The document discusses semantic construction with graphs. It provides background on the speaker including their engineering and entrepreneurial experience. It then discusses trends in the AECO industry toward increased digitalization and use of building information modeling (BIM). The document proposes that an integrated approach using a semantic graph database can help address information gaps and complexity issues across project stages in the AECO industry.

1. Semantic Construction with Graphs
GraphTour Amsterdam

2. ● Professional background
○ Engineer - Entrepreneur
○ Noise & Vibration Handling - Automotive (LMS)
○ Information Management - AECO Industry (iNFRANEA)
⇒ co-founder Neanex
● Personal
○ Family & Friends
○ Travel
○ Sports
A - Architects
E - Engineers
C - Contractors
O - Owner-Operators
Peter Imbrechts

3. Nea - nex
● +/- 20 FTE
● Antwerp - Breda - Valencia

4. Facts

5. Failure costs
10 - 15%

6. Productivity since the 50ties
Average
4 Mining
5 Construction
1 Agriculture
2 Manufacturing
3 Retail
Change(%)➜
Year ➜

7. Productivity since the 50ties
Lees verder Reinventing Construction: A Route to Higher Productivity

8. Poor digitalisation

9. Scope - Cost of change curve
Cost of Change
Flexibility
Time ➜
Low
High Committed Costs

10. Information gaps
Project stages ➜
Planning /
Conceptual
Design
Data,information,knowledge➜
Preliminary
Design
Final Design Construction &
As-Built
Maintenance &
operation

11. Functionality
Scope
Integrality
Constructability
Quality
Sustainability
Controllability
CostTime
Safety
Maintainability
Circularity
Increased complexity

12. Problem?

13. Traditional process
?
?
v1
v2
v2bis
!

14. Solution?

15. Trends in the AECO-Industry

16. BIM?
Building Information Modelling
(BIM) is a digital representation
of physical and functional
characteristics of a facility. A
building information model is a
shared knowledge resource for
information about a facility
forming a reliable basis for
decisions during its life-cycle;
defined as existing from earliest
conception to demolition.
Process??

17. BIM-levels
Level 0
CAD (lines & shapes)
Drawings, Paper
Level 1
2D & 3D
Only Files
Document Mgt System
Level 2
BIM (4D & 5D)
Files and libraries
Common Data Environment
(CDE) applying data and
documents
Level 3
Integrated BIM (6D)
Only data, libraries - Open
Standards
Cloud-collaboration
CDE applying only data

18. T
E
C
H
N
O
L
O
G
Y
K
N
O
W
L
E
D
G
E
PROCES
A logical sequence of tasks performed to
achieve a particular objective = WHAT
METHOD
Consists of techniques, practices and
procedures for performing a task = HOW
TOOL
Software to accomplish a task efficiently ,
based on a method = WITH
ORGANISATION
Integrates and supports the use of tools
and methods used on a project = BY
… based on the principles of a …
… supported by a …
… enabled by an …
skills
& abilities
capabilities
& potential
Estefan, J. A. Survey of model-based systems engineering (MBSE) methodologies. Incose MBSE Focus Group.
A methodology is a ‘recipe’ for the application of related processes, methods, and tools
to a class of problems that all have something in common.
More than just tools

19. Model Based Engineering (MBE)
“An approach to engineering that uses models as an integral part
of the technical baseline that includes the requirements, analysis,
design, implementation, and verification of a capability, system,
and/or product throughout the acquisition life cycle.”
⇒ Less human interpretation & more data associativity for more
control and less room for mistakes.
Processes!!

20. The life of an asset is like a trail ...
PDF
GIS
3D CAD
BIM
2D CAD
VR
DMS
Planning Performance
Compliance
ERP
Operation & MaintenancePreliminary DesignConcept
Detailed Design &
Construction

21. Model based?
- All information is decomposed in explicit data
- All explicit information is linked using ‘semantic’ relations
Requirement: “The training room has a minimum area of 32m2
, and
requires 4 double electrical sockets.”

22. Model based?
Room
Element
- number: 4
Criteria
Training room
Double socket
Area : 32m2
- All information is decomposed in explicit data
- All explicit information is linked using ‘semantic’ relations

23. Semantic database
A natural representation of information using 3 types of carriers
- Elements
- Properties
- Relations
Person
- Name
- Mobile
- E-mail
Activity
- Name
- description
- Startdate
- Enddate
Product
- Name
- Description
- Status
- ...
Is responsible for ... Has result
Is owner of

24. Semantic database
Room
- Number
Building
Element
- Number
- Number
Property
Require-
ment
Decision
Meeting
Is part of
Is located in
Is valid for...
Deals about...

25. ID
building
Obj-0001
Meeting room
Obj-0002
entrance
Obj-0005
Comfort class
Eis-0003
open
Verification
Dorpsstraat 1
Builder Ltd
peter@bouw.com
Peter
caroline@bouw.com
Caroline
Eis-0001
Accessibility
disabled
Object
Verification
status
Requirement
ID
Person
email
Organisation
address
exists of
exists of
has to comply with
is responsible for
Is employee of
Is employee of
is responsible for
has executor
has to comply with
Semantic database

26. Example 1:
A table consist of 4 legs & a flat top. Without the legs or the top, it
wouldn’t be a table! The table is made from wood and is placed in the
kitchen.
Table Leg Wood
Table Leg 1
Table Leg 2
...
Kitchen
Table
Chair
etc..
Table
Table Leg (4x)
Table Top (1x)
Managing complexity

27. Neanex - an integrated approach
Entity
Obj-0001Space
Obj-0002
Class
Akoes-0001
Eis-0001
Req
is decomposed in
must comply to
must comply to
Element
Ele-0002
...
CONSTRUCTION PROJECT
Task
management
Meeting
management
Organisation
management
Q&A
Verification &
validation
Communication
Change
management
Document
management

28. iBIM is 100% Integration

29. iBIM is 100% Collaboration
⇒ Reduce failure costs

30. Road to integrated BIM

31. User Research - Use Cases

32. Fast
Agile
Future Proof
User Research - Tool specs
Easy
Insightful

33. Academic research & Standardisation
- Best Practice Information Model
- ISO 15926, 15288, 19650, ... compliant
- COINS, CObie ready
- OpenBIM, IFC, BCF - BuildingSmart

34. Best fit-for-purpose Database
SQL DB Document DB Triple DB Graph DB
Data modeling tables JSON documents RDF triples native graph
Normalization normalized denormalized facts vertices/edges
Schema rigid schemaless OWL defined flexible
Language SQL (defacto) vendor specific SPARQL (open) CYPHER
Maturity proven emerging esoteric adopted
Scaling vertically horizontally horizontally horizontally
Performance joins denormalisation normalisation native graph
Use cases OLTP, OLAP big data, IoT data exchange highly related data
semantic applications, with complex relations and deep queries

35. Database As-desired
Characteristics
● Graph based data modeling
● Finding the sweet spot between Application
performance and integrating with Linked Data
● Choose the right tool for the right job
● Avoid Technology impedance mismatch
Strong points
● Think and model business domain in Graphs
● Native property Graph database allows for
performant queries at the application level
● Domain objects can be stored as Nodes with
properties, supporting denormalization
● Use JSON-LD to store denormalized RDF data
● ACID Transactions
● Performant denormalized join-less queries
● Flexible Schema or Schema less
● Add meaning and properties to Relations
● Great support for evolving Schemas
Property
Graph

36. Technical Research

37. Traditional Software
Technical Research
Front-end
Middleware
Database
Infrastructure
Stack layers Neanex
React.js (Facebook)
Node.js & JavaSpring
Native Graph - Neo4J
Docker
connectors
3rd party apps
Generic components
Open
Information model
AWS

38. User Experience is Key!
Frequency of usage
Complexity of task
Simple Moderate High
Monthly
Weekly
Daily
CEO
check project KPIs
Neighbour
activities next week
SE manager
verify V&V matrix
BIM manager
analyse & report clashes
Safety supervisor
report safety issues
Purchase assistant
order next batch

39. BIM Collaboration Platform

40. - Roles & Permissions
- Multi-tenancy <> Cost of hosting
- Speed of writing related data
- Hosting providers fear the
unknown
+ Build the schema
+ Build & execute complex queries
+ Explore the graph
+ Speed of reading the data
+ Scalability
+ Support to make it work
+ …
Lessons learned

41. Think as many, Work as one
THANK YOU!