The asphalt paving process

Language: English The Challenge At the outset the asphalt paving process seems to be simple. Trucks deliver hot mix asphalt from plants to the construction site. There the asphalt is loaded on asphalt pavers that evenly spread the asphalt. Finally, compactors compact the asphalt to its required density. Despite this relatively structured process our research shows that there are significant discrepancies in the final compaction grade and quality of newly asphalted road sections. It turns out that asphalt paving operations are to a certain extend knowledge intensive because of two reasons. First, compactor operators do have little explicit understanding about the temperature of the asphalt during the compaction processes. Such understanding, however, is crucial as asphalt can only be compacted in a certain temperature window. If the asphalt is too hot, the compactor will merely shove the asphalt forward. If the asphalt is too cold, little compaction can be reached. The second reason why asphalt paving operations are knowledge intensive is that asphalt compaction is team work. Seldom a road section is compacted only by one single compactor, most of the times multiple compactors are working together to reach the desired compaction grade. Again adequate information about the joint activities are not available to individual operators. Hence, it is hard to understand for the individual compactor operators which part of the road has been compacted how often. It is not surprising that roads currently are compacted highly inhomogeneous. Our Research To overcome this problem we developed a theoretical method to provide asphalt operators with adequate information about temperature and compaction processes that we developed to overcome the above described dilemma. In particular, we use a network of temperature sensors and GPS tracking devices to collect operational data. We then process this data to meaningful visualize the process to provide information to roller operators in real time during roller operations and in retrospect to enable organizational learning activities of asphalt paving crews. Adequate visualizations of this processed data is then provided to machine operators in real time and in retrospective feedback sessions with the goal to improve the operator's understanding about their paving processes. The Task In a next phase of this research, we aim at automating the data processing steps of the method. Currently, much of the processing is done manually and, hence, processing is quite work intensive. This hinders us currently to timely provide feedback to operators. To support …