基于模糊影响图方法的库存弹药事故概率分析

针对库存弹药安全的特殊性,在分析国内外库存弹药典型事故案例的基础上,指出FTA、ETA等传统概率计算方法存在的局限。提出一种基于模糊影响图(FID)的库存弹药事故概率分析方法,并对FID模型的构建过程和算法进行概述;以弹药被盗事故作为实例分析,并得到事故概率分布图和累积概率曲线。结果表明,FID的算法理论及建模过程能较好地克服FTA、ETA等传统方法的局限性,实例分析指出弹药被盗事故在10-4数量级的隶属度最高,且事故发生概率更趋向于10-3～10-1数量级,从而得到一旦仓库安全防范措施不到位,就很有可能发生弹药被盗事故的结论。     

多米诺效应的风险分析方法

介绍与分析多米诺效应的相关研究,并针对多米诺效应的定量风险分析,建立了从多米诺场景辨识到后果分析的多米诺效应定量分析方法.通过分析初始事故带来的物理效应对邻近设备的影响,计算了二次设备的损坏概率;分析了多米诺场景,计算了多米诺事故频率,并根据多米诺事故的后果得到个人风险和社会风险曲线.多米诺效应的风险是工业区一个潜在的严重风险,加强多米诺效应的研究对工厂和工业区的安全管理与规划都有重要意义.     

考虑人因差错影响的结构可靠度计算方法

通过对单个或任意多个人因差错影响下构件截面失效概率计算方法的分析讨论,推导出在理想已知条件下的数学解析求解公式;在考虑人因差错对结构参数影响程度的空间离散化后,得出了相应的简化计算方法;并以一个单筋梁的正截面为例进行了失效概率计算和人因差错影响程度分析.在目前人因差错的发生及其影响规律的研究不完善、缺乏必要的数学模型的现实条件下,该方法对人因差错影响下构件截面失效概率的分析计算有一定的实用价值.     

高墩大跨连续刚构桥施工期安全评价

针对高墩大跨连续刚构桥施工期结构概率失效的特性,提出了基于失效概率的安全评价方法.以徐水沟特大桥为工程背景,通过风险评定识别出施工最不利阶段--最大双悬臂时的主要风险因素即:节段特性模量的不确定性、梁体自重的不确定性以及预应力损失的不确定性.采用大型通用有限元程序ANSYS及蒙特卡洛法,计算徐水沟特大桥在最大悬臂时的失效概率,并确定施工期的目标可靠度.依据高墩大跨连续刚构桥施工期结构概率失效的评价结果,提出相应的风险控制措施.     

基于TVR的腐蚀油气管道失效概率及安全寿命研究

为避免因腐蚀导致油气管道失效,针对因管道特性和腐蚀尺寸的不确定性使得管道剩余强度成为概率模型的特点,建立了腐蚀管道强度损失随机模型;借助可靠性理论,通过分析管道腐蚀进程的时变性特点,将管道系统由损伤积累和抗力衰减导致的剩余强度随机化;提出基于穿越率的腐蚀油气管道失效评定及安全寿命预测方法。研究结果表明:腐蚀速率和运行压力对管道失效概率及安全寿命影响显著,管道尺寸影响适中,而相关系数和拉伸强度影响较小;若腐蚀速率Va=0.2 mm/a,VL=10 mm/a或局部腐蚀缺陷半径达到管道壁厚的0.5倍时,建议作为重点风险段监测并检修。所建方法是对腐蚀油气管道运营监控和风险评估的有益补充。     

中国典型河湖水体铅的水生生物安全基准与生态风险评价

采用物种敏感度排序法(SSR)对我国铅的淡水水生生物安全基准进行推导,并以太湖为例进行了流域水生生物安全基准推导。对于难以获得的本土生物毒性数据,开展了相应的毒性试验。获得了我国国家与太湖流域铅的水生生物安全基准值,基准最大浓度(CMC)分别为63.92、104.26μg·L-1,基准连续浓度(CCC)分别为1.21、4.06μg·L-1。同时,对我国主要河流以及太湖流域进行了铅的生态风险评价,联合概率曲线法显示影响5%水生生物种类的概率分别为66.22%和43.19%,熵值法则显示中国主要河流存在较大的铅暴露风险,因此,我国铅的潜在生态风险较大,主要河流与太湖流域存在铅污染问题。     

Monitoring Regional Riparian Forest Cover Change Using Stratified Sampling and Multiresolution Imagery1

Claggett, Peter R., Judy A. Okay, and Stephen V. Stehman, 2010. Monitoring Regional Riparian Forest Cover Change Using Stratified Sampling and Multiresolution Imagery. Journal of the American Water Resources Association (JAWRA) 46(2):334-343. DOI: 10.1111/j.1752-1688.2010.00424.x Abstract: The Chesapeake Bay watershed encompasses 165,760 km² of land area with 464,098 km of rivers and streams. As part of the Chesapeake Bay restoration effort, state and federal partners have committed to restoring 26,000 miles (41,843 km) of riparian forest buffers. Monitoring trends in riparian forest buffers over large areas is necessary to evaluate the efficacy of these restoration efforts. A sampling approach for estimating change in riparian forest cover from 1993/1994 to 2005 was developed and implemented in Anne Arundel County, Maryland, to exemplify a method that could be applied throughout the Bay watershed. All stream reaches in the county were stratified using forest cover change derived from Landsat imagery. A stratified random sample of 219 reaches was selected and forest cover change within the riparian buffer of each sampled reach was interpreted from high-resolution aerial photography. The estimated footprint of gross change in riparian forest cover (i.e., the sum of gross gain and gross loss) for the county was 1.83% (SE = 0.22%). Stratified sampling taking advantage of a priori knowledge of locations of change proved to be a practical and efficient protocol for estimating riparian forest buffer change at the county scale and the protocol would readily extend to much broader scale monitoring.     

基于多态模糊BN的石化动设备故障概率定量风险评估

石化行业的压缩机、泵等动设备故障模式多样,故障概率的不确定性较大,常用的风险评估方法无法实现对石化行业动设备失效概率的定量评估,限制了设备安全管理的准确性和有效性.基于贝叶斯网络(BN)可定量计算复杂系统失效概率的特点,利用故障模式与影响分析(FMEA)方法获取动设备风险事件的因果关系,将其映射为BN,并利用BN中节点...     

Value of information in product recovery decisions: a Bayesian approach

There is a widespread recognition of the need for better information sharing and provision to improve the viability of end-of-life (EOL) product recovery operations. The emergence of automated data capture and sharing technologies such as RFID, sensors and networked databases has enhanced the ability to make product information; available to recoverers, which will help them make better decisions regarding the choice of recovery option for EOL products. However, these technologies come with a cost attached to it, and hence the question ‘what is its value?’ is critical. This paper presents a probabilistic approach to model product recovery decisions and extends the concept of Bayes' factor for quantifying the impact of product information on the effectiveness of these decisions. Further, we provide a quantitative examination of the factors that influence the value of product information, this value depends on three factors: (i) penalties for Type I and Type II errors of judgement regarding product quality; (ii) prevalent uncertainty regarding product quality and (iii) the strength of the information to support/contradict the belief. Furthermore, we show that information is not valuable under all circumstances and derive conditions for achieving a positive value of information.