The environmental concerns of global warming and energy consumption are among the most severe issues and challenges facing human beings worldwide. Due to the relatively higher predicted temperatures (150–180 °C), the latest research on pavement energy consumption and carbon dioxide (CO₂) emission assessment mentioned contributing to higher environmental burdens such as air pollution and global warming. However, warm-mix asphalt (WMA) was introduced by pavement researchers and the road construction industry instead of hot-mix asphalt (HMA) to reduce these environmental problems. This study aims to provide a comparative overview of WMA and HMA from environmental and economic perspectives in order to highlight the challenges, motivations, and research gaps in using WMA technology compared to HMA. It was discovered that the lower production temperature of WMA could significantly reduce the emissions of gases and fumes and thus reduce global warming. The lower production temperature also provides a healthy work environment and reduces exposure to fumes. Replacing HMA with WMA can reduce production costs because of the 20–75% lower energy consumption in WMA production. It was also released that the reduction in energy consumption is dependent on the fuel type, energy source, material heat capacity, moisture content, and production temperature. Other benefits of using WMA are enhanced asphalt mixture workability and compaction because the additives in WMA reduce asphalt binder viscosity. It also allows for the incorporation of more waste materials, such as reclaimed asphalt pavement (RAP). However, future studies are recommended on the possibility of using renewable, environmentally friendly, and cost-effective materials such as biomaterials as an alternative to conventional WMA-additives for more sustainable and green asphalt pavements.

The fuzzy logic technique is one of the effective approaches for evaluating flexible and rigid pavement distress. The process of classifying pavement distress is usually performed by visual inspection of the pavement surface or using data collected by automated distress measurement equipment. Fuzzy mathematics provides a convenient tool for incorporating subjective analysis, uncertainty in pavement condition index, and maintenance-needs assessment, and can greatly improve consistency and reduce subjectivity in this process. This paper aims to develop a fuzzy logic-based system of pavement condition index and maintenance-needs evaluation for a pavement road network by utilizing pavement distress data from the U.S. and Canada. Considering rutting, fatigue cracking, block cracking, longitudinal cracking, transverse cracking, potholes, patching, bleeding, and raveling as input variables, the variables were fuzzified into fuzzy subsets. The fuzzy subsets of the variables were considered to have triangular membership functions. The relationships between nine pavement distress parameters and PCI were represented by a set of fuzzy rules. The fuzzy rules relating input variables to the output variable of sediment discharge were laid out in the IF–THEN format. The commonly used weighted average method was employed for the defuzzification procedure. The coefficient of determination ($R^2$), root mean squared error (RMSE), and mean absolute error (MAE) were used as the performance indicator metrics to evaluate the performance of analytical models.

Newfoundland and Labrador lies in the northeastern corner of North America. Road durability in this province is negatively affected by the harsh climate and ever-increasing traffic loads. The provincial five-year road plan emphasizes the maintenance and rehabilitation of existing pavements. For this purpose, basic parameters determining pavement condition should be evaluated. These include the determination of International Roughness Index (IRI), Present Serviceability Rating (PSR), and Pavement Condition Index (PCI) of roads in the city of St. John’s, Newfoundland. A smartphone application called TotalPave was used to measure IRI values. To compute PCI, ASTM International D6433-18 standard was adopted, and a questionnaire was distributed among drivers to obtain PSR. In addition, pavement distress data were collected for major and minor roads. Pavement distresses such as rutting, block cracking, fatigue cracking, longitudinal cracking, transverse cracking, delamination, potholes, and patching were analyzed, and a correlation was developed between the roughness and distress measurements. Roads were found to be in a noticeably inferior condition and PCI values correlated the most with the extent of pavement distresses.