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 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!

Thank you for your attention

pierre.brimont@tudor.lu

sylvain.kubicki@tudor.lu