Traditional visual approaches to condition surveys are time-consuming and still often include incorrect labelling, misdiagnosis and omission of defects. These anomalies are due to surveyors differing in how they report, linked to their experience – or sometimes inexperience – and the compressed time frames for completing surveys. Providing accurate surveys is vitally important, though, as incorrect identification and diagnosis of defects can result in inappropriate interventions that ultimately undermine the fundamental conservation objectives they mean to support.
In this context, progressive implementation of digital documentation and subsequent application of innovative data processing tools such as machine learning (ML) algorithms could transform surveying, repair and maintenance.
CyberBuild
https://cyberbuild.eng.ed.ac.uk/
>CyberBuild delivers Digital Engineering solutions for the AEC/FM sector.
CyberBuild is a research laboratory associated to the School of Engineering at The University of Edinburgh
In collaboration with various academic and industry partners, we research and develop technologies that deliver more efficient and effective design, construction and management of built environment assets.
Data Capture
Acquiring accurate and complete as-built/as-is data
Data Processing
Extracting value-adding information from the acquired data
Visualization
Effectively understanding and communicating the data and information
