Construction Progress Tracking Using Scan-vs-BIM from Visual and Sensed Data

The project aims to develop a prototype to process visual and sensed data for construction progress monitoring in a fully automated fashion, a process known as scan-vs-BIM. Our research hence employs BIM and computer vision methods, in addition to exploring the potential of deep learning in this context.

Automating manual progress and quality reporting processes in the construction industry would allow to identify problems early on and respond quickly, avoiding reworks and overruns of cost- and time-schedules. In contrast, it can currently take up to several weeks for critical information to reach stakeholders.

Objectives:

  1. A comprehensive, implementation and especially deep-learning focused review of the problem of scan-vs-BIM from visual and sensed data.
  2. The proposal of a new approach to progress tracking using recent methods from the fields of computer vision, robotics and deep learning

Funding:

The project is funded by Innovate UK. Research is conducted at the Centre for Systems Engineering and Innovation in collaboration with Contilio.

Cont