基于BP神经网络的苯储罐泄漏事故风险评价模型研究

为了在事故发生之前对苯储罐进行风险评价，提出1种基于BP神经网络的泄漏事故风险评价方法，利用该方法构建了苯储罐的风险评价模型，并对模型进行了训练及验证。研究结果表明:BP神经网络成功完成了建模任务，且模型训练结果较好，可利用基于BP神经网络所构建的苯泄漏事故风险评价模型对苯储罐发生泄漏事故的风险进行评价。     

某热处理厂火灾事故的故障树分析研究

故障树分析法(FTA)是根据系统可能发生的事故或已经发生的事故结果,来研究与事故发生有关的原因、条件和规律,进而实施危险辨识的系统工程方法。本文通过对一起火灾事故的FTA分析,研究相应的控制措施。     

Factors in offshore planning that affect the risk for major accidents

The purpose of this paper is to systematically analyse a typical planning process in the offshore industry from the perspective of causes of major accidents, with the ultimate aim of identifying factors that affect the risk for major accidents occurring. We first study and describe a typical planning process for offshore oil and gas operations in Norway. Then we analyse a number of theories of major accidents, to see how the different theories and their explanations of causes and contributing factors can be of relevance for future plans and planning processes. Finally, we review accident investigations to search for evidence of how weaknesses in planning processes can contribute to major accidents through the above identified factors. Also, we try to identify any additional factors that have not been recognised through the theoretical review. This provides empirical support for the theoretical basis. Thirteen factors which directly or indirectly can influence the planning process causing a major accident potential are identified. These are exemplified through a review of investigation reports. The paper suggests that planning process should focus more on increasing quality in the plans at an early phase, with examples from incidents, and illustrate the relation between planning quality and potential for major accidents.     

基于ARIMA的安全生产事故预测与实证研究

为了减少企业安全管理者在生产作业中由于不确定性导致错误决策所产生的风险,在企业历年安全生产事故数据基础上进行预测具有一定的现实意义。以某企业2008年至2011年的安全生产事故次数时序数据,采用EViews 5统计分析软件,基于ARIMA时间序列预测模型更加关注对事故发生是否平稳而相对于其他预测模型更关注于趋势研究的良好特点,建立安全生产事故ARIMA时序预测模型,并对2012年的安全生产事故发生次数进行预测,通过效果检验发现该模型预测结果基本上能够反映该企业安全生产事故发生的实际情况。通过ARIMA方法在某企业安全生产事故预测具体案例的实现,是对现有安全生产事故预测方法的补充和完善,可为企业安全管理和决策提供一定的指导。     

高速公路交通流实时安全性评价

根据多条高速公路近3年的事故数据及实时交通流数据,分析了高速公路运营安全性的相关影响因素,在此基础上提出了车速变异系数指标及其分级标准,建立了考虑交通流量、平均车速、车速变异系数、天气条件等多因素的高速公路交通流运行风险预测多元模型,并提出了高速公路交通流实时行车风险指数TRI指标,制定了交通流状态实时安全性评价标准,最后给出了高速公路交通流混合交通状态下的实时安全风险评价流程。通过西攀高速公路的交通流实时数据,验证了模型的有效性。     

连续长坡路段驾驶员心率与平曲线半径关系研究

为研究公路连续长坡路段驾驶员心率与曲线半径的关系,提高行车安全,选取9名驾驶员进行实际道路试验.用多导生理记录仪采集驾驶员的心率数据,按上、下坡方向将试验路段的每个曲线划分为7个位置,分别对上、下坡方向每个位置驾驶员的心率增长率和曲线半径的关系进行回归分析,建立相应的模型,并对每个位置的模型进行分析,提出合理的曲线半径设置建议.综合上、下坡方向各位置的曲线半径研究结论提出合理的曲线半径取值建议,公路连续长坡路段曲线半径在r≥900 m和200 m≤r≤300 m内取值.     

基于改进的HFACS和SPA的矿山安全人因分析

为了更好地服务于矿山安全管理,同时解决矿山人为因素调查、分析和评价方面存在的缺失,对部分矿山事故进行了统计分析,研究了诱发事故发生的人因因素,并分析了各因素发生的原因。在此基础上,结合对部分矿山安全管理现状的研究,建立了矿山HFACS分析模型。该模型包括5类事故致因、24个人因因素。应用该模型,结合集对分析方法,可实现矿山人因因素定量与定性分析。以陕西省某矿山为例,建立了该矿山的HFACS模型。采用专家评分法、层次分析法确定各因素的权重,计算出系统的联系度表达式。利用该表达式对该矿山的集对势、联系度、不确定性和悲观势进行了分析。结果表明,该矿山人因安全状况良好,个别人因因素指标需要改善和提高。矿山HFACS模型可以很好地涵盖矿山事故中的人因因素,通过对矿山人因现状的集对分析,可以有效提高矿山安全性。     

The Real-World Safety Potential of Connected Vehicle Technology

Objective: This article estimates the safety potential of a current commercially available connected vehicle technology in real-world crashes.

Method: Data from the Centre for Automotive Safety Research's at-scene in-depth crash investigations in South Australia were used to simulate the circumstances of real-world crashes. A total of 89 crashes were selected for inclusion in the study. The crashes were selected as representative of the most prevalent crash types for injury or fatal crashes and had potential to be mitigated by connected vehicle technology. The trajectory, speeds, braking, and impact configuration of the selected in-depth cases were replicated in a software package and converted to a file format allowing “replay” of the scenario in real time as input to 2 Cohda Wireless MK2 onboard units. The Cohda Wireless onboard units are a mature connected vehicle technology that has been used in both the German simTD field trial and the U.S. Department of Transport's Safety Pilot project and have been tuned for low false alarm rates when used in the real world. The crash replay was achieved by replacing each of the onboard unit Global Positioning System (GPS) inputs with the simulated data of each of the involved vehicles. The time at which the Cohda Wireless threat detection software issued an elevated warning was used to calculate a new impact speed using 3 different reaction scenarios and 2 levels of braking.

Results: It was found that between 37 and 86% of the simulated crashes could be avoided, with highest percentage due a fully autonomous system braking at 0.7 g. The same system also reduced the impact speed relative to the actual crash in all cases. Even when a human reaction time of 1.2 s and moderate braking of 0.4 g was assumed, the impact speed was reduced in 78% of the crashes. Crash types that proved difficult for the threat detection engine were head-on crashes where the approach angle was low and right turn–opposite crashes.

Conclusions: These results indicate that connected vehicle technology can be greatly beneficial in real-world crash scenarios and that this benefit would be maximized by having the vehicle intervene autonomously with heavy braking. The crash types that proved difficult for the connected vehicle technology could be better addressed if controller area network (CAN) information is available, such as steering wheel angle, so that driver intent can be inferred sooner. More accurate positioning in the real world (e.g., combining satellite positioning and accelerometer data) would allow the technology to be more effective for near-collinear head-on and rear-end crashes, because the low approach angles that are common in such crashes are currently ignored in order to minimize false alarms due to positioning uncertainty.     

2015年9-10月国内生产安全事故统计分析

统计了2015年9-10月国内发生的各种生产安全事故114起,其中包括交通事故、矿业事故、爆炸事故、火灾、毒物泄漏与中毒和其他事故.统计表明,在114起事故中,交通事故占64.91％,矿业事故占3.51％,爆炸事故占5.26％,火灾占2.63％,毒物泄漏与中毒占5.26％,其他事故占18.42％.114起事故共死亡520人.死亡人数的百分比分别为交通事故62.88％,矿业事故5.00％,爆炸事故5.00％,火灾4.62％,毒物泄漏与中毒3.85％,其他事故18.65％.2015年9-10月生产安全事故发生较多的5个省分别为广东(12起)、甘肃(9起)、安徽(9起)、云南(8起)和辽宁(7起);死亡人数较多的5个省分别为安徽(52人)、广东(47人)、河南(39人)、甘肃(38人)和山东(35人).     

On-Scene Injury Severity Prediction (OSISP) Algorithm for Truck Occupants

Objective: The aim of this study is to develop an on-scene injury severity prediction (OSISP) algorithm for truck occupants using only accident characteristics that are feasible to assess at the scene of the accident. The purpose of developing this algorithm is to use it as a basis for a field triage tool used in traffic accidents involving trucks. In addition, the model can be valuable for recognizing important factors for improving triage protocols used in Sweden and possibly in other countries with similar traffic environments and prehospital procedures.

Methods: The scope is adult truck occupants involved in traffic accidents on Swedish public roads registered in the Swedish Traffic Accident Data Acquisition (STRADA) database for calendar years 2003 to 2013. STRADA contains information reported by the police and medical data on injured road users treated at emergency hospitals. Using data from STRADA, 2 OSISP multivariate logistic regression models for deriving the probability of severe injury (defined here as having an Injury Severity Score [ISS] > 15) were implemented for light and heavy trucks; that is, trucks with weight up to 3,500 kg and ??16,500 kg, respectively. A 10-fold cross-validation procedure was used to estimate the performance of the OSISP algorithm in terms of the area under the receiver operating characteristic curve (AUC).

Results: The rate of belt use was low, especially for heavy truck occupants. The OSISP models developed for light and heavy trucks achieved cross-validation AUC of 0.81 and 0.74, respectively. The AUC values obtained when the models were evaluated on all data without cross-validation were 0.87 for both light and heavy trucks. The difference in the AUC values with and without use of cross-validation indicates overfitting of the model, which may be a consequence of relatively small data sets. Belt use stands out as the most valuable predictor in both types of trucks; accident type and age are important predictors for light trucks.

Conclusions: The OSISP models achieve good discriminating capability for light truck occupants and a reasonable performance for heavy truck occupants. The prediction accuracy may be increased by acquiring more data. Belt use was the strongest predictor of severe injury for both light and heavy truck occupants. There is a need for behavior-based safety programs and/or other means to encourage truck occupants to always wear a seat belt.