A framework for real-time compaction guidance system based on compaction priority mapping

Denis Makarov, Faridaddin Vahdatikhaki*, Seirgei Miller, Afshin Jamshidi, André Dorée

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

5 Citations (Scopus)
134 Downloads (Pure)

Abstract

The quality of hot mixed asphalt (HMA) is heavily dependent on the quality of the compaction. However, this operation is very complex, where a large number of parameters (e.g., logistics, weather condition, the skill level of operators, paver speed, etc.) play a role in the operation. To ensure high-quality compaction, operators and site managers need to process a large volume of data in real-time. The high dependency of compaction quality on the largely tacit knowledge of the operators introduces a considerable variability to the quality of compaction operations. In recent years, several compaction support systems are developed to provide operators and site managers with real-time temperature and compaction information. However, these support systems mostly provide the support data in a descriptive manner, i.e., just indicating the current status of the asphalt layer in terms of temperature and compaction maps. In this sense, the operators and managers still need to analyze and interpret the provided data and develop compaction strategies. As a result, it is argued that these systems not only do not reduce the dependency on the tacit knowledge but also can increase the cognitive load on the decision-makers and thus affect the compaction adversely. This research argues that for any compaction support systems to be more effective, it is necessary to provide more prescriptive feedback, i.e., translate the sensory data into actionable guidance that requires less interpretation from the operators and decision-makers. Therefore, the main objective of this research is to develop a framework for compaction guidance systems that can translate the temperature and compaction count (i.e., descriptive) data into clear suggestions for compaction strategies (i.e., prescriptive). A novel priority mapping method is developed to translate the data of (1) the temperature and compaction status of the asphalt mat, and (2) the location of compactors into an index representing the compaction priorities of different parts of the asphalt at any given time. Also, a novel effective compaction rate (ECR) index is proposed in this research to enable an objective and quantitative assessment of compaction operation quality. A prototype is developed and implemented in a case study to investigate the feasibility of the proposed framework. The effectiveness of the proposed guidance system is then assessed through a case study where the proposed method was tested in terms of the extent to which it improves the efficiency of the compaction. It is shown that the transition from the descriptive compaction support system to a more prescriptive guidance system can improve the efficiency of compaction up to 115%. Nevertheless, it is discovered that such a transition may not resonate well with the operators who tend to perceive a loss of control over the compaction strategy as a drawback of the guidance systems. This suggests that the technological transition to compaction guidance systems should coincide with a change of operators' mindset towards new ways of interaction with the collected sensory data

Original languageEnglish
Article number103818
JournalAutomation in construction
Volume129
Early online date15 Jul 2021
DOIs
Publication statusPublished - Sept 2021

Keywords

  • Compaction process control
  • Intelligent paving and compaction
  • IoT
  • Operator support systems
  • Real-time tracking systems
  • UT-Hybrid-D

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