Bayesian Networks‐Based Probabilistic Safety Analysis for Railway Lines

A Bayesian network model is developed, in which all the items or elements encountered when travelling a railway line, such as terrain, infrastructure, light signals, speed limit signs, curves, switches, tunnels, viaducts, rolling stock, and any other element related to its safety are reproduced. Due to the importance of human error in safety, especial attention is given to modeling the driver behavior variables and their time evolution. The sets of conditional probabilities of variables given their parents, which permits quantifying the Bayesian network joint probability, are given by means of closed formulas, which allow us to identify the particular contribution of each variable and facilitate a sensitivity analysis. The probabilities of incidents affecting safety are calculated so that a probabilistic safety assessment of the line can be done and its most critical elements can be identified and sorted by importance. This permits improving the line safety and saving time and money in the maintenance program by concentrating on the most critical elements. To reduce the complexity of the problem, an original method is given that permits dividing the Bayesian network in to small parts such that the complexity of the problem becomes linear in the number of items and subnetworks. This is crucial to deal with real lines in which the number of variables can be measured in thousands. In addition, when an accident occurs the Bayesian network allows us to identify its causes by means of a backward inference process. The case of the real Palencia–Santander line is commented on and some examples of how the model works are discussed.     

A Markovian–Bayesian Network for Risk Analysis of High Speed and Conventional Railway Lines Integrating Human Errors

The article provides a new Markovian–Bayesian network model to evaluate the probability of accident associated with the circulation of trains along a given high speed or conventional railway line with special consideration to human error. This probability increases as trains pass throughout the different elements encountered along the line. A Bayesian network, made up of a sequence of several connected Bayesian subnetworks, is used. A subnetwork is associated with each element in the line that implies a concentrated risk of accident or produces a change in the driver's attention, such as signals, tunnel, or viaduct entries or exits, etc. Bayesian subnetworks are also used to reproduce segments without signals where some elements add continuous risks, such as rolling stock failures, falling materials, slope slides in cuttings and embankments, etc. All subnetworks are connected with the previous one and some of them are multi‐connected because some consequences are dependent on previous errors. Because driver's attention plays a crucial role, its degradation with driving time and the changes due to seeing light signals or receiving acoustic signals is taken into consideration. The model updates the driver's attention level and accumulates the probability of accident associated with the different elements encountered along the line. This permits us to generate a continuously increasing risk graph that includes continuous and sudden changes indicating where the main risks appear and whether or not an action must be taken by the infrastructure manager. Sensitivity analysis allows the relevant and irrelevant parameters to be identified avoiding wastes of time and money by concentrating safety improvement actions only on the relevant ones. Finally, some examples are used to illustrate the model. In particular, the case of the Orense–Santiago de Compostela line, where a terrible accident took place in 2013.     

A Two‐Dimension Dynamic Bayesian Network for Large‐Scale Degradation Modeling with an Application to a Bridges Network

Modeling the stochastic evolution of a large‐scale fleet or network generally proves to be challenging. This difficulty may be compounded through complex relationships between various assets in the network. Although a great number of probabilistic graph‐based models (e.g., Bayesian networks) have been developed recently to describe the behavior of single assets, one can find significantly fewer approaches addressing a fully integrated network. It is proposed an extension to the standard dynamic Bayesian network (DBN) by introducing an additional dimension for multiple elements. These elements are then linked through a set of covariates that translate the probabilistic dependencies. A Markov chain is utilized to model the elements and develop a distribution‐free mathematical framework to parameterize the transition probabilities without previous data. This is achieved by borrowing from Cooke's method for structured expert judgment and also applied to the quantification of the covariate relationships. Some metrics are also presented for evaluating the sensitivity of information inserted into the covariate DBN where the focus is given on two specific types of configurations. The model is applied to a real‐world example of steel bridge network in the Netherlands. Numerical examples highlight the inference mechanism and show the sensitivity of information inserted in various ways. It is shown that information is most valuable very early and decreases substantially over time. Resulting observations entail the reduction of inference combinations and by extension a computational gain to select the most sensitive pieces of information.     

基于故障树和贝叶斯网络的仓储类建筑火灾风险评估

以降低仓储类建筑火灾风险为目的,通过分析影响仓储类建筑火灾发生原因,分析顶层事件与基层事件之间自上而下的因果关系,建立故障树,并将其映射到贝叶斯网络中,通过计算得出事件发生的先验和后验概率,并将两者有效的联系起来。给出各事件发生的重要度指标有结构指标、概率指标和关键指标,分别进行定量分析指出仓储类建筑最有可能存在的风险。文章以2010—2020年十年期间仓储类建筑火灾发生案例为样本对本文提出的模型进行验证。检验结果:文章提出贝叶斯网络模型能够有效评估仓储类建筑风险等级并降低火灾事故发生。     

分析公路路线设计中应注意的几点问题

公路质量是全社会共同关注的焦点。在公路建设中,公路的路线设计又尤为重要。公路路线设计要综合考虑地基土质情况、环保、安全、美观、舒适等。如果公路设计不过关,公路质量就无从谈起。日常发现的一些公路沉陷、交通事故、环境污染等故障很大一部分原因都是由于公路路线设计不完善。这些故障严重影响了人们的生活。因此,公路路线设计在公路工程中至关重要。     

关于公路桥梁设计中的耐久性分析

较好的质量安全性、耐久性是公路桥梁设计中最关键的控制点。设计师们要在进行公路桥梁设计的过程中,对出现的安全性问题和耐久性问题进行仔细的分析。有侧重性地提出设计方法,保证公路桥梁在投入使用后满足交通车辆对安全和稳定的需要。本文对公路桥梁设计中的一些问题及原因进行了分析,希望能为公路桥梁工程提供一些参考意见。     

Bridge condition modelling and prediction using dynamic Bayesian belief networks

The development of a condition-based deterioration modelling methodology at bridge group level using Bayesian belief network (BBN) is presented in this paper. BBN is an efficient tool to handle complex interdependencies within elements of engineering systems, by means of conditional probabilities specified on a fixed model structure. The advantages and limitations of the BBN for such applications are reviewed by analysing a sample group of masonry bridges on the UK railway infrastructure network. The proposed methodology is then extended to develop a time dependent deterioration model using a dynamic Bayesian network. The condition of elements within the selected sample of bridges and a set of conditional probabilities for static and time dependent variables, based on inspection experience, are used as input to the models to yield, in probabilistic terms, overall condition-based deterioration profiles for bridge groups. Sensitivity towards various input parameters, as well as underlying assumptions, on the point-in-time performance and the deterioration profile of the group are investigated. Together with results from ‘what if’ scenarios, the potential of the developed methodology is demonstrated in relation to the specification of structural health monitoring requirements and the prioritisation of maintenance intervention activities.     

Proactive,Backward Analysis and Learning in Road Probabilistic Bayesian Network Models

Some probabilistic safety assessment models based on Bayesian networks have been recommended recently for safety analysis of highways and roads. These methods provide a very natural and powerful alternative to traditional approaches, such as fault and event tree based methods. In this article, we present several new and original contributions to complement the inference engine tools of these models to provide new and relevant information about safety and backward analysis on one hand, and to learn the complex multidimensional joint probabilities of all variables, on the other hand. More precisely, we show how standard tools combined with the partitioning technique can be used in new ways to solve three relevant problems (1) to prognosticate the most probable combinations of variables leading to incidents, (2) to perform a backward analysis to identify the causes of accidents, and (3) to learn the model parameters using Bayesian conjugate methods (categorical and Dirichlet families). Finally, some real examples of applications are used to illustrate the proposed methods.     

A fuzzy Bayesian belief network for safety assessment of oil and gas pipelines

Safety assessment of oil and gas (O&G) pipelines is necessary to prevent unwanted events that may cause catastrophic accidents and heavy financial losses. This study develops a safety assessment model for O&G pipeline failure by incorporating fuzzy logic into Bayesian belief network. Proposed fuzzy Bayesian belief network (FBBN) model explicitly represents dependencies of events, updating probabilities and representation of uncertain knowledge (such as randomness, vagueness and ignorance). The study highlights the utility of FBBN in safety analysis of O&G pipeline because of its flexible structure, allowing it to fit a wide variety of accident scenarios. The sensitivity analysis of the proposed model indicates that construction defect, overload, mechanical damage, bad installation and quality of worker are the most significant causes for the O&G pipeline failures. The research results can help owners of transmission and distribution pipeline companies and professionals to prepare preventive safety measures and allocate proper resources.     

System loss assessment of bridge networks accounting for multi-hazard interactions

This paper details an integrated method for the multi-hazard risk assessment of road infrastructure systems exposed to potential earthquake and flood events. A harmonisation effort is required to reconcile bridge fragility models and damage scales from different hazard types: this is achieved by the derivation of probabilistic functionality curves, which express the probability of reaching or exceeding a loss level given the seismic intensity measure. Such probabilistic tools are essential for the loss assessment of infrastructure systems, since they directly provide the functionality losses instead of the physical damage states. Multi-hazard interactions at the vulnerability level are ensured by the functionality loss curves, which result from the assembly of hazard-specific fragility curves for local damage mechanisms. At the hazard level, the potential overlap between earthquake and flood events is represented by a time window during which the effects of one hazard type on the infrastructure may still be present: the value of this temporal parameter is based on the repair duration estimates provided by the functionality loss curves. The proposed framework is implemented through Bayesian Networks, thus enabling the propagation of uncertainties and the computation of joint probabilities. The procedure is demonstrated on a bridge example and a hypothetical road network.     

节约型交通建设高速公路改扩建问题探讨

从节约型交通建设的角度出发,以高速公路通行能力分析为基础,针对我国高速公路的改扩建是采用新建还是加宽方案,从社会效益、路网布局与结构、交通安全等多方面利弊的比较进行了综合分析,从而得出高速公路的拓宽工程方案宜采用在原有道上加宽拼接的结论。     

基于GIS的公路安全信息管理系统研究及应用

利用GIS技术手段建立了唐山市道路信息电子数据库和道路网络,在Visual Stidio2008平台基础上,进行Web GIS唐山市公路安全信息管理系统的开发,实现了集城市公路安全评价决策分析、交通事故影响范围缓冲分析、交通事故紧急救援最佳路径分析等功能于一体的数字化公路安全信息管理系统。     

Predicting Cost Escalation Pathways and Deviation Severities of Infrastructure Projects Using Risk‐Based Econometric Models and Monte Carlo Simulation

In the past decade, infrastructure‐related legislation in the United States has consistently emphasized the need to measure the variation associated with infrastructure project cost estimates. Such cost variability is best viewed from the perspective of the project development phases and how the project cost estimate changes as it evolves across these phases. The article first identifies a few gaps in the cost overrun literature. Then it introduces a methodology that uses risk‐based multinomial models and Monte Carlo simulation involving random draws to predict the probability that a project will follow a particular cost escalation pathway across its development phases and that it will incur a given level of cost deviation severity. The article then uses historical data to demonstrate how infrastructure agencies could apply the proposed methodology. Statistical models are developed to estimate the probability that a highway project will follow any specific cost escalation pathway and ultimately, a given direction and severity of cost deviation. The case study results provided some interesting insights. For a given highway functional class, larger project sizes are associated with lower probability of underestimating the final cost; however, such a trend is not exhibited by very large projects (total cost exceeding $30M). For a given project size, higher class roads were generally observed to have a lower probability of underestimating the final cost, compared to lower class roads and this gap in probability narrows as the project size increases. It was determined that a project's most likely pathway of cost escalation is not a guarantee that it will yield any particular direction of cost deviation. The case study results also confirmed the findings of a few past studies that the probabilities of cost escalation pathways and the cost overruns differ significantly across highway districts, and attributed this to differences in administrative culture and work practices across the districts. Infrastructure managers can use the developed methodology to identify which projects are likely to experience a particular pathway of cost escalation, the direction and severity of cost deviation, and to develop more realistic project contingency estimates.     

Location Optimization of Road Weather Information System (RWIS) Network Considering the Needs of Winter Road Maintenance and the Traveling Public

This study presents an innovative approach to the planning of a critical highway sensor infrastructure ‐ road weather information system (RWIS). The problem is formulated to minimize the spatially averaged kriging variance of hazardous road surface conditions while maximizing the coverage of accident‐prone areas. This optimization framework takes explicit account of the value of information from an RWIS network, providing the potential to enhance the overall efficacy of winter maintenance operations and the safety of the travelers. Spatial simulated annealing is used to solve the resulting optimization problem and its performance is demonstrated using a real‐world case study from Minnesota, United States. The case study illustrates the distinct features of the proposed model, assesses the effectiveness of the current location setting, and recommends additional stations locations. The findings of our study suggest that the proposed model could become a valuable decision‐support tool for planning a new RWIS network and evaluating the performance of alternative RWIS expansion plans.     

Predicting the probability of failure of timber bridges using fault tree analysis

The resilience of a community in an extreme event depends mainly on the robustness of the critical infrastructures. Road bridges are a critical link of the road network, which plays a focal role in Australia’s economy, prosperity, social well-being and quality of life. Timber bridges are a weaker link of the Australian road network and they often provide critical access to the rural communities. This research uses a number of bridge inspection reports to develop a method to predict the probability of failure of a timber bridge. The inspected condition states of the elements in the timber bridge are used to develop a Markov chain based model and Gamma process model to predict the deterioration of each element. The probability of condition state movement for each element thus calculated were used in fault tree analysis to estimate likelihood of failure of a bridge in a given time period. Although the developed method is based on limited data and it has several limitations, model can be further refined with the availability of more inspection reports. The method developed is demonstrated using an inspection report for a timber bridge, which was not used in the development of the models.     

高速公路护栏端部设计及其碰撞仿真分析

交通安全问题是全社会关注的一个热点问题,路侧交通事故占交通事故总数中相当大的比例.路侧护栏作为高速公路上防护事故车辆的有效手段,其端部设计与道路交通安全息息相关.结合我国路侧半刚性安全护栏端部的设计现状,提出解体消能优化设计方案,采用计算机仿真模拟软件和有限元结构分析软件进行碰撞仿真,模拟显示了端部解体消能结构在碰撞仿真过程中的运动状态,结果显示此设计具有很好的吸能效果及优越的可靠性和安全性.设计方案及仿真结果在陕蒙高速公路上进行了试验段施工,取得了成功.     

追求卓越质量理念 推广先进质量方法——先进质量管理方法解读

改革开放以来,我国工业产品质量总体水平有了很大提高,产品的可靠性、顾客的满意程度、降低成本、节能降耗、改进经营绩效及塑造先进质量文化等方面取得了明显的成效。但某些领域与国际先进水平相比还存在较大差距,工业产品质量安全事故仍时有发生。特别是2008年下半年以来,面对金融危机的影响及发生在我国产品质量方面的一些问题,胡锦涛总书记、温家宝总理和张德江副总理等党和国家领导人多次对质量工作做出重要的批示和指示。指出"质量是企业的生命",把加强产品质量工作,提到"中央应对国际金融危机的战略部署,保增长、扩内需,调整产业结构、转变发展方式的战略举措和加强企业管理、提高我国产品国际竞争力的战略任务"的高度,要求工信部和有关部门制定加强产品质量建设的工作方案,把提高产品质量工作作为工作重点,常抓不懈,抓出成果。为此工信部于2009年11月发布了《关于推广先进质量管理方法的指导意见》,国家质检总局也制定了2010年在全国开展"质量提升"活动计划,中国质量协会决定将2010年作为"先进质量方法推广年"。先进质量方法是指正在推广、使用的全面质量管理理论和方法,也包括目前应用还不普遍,但已被实践证实的科学的、有效的质量方法和工具。企业选择时也需要根据自身状况,适合的才是最好的。在今后的各期杂志中"质量服务"栏目,将邀请行业知名专家、走访成功的示范企业、介绍相关图书、创新成果为您详细解读先进质量管理方法。     

Seismic protection of constructed facilities: optimal use of resources

The problem is tackled of the allocation of the resources assigned for upgrading an ensemble of buildings or other constructed facilities, whose seismic reliability is not considered sufficient. The resources are allocated in order to minimize the predicted value of either the economic losses, the number of endangered persons or the risk of severance of a network. The optimization procedure, although formulated in general terms, is illustrated with reference to simple examples, in which realistic values are attributed to the relevant parameters. These examples refer to masonry buildings, whose damages can be summed with each other to obtain the total damage of the ensemble, and to a highway network, whose critical elements are reinforced concrete bridges. The presence of several discrete quantities and discontinuous relationships requires the use of specific algorithms, and in particular of dynamic programming.