk assessment (RA) based on expert knowledge and experience incorporates uncertainties that reduce its accuracy and effectiveness in implementing countermeasures. To support the construction of RA procedures and enhance associated decision-making processes, machine learning (ML) approaches have recently been investigated in the literature. Most ML approaches have difficulty processing dependency information from real-life construction datasets. This study developed a novel RA model that incorporates a graph convolutional network (GCN) to account for dependency information between construction accidents. For this purpose, the construction accident dataset was restructured into an accident network, wherein the accidents were connected based on the shared project type. The GCN decodes the construction accident network information to predict each construction activity’s severity outcome, resulting in a prediction accuracy of 94%. Compared with the benchmark feedforward network (FFN) model, the GCN demonstrated a higher prediction accuracy and better generalization ability. The developed GCN severity predictor allows construction professionals to identify high-risk construction accident scenarios while considering dependency based on the shared project type. Ultimately, understanding the relational information between construction accidents increases the representativeness of RA severity predictors, enriches ML models’ comprehension, and results in a more reliable safety model for construction professionals.

A Mobile Intelligent Mine Platform with a Hybrid Fuzzy NN and ATT-CNN Prewarning Model

Given the existence of coal production risk, effective prewarning is important to the reliability and safety of coal mine. So, the development of a risk prewarning system has become an important safety management tool. To improve the prediction ability and the supervision level of safety production, and handle different multidimensional (temporal and spatial) information for risk prewarning, we built a new platform based on the Internet, cloud platform, mobile communication, GIS, and artificial intelligence technology, i.e., a mobile intelligent mine platform. The terminal of the platform provides real-time queries and procedures of coal mine production and risk prewarning and provides data support and technical means for daily supervision, remote networking analysis, law enforcement inspection, and emergency rescue. The prewarning model of safety risk is an essential means to realize prewarning. The complexity of production of coal mine leads to the dynamic characteristics, fuzziness, and randomness of coal mine accidents. The complex nonlinear relationship between index and risk level leads to low accuracy of the traditional back propagation (BP) neural network prewarning method. A novel model based on a compensation fuzzy neural network (NN) and an attention mechanism-convolutional neural network (ATT-CNN) are a critical part of the new design. First, to full use of the convolutional network to get a larger receptive field, one-dimensional time series is transformed into two-dimensional matrix as the input of the CNN network by mapping. The neural network is utilized to extract the advanced features of the input signal. The results are finally output through a fully connected classifier. The model fuses multisource data at the feature level, employs the temporal and spatial relationships of monitoring data, and dynamically evaluates the risk. The experiment shows that the proposed model achieves impressive performance in both quantitative and qualitative evaluations and has improved the model generalization ability. The combination of data integration, remote examinations, and approval from existing information systems enables this platform to provide dynamic reminders of approval information, various risk prewarning, and management process automation through a mobile network.

A Review of Safety Risk Theories and Models and the Development of a Digital Highway Construction Safety Risk Model

This study conducts a systematic review of safety risk models and theories by summarizing and comparing them to identify the best strategies that can be adopted in a digital ‘conceptual’ safety risk model for highway workers’ safety. A mixed philosophical paradigm was adopted (that used both interpretivism and post-positivism couched within inductive reasoning) for a systematic review and comparative analysis of existing risk models and theories. The underlying research question formulated was: can existing models and theories of safety risk be used to develop this proposed digital risk model? In total, 607 papers (where each constituted a unit of analysis and secondary data source) were retrieved from Scopus and analysed through colour coding, classification and scientometric analysis using VOSViewer and Microsoft Excel software. The reviewed models were built on earlier safety risk models with minor upgrades. However, human elements (human errors, human risky behaviour and untrained staff) remained a constant characteristic, which contributed to safety risk occurrences in current and future trends of safety risk. Therefore, more proactive indicators such as risk perception, safety climate, and safety culture have been included in contemporary safety risk models and theories to address the human contribution to safety risk events. Highway construction safety risk literature is scant, and consequently, comprehensive risk prevention models have not been well examined in this area. Premised upon a rich synthesis of secondary data, a conceptual model was recommended, which proposes infusing machine learning predictive models (augmented with inherent resilient capabilities) to enable models to adapt and recover in an event of inevitable predicted risk incident (referred to as the resilient predictive model). This paper presents a novel resilient predictive safety risk conceptual model that employs machine learning algorithms to enhance the prevention of safety risk in the highway construction industry. Such a digital model contains adaptability and recovery mechanisms to adjust and bounce back when predicted safety risks are unavoidable. This will help prevent unfortunate events in time and control the impact of predicted safety risks that cannot be prevented.

Evaluation Method for Quality Risks of Safety in Prefabricated Building Construction Using SEM-SDM Approach.

The construction of prefabricated buildings is an effective and efficient approach to improving construction processes and productivity. However, there are practical problems in this approach, such as listing, safety risk levels, and quality control. This study aims to develop a systematic approach for determining the key factors affecting prefabricated building projects’ quality and safety risk and assessing this risk. Based on the literature review, a structured questionnaire was distributed to 408 China-based construction organizations. Considering the factors of safety risk evaluation systems for construction, the safety risk model of prefabricated buildings is established and combined with structural equation modeling (SEM) and a system dynamic model (SDM). A detailed case study was conducted to verify the empirical findings. The results show that pre-construction, during-construction, and after-construction significantly influence the quality risk (from high to low). The final comprehensive score is 92.71, indicating that the construction safety of the residential building is generally controllable and the quality is guaranteed. Furthermore, the investment risk of such projects can be assessed using SEM and SDM. This study contributes to the literature by considering quality-risk-influencing factors in this field. Furthermore, the findings provide an understanding of implementation and quality risk control for prefabricated building projects and provide valuable information to departments in charge of improving the safety risk performance of such projects.

A Novel Fuzzy Comprehensive Evaluation Model of Construction Safety Risk of Inclined Shaft in Underground Mine

A Novel Fuzzy Comprehensive Evaluation Model of Construction Safety Risk of Inclined Shaft in Underground Mine

Safety risk assessment and early warning of chemical contamination in vegetable oil

Edible vegetable oil is a necessity in people's daily diet, and its safety has attracted the attention of the government and consumers. In recent years, the safety incidents of edible vegetable oil are reported, and the safety problems of edible vegetable oil caused by chemical hazards such as benzopyrene, heavy metal and aflatoxin B1 are more serious. In this research, a risk assessment model through dietary exposure assessment and margin of exposure (MOE) were built to assess the health risks of benzopyrene, aflatoxin B1, and heavy metals in edible vegetable oils. And then an early warning model of food oil safety risk was established by using analytic hierarchy process (AHP) and back propagation (BP) neural network. According to the national standards and sampling data, the risk early warning model determined eight evaluation indexes of vegetable oil quality and safety, and calculated the risk value of chemical pollution in vegetable oil by Entropy Weight-Analytic Hierarchy Process(EW-AHP). The data of eight evaluation indexes were taken as the input of the model, and the comprehensive risk value was taken as the output of the model. The model learning process was carried out, and four algorithms, namely Support Vector Machine (SVM), Random Forest (RF), Radial Basis Function Neural Network (RBF) and Neural Network (BP), were selected for construction and comparison of model. The daily sampling data of chemical hazard factors in edible vegetable oil were quantified into specific chemical hazard risk levels. Thus the goal of predicting chemical hazard levels was achieved in edible vegetable oil. This study provides targeted reference suggestions for the safety supervision of edible vegetable oil, so as to improve the efficiency of supervision and ensure the consumption safety of edible vegetable oil.

Developing a new Safety Risk Model (SRM) methodology for the GB rail industry

This paper presents research recently undertaken by RSSB to develop a new Safety Risk Model (SRM) methodology. The SRM provides a network-wide risk profile for the mainline railway in Great Britain (GB) and has underpinned the industry’s evidence and risk-based approach to safety management for the best part of two decades. Firstly, users’ requirements were identified, collated and prioritised. The current SRM was then reviewed, summarised and appraised against these requirements to determine areas where there was potential to improve the current methodology. Potential new methodologies were then investigated to determine whether there were any which may enable the new SRM to better meet current user requirements. The feasibility and potential benefits of these alternative methodologies were assessed to determine the best approach for building the new SRM. This paper gives an overview and understanding of the processes that have been carried out in this research and will be of interest to others who may wish to undertake a similar appraisal of their risk models. Abbreviations: ALARP: as low as reasonably practicable; ALCRM: all level crossing risk model; BN: Bayesian network; CBA: cost benefit analysis; ETCS: European train control system; FWI: fatalities and weighted injuries; GB: Great Britain; HAZID: hazard identification; HEM: hazardous event movement accident; HEN: hazardous event non-movement accident; HET: hazardous event train accident; HS2: High Speed 2; PIM: Precursor Indicator Model; RPT: Risk Profile Tool; RSSB: Rail Safety & Standard Board; SMIS: safety management intelligence system; SRM: Safety Risk Model; SWOT: strengths, weaknesses, opportunities and threats

Hazard identification and risk modeling on runway bird strikes at Sardar-e-Jangal International Airport of Iran

In every Airport, management options are facing different issues that may cause problems for aviation operations. Hazard identification and risk modeling must be the priority of airport management. Our research focused on runway safety, so that is very important, and it has various risks. Factors affecting flight safety include runway incursion, runway excursion, loss of control on the ground, bird strike, etc. Collision with wildlife is one of the most critical factors affecting the environment. Our study goal was that bird strikes pose a significant threat to aviation safety worldwide. This issue has two perspectives; first, wildlife significantly affects safety by increasing collision risk with airplanes. Second, the aviation operation causes danger for animals' lives and, consequently, to the environment. To carry out the research method, we attempt to present a historical background and record of wildlife collision and their environmental risks and simultaneous a way to identify these hazards and control their impacts. An essential part of the research results was introducing two hazard identification forms and wildlife risk modeling at Sardar-e-Jangal international airport of the Rasht City of Iran. The risks summarized in this case, from March to September 2019. The results of daily observations showed that a variety of birds always used the runway. Based on observation, safety risk modeling has been done. Most birds that collide with aircraft were of the Pheasants type, as a relatively large bird species. Given all of these factors, the severity of this hazard is catastrophic. So the safety risk index is 5A and unacceptable. Therefore, it is necessary to make specific arrangements, especially, Abundant rivers, canals, and waterways in the Airport, creating diverse vegetation that attracts small insects and animals (rodents, amphibians, etc.) needed food, shelter in vegetation. In addition, making the right environment for childbirth, high rainfall in the Medium to a long-term action plan for each according to local circumstances.

Computation of Importance Measures Using Bayesian Networks for the Reliability and Safety of Complex Systems

Modern engineering systems have proven to be quite complex due to the involvement of uncertainties and a number of dependencies among the system components. Shortcoming in the inclusion of such complex features results in the wrong assessment of reliability and safety of the system, ultimately to the incorrect engineering decisions. In this paper, the usefulness of Bayesian Networks (BNs) for achieving improved modeling and reliability and risk analysis is investigated. The calculation of a number of Importance Measures with use of Fault Tree Analysis as well as BNs is provided for a complicated railway operation problem. The BNs based safety risk model is investigated in terms of quantitative reliability and safety analysis as well as for multi dependencies and uncertainty modeling.

Study on Dynamic Prediction Model of Small Reservoir Safety Risk

This paper comprehensively uses multi-source data such as small reservoir operation management information and mountain flood disaster investigation information to transform the disaster risk prediction of small reservoirs into disaster risk assessment of easy-to-observe indicators. On this basis, combined with the data of mountain flood investigation and reservoir operation management data, through the expert consultation method, the disaster risk state transition matrix and the disaster risk output matrix are studied and established to construct the Hidden Markov Model of mountain flood disaster risk prediction. Finally, combined with a reservoir in Zhejiang Province, the hidden Markov model constructed in this paper is verified. The verification results show that the model constructed in this paper is feasible and can effectively ze the dynamic prediction of small reservoir safety risks. The research results of this paper lay the foundation for perfecting the safety risk management theory of small reservoirs and realize the dynamic prediction of small reservoir safety risks.

Safety Risk Analysis of Unmanned Ships in Inland Rivers Based on a Fuzzy Bayesian Network

Risk factor identification is the basis for risk assessment. To quantify the safety risks of unmanned vessels in inland rivers, through analysis of previous studies, the safety risk impact factor framework of unmanned vessels in inland rivers is established based on three aspects: the ship aspect, the environmental aspect, and the management and control aspect. Relying on Yangtze River, a fuzzy Bayesian network of the sailing safety risk of unmanned ships in inland rivers is constructed. The proposed safety risk model has considered different operational and environmental factors that affect shipping operations. Based on the fuzzy set theory, historical data, and expert judgments and on previous works are used to estimate the base value (prior values) of various risk factors. The case study assessed the safety risk probabilities of unmanned vessels in Yangtze River. By running uncertainty and sensitivity analyses of the model, a significant change in the likelihood of the occurrence of safety risk is identified, and suggests a dominant factor in risk causation. The research results can provide effective information for analyzing the current safety status for navigation systems of unmanned ships in inland rivers. The estimated safety risk provides early warning to take appropriate preventive and mitigative measures to enhance the overall safety of shipping operations.

Anti-sliding Risk Model of Wet Surface for Asphalt Pavement and Test Verification

A layer of water film will form on the road surface in rainy weather. Due to the lubrication effect of water film, the anti-skid ability of driving vehicles decreases, which will lead to road traffic accidents. The contact between water film, tread rubber and pavement surface texture is the most direct and concrete when a vehicle travels on wet sliding road surface. Their respective characteristics and coupling effects provide the basis and essential source of tire-road friction. Based on this, in this paper a tire-road anti-skid safety risk model under rainy weather with friction coefficient as evaluation criterion was established by analyzing the fractal characteristics of asphalt pavement surface texture and the viscoelastic properties of rubber tire. Then the correctness of the model was verified by DF test. The test results show that the trend of friction curve calculated by the model is similar to that measured by DF test, which verifies that the model can be used to calculate and characterize the anti-sliding performance of road surface in rainy weather.