Frank Bijleveld

Aligning laboratory and field compaction practices for asphalt – the influence of compaction temperature on mechanical properties

The approach used to identify a compaction temperature in the laboratory, based on binder viscosity, provides a single compaction temperature whereas, on-site, a roller operates within a temperature window. The effect on the density and mechanical properties of rolling during a temperature window remains unclear. Consequently, asphalt concrete binder mixtures were compacted in different temperature windows in the laboratory using a Roller Sector Compactor, and the observed phenomena were then related to field study observations. The results show that while similar densities can be achieved in a broad range of temperature windows, other mechanical properties such as fracture energy may decline up to 30% if compacted outside the optimum temperature window. These results indicate that a compaction temperature window should form part of mix design and quality control. The paper proposes specifying a compaction window based on temperatures and the resulting mechanical properties rather than a single compaction temperature.

Making operational strategies of asphalt teams explicit to reduce process variability

The on-site construction process undertaken by asphalt teams has a critical impact on pavement quality. Process improvement and learning require explicit information about the process. However, current on-site operational activities and key parameters are, in general, not systematically monitored and mapped. The lack of process information makes it difficult for contractors and asphalt teams to distinguish between good and poor practices and to improve. Although technologies to make the on-site process explicit are becoming widely available, their adoption has been slow. To overcome this knowledge gap regarding explicit information about the on-site construction process, this paper proposes a framework and utilizes technologies for the systematic monitoring and mapping of on-site activities and key parameters. Various technologies and sensors, such as a global positioning system (GPS), a laser linescanner, and infrared cameras, make it possible to track the on-site movements of machinery and asphalt temperatures during construction. This framework was applied and refined during 29 asphalting projects in the Netherlands, creating an extensive set of on-site process data. Considerable variability was found in the delivered asphalt temperatures, the asphalt cooling, the compaction process and density progression, and the movements of machinery. This variability offers opportunities where action could be taken to improve process quality by reducing process variability. The framework and explicit data can help asphalt teams to verbalize their tacit knowledge and make their own processes and choices transparent and further promotes learning processes. This paper contributes to a deeper understanding of the on-site construction process and highlights how to encourage technology adoption in construction

Method-based learning: a case in the asphalt construction industry

Traditional working practices in the construction industry rely heavily on the onsite experience and craftsmanship (the tacit knowledge) of operators and teams. This results in implicit learning and lengthy learning cycles. The aims of the research are to develop a deeper insight into construction processes and to instigate a change from current implicit learning to explicit method-based learning. To change to explicit method-based learning, Kolb’s experiential learning model was introduced into current practices and ‘explicating the process’ was added to this learning cycle. Further ‘reflective observation’ and ‘abstract conceptualization’ were incorporated explicitly during an actual road construction project using feedback sessions with an asphalting team. The adopted learning framework was found to be applicable and useful in the quest for enhanced learning capabilities and improved process control. Fusing Kolb’s learning model with onsite collected data was vital in explicating tacit knowledge and implicit processes. The approach enabled a meaningful discussion with operators to unravel their intentions and reasoning behind the chosen strategies. Explicit method-based learning, as here, leads to improved quality awareness, better understanding of the processes and their interdependencies, and improved communication with and within the asphalting team.