Safety Evaluation Method Research Articles

Development of a Comprehensive Safety Evaluation Mechanism for the Highway Bus Industry

A suitable mechanism for evaluating the safety of the highway bus industry is crucial for ensuring public transportation safety in a country. In this study, we have combined indices from the Compliance Safety Accountability (CSA) program of the Federal Motor Carrier Safety Administration (FMCSA) in the US, the safety evaluation of renting buses in Japan, and some existing safety evaluation methods in Taiwan to develop a new safety evaluation mechanism for the highway bus industry. This paper introduces a two-stage decision making approach that includes the use of fuzzy analytic hierarchy process (Fuzzy AHP) and machine learning techniques such as decision trees, support vector machines, and random forests to develop this mechanism. The data used in this research were collected from the internet and directly from highway bus transportation companies. We calculated the safety performance of each company and assigned them different safety ranks. Compared with the causes of accidents in Taiwan, the results of this study showed that work hours, driver fitness, and administrative penalties are the three most important sub-attributes that affect the safety rank of the company.

The effect of low-temperature starting on the thermal safety of lithium-ion batteries

With the widespread application of lithium-ion batteries (LIBs) in the field of energy equipment, their probability of starting or operating in low-temperature environments is also increasing. However, there is currently a lack of research on the changes in thermal safety of batteries after use in corresponding environments. In this work, the thermal safety performance, degradation mechanisms and evaluation method of LIBs at low-temperature start-up conditions are studied. The results show that starting at low temperature leads to the decrease of cell capacity. Electrochemical characterization and cell disassembly analysis indicate that the loss of active lithium ions is mainly caused by the evolution of lithium and the growth of solid electrolyte interface films. In addition, the low-temperature startup results in the decrease of thermal runaway (TR) onset temperature and the advance of TR time. In order to quantitatively evaluate the safety state of the LIBs, an evaluation model based on Informer algorithm is established. The model extracts the discharge capacity, discharge energy and dV/dQ as aging characteristic parameters, and extracts TR temperature as thermal safety characteristic parameters. The data set is processed by cubic spline interpolation. After training and verification, the accuracy of the model reaches 80 %.

A hybrid approach combining UD and GA-CV-SVM to evaluate shear performance in high asphalt concrete core

The shear effect on high asphalt concrete core is significant. However, studies on the reliability of 100-meter-scale cores against shear damage remain limited. A key challenge in this research field is establishing the control criteria for the core and improving the computational efficiency of implicit limit state function (LSF). Additionally, the impact of material parameter uncertainty on the shear failure reliability of the core during the dam construction and impoundment stages remains unclear. To address this, a safety evaluation method based on time discretization was proposed, combining uniform design (UD), K-fold cross-validation (K-CV), and genetic algorithm (GA) to optimize the support vector machines (SVM). The core parameters of 52 asphalt concrete-core rockfill dams (ACCRDs) were analyzed, with the statistical values of the basic variables considered in determining the reliability index. The theoretical derivation of the critical shear failure safety index established a stability formula to assess the safety state of the dam core. The significance parameters were identified, and the sample points were generated at each stage using UD, and the Support Vector Regression (SVR) was applied to reconstruct the LSF, and reliability was calculated through the checking point method.

On-site monitoring of expansive soil slope protected by soilbags based on the universal Beidou monitoring system

To explore an effective method for deformation monitoring and behavior prediction of expansive soil slope, field tests are conducted for a flexible slope protection scheme with soilbags that has been implemented in an expansive highway soil slope. A new monitoring system, i.e., the universal Beidou deformation monitoring system, is developed to overcome the limitations of traditional Global Navigation Satellite System (GNSS) software and hardware, simplify the hardware structure and realize the power sharing mode; furthermore, this system can create and upload a large amount of monitored data to a cloud platform to enable real-time calculation. Compared with traditional GNSS, the volume of equipment required is reduced by approximately 75%, and the cost is reduced by approximately 80%. Secondly, a multilevel safety early-warning evaluation system is constructed by integrating the monitoring results of the universal Beidou deformation monitoring system, bag damage states, rainfall conditions, and slope fissure development; additionally, a deformation early-warning mechanism of flexible support of soilbags was established. Finally, the deformation and collapse of flexible supports of soilbags can be successfully predicted in the field. This research on flexible support of soilbags provides new ideas and methods of deformation monitoring, safety evaluation, and early warning for expansive soil slopes.

Pharmacovigilance Consideration for Ayurvedic Medicines in Pediatric Practice: Developing Protocols for Documenting Clinical Safety

Background The rising global trend of traditional medicine and complementary and alternative medicine use necessitates a structured approach to ensure patient safety and efficacy. Due to its unique theoretical framework, directly applying modern pharmacovigilance principles to Ayurveda may be insufficient. Purpose This study aims to highlight the need for a collaborative approach that integrates traditional Ayurvedic knowledge with modern safety and efficacy evaluation methods. It critiques the limitations of current pharmacovigilance practices, especially in the context of Ayurveda, and proposes improvements to better capture the complexities of traditional medicine. Methods An analysis of the “AyushSuraksha” program, a key initiative for the pharmacovigilance of Ayurveda, Siddha, Unani, and homoeopathy (ASU&H) medicines in India, was conducted. The program’s focus areas, including monitoring medications, specific materials, and procedure-based therapies, were reviewed in light of their efficacy and safety reporting mechanisms. Additionally, the applicability of current pharmacovigilance methodologies, particularly in the context of personalized Ayurveda treatments, was evaluated through case studies and literature reviews. Results The “AyushSuraksha” program, while focused on medication and therapy monitoring, overlooks the safety of food items used in Ayurveda. Current pharmacovigilance systems, designed for standardized medications, may be inadequate for Ayurveda, as demonstrated by positive outcomes in autism treatment despite the presence of microbiologically harmful products. Furthermore, existing pharmacovigilance proformas lack a distinction between physicians’ and end-users perspectives, limiting the reporting scope. Conclusion A transition to a holistic, evidence-based pharmacovigilance approach that incorporates Ayurvedic assessments and nonlinear herbal interactions is crucial. Separate proformas for physicians and end-users, along with disease-specific questions, are recommended to improve reporting accuracy. Expanding the scope of pharmacovigilance beyond standardized medication monitoring is essential for the evolution and refinement of Ayurvedic science.

Roundabout Safety Benefits, Design Considerations and Safety Evaluation Techniques

Roundabouts are increasingly replacing conventional signalized intersections due to their potential to enhance traffic safety and operational efficiency. This paper examines and reviews the safety benefits, design considerations, and evaluation methods for roundabouts. Studies consistently show significant reductions in crashes at roundabouts relative to other at-grade intersection control systems, particularly severe injury crashes, with reductions ranging from 37% to 88%. Roundabouts reduce conflict points, lower vehicle speeds, and improve safety for both motorized and vulnerable road users. The methodology for this paper involved a comprehensive review of literature on roundabout design and safety evaluation techniques. Data from global studies were analyzed to identify key design elements such as geometric layout, signage, and pedestrian provisions, which contribute to roundabout safety. Various safety evaluation methods, including crash data analysis, traffic conflict techniques, and surrogate safety measures, were also assessed for their effectiveness in evaluating roundabout performance. Findings indicate that proper geometric design, clear signage, and the use of innovative features like right-turn bypass lanes and roundabout metering systems can enhance safety and traffic flow. Based on these findings, the paper recommends integrating these design elements, prioritizing the safety of vulnerable users, and utilizing advanced safety evaluation techniques, including simulation modelling and intelligent transportation systems (ITS) to further optimize roundabout performance.

Risk assessment of bridge construction investigated using random forest algorithm

Bridge construction collapse is one of the most common bridge safety accidents. At present, evaluation results are often affected by the ability and experience of the assessor. Therefore, it is difficult to quickly, accurately and effectively evaluate the risk in the process of bridge construction. Moreover, key factors that can prevent accidents can hardly find from the existing bridge construction safety management and evaluation method. This paper analyzes and classifies the artificial and environmental risk factors that affect the bridge construction stage, and establishes 26 risk factors in 5 categories according to the characteristics of bridge construction and the actual situation of the project. Random forest (RF) algorithm is a non-parametric machine learning method based on decision tree, which does not need to be scored by experts in advance and avoids the influence of subjective factors. Compared with other analysis methods, random forest algorithm has the advantages of accurate and robust risk assessment results. Based on the advantages of random forest algorithm and the characteristics of bridge construction risk, this paper uses random forest algorithm to evaluate the bridge construction risk, and ranks the importance of indicators, and identify the index that has a greater influence on the risk. In order to verify the applicability and feasibility of the proposed method, a typical urban complex pedestrian bridge was taken as an example for actual engineering evaluation and verification. The results obtained are basically consistent with the actual risk assessment results of the pedestrian bridge.

Building Safety Evaluation and Improvement for Northern Vietnam Mountainous Environments Empirical Study Combining Japanese Experience with Local Conditions

This study addressed the insufficient structural strength and inadequate disaster resistance in building designs in the mountainous regions of Northern Vietnam. By integrating Japanese construction experience with local conditions, we proposed optimized building structures and simplified safety evaluation methods. Through an analysis of climate, terrain, geological hazards, soil conditions, and construction material costs, building design and foundation construction were optimized, and these optimizations were validated through wind tunnel experiments and finite element analysis. The results indicated that the optimized structures exhibited superior wind load stability, with external wind pressure coefficients ranging from −1.5 to −0.7, compared with the traditional structure’s range of −1 to −3.5. The redesigned foundation improved landslide resistance, reducing excavation and foundation construction costs relative to Japanese methods. The foundation’s safety factor reached 4.42–5.13, surpassing the standard of 2.5, and the retaining wall’s sliding resistance safety factor reached 1.87, exceeding the requirement of 1.5. These enhancements dramatically boosted building safety under extreme weather conditions. This study provides practical solutions for building design in Vietnam’s mountainous regions and serves as a valuable reference for similar research in other developing countries, underscoring significant practical and social implications.

Safety evaluation method for operational shield tunnels based on semi-supervised learning and a stacking algorithm

The safety assessment of structural defects in operational shield tunnels is crucial for ensuring their serviceability and safe operation. This study developed a novel comprehensive evaluation method for tunnel safety assessment based on semi-supervised learning and a stacking ensemble algorithm. First, in the membership degree calculation of the multidimensional normal cloud model, the importance coefficients of the tunnel safety evaluation indicators were used instead of their weights to improve the cloud model. This allowed generating unlabeled samples with patterns consistent with those of the collected samples with structural defects. Three classifiers, namely, random forest, extra trees, and gradient boosting, were employed for semi-supervised learning. This process converted unlabeled samples into pseudo-labeled samples, expanded the structural defects database, and ultimately optimized the classifier performance. Subsequently, a stacking algorithm was used to integrate the three optimized classifiers. This resulted in the creation of three stacking models, each containing multiple metamodels. Finally, the optimal metamodels were selected based on accuracy, precision, recall, and F1 score for a voting scheme to determine the tunnel safety level. The developed evaluation method was applied to a real-world engineering project. The evaluation results demonstrated consistency with those obtained from actual field conditions, thus validating the reliability and rationality of the proposed method.

Safety evaluation of genetically modified crops

This study focuses on the application of genetically modified (GM) crops in modern agricultural production, delving into the assessment of their safety and consumer acceptance issues, while analyzing the mechanisms through which these factors influence market dynamics. The background highlights that, despite the potential of genetic modification technology to enhance the overall performance of crops, public concerns regarding their safety significantly affect consumer acceptance and, consequently, market performance. An evaluation of existing literature on the safety evaluation methods for GM crops is first conducted, identifying shortcomings in integrating consumer acceptance, and market dynamics. To address this gap, an evaluation system that incorporates consumer acceptance into the safety evaluation of GM crops was developed, utilizing the FuzzyID3 algorithm. Furthermore, employing multi-attribute decision theory, a decision model for assessing the stances of market stakeholders towards GM crops was established. This model, through the calculation and weighting of the distances from positive and negative ideal solutions across various modules, offers a novel perspective for market analysis. The methodology employed herein provides a robust tool for the safety evaluation and market forecasting of GM crops, holding practical value for guiding policy formulation and industry development.

Safety Evaluation for Fabricated Small Box Girder Bridges Based on Fuzzy Analytic Hierarchy Process and Monitoring Data.

During the operation of fabricated small box girder bridges, which face safety issues such as structural degradation and failure, there is an urgent need to propose a safety evaluation method to cope with the possible risks. This article quantitatively evaluates the safety state of a fabricated small box girder bridge in Wuhan City based on Fuzzy Analytic Hierarchy Process (FAHP) and structural health monitoring (SHM) data. Firstly, the FAHP model is established, and stress, deformation, and temperature are selected as evaluation factors. The safety thresholds of stress and deformation are determined by combining the industry specifications and the historical statistical patterns of the massive SHM data. The temperature field of the bridge is simulated and analyzed by combining ANSYS, HYPERMESH, and TAITHREM, and the most unfavorable temperature gradient is determined as a threshold for the safety evaluation. Finally, the scores of indexes of the bridge are determined based on the measured SHM data, which in turn provides a quantitative description of the safety state. The results show that the thresholds determined by the joint industry specifications and the massive SHM data are reasonable; the temperature field simulation model established in this article is consistent with the measured results, and can accurately determine the temperature gradient of the bridge. The safety evaluation result from the FAHP model is the same as the field test results, which verifies the effectiveness and applicability of the proposed method to actual bridge projects.

Running safety assessment method of trains under seismic conditions based on the derailment risk domain

The accurate assessment of running safety during earthquakes is of significant importance for ensuring the safety of railway lines. Currently, assessment methods based on a single index suffer from issues such as misjudgment of operational safety and difficulty in evaluating operational margin, making them unsuitable for assessing train safety during earthquakes. Therefore, in order to propose an effective evaluation method for the running safety of trains during earthquakes, this study employs three indexes, namely lateral displacement of the wheel–rail contact point, wheel unloading rate, and wheel lift, to describe the lateral and vertical contact states between the wheel and rail. The corresponding evolution characteristics of the wheel–rail contact states are determined, and the derailment forms under different frequency components of seismic motion are identified through dynamic numerical simulations of the train–track coupled system under sine excitation. The variations in the wheel–rail contact states during the transition from a safe state to the critical state of derailment are analyzed, thereby constructing the evolutionary path of train derailment and seismic derailment risk domain. Lastly, the wheel–rail contact and derailment states under seismic conditions are analyzed, thus verifying the effectiveness of the evaluation method for assessing running safety under earthquakes proposed in this study. The results indicate that the assessment method based on the derailment risk domain accurately and comprehensively reflects the wheel–rail contact states under seismic conditions. It successfully determines the forms of train derailment, the risk levels of derailment, and the evolutionary paths of derailment risk.

Comparison of transfer function measurement methods for AIMD MRI safety evaluation

Abstract The transfer function is an important method in ISO/TS 10974 used to evaluate the safety of active implantable medical devices (AIMD). To measure the transfer function, there are two methods used during research and testing, which are the piecewise excitation method and the reciprocity method. In theory, they are equivalent. And for measurement, they have their own advantages and disadvantages. Simulations and experiments using both methods were performed and it is proved that they have good agreement in simulations and experiments.

Evaluation of Outdoor Sports Safety Risk Management Based on Embedded Systems

With the increasing popularity of outdoor sports, sports safety issues have gradually become prominent. In order to effectively manage safety risks in outdoor sports, this paper proposes an embedded system-based outdoor sports safety risk management and evaluation method. Intended to provide safety assurance for outdoor sports participants through real-time data collection, intelligent analysis, and early warning. This method identifies safety risk patterns in different sports scenarios through training and learning historical data. This paper also studied the construction method of an outdoor tourism security system and proposed an embedded system for encrypting the outdoor sports security database. An embedded system for encrypting outdoor sports security databases has been proposed. The simulation results show that the recall rate of the model is 92.8%, and the accuracy rate is 95.7%. The model can predict potential security risks based on real-time collected data and generate corresponding warning messages.

Research on Predicting the Safety Factor of Plain Shotcrete Support in Laneways Based on BO-CatBoost Model.

In general, the design of a safe and rational laneway support scheme signifies a crucial prerequisite for ensuring the security and efficiency of mining exploitation in mines. Nevertheless, the conventional empirical support system for mining laneways faces challenges in assessing the rationality of support methods, which can compromise the safety and reliability of the laneways. To address this issue, the safety factor was incorporated into research on laneway support, and a safety evaluation method for laneway support in line with the safety factor was established. In light of the data from a specific iron mine laneway in central China, the CRITIC method was employed to preprocess the sample data. Going one step further, a Bayesian algorithm was utilized to optimize the hyperparameters of the CatBoost model, followed by proposing a prediction model based on the BO-CatBoost model for evaluating laneway safety factors of plain shotcrete support. Furthermore, the performance indexes, such as the root mean square error (RMSE), the mean absolute error (MAE), the correlation coefficient (R2), the variance accounts for (VAF), and the a-20 index, were determined to examine the predictive performance of each proposed model. In contrast to the other models, the BO-CatBoost model demonstrated the optimal predictive output item for safety factors with the lowest RMSE and MAE, the largest R2 and VAF, and an appropriate a-20 index value of 0.5688, 0.4074, 0.9553, 95.25%, and 0.9167 in the test set, respectively. Therefore, the BO-CatBoost model was proven to be the most appropriate machine learning method that can more accurately predict the safety factor, which will provide a novel approach for optimizing laneway support design and laneway safety evaluation.

Safety evaluation method of bottom coal thickness in thick coal seam roadway

In recent years, the number of roadway floor rock burst accidents is increasing, which seriously restricts the safe production of the mine. Therefore, safety evaluation method of bottom coal thickness in thick coal seam roadway was studied. The research results shown that the stress concentration area of composite floor is distributed in coal seam or rock stratum with large elastic modulus. With the increase of floor rock strength, the stress of coal-rock composite floor increased gradually, but the displacement and energy decreased gradually. When floor rock strength was equal to bottom coal strength, the increase of floor stress and displacement with the change of bottom coal thickness was the smallest, which was 34.29% and 33.61% respectively. The elastic strain energy decreased from 14.58 to 9.85%. With the increase of bottom coal thickness, the stress and displacement of coal-rock composite floor increased first and then decreased, and the elastic strain energy decreased gradually. It puts forward the safety evaluation method of bottom coal thickness: floor failure depth → reasonable thickness of bottom coal → safety thickness of bottom coal. It can provide reference for design of roadway bottom coal retention and surrounding rock control in thick coal seam face.

Dam Deformation Prediction Model Based on Multi-Scale Adaptive Kernel Ensemble

Aiming at the noise and nonlinear characteristics existing in the deformation monitoring data of concrete dams, this paper proposes a dam deformation prediction model based on a multi-scale adaptive kernel ensemble. The model incorporates Gaussian white noise as a random factor and uses the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method to decompose the data set finely. Each modal component is evaluated by sample entropy (SE) analysis so that the data set can be reconstructed according to the sample entropy value to retain key information. In addition, the model uses partial autocorrelation function (PACF) to determine the correlation between intrinsic modal function (IMF) and historical data. Then, the global search whale optimization algorithm (GSWOA) is used to accurately determine the parameters of kernel extreme learning machine (KELM), which forms the basis of the dam deformation prediction model based on multi-scale adaptive kernel function. The case analysis shows that CEEMDAN-SE-PACF can effectively extract signal features and identify significant components and trends so as to better understand the internal deformation trend of the dam. In terms of algorithm optimization, compared with the WOA algorithm and other algorithms, the results of the GSWOA algorithm are significantly better than other algorithms and have the optimal convergence. In terms of prediction performance, CEEMDAN-SE-PACF-GSWOA-KELM is superior to the CEEMDAN-WOA-KELM, GSWOA-KELM, CEEMDAN-KELM, and KELM models, showing higher accuracy and stronger stability. This improvement is manifested in the decrease of root mean square error (RMSE), mean square error (MSE), and mean absolute error (MAE) and the improvement of the R square (R2) value close to 1. These research results provide a new method for dam safety monitoring and evaluation.

A BO-LSTM-Based Rapid Evaluation Method of PCCP Structural Safety under Falling Rock Impacts

A BO-LSTM-Based Rapid Evaluation Method of PCCP Structural Safety under Falling Rock Impacts

Traffic safety comprehensive evaluation of urban tunnel visual guiding system based on extension matter-element model: a case study in tunnel curves

Objective The visual guiding system, as a tunnel traffic safety improvement method by using visual guiding facilities to actively guide driving safely, has been widely used in countries with many tunnels, in recent years. This paper aims to quantitatively study the comprehensive evaluation of traffic safety of the visual guiding system in tunnels, which has certain engineering application value and can provide support for the quantitative evaluation and optimal design of tunnel traffic safety. Methods Based on the analysis of the relevant factors of urban tunnel traffic safety, a multi-factor comprehensive evaluation system with 5 upper-level indicators and 12 basic-level indicators was proposed. Considering the independent and incompatible indicators, a comprehensive evaluation method of traffic safety of the visual guiding system in urban tunnels was constructed by using the extension matter-element model. Taking the scene of 4 types of tunnel curves, such as no facilities, horizontal stripe, chevron alignment sign, and LED arch, as examples, the comprehensive evaluation of various schemes were carried out by using simulation tests. Results The traffic safety comprehensive evaluation system of visual guiding system in urban tunnels can be analyzed from five aspects: perception reaction, guidance ability, driver factor, driving task, and facility appearance. The results demonstrated significant the comprehensive evaluation result of the target level of scene 1 was L4, scene 2 was L3, scene 3 was L2, and scene 4 was L1. That is, the final results of the comprehensive evaluation of the four scenes were poor, medium, good, and very good, respectively. Conclusions In the scheme of visual guiding system for urban tunnel curves, the effectiveness of the three types of designs, from high to low, was the LED arch, chevron alignment sign, and horizontal stripe, and the safety of the scene without facilities was the lowest. Hence, setting the LED arch in the urban tunnel curve has a good effect in the aspects of guidance ability, sight distance, and sight zone, and is conducive to the driver’s perception reaction and driving task.

Research on Deformation Safety Risk Warning of Super-Large and Ultra-Deep Foundation Pits Based on Long Short-Term Memory

This paper proposes transforming actual monitoring data into risk quantities and establishing a Long Short-Term Memory (LSTM) safety risk warning model for predicting the deformation of super-large and ultra-deep foundation pits in river–round gravel strata based on safety evaluation methods. Using this model, short-term deformation predictions at various monitoring points of the foundation pits are made and compared with monitoring data. The results from the LSTM safety risk warning model indicate an absolute error range between the predicted deformation values and on-site monitoring values of −0.24 to 0.16 mm, demonstrating the model’s accuracy in predicting pit deformation. Additionally, calculations reveal that both the overall risk level based on on-site monitoring data and the overall safety risk level based on predicted data are classified as level four. The acceptance criteria for the overall risk level of the foundation pit are defined as “unacceptable and requiring decision-making”, with the risk warning control scheme being “requiring decision-making, formulation of control, and warning measures”. These research findings offer valuable insights for predicting and warning about safety risks in similar foundation pit engineering projects.