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
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
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 technologydriven 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
Air-moisture sensor (Savory et al. 2012)
An example:
•
Multi-layer wall panels in wood
construction
Source: Leverwood
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
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
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 sensorsspecific data modeling?)
www.buildingsmart-tech.org
Thank you for your attention
pierre.brimont@tudor.lu
sylvain.kubicki@tudor.lu