an approach to collect building sensors data based on building information models
DESCRIPTION
Some thoughts we shared at Business Meets Research BMR 2013 (Luxembourg) about Building Information Modelling and Big Data approaches.TRANSCRIPT
An approach to collect building sensors data based on Building
Information Models. Pierre Brimont & Sylvain Kubicki
CRP Henri Tudor
CRP Henri Tudor, three objectives Research: Contribute through scientific
excellence to the production and transfer of knowledge and to the international recognition of the scientific community in Luxembourg.
Innovation: Sustainably strengthen the innovation capacity of companies and public organisations.
Policy support: Support through research and innovation, the definition, implementation and evaluation of national public policies.
CRP Henri Tudor Scientific & Technological Domains:
Materials technologies
Environmental technologies
Health care technologies
Information and communication technologies
Business organisation and management
• Industrial Production and Manufacturing
• Construction and Building • Transport and Logistics • Service Industry
• IT, Multimedia and Communication • Finance and Banking
• Healthcare, Medical and Social • Governmental and Public
Organisations
Key Economic Sectors:
Construction @ CRP Henri Tudor Construction Program. Our competencies
• Business “experts” (Architects, Civil Engineer / Dr., PhD students)
• IT scientists
• Appropriation, networking, IPR
Our team is historically involved in CRTI-B innovation projects (http://www.crti-b.lu)
Today Tudor is co-animator of the NeoBuild innovation pole (http://www.neobuild.lu)
Context 2020 challenge in the construction industry
• Towards zero-energy buildings (EU regulations for new buildings)
Passiv/Positiv energy buildings characteristics
• Very high level of insulation and airtightness of interior spaces
• Heating, Ventilation and Air Conditioning become high-tech systems
Context Most of new-built houses are passiv houses,
with high control of:
• Heat recovery ventilation, insulation, solar gains
Issues are emerging from these technology-driven design choices (Hasselaar 2008)
• Comfort (overheating), noise (from installations/systems), health risks (legionella contamination of domestic water buffers, moistures because of low ventilation volumes)
Context Building pathology data
• Usually comes from the assessment of insurance agencies experience
• Could be widely collected from sensors implemented within buildings, buildings elements and equipments
An example: • Multi-layer wall panels in wood
construction
Source: Leverwood!
Air-moisture sensor (Savory et al. 2012)!
Big Data relevance
Challenges and Opportunities with Big Data!Computing Community Consortium !
www.cra.org/ccc !
Sensor mesures !Context metadata!
Linear and trustfull sources !
Security perspective !
No real time!
Modeling : use of the BIM!!!
BIM According to most of the practitioners and researchers, BIM is both
• Product modeling, i.e. modeling of building-related information,
• Process modeling, i.e. the way practitioners contribute to a single/interoperable model of the (future) building
Towards standardization (BuildingSMART, research community)
• IFC: standardizing product model (expected software interoperability)
• IDM: standardizing process model (understanding collaborative work process)
• IFD: effort towards common definitions and translations
Source: Autodesk!
BIM BIM through the life-cycle of a building/facility
Source: www.bccomfort.com!
BIM as a step to big data modeling buildingSMART data model standard
• IFC (ISO 16739:2013)
• Usually implemented by AEC software vendors
IFC Property Sets
• Define all dynamically extensible properties.
• Can be customely defined (e.g. for sensors-specific data modeling?)
www.buildingsmart-tech.org!
