高性能沥青路面Superpave级配设计方法分析

介绍了贝雷法的级配设计思想及其在苏嘉杭高速公路路面建设中的实践.贝雷法作为高性能沥青混合料Superpave级配设计理论基础,可对混合料粗细集料嵌挤程度进行评价分析,利用体积法设计出密实的骨架嵌挤沥青混合料.     

欢喜岭重交通道路沥青SHRP性能研究

应用Superpave沥青胶结料试验动态剪切试验、压力老化试验和弯曲梁流变试验评定欢喜岭重交通道路沥青的抗永久变形、抗低温开裂和抗疲劳开裂性能，确定欢喜岭重交沥青最高和最低路面设计温度及PG等级，表明欢喜岭重交沥青满足我国广阔地域的使用要求。     

同三国道主干线粤境开阳高速公路路面下面层SUPERPAVE配合比设计

结合同三国道主干道粤境开阳高速公路K170 000-K205 400的下面层Superpave配合比设计。介绍Superpave的应用情况．     

Superpave技术在季冻区重交通道路上的应用研究

简要介绍了Superpave技术的特点及优势,并结合该技术在吉林长珲（长春到珲春）高速公路上的应用成果,论证了该技术在吉林季冻区（季节性冻土区）重交通高速公路建设中的广泛应用前景。     

两种设计方法的最佳沥青含量关系探讨

对多组矿质混合料,按照沥青混合料马歇尔设计方法和Superpave设计方法分别确定最佳沥青含量.T检验表明,两种方法确定的最佳沥青含量有显著差异 .采用多元方差分析方法,研究了沥青混合料类型,矿料级配类型、沥青品种及矿料种类对两种方法确定的沥青含量差别的影响效果.结果表明,矿料级配类型是主要因素,其他因素影响不显著.根据试验结果的统计规律 ,提出马歇尔法和Superpave法确定的最佳沥青含量间的定量关系式,并对模型进行检验和实际应用.     

Superpave12．5沥青上面层施工技术

结合湖北襄荆高速公路工程实践，对Superpavel2．5沥青上面层施工技术、工艺方法进行了分析和小结，以推广该施工方法，可供类似工程参考。     

A dynamic oscillatory test that fulfills the objective of the elastic recovery test for asphalt binders

The ductilometer is currently being used by the asphalt pavement community for determination of elastic recovery of asphalt materials. Briquet specimens are pulled apart at 5 cm/min, held after a specified elongation, then severed at the center and allowed to recover undisturbed for 1 h with the idea of getting a measure of the ability of the asphalt material to recover after imposing a deformation. Materials with poor ability to recover are known to lend themselves to permanent deformation resulting in rutting of pavements. The Superpave specification parameter |G*|/sinδ recommended by the Strategic Highway Research Program was found not to relate well with observed rutting of mixtures that used polymer-modified asphalts with increased elastic behavior. This led researchers to seek methods to refine this parameter. While refinement efforts have been ongoing, there has been an inclination on the part of a number of State Departments of Transportation to use the elastic recovery test to complement the Superpave specification tests. However, the elastic recovery test is truly not very elegant and accurate, and often fails to discriminate between the behaviors of differently modified binders. The present work suggests a dynamic oscillatory test using the dynamic shear rheometer DSR that would fulfill the objectives of the elastic recovery test. The elastic recovery term and the condition under which it is determined from DSR data are selected by observation of master plots of a wide variety of asphalt binders. The criterion is developed by observing the ability of the selected term in best discriminating asphalt binders for their elastic recovery behavior. It is recommended that the term cosδ determined at a temperature T = 82°C and frequency ω = 1 radian/s be used to assess the elastic recovery of asphalt materials. Cosδ (T = 82°C, ω = 1) > 0.04 is recommended as the criterion for acceptance of required elastic recovery behavior. The opinions, findings, and conclusions expressed in this document are those of the author only and not necessarily of the Federal Highway Administration or any other researcher at the Turner-Fairbank Highway Research Center.     

用弯曲应变能评价沥青混合料低温抗裂性

采用低温弯曲破坏试验评价Superpave沥青混合料的低温抗裂性,并通过弯曲破坏试验的应力应变曲线求算应变能,结果表明用弯曲应变能作为评价指标可以很好表征沥青混合料的低温性能。     

矿料级配对沥青混合料压实性能影响研究

采用美国SHRP计划开发的Superpave旋转压实仪（SGC）,选取SMA13,DAC13,SJ13三种常用级配,通过分析在不同坐标系下沥青混合料的压实曲线及不同级配在各压实阶段特征参数的分布规律,对不同级配沥青混合料的压实性能进行了评价,为沥青混合料压实施工奠定基础。     

An artificial neural network model for virtual Superpave asphalt mixture design

This study presents an artificial neural network (ANN) model to predict the asphalt mixture volumetrics at Superpave gyration levels. The input data-set needed by the algorithm is composed of gradation of the mix, bulk specific gravity of aggregates, low- and high-performance grade of the binder, binder content of the mix and the target number of gyrations (i.e. N_ini, N_des and N_max). The proposed ANN model uses a three-layer scaled conjugate gradient back-propagation (feed-forward) network. The ANN was trained using data obtained from numerous roads with a total of 1817 different mix designs. Results revealed that the ANN was able to predict V_a within V_a (measured) ± 1.0% range 85–93% of the time and within V_a (measured) ± 0.5% range 60–70% of the time. Currently with the developed ANN model, Superpave mix design can take approximately between 1.5 and 4.5 days, which corresponds to 3–6 days of savings.