Experimental Characterization of Rutting Performance of HMA Designed with Aggregate Gradations According to Superpave and Marshall Methods

The permanent deformation (rutting) of pavement is a major distress in flexible pavement. It is related to vehicles properties and/or pavement materials and conditions. This article presents an extensive experimental investigation in order to compare between the aggregate gradation according to Superpave and Marshall methods of asphalt concrete mix design on pavement rutting and to examine the sensitivity of rutting resistance to aggregate gradation. A wheel truck machine has been used for measurement of pavement rutting (permanent deformation). The tests were carried out at two controlled different air temperature 55℃ and 25℃. The results obtained showed that the adopting of aggregate gradation procedure of Superpave method of pavement mix design for Marshall method of asphalt concrete mix design can reduce the pavement rutting by about 50%. This achievement may be related to missing of three sieves in aggregate gradation procedure of Marshall method which controls rounded and finer aggregate particles. These sieves provide more continuity for aggregate gradation to ensure filling unnecessary gaps and produce more contact points between the aggregates in Hot Mix Asphalt (HMA). The outputs of the research support modifying Marshall method of asphalt concrete mix design by adopting aggregate gradation proposed in Superpave method. The results of study also showed that the coarser aggregate provided more resistance to pavement rutting.

Injury severities from heavy vehicle accidents:An exploratory empirical analysis

Despite the importance of heavy vehicles in Australia’s transportation system,little is known on the factors influencing injury severity from accidents involving a single heavy vehicle.Heavy vehicular crashes have been one of the main causes of fatal injuries in Australia,and this raises safety concerns for transport authorities,insurance companies,and emergency services.Although there have been several potential attempts to identify the factors contributing to heavy vehicle crashes and injury severity,it is still necessary to reduce the number of traffic crashes and lower the fatality rate involving heavy vehicles.The aims of this study were investigating the effects of heavy trucks’presence in accidents on the injury severity level sustained by the vehicle driver and detecting the contributing factors that lead to specific injury severity levels.Fixed-and random-parameter ordered probit and logit models were applied for predicting the likelihood of three injury severity categories severe,moderate,and no injury based on data from crashes caused by heavy trucks in Victoria,Australia in 2012-2017.The results showed that the random-parameter ordered probit model performed better than the other models did.Twenty variables(i.e.,factors)were found to be significant,and 12 of them were found to have random parameters that were normally distributed.Since some of the investigated factors had different effects on the type of injury severity in Australia,this paper does not recommend generalizing the findings from other case studies.Based on the findings,Victoria state authorities can have insight and enhanced understanding of the specific factors that lead to various types of injury severity involving heavy trucks.Consequently,the safety of all road users,including heavy vehicle drivers,can be enhanced.