Hybrid As-Is Modelling of Existing Industrial Facilities
Lead Research Organisation:
UNIVERSITY OF CAMBRIDGE
Department Name: Engineering
Abstract
Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.
Publications
Agapaki E
(2018)
Prioritizing object types for modelling existing industrial facilities
in Automation in Construction
Evangelia Agapaki
(2018)
State-of-practice on As-is Modeling of Industrial Facilities
Studentship Projects
| Project Reference | Relationship | Related To | Start | End | Student Name |
|---|---|---|---|---|---|
| EP/N509620/1 | 30/09/2016 | 29/09/2022 | |||
| 1759297 | Studentship | EP/N509620/1 | 30/09/2016 | 30/03/2020 | Evangelia Agapaki |
| Description | I have discovered the following: 1) found the most important industrial shapes that need to be automatically modelled 2) generated a benchmark dataset (CLOI) that is currently used for deep learning experiments 3) automated detection and classification algorithms that identify the most important shapes in industrial plants. More specifically, starting from a point cloud as input, I automatically generate a geometric Digital copy (Digital Twin) of a facility. |
| Exploitation Route | My findings will save modelling time and cost for the modellers who wish to create a geometric model of a facility for maintenance and retrofitting purposes. |
| Sectors | Construction Digital/Communication/Information Technologies (including Software) Energy |
| Description | They will be used to support commercial modelling software of industrial facilities. |
| Sector | Energy |
| Impact Types | Economic |