High Level-of-Detail BIM and Machine Learning for Automated Masonry Wall Defect Surveying

Enrique Valero, Alan Mark Forster, Frédéric Nicolas Bosché, Camille Renier, Ewan Hyslop, Lyn Wilson

Research output: Contribution to conferencePaper

3 Citations (Scopus)
51 Downloads (Pure)

Abstract

Despite the rapid development of reality capture technologies and progress in data processing techniques, current visual strategies for defect surveying are time consuming manual procedures. These methods often deliver subjective and inaccurate outcomes, leading to inconsistent conclusions for defect classification and ultimately repair needs. In this paper, a strategy for monitoring the evolution of ashlar masonry walls of historic buildings through reality capture, data processing (including machine learning), and (H)BIM models is presented. The proposed method has been tested, at different levels of granularity, in the main façade of the Chapel Royal in Stirling Castle (Scotland), demonstrating its potential.
Original languageEnglish
Pages740-747
Number of pages8
DOIs
Publication statusPublished - 20 Jul 2018
Event35th International Symposium on Automation and Robotics in Construction 2018 - Berlin, Germany
Duration: 20 Jul 201825 Jul 2018
https://isarc2018.blogs.ruhr-uni-bochum.de/

Conference

Conference35th International Symposium on Automation and Robotics in Construction 2018
Abbreviated titleISARC 2018
CountryGermany
CityBerlin
Period20/07/1825/07/18
Internet address

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  • Cite this

    Valero, E., Forster, A. M., Bosché, F. N., Renier, C., Hyslop, E., & Wilson, L. (2018). High Level-of-Detail BIM and Machine Learning for Automated Masonry Wall Defect Surveying. 740-747. Paper presented at 35th International Symposium on Automation and Robotics in Construction 2018, Berlin, Germany. https://doi.org/10.22260/ISARC2018/0101