Unreadable to Actionable—Condensing Insights and Deriving Quality from Machine Raw Data in Asphalt Road Construction Processes

Modern construction machines are equipped with state-of-the-art sensors, like accurate GNSS receivers, thermal cameras or distance and vibration sensors, that record large volumes of data on their spatial and temporal properties, construction methods or the structural properties of the built material. While it is a challenge itself to condense insights from a single data source, the task grows exponentially more complex when several machines—and, with that, data sources—are involved. The present study covers the merging of data on different road and asphalt construction machines, collected on hundreds of construction sites for quality analysis, and describes relevant approaches in data engineering necessary to infer insights across the entire construction process. This publication proposes an approach to analyzing both individual metrics and spatially merged multi-variables. Depending on the step within the construction process, or the personnel involved, it can be important to perform actions based on detailed multi-variable information, or, when a timely decision is necessary, based on real-time high-level information. To enable this, data engineering methods and a proposed system have been developed, and concrete, actionable results from the data are presented. A representative sample project includes data covering a 14 h construction span with 62 material deliveries, thousands of temperature readings of the newly built asphalt (0.25 m × 0.25 m temperature grid at about 130 ± 15 °C), paver control (mostly steady speed at 3.55 m/min) and recorded track data on asphalt rollers, which are condensed into resulting rollovers.