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An approach to collect building sensors data based on Building Information Models.

An approach to collect building sensors data based on Building Information Models. Pierre Brimont & Sylvain Kubicki CRP Henri Tudor. CRP Henri Tudor, three objectives.

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An approach to collect building sensors data based on Building Information Models.

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  1. An approach to collect building sensors data based on Building Information Models. • Pierre Brimont & Sylvain Kubicki • CRP Henri Tudor

  2. CRP Henri Tudor, three objectives • Research: Contributethroughscientific excellence to the production and transfer of knowledge and to the international recognition of the scientificcommunity in Luxembourg. • Innovation: Sustainablystrengthen the innovation capacity of companies and public organisations. • Policy support: Support throughresearch and innovation, the definition, implementation and evaluation of national public policies.

  3. CRP Henri Tudor Scientific & Technological Domains: Key Economic Sectors: • 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 • Materials technologies Environmental technologies Health care technologies Information and communication technologies Business organisation and management

  4. Construction @ CRP Henri Tudor • Construction Program. Ourcompetencies • 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)

  5. Context • 2020 challenge in the construction industry • Towardszero-energy buildings (EU regulations for new buildings) • Passiv/Positivenergy buildings characteristics • Very high level of insulation and airtightness of interiorspaces • Heating, Ventilation and Air Conditioningbecome high-tech systems

  6. 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)

  7. 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 Air-moisture sensor (Savory et al. 2012) Source: Leverwood

  8. Big Data relevance Sensor mesures Context metadata Linear and trustfull sources No real time Security perspective Modeling : use of the BIM Challenges and Opportunities with Big Data Computing Community Consortium www.cra.org/ccc

  9. Source: Autodesk 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

  10. BIM • BIM through the life-cycle of a building/facility Source: www.bccomfort.com

  11. 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

  12. Thank you for your attention • pierre.brimont@tudor.lu • sylvain.kubicki@tudor.lu

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