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Multi-scale evaluation of the rheological and mechanical properties of asphalt binder has substantial importance in understanding the binder’s micro- and macro-scale properties. This study compares the macro- and micro-scale mechanistic properties of asphalt binders. Test samples used in this study include performance grade binders (PG 64-22) from two different sources along with their modified counterparts. The modifiers include polyphosphoric acid (PPA), styrene-butadiene-styrene (SBS), a combination of SBS and PPA, and reclaimed asphalt pavement. To achieve the goal of this study, atomic force microscope technology was utilized to estimate the asphalt binder’s micro-mechanical properties (e.g., Derjaguin, Muller, Toropov modulus and deformation). On the other hand, data on the macro-scale properties—such as rutting factor (G*/sinδ), consistency and penetration—of the selected binders were analyzed and compared with the aforementioned micro-level properties. The comparative analyses indicated that the micro-mechanical properties of asphalt binders followed a linear trend with the macro-scale properties. The findings of this study are expected to help researchers and pavement professionals in modeling asphalt materials when multi-scale effects are deemed to be necessary.

Automating pavement maintenance suggestions systems is challenging, especially for actionable recommendations such as patching location, depth, and priority. It is a common practice among state agencies to manually inspect road segments of interest and decide maintenance requirements based on the pavement condition index (PCI). However, standalone PCI only evaluates the pavement surface condition which, coupled with the variability in human perception of pavement distress, limits the accuracy and quality of current pavement maintenance practices. In this case, there is a need for multi-sensor data integrated with standardized pavement distress condition ratings. This study explores estimating the appropriate pavement patching strategy (i.e., patching location, depth, and quantity) by integrating pavement structural and surface condition assessment with pavement ratings of distress. In particular, it combines pavement structural condition parameters and falling weight deflectometer deflections with surface condition parameters, international roughness index, and cracking density, for a better representation of overall pavement distress conditions. Then, a pavement-specific, threshold-based patching suggestion algorithm is designed to suggest pavement maintenance operations. The thresholds were determined based on a reliability concept and were verified with the structural number ratio. The threshold values were then used in the patching suggestion algorithm to create patching suggestion tables. A web-based Patching Management Tool (PMT) was designed as an interactive tool to visualize these patching suggestion maps and analyze the pavement distress data using geographical maps and graphs. The PMT was validated with road surface and right-of-way images obtained from three-dimensional laser sensors, and it could successfully capture localized distresses in existing pavements.

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