Safety Assessment Model Research Articles

Nasonia vitripennis as a parasitoid wasp model for pest-specific dsRNA safety assessment

Nasonia vitripennis as a parasitoid wasp model for pest-specific dsRNA safety assessment

Parametric geological model update and probabilistic analysis of shield tunnel excavation: A borehole-based conditional random fields approach

Shield tunnel construction has faced considerable challenges due to the uncertain geological conditions in real scenarios. The borehole data obtained from geological investigations are often too limited to effectively evaluate uncertainties at nonsampled points. Moreover, the existing probabilistic safety assessment (PSA) frameworks that consider the uncertainties of soil properties have encountered limitations in engineering practice due to the complexity of conditional random field (CRF) theory and the operability of numerical modeling. Based on the digitization of the underground infrastructure, this study implements the object-oriented parametric modeling (PM) to address these limitations. A PM-CRF engine is first developed to automate the process of parametric geological modeling using CRFs. Then, a PM-CRF-PSA interoperability framework is proposed to automatically generate a PSA model for comprehensive safety assessment considering the uncertainties of soil properties. Finally, a case study is conducted with the proposed framework to assess the whole process of parametric modeling from CRF characterization to PSA. The results of this study highlight the clear advantages of this framework compared to traditional deterministic safety assessment (DSA) methods in terms of characterization of the uncertainty of soil properties and the efficiency of PSA modeling. Thus, the proposed framework enables the efficient and comprehensive exploration of shield tunnel design schemes by considering the uncertainties of soil properties.

Conflict Judgment and Safety Assessment at Unsignalized Intersections Based on Machine Vision

This article aims to explore an effective method for reducing vehicle collisions at unsignalized intersections. First, a monocular-binocular vision switching system is built to enable machine vision-based detection of obstacle vehicles in the left and right front directions. Then, the motion state and trajectory of each obstacle vehicle are predicted, and the intersection points of the trajectories of the obstacle vehicle and the ego vehicle are calculated. On this basis, a cross-conflict judgment model based on trajectories and collision times and a safety assessment model based on safety distance are established. Finally, the conflict judgment and safety assessment for the obstacle vehicles are simulated. The results of the simulation demonstrate that the monocular-binocular vision switching system proposed in this article can achieve a detection accuracy of 95%, a ranging accuracy of 96%, and a cross-conflict detection accuracy of 97%, while ensuring a maximum detection area, which can meet the requirements of traffic safety assurance at unsignalized intersections.

Construction of Real-time Dynamic Reversible Lane Safety Control Model in Intelligent Vehicle Infrastructure Cooperative System

ABSTRACT Reversible lanes play an important role in alleviating urban traffic congestion and improving the utilization of road resources. However, most of the reversible lanes currently implemented are timed and fixed, and there is little research on the safety of reversible lanes without isolation facilities. In view of the existing problems of reversible lanes, this paper proposed a real-time dynamic reversible lane safety control model. The influencing factors of reversible lane safety control are analyzed first, and then determine model parameters of the no-entry zone and the buffer zone in the conflict area. Moreover, model parameters of emptying, exit, and recovery zones in the operation process were determined as well. Additionally, this paper discussed the length of reversible lanes and the timing of signals. The surrogate safety assessment model (SSAM) is adopted to analyze vehicle trajectory data for traffic simulation under different traffic flows. The experiment results show that the proposed reversible lane safety control scheme can keep the vehicle’s traffic conflict rate below 5%, which can effectively ensure the safe operation of the reversible lane.

Overview of omics applications in elucidating the underlying mechanisms of biochemical and biological factors associated with meat safety and nutrition

Over the years, significant technological discoveries have facilitated the improvement of meat-related research. Recent studies of complex and interactive factors contributing to variations in meat safety are increasingly focused on data-driven omics approaches such as proteomics. This review highlighted omics advances in elucidating the biochemical and biological actions on meat safety. Also, the impacts of the nutritional characteristics of meat and meat products on human health are emphasized. Future perspectives should explore multi-omics and in situ investigations to elucidate the implications in microbiological studies, including nutritional and health-related assessments. Also, creating meat safety assessment and prediction models based on biomarkers of meat safety traits will help to mitigate application constraints, thereby evaluating meat quality more accurately. This could provide a scientific basis for increasing the meat industry's profitability and producing high-quality meat and meat products for consumers. Significance of the reviewThis review highlighted omics advances in elucidating underlying mechanisms of biochemical and biological factors associated with meat safety. Also, the impacts of meat proteins on human health are emphasized. Future perspectives should explore multi-omics and in situ investigations to elucidate the implications in microbiological studies, including nutritional and health-related assessments. Also, creating meat safety assessment and prediction models based on biomarkers of meat safety traits will help to mitigate application constraints, thereby evaluating meat quality more accurately. This could provide a scientific basis for increasing the meat industry's profitability and producing high-quality meat and meat products for consumers.

A multi‐source information fusion model for safety assessment of bamboo‐integrated products

AbstractA multi‐source information fusion model is proposed to assess the safety and reliability of bamboo‐integrated products, which, unlike metal materials, hold different properties in longitudinal and transverse surfaces/directions. First, a weight factor is proposed to estimate the importance of each data source. Subsequently, a multi‐source information fusion model is proposed to compute the weight factors of the bamboo‐based materials in longitudinal and transverse directions. The reliability and failure probability of a bamboo‐integrated product, in longitudinal and transverse directions, are computed according to the multi‐source information fusion model‐based safety assessment approach with the assistance of the Stress and Strength interface model. Finally, a comparison between the proposed safety assessment method and the conventional Stress and Strength interface model is finalized, and the results indicate that the proposed method computes more reliable results than that of the conventional method. Overall, the proposed method provides a framework for safety and reliability assessment under multiple data sources and contributes to the comprehensive application of diverse data.

Construction Risk Assessment for Old Town Renovation Based on AHP-LEC

Old city renovation can improve the living environment and quality of residents and enhance the image of the city, and in the process of urbanisation, old city renovation is always a people’s livelihood project that cannot be ignored. This paper takes the old city renovation in County H as an example, through the combination of hierarchical analysis method (AHP) and construction safety evaluation method (LEC) in the renovation process, constructs the safety assessment model, uses the Delphi method to determine the factors that lead to the occurrence of safety accidents in the construction, assigns scores to each index and realises the quantitative analysis, and finally analyses the weighted size of the factors that lead to the occurrence of safety accidents and carries out the safety assessment. Based on the results of the safety assessment, it provides certain reference significance for the future transformation of the old city.

The use of microscopic simulators for safety assessment in automated and partially automated scenarios: a comparison

In the field of safety assessments, the use of simulators to predict unknown scenarios is becoming crucial, due to the ability of predicting conflict points. The microscopic simulators, accountable for modeling single-vehicle interactions, differ for the type of car-following and lane-changing model, hence for the number of definable parameters. The possibility of modifying all the parameters inside the model equations makes them suitable for simulating autonomous vehicles too. To evaluate the road safety of a site, the output of traffic simulators can become the input of the SSAM (Surrogate Safety Assessment Model), which relies on surrogate safety measures (SSMs). Thus, the choice of the simulator is fundamental. The focus of this dissertation concerns the comparison of the car-following and lane-changing models at the basis of the most used commercial microscopic simulators (VISSIM, AIMSUN, PARAMICS) and the related SSAM outputs in the automated and partially automated traffic environment. The Gipps Model, valid for the car-following and the lane-changing model, aims at always achieving the safety distance, intervening on kinematic parameters like the acceleration, speed, and distance. It is possible to intervene on a limited number of parameters of this model. The Wiedemann model belongs to the family of psycho-physical one and it is governed by several parameters, which makes it useful to remodel complex situations, simulating the human mental workload in driving. The Gipps model and the Wiedemann one (implemented respectively in AIMSUN and VISSIM) have already been used for safety purposes, while the Fritszche one (implemented in PARAMICS) has been currently used only for environmental issues related to automation. It is possible to modify up to 16 parameters in the Gipps and Wiedemann model to simulate the automated and partially autonomous vehicles, even if these models belong to different car-following model family. Based on the site variables, each simulator showed potential strengths. Scenarios involving automated vehicles, which do not account for human behaviors, might be better represented by the Gipps model, which aims at reproducing only the safety distance and not the human mental behavior. Thus, this model could also be the most suitable for the general purpose of crash analysis above mentioned.

Establishment and Application of City Safety Assessment Model Based on Risk Matrix

Urban safety is a common concern in the world, and it is important to evaluate the safety of cities. Based on the existing research results and China's national conditions, this paper establishes an urban safety evaluation model based on "risk matrix" and verifies the scientific validity of the model by using examples. This paper provides a reference for future urban safety management.

Research on safety evaluation of assembly building construction by integrating entropy power method and network analysis model

Unlike the traditional construction mode of rough operation, assembly building construction implements the concept of green development in terms of energy consumption and environmental adaptability. Although assembly construction can effectively reduce construction energy consumption and improve the environmental resilience of building construction work, there is an urgent need for an effective safety assessment model for construction development due to the imperfect operation system and harsh construction environment in the construction industry. Therefore, the study analyzes the relationship of construction safety factors by using Analytic Network Process (ANP) to filter safety evaluation indexes according to the importance ranking. At the same time, the objective weights of safety indicators were determined by the entropy weight method, and the subjective weights determined by the ANP method were combined to construct the safety evaluation model for the construction of assembled buildings. The experiment shows that the maximum similarity between the comprehensive evaluation results of the model in the simulation of safety evaluation of high-rise residential construction and the actual evaluation criteria is 0.772. The experiment proves the reliability of the evaluation of the model, which reduces the safety loopholes and operation hazards for the construction of assembled buildings.

Safety Performance Evaluation of Continuous Flow Intersections in the Era of Connected Vehicles: A Microsimulation Modelling Approach

This study employed Federal Highway Administration’s Surrogate Safety Assessment Model (SSAM) to investigate the safety of implementing Connected Vehicles (CVs) at the Continuous Flow Intersection (CFI), by reproducing a real-world corridor, that has multiple successive implementations of CFIs, in VISSIM. Econolite’s ASC/3 Software-in-the-Loop signal controllers and Python-programmed Vehicle to Infrastructure (V2I) communications were embedded in VISSIM. Additionally, the effect of CV-Market Penetration Rate (CV-MPR) on safety is taken into consideration. The study shows that CV deployments at partial and full CFIs leads to notable reductions in crash likelihoods and severities. The total number of conflicts, rear-end and lane change conflicts dropped by 23.8%, 23.6% and 24.4%, respectively at full CFIs and 100% MPR, whereas those were reduced by 6.4%, 4.8% and 17.9%, respectively at partial CFIs and 100% MPR. It was also found that at least a 50% MPR of CVs is required for safety improvements to be influential.

An Established Theory of Digital Twin Model for Tunnel Construction Safety Assessment

In traditional construction safety assessment, it is difficult to describe the safety status of different construction stages. To solve this problem, this paper proposes a digital twin modeling theory for construction safety assessment. Firstly, this paper analyzes the requirements of a digital twin model. Secondly, the required information is collected by IoT. Finally, the DT model is established based on the collected information. This DT model analyzes the collected information by ML, which aims to conducting the assessments of construction safety. To verify this method, this paper analyzes the vault settlement during tunnel construction. The analysis results show that the DT model can predict the settlement value with high accuracy. Moreover, the safety state is assessed dynamically based on the settlement value by DT.

Analysis of LNG Storage Tank Safety: A Comprehensive Model Approach with ANP and Normal Cloud

Global energy shortages continue in the post-epidemic era. As clean primary energy, LNG plays an important role in the energy structure adjustment. However, with the widespread use of LNG, storage tank leaks occur frequently. In order to highlight the risk status of critical risk areas of LNG tank leakage, the “Five-high” risk system for the LNG industry, with high-risk locations, high-risk equipment, high-risk processes, high-risk personnel, and high-risk substances as first-level elements, was built. Combined with the Analytic Network Process (ANP) and normal cloud model for quantitative analysis, a safety risk analysis and assessment model for LNG storage tanks based on the ANP-normal cloud model was developed. Safety analysis was conducted with the example of an LNG storage tank area of a factory. The results show that the risk level for LNG tank leakage is level IV, with high-risk equipment being the first-level element with the highest weight and risk. The reliability of the comprehensive model was verified by AHP and fuzzy integrated analysis, and the results were basically consistent with the factory safety evaluation report. The study realizes the quantitative and visual analysis considering the correlation of risk elements and provides a new quantitative method for the safety analysis of LNG energy.

Research on the Safety of the Left Hard Shoulder in a Multi-Lane Highway Based on Safety Performance Function

The left hard shoulder plays an important role in the event of an emergency on the inside of a multi-lane highway, but past studies have not been able to clarify the criteria for its installation or quantify the safety impact of its installation on the left side. In order to study the influence of the left hard shoulder on the safety of vehicles traveling on multi-lane highways, based on past studies that only studied the situation of four-lane highways, this paper firstly constructs a multi-lane highway simulation model under different numbers of lanes based on the VISSIM traffic simulation and uses Surrogate Safety Assessment Model (SSAM) to study the conflict characteristics of multi-lane highway vehicles under different numbers of lanes. Based on the above findings, this paper introduces the Safety Performance Function (SPF) to construct a multi-lane freeway accident prediction model, calibrates the model by adding the indexes affected by the left side hard shoulder to the basic prediction mode, and uses the historical accident data of the Badou-Shihu section of the Guangdong Northern Second Ring Highway as the basis to study the differences in accident rates of the investigated section before and after setting the left hard shoulder. The study showed that the average Time to Collision (TTC) increased by 57.2%, Maximum Deceleration (MaxD) increased by 19.2%, and Delta Speed (DeltaS) increased by 15.3% after setting hard shoulders on the left side of multi-lane freeways, and traffic conflicts on multi-lane freeways were significantly reduced, and safety was improved considerably. In addition, the rear-end conflict rate decreased by 0.17%, 0.75%, and 4.6% after setting hard shoulders on the left side of one-way three, four, and five lanes, respectively, indicating that hard shoulders on the left side are the most effective in improving the safety of one-way five-lane freeways. The accident prediction results show that within the reasonable setting range of the left hard shoulder width (0~4 m), the accident rate decreases by about 1.5% for every 0.5 m increase if only the influence of the left hard shoulder width is considered. Without considering other factors, increasing the width of the hard shoulder on the left side can reduce the number of accidents. This indicates a significant safety improvement for a one-way five-lane highway after setting the hard shoulder on the left side, and the conclusion is consistent with the simulation results. In this paper, based on past research, the research object is extended to one-way three-, four-, and five-lane highways. The findings of this paper can help the road authorities develop specifications for installing hard shoulders on the left side of multi-lane freeways and adopt strategies to improve the traffic safety level of multi-lane freeways. In addition, the models and methods used in this paper can also help build a framework for future intelligent networked vehicle avoidance systems and promote the development of intelligent networked technologies.

Detection Method for All Types of Traffic Conflicts in Work Zones

Traffic conflict technology (TCT) is widely used to assess the safety of work zones. The current TCT is temporal and (or) spatial proximity defined based, which can only detect two-vehicle or multi-vehicle conflicts, and is not competent for single-vehicle conflicts. However, single-vehicle accidents in work zones are also severe. This study proposes a detection method for all types of traffic conflicts in work zones. Based on vehicle micro-behavior data, evasive behavior is detected by automatic segmentation, Support Vector Machine (SVM)-based behavior identification, and threshold-based judgment methods. Two-vehicle or multi-vehicle conflicts are detected by classical proximity defined-based method, i.e., the surrogate safety assessment model (SSAM). By comparing the analysis results of the evasive behavior with the one of SSAM, single-vehicle conflicts can be detected. Taking a practical work zone as an example, the effectiveness of this method in detecting all types of traffic conflicts in work zones is verified. The single-vehicle conflict can be subdivided into 10 types according to basic behavior types, such as straight-line driving and decelerating. The two or multi-vehicle conflicts can be subdivided into three types, such as rear-end conflict. The example also verifies the applicability of this method under different traffic volume scenarios.

A new proposal for controlled recycling of decommissioning concrete waste as part of engineered barriers of a radioactive waste repository and related comprehensive safety assessment

As an alternative to conventional management options for a lot of concrete waste from decommissioning of nuclear power plants, a set of scenarios for controlled recycling of decommissioning concrete waste as engineered barriers of a radioactive waste repository was proposed, and a comprehensive safety assessment model and framework covering both pre-and post-closure phases was newly developed. The new methodology was applied to a reference vault-type repository, and the ratios of derived concentration limits to unconditional clearance levels of eighteen radionuclides for controlled recycling were provided for three sets of dose criteria (0.01, 1, and 20 mSv/y for the pre-closure and 0.01 mSv/y for the post-closure phases). It turns out that decommissioning concrete waste whose concentration is much higher than the unconditional clearance level can be recycled even when the dose criterion 0.01 mSv/y is applied. Moreover, a case study on ABWR bio-shield shows that the fraction of recyclable concrete waste increases significantly by increasing the dose criterion for the radiation worker in the pre-closure phase or the duration of storage prior to recycling. The results of this study are expected to contribute to demonstrating the feasibility of controlled recycling of a lot of decommissioning concrete waste within nuclear sectors.

A traceable process to develop Bayesian networks from scarce data and expert judgment: A human reliability analysis application

The present paper develops a Bayesian Belief Network (BBN) for quantification of aggravating actions, as outcomes of inappropriate decisions, to be integrated in probabilistic safety assessment (PSA) models (i.e., the so-called errors of commission, EOCs). The BBN connects analyst ratings on influencing factors to the error forcing impact of a specific scenario, supporting the CESA-Q method (the Quantification module of the Commission Error Search and Assessment method). While contributing to the quantification of EOCs, this paper presents a novel process for the quantification of the BBN parameters (the Conditional Probability Distributions, CPDs), striving for traceable integration of expert knowledge and (scarce) data, in the form of retrospective analyses of operational events involving EOCs. The process combines the functional interpolation method for populating CPDs and Bayesian updates to adjust the BBN response to the available evidence. A first, prior BBN is developed, then sequentially updated to adjust to two data sets. This allows some intermediate validation and puts forwards the steps for future BBN updates as new EOC events (or new analyst assessments) become available.

Fire safety assessment models based on machine learning methods for the coal industry

One of the major coal industry problems is the auto-oxidation of coal, which leads to fires and human-induced disasters. This paper aims to use machine learning techniques and the most effective algorithm for preventing fires in mines. Two machine learning techniques, the naive Bayes classifier and support vector machines (SVMs), were employed to achieve the objective. The algorithm was developed based on the dependency of the indicating gas amount on the coal temperature. The accuracy of the techniques was assessed using the non-conformity matrix and related parameters. The data collected was processed using Microsoft Office. It was discovered that before the appearance of an open fire, there were two other temperature intervals without combustion and smoldering, demonstrating a clear dependence of smoke and carbon monoxide concentrations on the coal temperature. This technique is excellent for forecasting the first step of combustion, which is confirmed by the corresponding parameter values. The results demonstrated that the methodology was excellent in laboratory studies. The algorithm proposed in this paper can be used as a new method of predicting fires in coal mines and elaborating a branch of machine learning to establish more effective ways for detecting combustion in its early stages.

Estimation of Appropriate Acceleration Lane Length for Safe and Efficient Truck Platooning Operation on Freeway Merge Areas

The length of an acceleration lane is one of the dominant freeway geometric design parameters. This length requires new analyses to anticipate the needs of heavy commercial vehicle (HCV) platooning. We evaluated the safety and operational impact of HCV platooning on acceleration lane length for a freeway ramp in Ontario, Canada. This study modified the 2018 AASHTO’s acceleration lane length estimation analytical model. Furthermore, this study used a VISSIM micro-simulation model and surrogated safety assessment model (SSAM) to examine the safety and operational impact on the real-world circumstances of HCV platooning at 0.6 s and 1.2 s headways and different market penetration rates of 0%, 5%, and 10%. The results suggest a minimum acceleration lane length of 600 m for platooned HCVs, which is inadequate compared to American and Canadian design guidelines. An extended acceleration lane length (600 m) will improve safety by reducing conflict by 19.2% and operational performance by reducing 3.9% of 85th percentile merging time for the operation of 5% HCV platooning with 0.6 s headway compared with 350 m acceleration lane length. This study suggests 5% of traffic containing two HCV platoons with 0.6 s headway may be reasonable for operation during certain hours of the day under existing conditions.

A Simulation-Based Study of the Influence of Low-Speed Vehicles on Expressway Traffic Safety

To reveal the impact mechanism of low-speed vehicles (LSVs) on expressway traffic safety, this paper uses the polynomial fitting method to establish evolution models of traffic density and average speed at different LSV speeds in order to explore the queuing and dissipation characteristics of vehicles affected by LSVs and investigate the impact range of LSVs on expressways. Based on the findings above, this paper builds a Surrogate Safety Assessment Model (SSAM)-based model to quantify driving safety and further explore the differences in vehicle conflicts when an LSV moves in different lanes at the same speed. The simulation experiment is conducted based on the field data from the Inner Ring North Road located along the Nanjing Inner Ring High Speed Road. The results show that the evolutionary features of lane traffic density and average speed under different LSV speeds satisfy the octuple polynomial law, reflecting the spatial heterogeneity of vehicle distribution at different LSV driving speeds. Meanwhile, LSVs with different speeds produced the most significant negative impact on the roadway within 400 m of the expressway entrance. The lower the speed of the LSV, the more significant the adverse effect. In addition, this paper finds that when an LSV travels in different lanes at the same speed, the inner, middle, and outer lanes have the highest number of total conflicts, rear-end conflicts, and lane-change conflicts, respectively. Meanwhile, vehicles in the outer lane are the most significantly affected by LSVs, while vehicles in the middle lane are the least affected with the highest traffic efficiency. Additionally, the Maximum Speed (MaxS) and Difference in Vehicle Speed (DeltaS) for the middle lane are 47.9% and 60.5% higher than the outer lane, respectively. Nevertheless, based on the Probability of Unsuccessful Evasive Actions, i.e., P(UEA), vehicles in the middle lane have the highest probability of potential traffic conflicts. The methods used in this paper will have positive implications for establishing autonomous vehicle risk avoidance systems which can improve the safety levels of expressways.