Multibody model of the human upper extremity for fracture simulation

About 3.8 million people are injured in accidents at work in Europe every year. The resulting high costs are incurred by the victims themselves, their families, employers and society. We have used a numerical simulation to reconstruct accidents at work for several years. To reconstruct these accidents MADYMO R7.5 with a numerical human model (pedestrian model) is used. However, this model is dedicated to the analysis of car-to-pedestrian accidents and thus cannot be fully used for reconstructing accidents at work. Therefore, we started working on the development of a numerical model of the human body for the purpose of simulating accidents at work. Developing a new numerical model which gives an opportunity to simulate fractures of the upper extremity bones is a stage of that work.     

Analysis of car-to-bicycle approach patterns for developing active safety devices

Objective: To reduce the severity of injuries and the number of cyclist deaths in traffic accidents, active safety devices providing cyclist detection are considered to be effective countermeasures. The features of car-to-bicycle collisions need to be known in detail to develop such safety devices.

Methods: The study investigated near-miss situations captured by drive recorders installed in passenger cars. Because similarities in the approach patterns between near-miss incidents and real-world fatal cyclist accidents in Japan were confirmed, we analyzed the 229 near-miss incident data via video capturing bicycles crossing the road in front of forward-moving cars. Using a video frame captured by a drive recorder, the time to collision (TTC) was calculated from the car's velocity and the distance between the car and bicycle at the moment when the bicycle initially appeared.

Results: The average TTC in the cases where bicycles emerged from behind obstructions was shorter than that in the cases where drivers had unobstructed views of the bicycles. In comparing the TTC of car-to-bicycle near-miss incidents to the previously obtained results of car-to-pedestrian near-miss incidents, it was determined that the average TTC in car-to-bicycle near-miss incidents was significantly longer than that in car-to-pedestrian near-miss incidents.

Conclusions: When considering the TTC in the test protocol of evaluation for safety performance of active safety devices, we propose individual TTCs for evaluation of cyclist and pedestrian detections, respectively. In the test protocols, the following 2 scenarios should be employed: bicycle emerging from behind an unobstructed view and bicycle emerging from behind obstructions.     

Traffic accident reconstruction and an approach for prediction of fault rates using artificial neural networks: A case study in Turkey

Objective: Currently, in Turkey, fault rates in traffic accidents are determined according to the initiative of accident experts (no speed analyses of vehicles just considering accident type) and there are no specific quantitative instructions on fault rates related to procession of accidents which just represents the type of collision (side impact, head to head, rear end, etc.) in No. 2918 Turkish Highway Traffic Act (THTA 1983). The aim of this study is to introduce a scientific and systematic approach for determination of fault rates in most frequent property damage–only (PDO) traffic accidents in Turkey.

Methods: In this study, data (police reports, skid marks, deformation, crush depth, etc.) collected from the most frequent and controversial accident types (4 sample vehicle–vehicle scenarios) that consist of PDO were inserted into a reconstruction software called vCrash. Sample real-world scenarios were simulated on the software to generate different vehicle deformations that also correspond to energy-equivalent speed data just before the crash. These values were used to train a multilayer feedforward artificial neural network (MFANN), function fitting neural network (FITNET, a specialized version of MFANN), and generalized regression neural network (GRNN) models within 10-fold cross-validation to predict fault rates without using software. The performance of the artificial neural network (ANN) prediction models was evaluated using mean square error (MSE) and multiple correlation coefficient (R).

Results: It was shown that the MFANN model performed better for predicting fault rates (i.e., lower MSE and higher R) than FITNET and GRNN models for accident scenarios 1, 2, and 3, whereas FITNET performed the best for scenario 4. The FITNET model showed the second best results for prediction for the first 3 scenarios. Because there is no training phase in GRNN, the GRNN model produced results much faster than MFANN and FITNET models. However, the GRNN model had the worst prediction results. The R values for prediction of fault rates were close to 1 for all folds and scenarios.

Conclusions: This study focuses on exhibiting new aspects and scientific approaches for determining fault rates of involvement in most frequent PDO accidents occurring in Turkey by discussing some deficiencies in THTA and without regard to initiative and/or experience of experts. This study yields judicious decisions to be made especially on forensic investigations and events involving insurance companies. Referring to this approach, injury/fatal and/or pedestrian-related accidents may be analyzed as future work by developing new scientific models.     

Driver's obesity and road crash risks in the United States

Objective: We assessed obesity trends in U.S. drivers involved in fatal crashes since 1999 and distinguished whether crash risk factors were different between obese and nonobese drivers.

Methods: We included only drivers of passenger cars involved in fatal traffic crashes between January 1, 1999, and December 31, 2012. Obesity was classified according to the World Health Organization guidelines and profiled between 1999 and 2012 using the adjusted prevalence ratio (aPR) from log-binomial regression models. Differences in crash risks (e.g., driver's fatality, drunk driving, seat belt nonuse) between obese and nonobese drivers were estimated as adjusted odds ratios (aORs) using logistic regression models.

Results: A total of 753,024 U.S. drivers were involved in fatal crashes, for which obesity information was available for 534,887. About 56% (n = 299,078) were driving passenger cars. The prevalence of class I obesity increased from 10% in 1999 to 14% in 2012 (aPR = 1.50, 95% confidence interval [CI], 1.42–1.58), class II obesity from 3 to 5% (aPR = 2.22, 95% CI, 2.05–3.01), and class III obesity from 1 to 2% (aPR = 2.65; 95% CI, 2.27–3.10). Compared to nonobese controls, obese drivers had significantly higher risks for fatality (1.10 ≤ aOR ≤ 1.47), seat belt nonuse (1.00 ≤ aOR ≤ 1.21), need for extrication (1.01 ≤ aOR ≤ 1.23), and ambulance transport time ≥30 min (1.01 ≤ aOR ≤ 1.28). Compared to nonobese controls, obese drivers were less likely to drink drive (0.41 ≤ aOR ≤ 0.72) or speed >65 mph (0.78 ≤ aOR ≤ 0.93).

Conclusion: The rising national prevalence of obesity extends to U.S. drivers involved in fatal crashes and indicates the need to improve seat belt use, vehicle design, and postcrash care for this vulnerable population.     

Risk management in operations of petrochemical plants: Can better planning prevent major accidents and save money at the same time?

This paper presents an argument that improvement in operational safety can be achieved concurrently with increased operational efficiency. This is a fundamentally different viewpoint on the investment in safety. Traditionally, the cost of providing safety barriers is offset by the expected benefits of reducing the occurrence and severity of accidents. Our approach departs from this method of accounting for safety improvements and focuses on planning as a means of managing systems' response uncertainty and consequently reducing both major accident risk and the cost of operations. The scope of the paper is limited to interventions such as maintenance and repairs and defined in the context of major accident prevention e.g. hydrocarbon leaks. However, the developed methodology is general enough to be applied across the spectrum of process industry facilities and operations.     

钱塘江突发水污染事故应急系统开发与应用

采用网络信息平台和水环境模型相结合的技术手段,构建了钱塘江突发水污染事故应急系统。该系统旨在帮助应急管理部门快速应对钱塘江流域突发性水污染事故,以及保障G20峰会期间钱塘江流域的饮用水源地安全。该系统以水动力模型、溢油和化学品泄漏模型为核心,采用空间关系型中央数据库作为数据管理系统,基于网络GIS技术开发而成。通过对钱塘江流域历史事故的反演,验证了该系统的准确度和可靠性。在该系统的基础上,构建了钱塘江流域典型流场库和典型突发水污染事故情景库,以实现快速应对G20峰会期间钱塘江流域突发水污染事故风险。     

试论突发性环境污染事故的应急监测

文章集中阐述了突发性污染事故的类型以及具有的基础特性,分析了环境污染事故的严重性与危害性。论述了突发性污染事故的应急监测体系的建立,应急监测工作的开展,应急监测的要求,为污染事故的应急监测起到了重要指导作用。     

水环境污染事故的危害指数评价与计算方法

针对频发的突发性水污染事故,阐述了在风险预警系统建立的基础上,如何依据水体中污染物迁移转化基本方程进行合理简化和数学推导,得出用于鉴别环境危害状态、表征环境危害程度、描述事故危害等级、估算事故危害区范围和危害时间的风险评估模式。该模式可为环境风险管理以及突发性环境污染应急预案的制定提供科学依据。     

2011年1-7月国内外石油与化工事故分析

基于2011年1-7月国内外石油与化工事故,从事故发生时间、经营类型和事故类型等方面进行了分析,并分析了重大自然灾害的影响。结果表明：7月是事故高发的月份,化工生产事故最多,泄漏是主要的事故类型;日本地震等重大自然灾害对石油与化工行业产生很大的影响。从推进中小化工企业的安全标准化工作、加强储存运输的安全运营保障、提高企业应对自然灾害的能力等方面提出了建议,以预防或减少事故发生。