Automated Geometric Digital Twin Construction for Existing Buildings from Point Cloud Datasets
type: CreativeWork
description: The Architecture, Engineering, Construction, and Operation (AECO) industry significantly impacts the economy and environmental sustainability. However, it faces ongoing challenges in productivity and sustainability caused by outdated and inefficient information management methods. Digital Twins (DTs) offer a promising solution by enhancing decision-making processes throughout the building lifecycle. They have proven their value in all stages of the building lifecycle, including the operation and maintenance stages. However, most existing buildings lack DTs because they were constructed before the widespread adoption of digital technologies, while their design models are unreliable in representing as-is conditions of assets. Creating DTs requires capturing the as-is geometry of buildings (geometric DTs or gDTs). This process involves capturing Point Cloud Datasets (PCDs) and modelling these datasets to represent current building geometry accurately. However, this process requires extensive manual labour and remains a barrier to the broader adoption of DTs for the operation and maintenance of existing buildings. This thesis proposes a novel framework to automate the digitisation of building geometry from PCDs, comprising three main components: space detection, relation detection, and object detection. The space detection method uses empty blob detection and expansion to model volumetric spaces directly from large-scale PCDs, identifying walls, doorways, and windows. The relation detection method identifies primitive surfaces and establishes topological relations between them using line-casting and distance approaches followed by data-driven context-aware classification. This approach constructs a graph that interconnects building elements into a coherent structure. The object detection method combines data-driven and model-driven approaches and outcomes of the previous methods to detect and generate volumetric models for the most common building object types. Lastly, …
abstract: The Architecture, Engineering, Construction, and Operation (AECO) industry significantly impacts the economy and environmental sustainability. However, it faces ongoing challenges in productivity and sustainability caused by outdated and inefficient information management methods. Digital Twins (DTs) offer a promising solution by enhancing decision-making processes throughout the building lifecycle. They have proven their value in all stages of the building lifecycle, including the operation and maintenance stages. However, most existing buildings lack DTs because they were constructed before the widespread adoption of digital technologies, while their design models are unreliable in representing as-is conditions of assets. Creating DTs requires capturing the as-is geometry of buildings (geometric DTs or gDTs). This process involves capturing Point Cloud Datasets (PCDs) and modelling these datasets to represent current building geometry accurately. However, this process requires extensive manual labour and remains a barrier to the broader adoption of DTs for the operation and maintenance of existing buildings. This thesis proposes a novel framework to automate the digitisation of building geometry from PCDs, comprising three main components: space detection, relation detection, and object detection. The space detection method uses empty blob detection and expansion to model volumetric spaces directly from large-scale PCDs, identifying walls, doorways, and windows. The relation detection method identifies primitive surfaces and establishes topological relations between them using line-casting and distance approaches followed by data-driven context-aware classification. This approach constructs a graph that interconnects building elements into a coherent structure. The object detection method combines data-driven and model-driven approaches and outcomes of the previous methods to detect and generate volumetric models for the most common building object types. Lastly, …
author: Drobnyi, Viktor
language: eng
keywords: Digital Twin, existing buildings, geometric Digital Twin, geometry digitisation, Point Cloud Datasets
license: https://apollo8-f-pro.lib.cam.ac.uk/bitstreams/4af686b1-d856-49cc-8f77-e7491a17ea3b/download
license: https://creativecommons.org/licenses/by/4.0/
date published: 2024-07-31
date modified: 2026-07-22T22:24:14Z
provider: Cambridge University
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: University of Cambridge

PropertyValue
property ID: DOI
value: 10.17863/CAM.115061