Building Information Modelling (BIM) Services
On 31st May 2011 UK Government published its plans to deliver a 20% reduction in capital cost and carbon burden from the construction and operation of the built environment. At the centre of this report was a mandate for the creation of a collaborative 3D BIM environment, an environment in which all project and asset information, documentation and data are captured, stored, shared and updated electronically. The first milestone – level 2 BIM compliance – was set for March 2016.
As businesses, contractors, sub-contractors and partners adjusted to this far-reaching change we of course saw a significant increase in requests for scanning of paper material. Paper archives are, by definition, non BIM compliant. However in the 18 months since the level 2 milestone we have also seen a marked increase in requests for our data conversion and data structuring services. This demand is no doubt being driven by the next two BIM milestones: level 2 business-as-usual; level 3 implementation. Having created a digital image archive, businesses and organisations are now turning their attention to how they might best extract the full value of the data contained within the images. To work effectively BIM must be structured around a Common Database Environment (CDE) with linked databases. Converting unstructured images to structured data models is essential to this.
Typical customers include the AEC sector, local government and central government. However BIM for Heritage is already well-established as an SIG in its own right and we are playing an active part in this.
- Conversion of scanned drawings into CAD files
- Conversion of unstructured attribute information into structured data
- Cleansing of unstructured datasets to follow standardised hierarchies and naming conventions
- 3D modelling with attributes
- Geo-referencing of scanned maps and plans
- Feature extraction from maps and plans (into CAD or GIS file formats)
- Web-based 3D GIS platforms
- Virtual tours
- High-volume data extraction through semi-automated processes (machine learning). For instance extraction of data fields from Word/ pdfs (e.g. planning applications, contracts, AIRs and EIRs)