The research done in this study, aimed to examine how management of point cloud data in the Danish AEC industry, could be improved by applying artificial intelligence (AI), during the construction phase.
The research was based on the problem statement: “How can the utilization of point cloud data in the construction phase in the AEC industry, be improved by applying Deep Learning, to segment and classify the point cloud data?”
To answer this question and gain an understanding about point cloud data and artificial intelligence (AI), a literature review was conducted. In addition, an interview was carried out with appropriate participants in the construction industry, to gain empiric data.
The collected data was analyzed using work models based on the method Contextual Design (Holtzblatt and Beyer, 2017) and segmented using interview method by (Brinkmann and Tanggaard, 2020).
The analyzed empiric data was used, to discuss the data found in the literature study. In addition to this, an early prototype was constructed based on the Contextual Design method (Holtzblatt and Beyer, 2017). Afterwards this prototype was tested and evaluated.
The research concluded that compiling and processing point cloud data can be challenging. To simplify the process, Deep Learning can be applied to automate some of the processes. To understand if this could be useful in the AEC, a prototype for controlling the schedule, improving the workers safety and automatic quality control, was developed, and tested. The test results showed that an automation of these processes would be useful to the supervisors, but only if it could be automated. By utilizing Deep Learning, this should be possible, but more research is needed, especially in larger datasets.