an approach to collect building sensors data based on building information models. pierre brimont...
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An approach to collect building sensors data based on Building
Information Models.Pierre Brimont & Sylvain Kubicki
CRP Henri Tudor
CRP Henri Tudor, three objectivesResearch: 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 TudorConstruction 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)
Context2020 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
ContextMost 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)
ContextBuilding 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 DataComputing Community Consortium www.cra.org/ccc
Sensor mesures Context metadata
Linear and trustfull sources
Security perspective
No real time
Modeling : use of the BIM
BIMAccording 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
BIMBIM through the life-cycle of a building/facility
Source: www.bccomfort.com
BIM as a step to big data modelingbuildingSMART 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
