Risk factors associated with truck-involved fatal crash severity: Analyzing their impact for different groups of truck drivers

Introduction: Fatal crashes that include at least one fatality of an occupant within 30 days of the crash cause large numbers of injured persons and property losses, especially when a truck is involved. Method: To better understand the underlying effects of truck-driver-related characteristics in fatal crashes, a five-year (from 2012 to 2016) dataset from the Fatality Analysis Reporting System (FARS) was used for analysis. Based on demographic attributes, driving violation behavior, crash histories, and conviction records of truck drivers, a latent class clustering analysis was applied to classify truck drivers into three groups, namely, ‘‘middle-aged and elderly drivers with low risk of driving violations and high historical crash records,” ‘‘drivers with high risk of driving violations and high historical crash records,” and ‘‘middle-aged drivers with no driving violations and conviction records.” Next, equivalent fatalities were used to scale fatal crash severities into three levels. Subsequently, a partial proportional odds (PPO) model for each driver group was developed to identify the risk factors associated with the crash severity. Results' Conclusions: The model estimation results showed that the risk factors, as well as their impacts on different driver groups, were different. Adverse weather conditions, rural areas, curved alignments, tractor-trailer units, heavier weights and various collision manners were significantly associated with the crash severities in all driver groups, whereas driving violation behaviors such as driving under the influence of alcohol or drugs, fatigue, or carelessness were significantly associated with the high-risk group only, and fewer risk factors and minor marginal effects were identified for the low-risk groups. Practical Applications: Corresponding countermeasures for specific truck driver groups are proposed. And drivers with high risk of driving violations and high historical crash records should be more concerned.     

钢筋混凝土旧工业厂房主体结构的改造加固风险研究

为探索钢筋混凝土旧工业厂房的改造与加固风险,进行了实地调研和基础理论研究,借助SPSS 22.0统计软件对32个二级指标进行筛选,确立6个方面29个指标的改造加固风险评价指标体系,运用结构方程模型（SEM）实证分析关键影响因素及其作用路径。研究结果表明:设计方案、结构特征和施工技术对改造加固风险具有影响;结构特征与设计方案之间的强关联形成了隐性且重要的风险路径。基于关键影响因素及因素间关联关系,对宝鸡市某U型厂房主体结构加固实例提出建议,为此类项目的改造施工提供理论依据。     

Corrosion failure probability analysis of buried gas pipelines based on subset simulation

Corrosion is the main reason for the failure of buried gas pipelines. For effective corrosion failure probability analysis, the structural reliability theory was adopted in this study to establish two calculation models for pipeline corrosion failure: the pressure failure model and von Mises stress failure model. Then, two calculation models for the corrosion failure probability were established based on a corrosion depth growth model obtained from actual survey data of soil corrosion characteristics. In an example, Monte Carlo simulation (MCS) and subset simulation (SS) were used to analyze the corrosion failure probability of pipelines, and the results were compared. SS can compensate for the shortcomings of MCS as it has higher computational efficiency and accuracy. Therefore, SS was adopted to simulate variations in the corrosion failure probability of buried pipelines with the service time for the two failure probability calculation models, which were applied to a natural gas pipeline located in a chemical industry park in Zhuhai, China. A sensitivity analysis was carried out on the relevant parameters that affect the failure probability. The results showed that multiple loads caused by the covering soil, residual stress, temperature differential, and bending stress have a non-negligible effect on the pipeline reliability. The corrosion coefficients gradually become the most important factors that affect the failure probability with increased service time. The proposed methodology considers the actual operating conditions of pipelines to provide a reliable theoretical basis for integrity management.     

Pipeline leakage detection and isolation: An integrated approach of statistical and wavelet feature extraction with multi-layer perceptron neural network (MLPNN)

Leakage diagnosis of hydrocarbon pipelines can prevent environmental and financial losses. This work proposes a novel method that not only detects the occurrence of a leakage fault, but also suggests its location and severity. The OLGA software is employed to provide the pipeline inlet pressure and outlet flow rates as the training data for the Fault Detection and Isolation (FDI) system. The FDI system is comprised of a Multi-Layer Perceptron Neural Network (MLPNN) classifier with various feature extraction methods including the statistical techniques, wavelet transform, and a fusion of both methods. Once different leakage scenarios are considered and the preprocessing methods are done, the proposed FDI system is applied to a 20-km pipeline in southern Iran (Goldkari-Binak pipeline) and a promising severity and location detectability (a correct classification rate of 92%) and a low False Alarm Rate (FAR) were achieved.     

Modelling outflow from a ruptured pipeline transporting compressed volatile liquids

The paper presents a mathematical model for predicting outflow rates from a ruptured pipeline transporting compressed volatile liquids. The main focus of the paper is the methodology used to predict thermodynamic properties of interest. The model is validated using experimental data in the open literature. As the field scale outflow data does not include typical operating conditions the model is further validated at higher pressures and longer pipelines by comparing outflow rates calculated using a commercial pipeline simulation package, PROFES. The mathematical model predictions of mass flow rate and pipeline inventory agree well with the measured data and the more sophisticated pipeline model.

The simple pipeline rupture model is a useful tool for consequence analysis as it has a fast runtime on a standard PC. A further advantage is it is more easily, without having to address all of the numerical issues that arise when using a more sophisticated pipeline model. This allows a safety engineer to focus on the potential hazard rather than driving the model.     

基于MIKE 21的长江江山制药排污口水环境影响分析——以江山制药公司污水处理站为例

为分析长江沿岸入河排污口设置对水环境的影响,采用MIKE 21建立了长江马鞍山-高桥段二维非稳态水量水质模型,并以江山制药公司污水处理站为例,对其排污口迁址改建前后的水环境影响进行模拟.结果 表明,较于原排污口排放,拟设排污口在执行新排放标准情况下,对九圩港入江断面水质有改善作用,COD、NH3-N、TP的改善率分别为...     

外源性污染对太湖梅梁湾水质影响的定量化

通过太湖的水量水质数学模型,模拟了梅梁湾内主要的入湖污染源———直湖港、武进港和梁溪河排入的污染物质的迁移、转化规律;分析了梅梁湾中梅园、小湾里、闾江口、拖山4个监测点水质浓度受直湖港、武进港和梁溪河排污影响程度的大小;并分别建立了监测点污染物浓度与排污口排污量的响应关系曲线和响应关系表达式。通过这些结论可以方便的定量的计算出在排污口排污量发生变化的情况下监测点水质浓度的变化量,为合理控制外源污染物质的入湖量提供了技术支持。     

长江三峡水库气候效应数值模拟

在地形坐标系中建立了一个三维静力平衡的大气-土壤耦合模式,模式详细考虑了复杂地形、植被和水面的热力、动力过程。与其它中、小尺度模式相比其独特之处是：此模式在地表与植被冠层建立的辐射平衡方程和能量平衡方程中,均详细地考虑了坡度、坡向的影响。观测表明,长江三峡江面水温的日变化小于3℃。长江三峡段水流湍急,江水上下交换十分剧烈,各处水深变化较大,使模拟江水的动力、热力过程变得十分困难。所以,在研究中忽略了江水温度的日变化,模式中作为动力、热力外强迫因子,水库建成后的改变仅为水面海拔高度的升高和水面区域的扩大,水面温度是常数。用此模式模拟研究长江三峡建成前后气象要素场的日变化过程,通过计算它们的差别来分析水库的小气候效应。结果表明风、温、湿气象要素场在方圆近10 km范围内均有不同程度的改变,但变化幅度不大。     

MR阻尼器的简化参数模型及其应用

MR阻尼器应用于控制风振、地震等引起的随机振动时需用参数化表达式来描述MR阻尼器简化的力学模型 ,从而更好地揭示MR阻尼器的减振机理。本文采用偏最小二乘法误差估计、Chebyshev多项式拟合建立了MR阻尼器简化模型的参数化表达式 ,并应用于斜拉索振动控制中 ,为合理选择MR阻尼器提供了便捷、可靠的手段     

金岭铁矿加强安全管理的措施与经验

山东金岭铁矿通过建立科学合理的安全管理模式,开创了安全生产的新局面,连续3年实现安全生产,社会和经济效益显著。