Built Environment Digital Twinning

The Focus Group Built Environment Digital Twinning comprises Hans Fischer Senior Fellow Dr. Ioannis Brilakis (University of Cambridge) and his host Prof. André Borrmann.

The adoption of Digital Twins (DTs) in the built environment sector is gradually increasing, as DTs can offer substantial value to all of the associated stakeholders. DTs can be used to construct, manage, maintain, and monitor physical built facilities. However, there are only a few built facilities with available digital models. There are mainly two reasons for this situation. The first reason is that many facilities have no pre-existing digital models from when they were constructed. Secondly, even if digital models exist, they were not updated through the assets’ lifecycle. Hence, digital models are missing all asset modifications. This substantially reduces the reliability and usability of the data.

Existing capturing technologies such as laser scanning or photogrammetry allow the automation of data acquisition for geometric models.  However, the generation of semantically rich DTs is a complex process that is extremely labour-intensive. In this Focus Group, the aim is to develop methods that can reconstruct 3D models of the built environment from point clouds automatically or semi-automatically, using interdisciplinary methods across construction engineering and computer vision. The ultimate objective of the work is to automatically construct accurate geometric models enriched with semantic and contextual information through employing state-of-art artificial intelligence techniques.

TUM-IAS funded doctoral candidate:
Yuandong Pan, Computational Modeling and Simulation

Publications by the Focus Group

2022

  • Argyroudis, Sotirios A.; Mitoulis, Stergios Aristoteles; Chatzi, Eleni; Baker, Jack W.; Brilakis, Ioannis; Gkoumas, Konstantinos; Vousdoukas, Michalis; Hynes, William; Carluccio, Savina; Keou, Oceane; Frangopol, Dan M.; Linkov, Igor: Digital technologies can enhance climate resilience of critical infrastructure. Climate Risk Management 35, 2022, 100387 mehr…
  • Assadzadeh, Amin; Arashpour, Mehrdad; Brilakis, Ioannis; Ngo, Tuan; Konstantinou, Eirini: Vision-based excavator pose estimation using synthetically generated datasets with domain randomization. Automation in Construction 134, 2022, 104089 mehr…
  • Fitzsimmons, John Patrick; Lu, Ruodan; Hong, Ying; Brilakis, Ioannis: Construction schedule risk analysis – a hybrid machine learning approach. Journal of Information Technology in Construction 27, 2022, 70-93 mehr…

2021

  • Agapaki, Eva; Brilakis, Ioannis: CLOI: An Automated Benchmark Framework for Generating Geometric Digital Twins of Industrial Facilities. Journal of Construction Engineering and Management 147 (11), 2021, 04021145 mehr…
  • Agapaki, Eva; Brilakis, Ioannis: Instance Segmentation of Industrial Point Cloud Data. Journal of Computing in Civil Engineering 35 (6), 2021, 04021022 mehr…
  • Disser, Michael; Hoffmann, André; Kuhn, Luisa; Scheich, Patrick; Institut Für Numerische Methoden Und Informatik Im Bauwesen, TU Darmstadt: 32. Forum Bauinformatik 2021. Towards applicable Scan-to-BIM and Scan-to-Floorplan: An end-to-end experiment, 2021 mehr…
  • Hong, Ying; Xie, Haiyan; Bhumbra, Gary; Brilakis, Ioannis: Comparing Natural Language Processing Methods to Cluster Construction Schedules. Journal of Construction Engineering and Management 147 (10), 2021 mehr…
  • Pan, Yuandong; Braun, Alex; Borrmann, André; Brilakis, Ioannis: Void-growing: a novel Scan-to-BIM method for manhattan world buildings from point cloud. Proceedings of the 2021 European Conference on Computing in Construction, University College Dublin, 2021 mehr…
  • Swanborough, Jack; Kim, Min-Koo; Agapaki, Eva; Brilakis, Ioannis: Automated optimum visualization system for construction drawing reading. Journal of Information Technology in Construction 26, 2021, 681-696 mehr…