Research on the prediction model of hazardous chemical road transportation accidents

In recent years, hazardous chemicals road transport accidents have occurred frequently, causing huge casualties and property losses, and accident risk assessment has become the focus of researchers' research. To predict the risk probability value of hazardous chemical road transport accidents, first, we compiled data on road transportation accidents of hazardous chemicals in China in the past five years. And the nine nodes in the Bayesian network (BN) structure were defined in combination with relevant classification standards. The optimal Bayesian network structure for hazardous chemical road transport accidents was determined based on the K2 algorithm and the causalities between the nodes. Second, the node conditional probabilities were derived by parameter learning of the model using Netica, and the validity of the model was verified using the 5-fold cross-validation method. Last, the Bayesian network model of hazardous chemical road transport accidents is used to analyze accident examples, and the accident chain of “rear-end-leakage” is predicted, and the accident is most likely to be disposed of within 3–9 h. The study shows that the derived accident prediction model for hazardous chemical road transportation can reason reasonably about the evolution of accident scenarios and determine the probability values of accident risks under different parameter conditions.     

Developing a chaotic pattern of dynamic risk definition for solving hazardous material routing-locating problem

In the case of determining routes and locations for constructing distribution centers on hazardous materials (Hazmat) transportation, risk and cost are considered as the main attributes for developing mathematical models. Since, Hazmat transport risk may be defined as a chaotic factor, using dynamic risk changes the selected routes and optimized locations for constructing distribution centers.In the present paper, an iterative procedure has been proposed to determine the best routes and optimized locations of distribution centers for transporting hazardous materials based on the concept of chaos theory in which hazmat transport risk is defined as a dynamic variable. A mathematical model has been developed for solving Hazmat routing and locating problems, simultaneously. Daily transport risk, defined as a chaotic variable, is iteratively updated using one-dimensional logistic map equation over the time period (year). An experimental road network, consists of eighty nine nodes and one hundred and three two-way edges, has been selected for analytical process and model validation. Results revealed that although different amounts of risk and cost priorities change optimized locations of distribution centers and their associated supplies, but the most frequent set of optimized centers remains independent. Therefore, the proposed procedure is capable to determine the best routes and optimized locations for distributing hazardous materials. While risk is iteratively updated over a specific time period, results show that the main property of chaos theory known as dependency upon initial condition would not be a serious concern for decision makers who are dealing with Hazmat management.     

危险品道路运输风险分级指数法研究

危险品道路运输过程风险影响因素多,随机性和不确定性大,定量风险评价比较复杂,至今没有统一的风险评估模型.为了有效预防危险品运输事故和进行快速风险评估,基于运输危险品本身特性和相关的风险影响因素探讨了运输危险品现实风险分级指数法.该方法由危险品风险分级指数、路线影响因素和安全措施补偿因子3部分构成.危险品风险分级指数由危险品加权平均风险等级和运输危险品量、泄漏点与居民区距离以及危险品扩散因子等级确定.危险品加权平均风险等级涉及其健康危害性、可燃性、化学反应活性以及特殊危险性(氧化性和与水反应性)等级的确定.路线影响因素包含运输道路特征、气象条件、交通状况和影响人员分布4类.安全措施补偿因子为车辆、设备、容器、包装因子,人员素质因子和安全管理因子3类.该方法可对运输危险品的潜在风险进行快速分级,有利于采取有效的安全预防和控制措施,降低运输事故概率和沿线影响人员风险.     

基于贝叶斯网络的煤矿事故人因推理

煤矿事故的不可重现性决定了事故原因的调查具有很强的不确定性,如何通过事故发生后的相关信息提高事故深层次原因调查的准确性是非常重要的。将HFACS与贝叶斯网络相结合,以煤矿事故HFACS分析结果为样本,通过卡方检验和让步比分析建立人因的贝叶斯网络因果图,进一步利用最大似然估计算法确定了煤矿事故人因的贝叶斯网络参数。最后,以双柳煤业顶板事故的调查信息为证据推理导致煤矿事故发生的深层次原因,提高事故原因调查的准确性,从而验证模型的有效性。     

基于STAMP-ISM的铁路危险品运输系统风险-事故分析方法*

为预防铁路危险品运输系统事故的发生，采用STAMP-ISM模型分析铁路危险品运输系统风险-事故。首先，利用STAMP模型详细分析事故，得到控制结构的相关安全约束、不充分控制行为及产生原因，以及系统的安全动态变化；其次，采用ISM分析事故致因因素之间的关联关系并划分层级；最后，基于STAMP和ISM分析结果提出系统改进方案。以我国一起匿名夹带事故为背景进行案例研究，结果表明，ISM模型的加入可深入挖掘事故因素间的相互关系，为事故致因因素重要度划分层级，分析结果及建议更具针对性。     

基于Apriori算法的易燃易爆危险化学品储运火灾爆炸事故关键致险因素的挖掘

导致易燃易爆危险化学品储运火灾爆炸事故的因素繁多、关系复杂,挖掘关键致险因素是减少管理成本、提高防控效率的关键。研究了200例事故等级为较大事故以上的易燃易爆危险化学品储运火灾爆炸事故的原因,采用事故树分析法建立易燃易爆危险化学品储运火灾爆炸事故树,并运用频度统计法遴选出致险因素;在此基础上,建立基于Apriori算法的关联规则模型进行数据挖掘,共得到14个关键致险因素。通过对关键致险因素与易燃易爆危险化学品储运火灾爆炸事故之间关联规则的分析表明,关键致险因素与事故之间存在强关联规则,单一关键致险因素或其组合的存在必将导致事故的发生,为实现危险化学品储运精准化安全管理提供参考。     

营运驾驶员驾驶事故影响因素仿真研究

解决营运驾驶员驾驶事故频发的不安全行为问题,基于复杂适应系统（CAS）理论和多主体建模与仿真（ABMS）方法,构建营运驾驶员驾驶事故模型。通过调节车辆状况、管理者管理监督水平、营运驾驶员情绪状态、安全教育水平、驾驶经验和环境综合条件等因素,运用NetLogo仿真平台分析安全管理水平、营运驾驶员安全责任意识、心理负荷水平对驾驶事故的影响。研究表明:营运驾驶员驾驶事故的发生系统是一种典型的CAS,营运驾驶员驾驶事故是三大主体属性和行为环境交互的结果,且交互作用程度不同。因此,运输企业要考虑多种因素,采取相应的措施来降低营运驾驶员驾驶事故的发生。     

基于多因素耦合的航空运输系统脆弱性分析*

为分析航空运输系统脆弱性，提升航空运输系统安全水平。基于脆弱性理论，针对航空运输系统运行特点，提出航空运输系统脆弱性概念，并归纳分析航空运输系统安全影响因素；采用触发器原理建立基于航空运输系统脆弱性影响因素耦合的事故形成机理模型，选取1973—2019年的120起全球重大航空事故为数据基础，构建可量化航空运输系统脆弱性影响因素耦合关系的N-K模型。结果表明:影响航空运输系统耦合关系的关键因素为管理因素，有针对性地加强航空运输系统安全脆弱性关键耦合因素的管控，能更好地提高航空运输系统安全水平。     

基于ISODATA算法的水上交通事故黑点识别

针对水上交通安全问题,基于ISODATA算法和水上交通事故等级综合加权平均法,构建水上交通事故黑点识别模型。结合道路交通黑点及现有水上交通多发区的研究方法,定义水上交通事故黑点;采用ISODATA算法分析水上交通事故空间分布特征,实现对水上交通事故空间的构建;对水上交通事故等级梯度赋值,以量化事故的严重程度,利用等级综合加权平均法确定黑点阈值。并以深圳西部港区水上交通事故为例进行模型应用,共识别出10处事故黑点及其边界,表明水上交通黑点识别模型能有效识别水上交通黑点的空间分布及特征,为水上交通安全状态分析提供了一种度量方法。     

Evaluation on consequences prediction of fire accident in emergency processes for oil-gas storage and transportation by scenario deduction

This paper takes the safety in emergency processes as the starting point, from the perspective of scenario deduction, to study the consequences of fire accidents for oil-gas storage and transportation. Through the statistical analysis of actual accident cases, 19 frequently occurring basic scenarios in emergency processes are summarized. The scenario evolution paths of fire accidents for oil-gas storage and transportation are given by analyzing the evolution law of the accident development. Fuzzy numbers are introduced to express experts' qualitative judgment on accident scenarios. The empirical probabilities of scenario nodes are obtained by defuzzification calculation, and the state probability of each scenario node is calculated by using the dynamic Bayesian network joint probability formula. Under the comprehensive consideration about the probability statistics of actual accident cases, the critical scenario nodes on the evolution path and their final scenario probabilities are jointly determined to realize the optimization of the scenario evolution path. By constructing the correlation between the optimized scenario evolution path and the accident consequences, an accident consequence prediction model is established. The occurrence probability of accident consequences is calculated by the defuzzification method and dynamic Bayesian network. The accuracy of the consequence prediction model is verified by the July 16 Dalian's Xingang Harbor oil pipeline explosion accident. The research results provide scientific basis for helping decision makers to make the effective emergency measures that are most conducive to the rapid elimination of accidents and reducing the severity of accident consequences.     

A Bayesian network-based approach for the assessment and management of ageing in major hazard establishments

Currently, there is an increasing attention towards ageing of industrial equipment, as the phenomenon has been recognised as a cause of severe accidents, recorded in the last years in many process establishments. Recent studies described ageing through a number of key-factors affecting the phenomenon by accelerating or slowing it down. The Italian Competent Authority for the prevention of chemical accidents (Seveso III Directive) adopted a short-cut method, accounting for the assessment of these factors, to evaluate the adequateness of ageing management during inspections at Seveso sites. In this paper, a Bayesian Network was developed, by using the data gathered during the first application of the short-cut method, with the aim to verify the robustness of the approach for ageing assessment and the validity of the a priori assumptions used in assessing the key-factors. The structure of the Bayesian network was established by using experts’ knowledge, whereas the Counting Learning algorithm was adopted to execute the parameter learning by means of the software Netica. The results showed that this network could effectively explore the complex logical and uncertain relationships amongst factors affecting equipment ageing. Results of the present study were exploited to improve the short-cut method.     

基于零膨胀负二项回归模型的内河水上交通人命安全分析

运用零膨胀负二项回归模型,分析水上交通事故中人员死亡失踪数量的各影响因素的影响程度。通过Pearson相关分析得到与水上交通事故中人员死亡失踪人数密切相关的因素,建立ZINB回归模型得到各因素的参数估计值。运用弹性分析方法对不同影响因素的影响程度进行评估,根据影响程度对各影响因素进行排序。将该方法应用于长江海事局辖区范围内的水上交通事故人员死亡失踪分析,取得了理想效果。     

基于改进贝叶斯网络的加油站火灾爆炸分析

为了找出导致加油站发生火灾爆炸事故的基本事件及其可能性大小,以加油站火灾爆炸故障树为基础建立相应的贝叶斯网络风险模型。在FTA向BN转化算法的基础上对条件概率做出了修正。利用GeNIe软件计算加油站火灾爆炸事故基事件的后验概率,同时进行灵敏度和影响力分析。最后通过实例分析,找出了导致某加油站发生火灾爆炸事故危险性最大的因素集为:加油站接打手机、机械碰撞、给塑料容器加油、加油冒油、油枪渗漏等。结果表明,注重基事件的多态性和事件间逻辑关系合理性的新模型,能推算出更准确的基事件概率分布,同时可以找出导致事故发生的最有可能途径,为加油站事故预防,系统改进提供较为合理性建议。     

基于动态贝叶斯网络的石化装置火灾事故动态风险分析

为揭示石油炼化装置事故风险动态特性和事故情景演变路径,在对石化装置进行风险因素分析的基础上构建石化装置火灾事故故障树,基于贝叶斯网络非常规突发事故的演变过程,构建情景演变下的动态贝叶斯网络模型,在综合考虑应急措施的基础上,利用MATLAB软件和联合概率公式计算出各种事故场景的状态概率.以丙烯精馏装置火灾事故为例,结果表...     

Risk assessment of safety level in university laboratories using questionnaire and Bayesian network

Accidents in university laboratories not only create a great threat to students’ safety but bring significant negative social impact. This paper investigates the university laboratory safety in China using questionnaire and Bayesian network (BN) analysis. Sixteen influencing factors for building the Bayesian net were firstly identified. A questionnaire was distributed to graduate students at 60 universities in China to acquire the probability of safe/unsafe conditions for sixteen influencing factors, based on which the conditional probability of four key factors (human, equipment and material, environment, and management) was calculated using the fuzzy triangular theory and expert judgment. The determined conditional probability was used to develop a Bayesian network model for the risk analysis of university laboratory safety and identification of the main reasons behind the accidents. Questionnaire results showed that management problems are prominent due to insufficient safety education training and weak management level of management personnel. The calculated unsafe state probability was found to be 65.2%. In the BN analysis, the human factor was found to play the most important role, followed by equipment and material factor. Sensitive and inferential analysis showed that the most sensitive factors are personnel incorrect operation, illegal operation, and experiment equipment failure. Based on the analysis, countermeasures were proposed to improve the safe management and operation of university laboratories.     

基于N-K模型的海上交通安全风险因素耦合分析

为定量分析海上交通安全风险因素间的影响关系,识别导致海上交通事故的关键因素,分别从单因素、双因素和多因素的角度分析了人、船、环境、管理等风险因素间的耦合关系,运用N-K模型构建了海上交通安全风险耦合度量模型,结合中国海事局、德国联邦海事事故调查局、英国船舶事故调查局和美国国家运输安全委员会等海事调查机构公布的710起海上交通事故,利用所构建的模型计算出不同风险耦合的发生概率和风险值。结果表明:风险耦合因素越多,海上交通事故的发生概率越高;人的因素是引发海上交通事故的重要原因。     

基于贝叶斯网络的化工园区地震次生灾害情景分析

为了研究化工园区内发生地震灾害后的事故演化过程,利用事故链模型对地震次生灾害演化过程进行分析,并将其转化为贝叶斯网络,确定各节点的变量与状态取值范围;通过相关文献及专家经验判断分析,获得贝叶斯网络中各节点的条件概率;依据贝叶斯网络的推理策略,比较不同地震烈度下,各化工事故发生的后验概率值,并探讨应急救援的及时性对地震次生灾害发生概率的影响,从关键要素遏制化工事故的发生,采取针对性的应对措施,尽可能的降低化工事故,有效的降低人员伤亡及财产损失。     

Probabilistic analysis of aluminium production explosion accidents based on a fuzzy Bayesian network

Explosion accidents of molten aluminium in contact with water during aluminium production often occur and may cause injury and death. In this paper, a fuzzy Bayesian network (BN) was employed to probabilistically analyse the explosion accident of molten aluminium in contact with water. A fault tree-Bayesian network (FT-BN) model was established in the cause-effect analysis of the explosion accident, including three processes: electrolysis, molten aluminium transportation and aluminium casting. Fifty-three nodes were proposed in the model to represent the evolution process of the explosion accident from failure causes to consequences. Furthermore, the occurrence probabilities of basic events (BEs) were determined by expert judgement with weighted treatments based on fuzzy theory. By giving certain occurrence probabilities of each BE, the probability of an explosion accident was estimated. Subsequently, importance measures were assessed for each BE, which could reflect the impact on the occurrence of the top event (TE), and the final ranks were provided. The results indicate that using wet ladles and tools, water on the ground, breakage of the tap hole, damage to the casting mould, and leakage of circulating water are five main problems that cause explosion accidents. Safety advice was provided based on the analysis results. This study can help decision makers improve the safety management of aluminium production.     

模糊综合评价法在道路运输企业安全评价中的应用

我国道路运输企业交通安全形势比较严峻,推行道路运输企业安全综合评价是降低道路运输企业交通事故的有效途径之一.本文运用Delphi法和AHP法建立了道路运输企业安全层次分析模型,构建了安全综合评价指标体系.选用基于"差动"原理的赋权法--G1法计算各指标的权重,应用模糊综合评价法对其进行安全综合评价.采用平均加权法对评价因素的各项指标进行量化处理,从而得到道路运输企业安全的综合评价结果.最后,对陕西省一家道路运输企业进行了实际验证,比较真实地反映了此企业的安全状况.     

基于UHBs的输气管道高后果区泄漏燃爆事故分析

为探究输气管道高后果区中人的不安全行为（Unsafe Human Behaviors,UHBs）对输气管道泄漏燃爆事故发生的影响,结合模糊Bow-tie模型和贝叶斯网络对输气管道泄漏燃爆事故进行分析。构建基于T-S模糊故障树的输气管道泄漏燃爆模糊Bow-tie模型,并转化为贝叶斯网络;从人的不安全行为发生的可能性出发,将不同等级高后果区划分为不同等级人口敏感区;利用专家经验评判法得到不同等级人口敏感区基本事件的先验概率和中间事件的条件概率表;运用贝叶斯网络双向推理算法求解模糊Bow-tie模型。结果表明:随着地区人口敏感等级的提高,输气管道泄漏燃爆事故发生的概率随之增大,发现导致输气管道失效泄漏事故发生的最主要原因为施工破坏,失效原因与EGIG分析的结果基本相符,验证该方法在高后果区输气管道泄漏燃爆事故分析上的可行性,可为输气管道高后果区的安全管理提供决策依据。