Automatic As-built Digital Twin

The goal of this project is to create a novel approach to automatically generate as-built Building Information Models – also called Digital Twin, of built environment assets.
Description of the Project: 

Key to effective facilities management and visions like Smart Cities or Digital Britain is the availability of reliable Digital Twins of built environment assets (buildings, infrastructure industrial complexes). Such semantically-rich 3D models are important to support Operations and Maintenance activities, particularly when robots are to be deployed (e.g. to operate a valve or simply navigate some environments during dangerous or emergency operations).

However, while these assets may be designed with care and detail, their actual construction often results in changes that must be recorded in the Digital Twin that then reflects the as-built state of the facility. Detecting such variations is a challenge and recording them is often not done at all or requires significant work and capital. Some methods (and commercial software) have been proposed to process point cloud data of as-built assets to generate digital twins, but these are very limited in capacity and level of automation.

Method: the project will aim to interpret coloured point clouds of a facility given a design model to identify and correct small and large discrepancies. This will be achieved by integrating Scan-to-BIM [1] and Scan-vs-BIM [2]. In particular, the approach shall uniquely integrate geometry, topological, and wider semantic analysis.

Evaluation: We have access to a number of case study point clouds that can be provided by the company Scan&BIM in Ireland (that works in this area), as well as other interested construction organisations, such as Laing O’Rourke, Jacobs and AECOM (who demand such services).

Resources required: 
Possibly access to a terrestrial laser scanner
Project number: 
300004
First Supervisor: 
University: 
Heriot-Watt University
Second Supervisor(s): 
First supervisor university: 
Heriot-Watt University
Essential skills and knowledge: 
2D-3D data processing
Desirable skills and knowledge: 
Machine learning / AI
References: 

[1] Murali S., Speciale P., Oswald M. R., Pollefeys M. (2017), “Indoor Scan2BIM: Building Information Models of House Interiors”, IEEE/RSJ Proceedings of IROS, 2017. [pdf]

[2] Bosché F., Ahmed, M., Turkan, Y., Haas, C., Haas, R. (2015), "The value of integrating Scan-to-BIM and Scan-vs-BIM techniques for construction monitoring using laser scanning and BIM: The case of cylindrical MEP components", Automation in Construction, Vol. 49, pp. 201-213. [pdf]