DECISION MAKING ON CULTURAL HERITAGE
CONSOLIDATION MATERIALS USING
COMPUTATIONAL INTELLIGENCE TOOLS
Anastasia Kioussi1, Maria Karoglou1, Anastasios Doulamis2, Klio Lakiotaki3, Nikolaos Matsatsinis3 and Antonia Moropoulou1
1National Technical University of Athens, School of Chemical Engineering,
[email protected]
2NTUA scientific collaborator & Professor of Technical University of Crete
[email protected],
3Technical University of Crete, Decision Support Lab, Technical University Campus, 73100, Chania, Crete, Greece, Email:
[email protected]
Aim of this work
 In this paper a support scenario for consolidation
treatments is proposed, using computational
intelligence tools, based on the criteria provided by
the Conservation Intervention documentation
protocol(including international standards and
recommendations).
 This approach enables decision-makers to
understand the complex relationships of the
relevant attributes in conservation problems (like a
consolidation treatment), which may subsequently
improve the acceptability of the decision.
Consolidation


ASTM E2167-01 (Reapproved 2008):Standard Guide for Selection and Use of Stone Consolidants- Definition: Consolidant is a
material applied to stone to re-establish the bond between particles that may have been lost through weathering or decay
mechanisms. The consolidant treatment aims to reduce the rate of decay of stone.
The use of a consolidant should be considered only after developing a thorough understanding of all factors contributing to the
deterioration of the stone (Comprehensive survey of existing conditions, environment, and a definition of stone performance
requirements, followed by laboratory and in site investigation analysis of the stone)
1.Performance goals
for stone consolidants
Depth of penetration
Consolidation ability
Water vapour permeability
Resistance to internal expansive forces
Thermal expansion characteristics
Appearance (texture, colour, reglectivity)
Durability
Water resistance
Biodegradation
2. Selection criteria:
Historic preservation guidelines
Stone characteristics
Past performance history
Consolidant application method
Environmental protection considerations
3.Analysis of untreated stone
Petrographic examination
Water absorption
Soluble salt analysis
4.Analysis of and comparison
of treated and untreated
stone samples (laboratory)
Depth of penetration
Consolidant loading
Strength properties (compressive, flexural,
modulus of elasticity)
Abrasion resistance
Water absorption
Water vapor transmission
Salt crystallization damage resistance
Appearance change
Accelerated weathering tests
Microstructure parameters(porosity, pore
size distribution)
5. Evaluation of pilot test
areas
Consolidant consumption
In situ evaluation (ndt testing)
Sampling(laboratory analysis-depth of
penetration, petrographic
and SEM examination, water absorption.
6.Final evaluation of
consolidant performance
Questions like:
Did the consolidant meet the performance
goals identified in 1?
Were there any adverse effects on the
structure during the in-situ test?
In the test areas, did the treated stone
show less deterioration than the untreated
stone?
Is the treatment practical and
economically feasible?
Consolidation interventions
How to support the decision making in choosing the most appropriate
consolidation material
 Multidimensional problem
Why to use computational intelligence & decision support tools?
•
Assist experts to take decisions based on expert knowledge and experience
•
Reject non-preferable solutions
– Reduces the costs
•
Identify hidden knowledge
•
Improve validation performances
•
Results on optimal use of conservation materials such consolidants
•
Identify and quantify all related parameters, estimating of the weight of each
parameter
•
Systematize all related data(text, numerical, images etc)

Methodology
1.Web-research consolidation
Collection of technical sheets
of consolidation products
3.DataBase of Consolidants
2.Criteria set based on
expert Knowledge and
Bibliographical Data
•Inorganic Materials
•Nano-limes
•Organic Materials
•Alkoxysilanes
Materials ranking with a climax that was created by
data provided by the official technical sheets of
products, as well at bibliographical data concerning
the application of some products, giving both
qualitative and quantitative data.
Decision support systems
1.Utah star methodology
based on linear regression
2. Supervised non-linear
classifiers (feed-forward
neural networks
)
3. Unsupervised clustering
methodologies (k means).
Validation in Lab and in the Monument Scale
Criteria Adopted
Criteria Adopted for materials ranking
Availability
Inversibility
Resistance
to Chemical
Environmenta Compatibility
Standardized
Rules
l Loads
Yes/No
Numerical
Numerical
Numerical
/Unknown
Values
Values
Values
Filming
Penetration
Discolored
Water
depth
Numerical
Values
Quality Values
Binary Values
Hardness
absorption
Quality Values
Quality Values
Quality Values
Climax
Parameter
Climax
Availability in Greece
Parameter
Penetration depth
Unknown / Not satisfactory /
depend on any factors/
depends on the solvent / good
/ very good with the use of
specific solvent / very good/
Color change
No change /depend on the
surface / depend on the
excess of material at surface/
medium / high/unknown
Yes/No /
Unknown
Irreversibility
Climax
0-10
Durability at environmental
loads
0-10
Capillary absorption of
water
Low / medium / high/unknown
Chemical compatibility
0-10
Change of hardness
Low / medium / high/unknown
Standards
Yes/No /
Unknown
Creation of film
0/1/2
A Decision Support System for Material
Consolidation
To suggest to the expert user the most suitable materials used
for consolidation
 We use different criteria for each material
 We train a classifier to relate the material values to their
consolidation performance
 We use a k-means algorithm to select the most representative
samples used as inputs to the classifier
 For each material we assign an output which declare how
important is the consolidation performance of this material


the values are ranked from 1 to 20 (1 the best, 20 the worst)
The Computational Intelligent Architecture
The Training Phase
Cultural Data
Unsupervised Clustering
Fuzzy C-Means
Two phases: The training
phase- We combine Fuzzy Cmeans with neural networks
to efficiently train a classifier
The testing phase-The neural
is tested on data outside the
training set. New weights
are estimated using
constructive training
techniques.
A set of representative
samples
Neural Network
Training
The Testing Phase
Neural Network Testing
Constructive Training
Decision Support output
Experiments Set-Up
Two scenarios for different application
substrate:
• The ranking is primarily based on chemical
composition of stones
•
 1st Scenario: Substrate limestone(35 samples)
 2nd Scenario: Substrate sandstone(34 samples)
 In future, more parameters
will be included like microstructural characteristics of material, mechanical properties
etc,
Neural Networks Set-Up
We use either three or
two categories for the
performance of each
material
• Three categories
(preferred, neutral nonpreferred)
• Two categories
(preferred, nonpreferred)
Quantized
Outputs
We use either three or
two categories for the
performance of each
material
Continuous
Outputs
• Preference Order
The network size is
properly selected
through cosntructive
training methods
• Two layers networks
• Constructive training
method
Different
Network Sizes
Results- Generic
2
1.4
Neural Output in Training Samples
Neural Output inTesting Samples
Desired output in Training Samples
Desired Output inTesting Samples
1.5
1.2
1
0.5
Selection Preference
Selection Preference
1
0
0.8
0.6
-0.5
0.4
-1
0.2
-1.5
0
5
10
15
20
25
Number of Samples
We test the scheme over 25 samples of a
consolidation material (training set). The
performance is almost perfect. We cannot
notice the difference between the desired
outputs and the ones obtained by the system
0
1
2
3
4
5
Number of Samples
6
7
We test the scheme over 8 samples of a
consolidation material outside the training
set. Good performance is noticed.
8
Results –Scenarios 1,2
Average Error over 70 randomly
partitioned datasets when 20 hidden neurons are selected
Average Error over 70 randomly
partitioned datasets when 20 hidden neurons are selected
SCENARIO 1
SCENARIO 2
Training error
Testing Error
0.4%
5.7%
Training error
Testing Error
0.7%
2.6%
Quantitative performance: Overall error over all datasets. We use 70
randomly selected experiments.
The UTA* algorithm
Automatic Extraction of
Criteria Importance
The UTA* algorithm
 The idea is to find which criterion is the most
important
 Which criterion contributes more to the final
decision
 We use a linear regression scheme form
statistical analysis
 A Multi-criterion decision making approach
Results-Scenario 1
Criteria Adopted
Availability Inversibility Resistance
Chemical
Standardize
to
Compatibili d Rules
Environme
ty
ntal Loads
Yes/No
Numerical
Numerical
Numerical
Binary
/Unknown
Values
Values
Values
Values
Filming
Penetration Discolored
Water
Hardness
depth
absorption
Numerical
Quality
Quality
Quality
Quality
Values
Values
Values
Values
Values
Values 1 to 10 are the ten
criteria adopted in our case.
The ranking of the criteria is
exactly the same as in next
Table.
Results-Scenario 2
Scenario 1
0,090789818
0,120704544
0,054324815
0,096816889
0,097848487
availability in Greece
reversibility
0,062514711
0,120795954 0,0085
0,067767522
Scenario 2
0,10829301
0,114843951
durability
chemical compatibility
chemical compatibility
standards
standards
film
0,088442145
penetration depth
0,048352749
0,299685734
color change
availability in Greece
reversibility
durability
0,09743482
0,103792321
film
0,030142594
0,059719141
penetration depth
0,323630796
color change
We demonstrate the significance of the criteria for both scenarios examined.
The importance of the criteria as derived by the algorithm are next assessed
by an expert user. The results shows that the system have correctly define the
criteria importance.
Porous Biocalcarenite
 Pilot scale treatments for porous
stone
consolidation in the Medieval City of Rhodes
 Materials
 LUDOX HS30 (PL)
 Silbond HT20 (PH)
 Rhodorsil RC70 (RP)
 Acryl Siliconic Resin (EU)
Evaluation of the Compatibility of Conservation Interventions in lab
Water Absorption of Porous Stone & Consolidated Porous Stones
25
Imbibed Water, wgt. %
RPS
CSPH2
20
CSPL3
15
10
CSEU1
5
CSRP4
0
0
10
20
30
40
50
60
70
80
(Time, sec) 1/2
Changes of water absorption curves (capillary) of consolidated porous
stones and monitoring by infrared thermography in the laboratory
IR Thermography
Investigation of Capillary Rise, Monument Scale
Investigated Surface: Gate
of St. Paul, Medieval
Fortifications of Rhodes
Evaluation of Pilot Consolidation Interventions, Monument Scale
Investigated
Surface:
Entrance of
Moat, Medieval
Fortifications
of Rhodes
15 months after the applications
28 months after the applications
Consolidation Materials: LUDOX HS30 (PL), Silbond HT20 (PH), Rhodorsil RC70 (RP)
acryl siliconic resin (EU), evaluation of their performance in real scale.
Validation of the results
-in laboratory (various
analytical
techniques like capillary absorption test,
mercury intrusion porosimetry etc)
-in monument scale (non-destructive
testing)
Feedback: Changes in
materials ranking
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conditions in combination with consolidation treatments on highly porous stone”, Science and Technology for Cultural Heritage, 3 (1994) pp.
113-122
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scale”, PACT, J. European Study Group on Physical, Chemical, Biological and Mathematical Techniques Applied to Archaeology, 59 (2000)
pp. 209-230
Moropoulou, A., Kouloumbi, N., Bakolas, A., Haralampopoulos, G., «Performance evaluation of conservation interventions to porous stone
facades of historic buildings in heavily polluted urban centers», Pitture e Vernici European Coatings, 12/13 (2001) pp. 19-28
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in the Medieval City of Rhodes porous stone”, J. Mediterranean Archaeology and Archaeometry, 1 [1] (2001) pp. 37-68
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performance evaluation of consolidation materials applied on porous stones”, Materials and Structures, 36 (2003) pp. 210-217
Moropoulou, A., Kouloumbi, N., Haralampopoulos, G., Konstanti, A., Michailidis, P., “Criteria and methodology for the evaluation of
conservation interventions on treated porous stone susceptible to salt decay”, Progress in Organic Coatings, 48 [2-4] (2003) pp. 259-270
Avdelidis, N.P. Moropoulou, A., “Detection of moisture in stonework and the effeectiveness of conservation methods in historic structures”,
INSIGHT, J. of the British Institute of non-destructive testing, 46 [6] (2004) 360-363
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treatments in situ”, Scienza e Beni Culturali XVI, ed. G. Biscontin, G. Driussi, Publ. Arcadia Ricerche S.r.l. (2000) pp. 239-255
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prerequisite to a reversible conservation intervention”, Scienza e Beni Culturali XIX, ed. G. Biscontin, G. Driussi, Publ. Arcadia Ricerche,
(2003) pp. 375-382
Moropoulou, A., Theoulakis, P., Kokkinos, Ch., Papasotiriou, D., Daflos, E., “The performance of some inorganic consolidants on the
calcareous sandstone of the Medieval City of Rhodes”, in Proc. 7th International Congress on Deterioration and Conservation of Stone, ed.
J.D. Rodrigues, F. Henriques and F. Telmo Jeremias, Publ. Laboratorio Nacional de Engenharia Civil, Lisbon (1992) pp. 1231-1242
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pp. 410
Programs
•1988-1991, Municipality of Rhodes, Study for the restoration and protection of porous
stone of Medieval town of Rhodes.
•1993-1997, Municipality of Rhodes, Pilot program for the restoration of Medieval town of
Rhodes.
Presentations
EU-CHIC Steering Committee and Advisory Committee Meeting
Internal review meeting , Computational Intelligence Methods & Decision Support Tools in
Cultural Materials, A. Doulamis, A. Kioussi, M. Karoglou,K. Lakiotaki, E.Delegou, N.
Matsatsinis , A.Moropoulou, Feb.2012, Athens, Greece.
Upcoming conferences
E. Ksinopoulou, A. Bakolas, A. Moropoulou, "Particle modified consolidants in the
consolidation of porous stones" , In 12 Int. Con. on the deterioration and conservation of
stone, NY, Oct.2012
A. Doulamis, A. Kiousi, M. Karoglou, A. Moropoulou, Semi-supervised learning approaches
for optimally selecting consolidation materials, In Int. Conf. On Cultural Heritage- Euromed,
Cyprus 29Oct-3Nov. 2012.