Learning from data to improve asphalt road construction processes

Abstract

Asphalt road construction projects pose a lot of challenges in different disciplines, from the choice of construction methods, type and quantity of machines and personnel, pre-planning of all processes, ordering material from asphalt mixing plants, real-time logistics of handling material transport, and the asphalt paving and compaction process itself. Depending on the complexity and size of the construction site, preparation and execution can be influenced by a lot of unknown variables, like weather impact, material properties of the delivered asphalt or delays in the deliveries themselves. These circumstances might lead to irregularities in the asphalt material temperatures and thus suboptimal conditions for compaction or even construction stoppage - in any case, they lead to a reduction in quality of the built asphalt road. In recent years, a lot of mobile devices, sensors and software have been emerging as viable solutions in the construction industry to digitize and support these processes. Those real-time applications allow deeper insight into logistics or material properties on site and acquire data for downstream analysis. These endeavours in process optimization now benefit construction companies to increase efficiency, and for work safety and environmental aspects, it is crucial to optimize energy and CO2 consumption. Improved logistics and building roads with low-temperature asphalt will have to become a standard in road construction. The present study gives an overview of the digitalized processes in asphalt road construction with market-available solutions focusing on logistics data of the material delivery chain from mixing plant to paver, thermal imaging data of the built asphalt to validate asphalt temperatures, as well as machine and location data from asphalt paver and compaction rollers.