Effect of particle morphology on metal dust deflagration sensitivity and severity

Combustible dust explosions continue to present a significant threat toward industries processing, storing, or pneumatically conveying metal dust hazards. Through recent years, investigations have observed the influence of particle size, polydispersity, and chemical composition on dust explosion sensitivity and severity. However, studies characterizing the effect of particle shape (or morphology) on metal dust explosibility are limited and merit further consideration. In this work, high-purity aluminum dust samples of three unique particle morphologies were examined (spherical granular, irregular granular, and dry flake). To maintain consistency in results obtained, all samples were procured with similar particle size distribution and polydispersity, as verified by laser diffraction particle size analysis. Scanning electron microscopy (SEM) imaging and Brunauer-Emmett-Teller (BET) experiments were executed to confirm supplier claims on morphology and to quantify the effective surface area associated with each sample, respectively. Investigations performed in a Kühner MIKE3 minimum ignition energy apparatus and a Siwek 20 L sphere combustion chamber resulted in the direct characterization of explosion sensitivity and severity, respectively, as a function of suspended fuel concentration and variable particle morphology. Recommendations to standard risk/hazard analysis procedures and to existing design guidance for the mitigation of deflagrations that originate from ignition of distinctively processed metal dust fuels have been provided.     

Exploring bicyclist injury severity in bicycle-vehicle crashes using latent class clustering analysis and partial proportional odds models

Introduction: Bicyclists are more vulnerable compared to other road users. Therefore, it is critical to investigate the contributing factors to bicyclist injury severity to help provide better biking environment and improve biking safety. According to the data provided by National Highway Traffic Safety Administration (NHTSA), a total of 8,028 bicyclists were killed in bicycle-vehicle crashes from 2007 to 2017. The number of fatal bicyclists had increased rapidly by approximately 11.70% during the past 10 years (NHTSA, 2019). Methods: This paper conducts a latent class clustering analysis based on the police reported bicycle-vehicle crash data collected from 2007 to 2014 in North Carolina to identify the heterogeneity inherent in the crash data. First, the most appropriate number of clusters is determined in which each cluster has been characterized by the distribution of the featured variables. Then, partial proportional odds models are developed for each cluster to further analyze the impacts on bicyclist injury severity for specific crash patterns. Results: Marginal effects are calculated and used to evaluate and interpret the effect of each significant explanatory variable. The model results reveal that variables could have different influence on the bicyclist injury severity between clusters, and that some variables only have significant impacts on particular clusters. Conclusions: The results clearly indicate that it is essential to conduct latent class clustering analysis to investigate the impact of explanatory variables on bicyclist injury severity considering unobserved or latent features. In addition, the latent class clustering is found to be able to provide more accurate and insightful information on the bicyclist injury severity analysis. Practical Applications: In order to improve biking safety, regulations need to be established to prevent drinking and lights need to be provided since alcohol and lighting condition are significant factors in severe injuries according to the modeling results.     

Exploring pedestrian injury severities at pedestrian-vehicle crash hotspots with an annual upward trend: A spatiotemporal analysis with latent class random parameter approach

Introduction: With the increasing trend of pedestrian deaths among all traffic fatalities in the past decade, there is an urgent need for identifying and investigating hotspots of pedestrian-vehicle crashes with an upward trend. Method: To identify pedestrian-vehicle crash locations with aggregated spatial pattern and upward temporal pattern (i.e., hotspots with an upward trend), this paper first uses the average nearest neighbor and the spatial autocorrelation tests to determine the grid distance and the neighborhood distance for hotspots, respectively. Then, the spatiotemporal analyses with the Getis-Ord Gi* index and the Mann-Kendall trend test are utilized to identify the pedestrian-vehicle crash hotspots with an annual upward trend in North Carolina from 2007 to 2018. Considering the unobserved heterogeneity of the crash data, a latent class model with random parameters within class is proposed to identify specific contributing factors for each class and explore the heterogeneity within classes. Significant factors of the pedestrian, vehicle, crash type, locality, roadway, environment, time, and traffic control characteristics are detected and analyzed based on the marginal effects. Results: The heterogeneous results between classes and the random parameter variables detected within classes further indicate the superiority of latent class random parameter model. Practical Applications: This paper provides a framework for researchers and engineers to identify crash hotspots considering spatiotemporal patterns and contribution factors to crashes considering unobserved heterogeneity. Also, the result provides specific guidance to developing countermeasures for mitigating pedestrian-injury at pedestrian-vehicle crash hotspots with an upward trend.     

Injury-severity analysis of lane change crashes involving commercial motor vehicles on interstate highways

Introduction: One of the challenging tasks for drivers is the ability to change lanes around large commercial motor vehicles. Lane changing is often characterized by speed, and crashes that occur due to unsafe lane changes can have serious consequences. Considering the economic importance of commercial trucks, ensuring the safety, security, and resilience of freight transportation is of paramount concern to the United States Department of Transportation and other stakeholders. Method: In this study, a mixed (random parameters) logit model was developed to better understand the relationship between crash factors and associated injury severities of commercial vehicle crashes involving lane change on interstate highways. The study was based on 2009–2016 crash data from Alabama. Results: Preliminary data analysis showed that about 4% of the observed crashes were major injury crashes and drivers of commercial motor vehicles were at-fault in more than half of the crashes. Acknowledging potential crash data limitations, the model estimation results reveal that there is increased probability of major injury when lane change crashes occurred on dark unlit portions of interstates and involve older drivers, at-fault commercial vehicle drivers, and female drivers. The results further show that lane change crashes that occurred on interstates with higher number of travel lanes were less likely to have major injury outcomes. Practical Applications: These findings can help policy makers and state transportation agencies increase awareness on the hazards of changing lanes in the immediate vicinity and driving in the blind spots of large commercial motor vehicles. Additionally, law enforcement efforts may be intensified during times and locations of increased unsafe lane changing activities. These findings may also be useful in commercial vehicle driver training and driver licensing programs.     

Do NHTSA vehicle safety ratings affect side impact crash outcomes?

Introduction: Side impact crash injuries tend to be severe, mainly due to the effects of the mechanism of such crashes. This study addresses the relationship between side impact crash injury severities and side impact safety ratings of the passenger cars involved in such crashes. It is motivated by the lack of research on side impact safety ratings in relation to the real-world crash outcomes. Method: Analysis of Crashworthiness Data System’s (CDS) data show the head and thorax are the most common regions of impact of severe injuries, while the neck is the least. Irrespective of body regions, higher-rated vehicles were found to provide better occupant protection to both younger and older driver age groups. Assessment based on injury severity score (ISS) indicates that higher-rated vehicles have an overall lower average ISS compared to lower-rated vehicles. Results: Ultimately, this study shows that vehicles rated with National Highway Traffic Safety Administration’s (NHTSA) new criteria had lower average ISS compared to vehicles rated under the old criteria. The 2011 NHTSA side impact rating criteria being relatively new, it has very few crashes to draw meaningful statistically significant conclusions. However, this paper establishes the fact that vehicles with higher star ratings (under experimental conditions) indeed offer increased occupant protection in the field conditions. Practical applications: Previous studies have found that safety was given priority while buying new vehicles. However, people associated vehicle safety with technologies and specific safety features rather than the vehicle’s crash test results or ratings (Koppel, Charlton, Fildes, & Fitzharris, 2008). The results from this study provide a point of reference for safety advocates to educate the drivers about the importance of considering vehicle safety ratings during a vehicle purchase.     

Modelling the elements of country vulnerability to earthquake disasters

Earthquakes have probably been the most deadly form of natural disaster in the past century. Diversity of earthquake specifications in terms of magnitude, intensity and frequency at the semi-continental scale has initiated various kinds of disasters at a regional scale. Additionally, diverse characteristics of countries in terms of population size, disaster preparedness, economic strength and building construction development often causes an earthquake of a certain characteristic to have different impacts on the affected region. This research focuses on the appropriate criteria for identifying the severity of major earthquake disasters based on some key observed symptoms. Accordingly, the article presents a methodology for identification and relative quantification of severity of earthquake disasters. This has led to an earthquake disaster vulnerability model at the country scale. Data analysis based on this model suggested a quantitative, comparative and meaning full interpretation of the vulnerability of concerned countries, and successfully explained which countries are more vulnerable to major disasters.     

基于随机森林的高速公路运营隧道风险判别法则研究*

为快速、准确识别山区高速公路隧道不同区段风险因素组合,基于交通事故调研数据,分析其时空变化规律;从人-车-路-环境角度系统筛选风险影响因素,采用随机森林理论构建风险等级预测模型;以决策树与规则相互关系为基础,结合自变量重要评分法,提出基于随机森林的高速公路运营隧道风险判别法则。研究结果表明:判别法则集预测精度较好且运行时间短,能够明确高速公路不同隧道区段事故成因,为预防隧道严重交通事故发生提供参考。     

Driver aging and its effect on male and female single-vehicle accident injuries: some additional evidence

INTRODUCTION: This study explores the differences in injury severity between male and female drivers, and across the different age groups, in single-vehicle accidents involving passenger cars. METHOD: Given the occurrence of an accident, separate male and female multinomial logit models of injury severity (with possible outcomes of no injury, injury, and fatality) were estimated for young (ages 16 to 24), middle-aged (ages 25 to 64), and older (ages 65 and older) drivers. RESULTS: The estimation results show statistically significant differences in the factors that determine injury-severity levels between male and female drivers and among the different driver age groups. CONCLUSIONS: We discuss a number of plausible explanations for the observed age/gender differences and provide suggestions for future work on the subject. IMPACT ON INDUSTRY: A better understanding of age and gender differences can lead to improvements in vehicle and highway design to minimize driver injury severity. This paper provides some new evidence to help unravel this complex problem.     

尾矿坝溃坝后果严重度评价模型研究

尾矿库是一个具有高势能的人造泥石流,一旦溃坝将给当地的工农业生产及尾矿库下游的人民生命财产造成巨大的灾害和损失.分析并提出了影响尾矿坝溃坝后果严重程度的4个主要影响因子(尾矿坝规模、生命损失、经济损失和社会环境影响),依据现行法规,对各影响因子进行了分级,利用综合因子加权法构造了尾矿坝溃坝后果严重度的评价模型,给出了判别严重程度的定量指标.该模型的建立,为尾矿库的风险管理提供了依据.