Gender balance in k-12 American history textbooks

Abstract This research study evaluated K-1

Smart Compaction for Infrastructure Materials

69A3551847103Compaction is a process of rearranging material particles by various mechanical loadings to densify the materials and form a stable pavement structure. Current methods to assess the compaction quality rely heavily on engineers' experiences or post-compaction methods at selected spots. The experience-based method is prone to cause compaction problems and pavement distresses, particularly when new materials are implemented. Due to the complicated interactions between the compactors and materials, the compaction mechanism of the particulate materials is still unclear. This gap hinders the improvement of compaction quality and the development of intelligent construction. This project was undertaken to investigate the compaction mechanism of the infrastructure material from the mesoscale (particle scale) and develop an innovative compaction monitoring method that determines the compaction condition based on particle kinematics. With the development of sensing technologies, wireless particle-size sensors have become available in research and industry for monitoring particle behaviors during compaction. A wireless sensor, SmartRock, was applied in the project and collected the mesoscale behaviors during compaction. Several lab and field compaction projects were carried out using asphalt mixtures and granular materials, various compaction machines, and pavement structures. It was found that internal particle kinematic behavior is closely correlated to material densification during compaction. The lab and field compaction can be reasonably connected by the particle rotation, and similar three-stage compaction patterns were identified. Three machine learning models were built to predict the compaction condition and the density of the asphalt pavement both in the lab and in the field. The reasonable predictions confirm that the machine learning algorithm is appropriate for compaction prediction. The density results from the pavement cores further verify the applicability and robustness of the intelligent model for compaction prediction. Future studies are still needed to evaluate the model's robustness based on more mixture varieties and field applications

Wings to the Future: Teaching Strategies to Grow Lifelong Learning

Teaching and Learning Conference