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

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.

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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Surveying
Learning systems
Defects
Facades
Repair
Monitoring

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.DOI: 10.22260/ISARC2018/0101
Valero, Enrique ; Forster, Alan Mark ; Bosché, Frédéric Nicolas ; Renier, Camille ; Hyslop, Ewan ; Wilson, Lyn. / High Level-of-Detail BIM and Machine Learning for Automated Masonry Wall Defect Surveying. Paper presented at 35th International Symposium on Automation and Robotics in Construction 2018, Berlin, Germany.8 p.
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Valero, E, Forster, AM, Bosché, FN, Renier, C, Hyslop, E & Wilson, L 2018, 'High Level-of-Detail BIM and Machine Learning for Automated Masonry Wall Defect Surveying' Paper presented at 35th International Symposium on Automation and Robotics in Construction 2018, Berlin, Germany, 20/07/18 - 25/07/18, pp. 740-747. DOI: 10.22260/ISARC2018/0101

High Level-of-Detail BIM and Machine Learning for Automated Masonry Wall Defect Surveying. / Valero, Enrique; Forster, Alan Mark; Bosché, Frédéric Nicolas; Renier, Camille ; Hyslop, Ewan; Wilson, Lyn.

2018. 740-747 Paper presented at 35th International Symposium on Automation and Robotics in Construction 2018, Berlin, Germany.

Research output: Contribution to conferencePaper

TY - CONF

T1 - High Level-of-Detail BIM and Machine Learning for Automated Masonry Wall Defect Surveying

AU - Valero,Enrique

AU - Forster,Alan Mark

AU - Bosché,Frédéric Nicolas

AU - Renier,Camille

AU - Hyslop,Ewan

AU - Wilson,Lyn

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AB - 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.

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Valero E, Forster AM, Bosché FN, Renier C, Hyslop E, Wilson L. High Level-of-Detail BIM and Machine Learning for Automated Masonry Wall Defect Surveying. 2018. Paper presented at 35th International Symposium on Automation and Robotics in Construction 2018, Berlin, Germany. Available from, DOI: 10.22260/ISARC2018/0101