水工环地质灾害危险性评估的策略研究

随着我国各方面持续的发展,工程建设中的基础建设也不断深入,在后续的使用过程中,因水利工程、矿山开采等对地质环境造成了极其严重的影响,如果不及时改变这些不良的地质环境问题,很有可能对我们以后的生活产生影响。所以,我们要在施工前对工程建设的水利地质灾害进行危险评估,通过科学并且合理的评估,分析当前我国水工环地质灾害的危险性,希望可以对改善我国不良环境提出一些针对性的解决方案。     

A multinomial logit model of motorcycle crash severity at Australian intersections

Introduction: Motorcyclists are exposed to more fatalities and severe injuries per mile of travel as compared to other vehicle drivers. Moreover, crashes that take place at intersections are more likely to result in serious or fatal injuries as compared to those that occur at non-intersections. Therefore, the purpose of this study is to evaluate the contributing factors to motorcycle crash severity at intersections. Method: A data set of 7,714 motorcycle crashes at intersections in the State of Victoria, Australia was analyzed over the period of 2006–2018. The multinomial logit model was used for evaluating the motorcycle crashes. The severity of motorcycle crashes was divided into three categories: minor injury, serious injury and fatal injury. The risk factors consisted of four major categories: motorcyclist characteristics, environmental characteristics, intersection characteristics and crash characteristics. Results: The results of the model demonstrated that certain factors increased the probability of fatal injuries. These factors were: motorcyclists aged over 59 years, weekend crashes, midnight/early morning crashes, morning rush hours crashes, multiple vehicles involved in the crash, t-intersections, crashes in towns, crashes in rural areas, stop or give-way intersections, roundabouts, and uncontrolled intersections. By contrast, factors such as female motorcyclists, snowy or stormy or foggy weather, rainy weather, evening rush hours crashes, and unpaved roads reduced the probability of fatal injuries. Practical Applications: The results from our study demonstrated that certain treatment measures for t-intersections may reduce the probability of fatal injuries. An effective way for improving the safety of stop or give-way intersections and uncontrolled intersections could be to convert them to all-way stop controls. Further, it is recommended to educate the older riders that with ageing, there are physiological changes that occur within the body which can increase both crash likelihood and injury severity.     

Drowsiness and distraction while driving: A study based on smartphone app data

Introduction: Due to the negative impact on road safety from driver drowsiness and distraction, several studies have been conducted, usually under driving simulator and naturalistic conditions. Nevertheless, emerging technologies offer the opportunity to explore novel data. The present study explores retrospective data, which was gathered by an app designed to monitor the driver, which is available to any driver owning a smartphone. Method: Drowsiness and distraction alerts emitted during the journey were aggregated by continuous driving (called sub-journey). The data include 273 drivers who made 634 sub-journeys. Two binary logit models were used separately to analyze the probability of a drowsiness and distraction event occurring. Variables describing the continuous driving time (sub-journey time), the journey time (a set of sub-journeys), the number of breaks, the breaking duration time and the first sub-journey (categorical variable) were included. Additionally, categorical variables representing the gender and age of the drivers were also incorporated. Results: Despite the limitations of the retrospective data, interesting findings were obtained. The results indicate that the main risk factor of inattention is driving continuously (i.e., without stopping), but it is irrelevant whether the stop is long or short as well as the total time spent on the journey. The probability of distraction events occurring during the journey is higher than drowsiness events. Yet, the impact of increasing the driving time of the journey and stopping during the journey on the probability of drowsiness is higher than the probability of distraction. Additionally, this study reveals that the elderly are more prone to drowsiness. The data also include a group of drivers, who did not provide information on gender and age, who were found to be associated to drowsiness and distraction risk. Conclusions: The study shows that data gathered by an app have the potential to contribute to investigating drowsiness and distraction. Practical applications: Drivers are highly recommended to frequently stop during the journey, even for a short period of time to prevent drowsiness and distraction.     

Self healing databases for predictive risk analytics in safety-critical systems

Assuring the quality, consistency and accuracy of safety data repositories is essential in safety-critical systems. In many systems, however, significant effort is required to identify, address, clean and repair data errors and inconsistencies, and to integrate safety data sets and repositories, particularly for risk analyses. Although some self healing and self repairing capabilities leveraging machine learning and predictive analyses have been employed to identify anomalies and monitor quality in structured safety-critical data sets, little attention has been focused on addressing shortcomings in heterogeneous—structured and unstructured—safety data sets, the focus of this work. The text mining and classification analysis employed in this research indicates that machine learning techniques can be employed to improve the accuracy and robustness of large-scale structured and unstructured database repositories, and to improve the effectiveness and efficiency of safety data repository maintenance. Hybrid machine learning approaches, leveraging machine learning, text mining and natural language processing, offer additional promise in future work.     

Application of risk analysis in the liquefied natural gas (LNG) sector: An overview

In recent years, the global demand for liquefied natural gas (LNG) as an energy source is increasing at a very fast rate. In order to meet this demand, a large number of facilities such as platforms, FPSO (floating production, storage and offloading), FSRU (floating storage and regasification unit) and LNG ships and terminals are required for the storage, processing and transportation of LNG. Failure of any of these facilities may expose the market, companies, personnel and the environment to hazards, hence making the application of risk analysis to the LNG sector a very topical issue throughout the world. To assess the risk of accidents associated with LNG facilities and carriers, various risk analysis approaches have been employed to identify the potential hazards, calculate the probability of accidents, as well as assessing the severity of consequences. Nonetheless, literature on classification of the risk analysis models applied to LNG facilities is very limited. Therefore, to reveal the holistic issues and future perspectives on risk analysis of LNG facilities, a systematic review of the current state-of-the-art research on LNG risk analysis is necessary. The aim of this paper is to review and categorize the published literature about the problems associated with risk analysis of LNG facilities, so as to improve the understanding of stakeholders (researchers, regulators, and practitioners). To achieve this aim, scholarly articles on LNG risk analysis are identified, reviewed, and then categorized according to risk assessment methods (qualitative, semi-qualitative or quantitative; deterministic or probabilistic; conventional or dynamic), tools (ETA, FTA, FMEA/FMECA, Bayesian network), output/strategy (RBI, RBM, RBIM, facility siting, etc.), data sources (OREDA handbook, published literature, UK HSE databases, regulatory agencies' reports, industry datasets, and experts’ consultations), applications (LNG carriers and LNG fuelled ships, LNG terminals and stations, LNG offshore floating units, LNG plants), etc. Our study will not only be useful to researchers engaged in these areas but will also assist regulators, policy makers, and operators of LNG facilities to find the risk analysis models that fit their specific requirements.     

A novel Dempster-Shafer theory-based approach with weighted average for failure mode and effects analysis under uncertainty

Risk priority number (RPN) is a commonly-used prioritization method in failure mode and effects analysis (FMEA) for systemic reliability and safety study. However, conventional RPN confronts wide criticisms, due to the neglect of the uncertainties of experts' opinions. It is important to handle the conflicts among experts' multi-opinions which could be described in imprecise, incomplete or crisp forms in lack of knowledge. In this paper, a novel method is proposed to alleviate and/or eliminate counter-intuitive behaviors against conflicts among multi-opinions under Dempster-Shafer theory. Firstly, the abnormity test for experts’ opinions is undertaken to identify and discard those distinct judgments. Then, an approach to determining the subjective weights of experts is proposed, which is used in weighted average for multi-opinions before calculating RPNs. Finally, the risk priority evaluation on main engine crankcase explosion failure on-board ship is implemented to verify the feasibility of the proposed approach.     

如何提高大型游乐设施设备本质安全

大型游乐设施事故主要是由于人的不安全行为和物的不安全状态导致的,目前部分设计制造单位对游乐设施危险源识别不充分,风险分析不到位,防控措施流于形式,不能从设计制造阶段彻底消除不安全因素,希望使用单位的规范管理来规避风险,这不能从根本上控制风险,游客和作业人员的不安全行为必须通过设备本体的安全功能来约束和控制.本文结合标准...     

A theory of chemicals regulation and testing

Risk management of chemicals requires information about their adverse effects such as toxicity and persistence, for example. Testing of chemicals allows for improving the information base for regulatory decision‐making on chemicals' production and use. Testing a large number of chemicals with limited time and resources forces a prioritization of chemicals. This paper proposes a decision model that provides a ranking of chemicals according to “urgency to test”. The model adopts a value‐of‐information approach describing the expected welfare gains from regulatory actions that respond to test information. We determine the value‐of‐information of tests revealing chemicals' levels of toxicity and persistence. We compare our findings to the prioritization of chemicals in the new European Chemicals Regulation “REACH”, where several tens of thousands of chemicals are to be tested in order to fill existing information gaps and to implement more effective risk management. We find that the main lines of chemicals' prioritization under REACH receive backing from our decision model. However, prioritization for testing can be further improved by accounting for testing costs and the sensitivity of regulatory action with respect to the test information.     

The effect of calcium and pH on nickel accumulation in and rhizotoxicity to pea (Pisum sativum L.) root-empirical relationships and modeling

The accumulation and rhizotoxicity of Ni to pea were investigated. Calcium, H, and Ni competed for root-binding sites with high pH and low Ca favoring more Ni accumulation. At low pH, Ca accumulation is the key factor determining root growth, while at medium to high pH, root elongation is more sensitive to Ni concentration. The tissue concentration of Ni and Ca ([Ni]t or [Ca]t, μmol g⁻¹ dry root) can be predicted from total dissolved Ni ([Ni]_T, μM), pH, and total dissolved Ca ([Ca]_T, mM) by two approaches. Approach 1 is the empirical equations [Ni]t = (0.361 pH-0.695[Ca]_T)*[Ni]_T and [Ca]t = 8.29 pH + 10.8 [Ca]_T. The second approach involves a two-step model. The surface-bound Ni and Ca are estimated from a surface adsorption model with binding constants derived from independent ion adsorption experiments. Then transfer functions are used to predict internal root Ni and Ca accumulation.