A presentation looking back at a decade of research into Information Retrieval to manage digital content in construction.

1. The Future of Construction and
Infrastructure Information:
BIM, the Death of the Document, the Importance of Information Retrieval
and Future-proofing in Design
Dr Peter Demian, Senior Lecturer, Loughborough University
Cambridge. 13 March 2013

2. Outline
 Today’s aim
(“Documents” and IR in the era of BIM)
 CoMem: Measuring relevance
 Needles: Presenting search results
 3DIR: Exploiting 3D visualisations
 The future: BIM for decision-making (e.g.
future-proofing of designs)
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3. AIM: “Documents” and IR in the era of BIM
 Construction is information intensive
 Complexity, fragmentation, multiplicity of
stakeholders, project orientation, adversarial….)
 Information Retrieval: help humans fulfil their
information needs
 Mostly: “Documents”
 BIM: paradigm shift in the way we manage
information about buildings and constructed things
 Finer grains, smarter way to manage information
 IR, documents irrelevant Semantics: can treatas a
grain of information small
document
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4. CoMem
 Design reuse from a Corporate Memory
 How can you make use of “digital content”
accumulated from previous projects
 Visual interfaces….
 ... but large element of Information Retrieval
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5. Measuring Relevance in CoMem
 Archive of building (information) models
 Treat each object as a (short) document
 Lots of short documents
 Can we still apply IR techniques?
 Relevance measure evaluation:
 Precision
 Recall

6. Relevance Measures Tested
 Different Vector Model Weights
 Binary
 TF-IDF
 Log Entropy
 Latent Semantic Indexing (LSI)
 Additional Corpus vs. No Additional Corpus
 Context-sensitive comparisons:
 Concatenation
 Tree Isomorphism / Tree Matching
 Stemming vs. No Stemming (Porter’s Algorithm)

7. Precision-Recall for Component Queries
Differing Term Weighting Approaches For
Component Queries
No Stemming, Binary
No Stemming, Log-Entropy
1 No Stemming, Tf-Idf
0.9 Stemming, Binary
Stemming, Log-Entropy
0.8 Stemming, Tf-Idf
Precision
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Recall

8. Evaluation of Relevance Measures

9. Latent Semantic Indexing
Mean Precision Over 11 Standard Recall
Levels Against Number of LSI Factors for
Project Queries
0.40
0.35
Mean Precision
0.30
0.25
0.20 No Stemming, No Additional Corpus
Stemming, No Additional Corpus
0.15 No Stemming, Discussion Forum Corpus
0.10 Stemming, Discussion Forum Corpus
No Stemming, Textbook Corpus
0.05 Stemming, Textbook Corpus
Vector Model, No Stemming
0.00 Vector Model, Stemming
0 100 200 300 400 500 600 700 800
Number of Factors

10. Mean Precision
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0.40
0.45
0.50
Simple,
No Stemming
Simple,
Stemming
Ancestors,
No Stemming
Ancestors,
Stemming
Descendants,
and LSI Runs
No Stemming
Descendants,
Stemming
Both,
LSI - Tf-idf
No Stemming
Project Queries, Comparison of Vector
Both,
LSI Relevance vs. Vector Model
Stemming
Vector Model - Tf-idf

11. Tree Isomorphism Example
rpi , p j
P P
rd i , d j
D D
g (d i , d j )
C C
C C
C

12. Tree Isomorphism vs. Vector Model
Mean Precision Over 11 Standard Recall
Levels
1
Vector Model, Tf-idf weights, Stemming
0.9
Tree Matching
0.8
Mean Precision
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
PROJECT DISCIPLINE COMPONENT

13. Summary of Relevance Measure Evaluation
 Methods
 Simple Vector Model Techniques
 Binary Weights, TF-IDF Weights, Log-Entropy Weights
 Context-sensitive Techniques
 Concatenation , Latent Semantic Indexing (LSI), Tree
Isomorphism Method
 Findings
 Good performance is possible with sparse semantic data /
annotations
 LSI yields similar performance as does the Vector Model
 Innovative Tree Isomorphism Method to evaluate relevance
 Tree Isomorphism Method performs well compared to the Vector
Model

14. Needles: Search Results Interfaces
Relevant in Content interface: Focused interface: Baseline interface:
Just right context Too little context Too much context
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15. Needles Findings: RIC Interface, best
time and accuracy
Time Accuracy
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16. 3DIR – Background
 We are cramming more information into our
models (BIM)
 Beyond modelling and design, the average
human needs to manage unprecedented
amounts of information
 Research has identified human strengths in
vision, spatial cognition and visual memory;
these strengths can be exploited when
managing information linked to 2D/3D space
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17. “There‟s no such thing as a „true‟ model”
 This idea comes up in many contexts…. even
in BIM
 To propose new BIM ideas, we need to
understand the decisions professionals need
to make
 “Modelling positivism”
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18. Map
“There‟s no such thing as a „true‟ model”
http://commons.wikimedia.org/wiki/
London_Underground_geographic_
maps
TFL web site
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19. Bim for Future-proofing – A BIM Tool
Healthcare trends NHS responce
DYNAMIC FACILITIES
(hospitals)
Push-Pull
Process interconnecti Process
field field
ons
INFORMATION
DESIGN PROCESS
TECHNOLOGY
(BIM)
Technology
Policy field
field
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20. BIM for Future-proofing – Design
Theory
Integrative Concept of Change-
ready Process
BIM Plan
Outer Environment
System
requirements
Lawson,
2005
Brief
Problem
Space
Krishnamurti,
2006
Simon, 1996 Design
Krishnamurti,
Krishnamurti Space
2006
, 2006 Woodbury and Burrow, 2006
Inner Environment
Solution
Conceptual Design
Design
Akin, 2001
Akin, 2001
Process Space
Design Design
Lawson,
2005
Strategy Knowledge
Candidate solution/
Fricke, 1996
Fricke, 1996
Ahmad et
Ahmad et
al., 2013
given problem
al., 2013
Function Step Design
oriented wise Flexibility standardisation
Simon, 1996
BIM Implementation
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21. Integrative Concept
BIM Plan
X problem space y problem space
z problem space
Outer Environment
Brief
All arbitrary
Any initial layout that
substitutions of ADB
constraints flexibility
rooms
Narrowed
design space
List of proposed ADB rooms
based on selected filters
Design Process
Inner Environment
Conceptual Design
Automated design
Design section knowledge
Working out the Change
embodiment effect
design
structural Grouping ADB rooms
Designing the entire regarding numerical and
concept
spatial text values. E.g. area space
(10-15m2 or a function that
Searching for working
principles takes place on specific
flow rooms (clinical function).
Function B
Function A
Function C
Problem
area Short-term Medium-term Long-term Time frame
effect
BIM Implementation
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22. Conclusions
 Building models can be like collections of
documents
 BIM is all about non-geometric information
(parametric models, object orientation, etc.)
 Applications of IR are important
 Beyond IR, must understand decisions to
provide decision support
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23. Acknowledgements
 CoMem: Own PhD research under supervision of Dr Renate
Fruchter, Stanford
 Demian, P. and R Fruchter, 2005. “Measuring Relevance in Support of Design Reuse from Archives of Building Product
Models.” ASCE Journal of Computing in Civil Engineering, 19(2), 119-136.
 Needles: Collaboration with Dr Panos Balatsoukas, now at
University of Manchester
 Demian P and P Balatsoukas, 2012. “Information retrieval from civil engineering repositories: the
importance of context and granularity.” Journal of Computing in Civil Engineering, 26(6), pp. 727–740
 3DIR: On-going collaboration with Dr Kirti
Ruikar, Professor Anne Morris, Dr Ann
O’Brien, Loughborough
 Royal Society poster: http://royalsociety.org/uploadedFiles/Royal_Society_Content/grants/labs-to-
riches/2012/Peter%20Demian.pdf
 Future-proofing is the doctoral research of Mr Ilias Kristallis
 Krystallis I, Demian P, Price ADF, 2012. “Design of flexible and adaptable Healthcare Buidlings of the
Future – A BIM Approach”. FIRST UK ACADEMIC CONFERENCE ON BIM: CONFERENCE
PROCEEDINGS, Newcastle, 05 Sep 2012 - 09 Sep 2012. 222-232. 03 Sep 2012.
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24. Thank you.
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