Photo taken during one of the annual ASPARi symposia at the SOMA college
Significant changes are currently occurring in the construction industry, resulting in changing roles for road agencies (clients) and contractors. Agencies are shifting towards service-level agreements with lengthy guarantee periods. Hence it is important for contractors to improve process and quality control during construction.
However, as in many domains in the construction industry, traditional working practices lean heavily on the on-site experience and tacit knowledge of operators and teams. Operators may implicitly learn based on experience from previous construction projects, but this is based on limited observations and data. To develop a deeper insight into construction processes and improve process and quality control, this tacit knowledge needs to be made explicit to prompt a change towards explicit improving and learning methods.
The construction process is still mainly carried out without the use of high-tech instruments to monitor key process parameters, and little research effort has been put into the systematic mapping and analysis of construction processes. Contractors do not explicitly know what transpired during construction, how the operations were carried out, and therefore it is also near impossible to identify good or for that matter, poor operational practices. In this project, new technologies such as Differential GPS, Laser Line Scanners and Infrared Cameras, are used to make the asphalt construction process explicit and make operational behavior explicit through the use of innovative visualizations.
The explicit data and visualizations will help contractors to improve their understanding of on-site construction processes and therefore work towards improved process control. It also helps to move from current implicit, experience-based knowledge to explicit knowledge. Finally, the research also provides pointers for other domains of the construction industry to move from traditional experience-driven practices towards method-based practices