ASphalt PAving, Research & innovation (ASPARi):
Towards professionalising the asphalt paving process

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About ASPARi

The ASPARi knowledge network established in 2007, includes researchers of the University of Twente, several Dutch contractors and Rijkswaterstaat, the national road agency. The contractors altogether pave about 8 million tonnes of asphalt annually, which represents about 80% of the Dutch asphalt industry. The network aims to significantly raise the quality of constructed asphalt layers and therefore, increase the durability of asphalt. It does so by filling the gap between rapid technology development and digitalisation, the implementation thereof, raising the education and workmanship of operators and appropriate research for current and future needs of the road construction industry. 

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Photo of ASPARi researchers (Farid, 2021)

Research focus

Network members and the University work together in research projects and technology development to improve the performance of the asphalt paving and compaction processes. We utilize advanced technologies, such as GPS, thermography, digital imaging, virtual reality tools, sensors and sensor modalities to improve the construction process and aim to:

  • Develop insights into the asphalt construction process and provide feedback to operators.

  • Reduce variability in key parameters, improve process control, and continuously advance productivity.

  • Reduce risks for paving companies to improve product quality and value for the public clients.

 

The network has the strong belief that professionalization in the industry can only happen when research and technology development is driven by practice and guided by scientific rigor.

ASPARi goes with the times

The research corresponds to on-going changes in the Dutch asphalt construction industry:

  • Focus on quality and risks: Longer guarantee periods, shifting design tasks and risks towards contractors, more focus on value instead of costs, higher penalties.

  • Decreasing availability of time and space to complete the work at the construction site.

  • High variability in working methods and results.

  • Inflow of many (high-tech) technologies from manufactures.

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Previous research

This research network already provided several valuable outcomes and insights:

  • An action research approach that captures the operational characteristics of the asphalt construction process in detail and in a more holistic manner. This approach involves the researcher and the construction team directly in process improvement initiatives, which underscores that the asphalt construction team needs to be involved in and take responsibility for process improvement. Through alternating steps of technology introduction and making operational strategies explicit, the construction team gradually becomes used to new technologies and the benefits that new technologies bring. Rather than just being recipients of technology, they are part of the development of technology and more method-based work strategies.

  • A systematic framework, called the Process Quality Improvement (PQi), is used for improving process quality and can be used for monitoring and exposing variability in the HMA construction process. This enables asphalt construction teams to systematically work towards professionalization of their primary processes.

  • Systematic procedures are used to [1] monitor the movements of machinery at the construction site, [2] continuously monitor the surface temperature in real-time, [3] monitor the in-asphalt temperature relative to the surface temperature, [4] systematically monitor the density progression during the compaction process, and [5] continuously monitor the weather conditions. Within the PQi-framework and the developed procedures, several SMART technologies including GPS, laser line scanners, infrared cameras and thermocouples are successfully used to monitor the working methods of the asphalt team and the temperature differentials during the paving process. The temperature profiling highlights the resultant variability in temperature homogeneity and identifies potentially segregated areas. Temperature Contour Plots and Compaction Contour Plots (see examples below) are digitally “georeferenced in layers” and saved in permanent records for future reviewing of on-site pavement distress and failure.

  • Several visualisation tools have been developed and can be used to make operational behaviour explicit. Mapping the heuristics the operators use allows a deeper understanding of the on-site paving process. The developed tools include: [1] Innovative plots that visualises actual asphalt temperature and compaction data collected during the construction process, [2] 2D animations showing all asphalt equipment movements during construction and in so doing provide evidence of the rolling patterns and of how compaction is undertaken during the construction process, and [3] a Virtual Reality Training Tool and Gaming Software that can be used to train roller compactor operators.

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Figure 1 - Typical georeferenced Temperature Contour Plot (TCP)
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Figure 2 - Typical georeferenced Compaction Contour Plot (CCP) and GPS mounted on a roller compactor

On-going research projects

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  • The explicit data gathered on the construction site shows variability in working methods. How different    strategies influence the final quality of the pavement still is unclear. A method has been developed to simulate the compaction process in the laboratory and hence better design the compaction process in the laboratory, instead of trail-and-error in actual paving projects. This method is being applied in the development of guided operational strategies for a selection of Dutch asphalt mixes.

  • The asphalt construction sector is being flooded with new technologies that support the asphalt construction process using big data and data analytics. An advantage of using the collected data is that it makes asphalt construction operations more explicit in terms of their structures and interconnections. Also, a better understanding of construction operations and possibilities to translate them into algorithms, opens the stage for further process automation. Although, the automation trend is present implicitly in the area of pavement construction, it is barely defined in terms of metrics, standards and use protocols when compared to the Unmanned Aerial Vehicles (UAV) industry and widely used metrics of autonomous control levels (ACL). Therefore, we develop frameworks that can be used to define autonomy classification (autonomy levels) for the asphalt construction industry and design system architectures to further shift towards higher levels of automation.

  • We plan to improve and enrich the PQi method, test the learning effects of the PQi-framework and introduce new sensors into the measurement process.

  • We developed a prototype Real-Time Process Control System that will provide paver and roller operators with the appropriate data visualizations to guide the construction process. Successful experiments have been conducted to deliver 'real-time' information to the operators at the construction site.

  • We plan to improve and enrich the PQi method, test the learning effects of the PQi-framework and introduce new sensors into the measurement process.

  • We developed a prototype Real-Time Process Control System that will provide paver and roller operators with the appropriate data visualizations to guide the construction process. Successful experiments have been conducted to deliver 'real-time' information to the operators at the construction site.

  • The explicit data gathered on the construction site shows variability in working methods. How different strategies influence the final quality of the pavement still is unclear. A method has been developed to simulate the compaction process in the laboratory and hence better design the compaction process in the laboratory, instead of trail-and-error in actual paving projects. This method is being applied in the development of guided operational strategies for a selection of Dutch asphalt mixes.

  • The asphalt construction sector is being flooded with new technologies that support the asphalt construction process using big data and data analytics. An advantage of using the collected data is that it makes asphalt construction operations more explicit in terms of their structures and interconnections. Also, a better understanding of construction operations and possibilities to translate them into algorithms, opens the stage for further process automation. Although, the automation trend is present implicitly in the area of pavement construction, it is barely defined in terms of metrics, standards and use protocols when compared to the Unmanned Aerial Vehicles (UAV) industry and widely used metrics of autonomous control levels (ACL). Therefore, we develop frameworks that can be used to define autonomy classification (autonomy levels) for the asphalt construction industry and design system architectures to further shift towards higher levels of automation.

  • PQi measurements have been established for a while as a baseline for the evaluation of the quality of asphalt paving operations. It is a proven method for assessing the homogeneity and consistency of the asphalt construction process. However, the correlation between process and product quality has always been treated as implicit and intuitive. Given that the ultimate goal of the ASPARi network is to improve the quality of the final product, i.e. the asphalt layer, it is of a cardinal importance to explicitly couple the process quality indicators with the product quality indicators to help practitioners (contractors) better assess the consequences of their operational strategies and decisions on the final quality of the asphalt.

  • A requirement for the successful implementation of digital technologies is the underlying data structure that can accommodate, align, and link the plethora of heterogeneous data that will be generated digitally. Without such a structure, data inundation can paralyze the implementation of any digital technologies and possibly create additional barriers to successful adoption. One example of a systematic effort for data structuring in the civil engineering domain is Building Information Modelling paradigm (BIM), where semantic technologies are used to streamline the digital communication of building data over its lifecycle. While BIM is en route to becoming mainstream in the building sector, other branches of civil engineering are lagging. In pavement operations too, while there is much effort in applying new digital technologies to improve the design, construction and maintenance of paved roads in recent years, there is very little done to systematically and semantically structure pavement lifecycle data. Hence, our researchers are currently developing digital twins for road construction and an ontology for life-cycle data management support.

  • Rapid digitalisation of construction processes has resulted in education curricula lagging in terms of relevance, content and quality. We use our research results to continually develop and update vocational and higher education programmes for various levels and competencies.

  • User-oriented visualization is a strategy to increase understanding and improve the paving process. Based on the on-site sensor readings, data fusion and visualization tools are being developed to form a foundation for a virtual reality training environment. This environment will be used to train asphalt machine operators.

  • Rolling resistance is a longitudinal resistive force to a tire rolling over a pavement. Depending on the environmental, tire, driving and pavement conditions, it is responsible for the additional consumption of significant amounts of fuel, and therefore greenhouse gas emissions. Being able to measure rolling resistance is thus the first step towards mitigating its undesirable effects. While different methods exist for that purpose, they all fall short in capturing the dynamic interplay of the above-mentioned factors during real driving conditions. To overcome this limitation, ASPARi researchers are currently developing a data-driven model to predict rolling resistance by combining real-time measurements of vehicle dynamic states and tyre-road contact parameters with the potentialities of data analytics and machine learning.

  • The construction and regular road pavement maintenance activities require the consumption of considerable quantities of materials and energy resources and release undisputable amounts of harmful substances. Further, contrary to common belief, the environmental impacts related to the pavement life cycle are not constrained to the above phases as they continue to be generated during the use phase. Being able to quantify the environmental, economic and social impacts is therefore of paramount importance for the road authorities and contractors (1) to identify burdensome construction processes and pavement management strategies and (2) to deploy solutions able to reduce the negative impacts. In this domain, ASPARi researchers develop and apply customized and parametric life cycle sustainability assessment methodologies to understand and reduce the life cycle environmental impacts and costs and to exploit the beneficial social impacts of road pavement systems.

ASPARi facilities and equipment

The ASPARi research unit, owns several Trimble GNSS systems for the tracking of all construction equipment (e.g. roller compactors), an RTK bridge, a laser-linescanner, weather stations, sensor interrogators, and infrared cameras, thermocouples and automatic data loggers for the monitoring of the asphalt temperature and other parameters. Contractors, in addition to providing access to their construction sites for essential data collection activities, also provide laboratory assistance (space and technical expertise) for all asphalt laboratory experiments. Also, the SOMA vocational training college provides facilities for the testing of simulator and virtual reality tools developed by our researchers.