Computational Intelligence Methods & Decision Support Tools in Cultural Materials

1. Computational Intelligence Methods & Decision Support Tools in Cultural Materials Anastasios Doulamis, Anastasia Kioussi, Maria Karoglou, Klio Lakiotaki, Ekaterini Delegou, Nikolaos Matsatsinis and Antonia Moropoulou National Technical University of Athens

2. Computational Intelligence & Decision Support Tools • Assist experts to take solid decisions • Reject non-preferable solutions • Reduces the costs • Identify hidden knowledge • Image processing/analysis, computer vision • Improve validation performances • Results on optimal consolidation of cultural material

3. Outlines

4. Optimal Consolidation of Cultural Heritage Material • Cultural heritage protection => targeted restoration actions to increase monuments’ lifetime. • conservation materials • The performance of each material on the restoration significantly differs with respect to its type, chemical properties and the building substrate. • Design phase: A decision support system which will assist the engineer to extract optimal conclusions. • Today, such section is expert-dependent process mainly exploiting her/his experience.

5. Computational Intelligence inCultural Material Consolidation • We have applied different types of intelligent tools for optimal selecting the most suitable conservation materials

6. AI & DSS for Conservation Interventions • Applications to two Cases of Conservation Interventions • Consolidation of Materials/Structures • Cleaning of architectural surfaces

7. Consolidation interventions • How to support the decision making in choosing the most appropriate consolidation material • The consolidation materials and interventions used intend to the : • Modification of micro structural characteristics of the stone, leading to lessening of stone susceptibility to salt decay • Prevention of decay due to grains de-cohesion • Amelioration of mechanical characteristics of the stone • Main categories of consolidation products are: • Inorganic Materials • Nano-limes • Organic Materials • Alkoxysilanes

8. DDS OUTPUT Validation in Lab and in the Monument Scale

10. Criteria Adopted

11. Experiments Set-Up • Two scenarios for different application substrate: • The ranking is primarily based on chemical composition of stones • 1st Scenario: Calcareous Stone (35 samples) • 2nd Scenario: Silicon-based Stone (34 samples) • In future, more parameters will be included like micro-structural characteristics of material, mechanical properties etc,

12. A Feed-Forward Neural Network

13. Neural Networks Set-Up

14. Results- Generic Results in Training Set Results in Test Set

15. Effect of Network Size-Generic Effect of Neural Size

16. Results- Scenario 1 test_output Results in Test Set

17. Combined Fuzzy K-means &Neural Networks

18. Results –Scenarios 1,2

19. The UTA* algorithm AR g1 g2 g3 g4 Reference set 3 1 Criteria weights 2 4 Post-optimality analysis

20. Results-Scenario 1

21. Results-Scenario 2

22. 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)

23. Evaluation of the Compatibility of Conservation Interventions in lab Changes of water absorption curves (capillary) of consolidated porous stones and monitoring by infrared thermography in the laboratory

24. 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)

25. 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

26. Computational Intelligence on Cleaning Interventions Assessment • We have applied the aforementioned methodology for supporting the decision making on the assessment of pilot cleaning interventions on marbles surfaces • The application sites are located on the historic buildings of National Library of Greece (NLG), and National Archaeological Museum in Athens-Greece (NAM) • The diagnosed decay patterns are black grey crusts, washed out surfaces, and fractured surfaces of marble.

27. Presentation of Applications Sites Smooth Marble Architectural Surface NLG facade Different protection degree from rain wash North Facade

28. Presentation of Applications Sites Relief Marble Architectural Surface East Side, Full Protected from rain Wash NAM West Façade, North Column Relief Marble Architectural Surface Column Capital East Side, Full Protected from rain Wash Relief Marble Architectural Surface North Side, Different Protection Degree from Rain Wash North Column East Side

29. Depending on the protection degree from the projected horizontal geison FOM SEM FTIR Friable black-grey crust Cohesive black-grey crust Inter-granular fissured marble Decay Diagnosis - NLG

30. FOM SEM Black-grey crust East orientation FTIR Washed out surfaces FOM SEM North orientation FTIR Decay Diagnosis - NAM

31. Black-grey crust of great variety regarding: the width, the presence or not of Barite (patina), the location in the anthemia relief, relief side FOM FOM SEM East orientation Relief face right part FTIR Relief side,central part Relief face central part FOM FOM SEM SEM FTIR FTIR Decay Diagnosis - NAM

32. Pab22, P. ΑΒ57, 2h Pnc22, P. (NH4)2CO3, 2h Pm22, P. Mora, 2h Ps32 P. Sepiolite, 3.5h Pat2, Atomized water Pm24, P. Mora, 1.5h Ps34 P. Sepiolite, 3h Pab24, P. ΑΒ57, 1,5h Pnc24, P. (NH4)2CO3, 1.5h In situ application of pilot cleaning interventions – Monument scale - NLG Smooth marble architectural surfaces, black-grey crust, inter-granular fissured marble

33. Pnc12, P. (NH4)2CO3, 1h Pab12, P. ΑΒ57, 1h Ped12, Π. EDTA, 1h Pab14, P. ΑΒ57, 1h Ped14, P. EDTA, 1h Pnc14, P. (NH4)2CO3, 1h In situ application of pilot cleaning interventions – Monument scale - NLG Smooth marble architectural surfaces, black-grey crust, inter-granular fissured marble

34. Ke Ke Ke2a Ke3b Ke1c KeG3 Ke In situ application of pilot cleaning interventions – Monument scale - NAM Relief marble architectural surfaces, black-grey crust, east orientation Ion exchange resin with solution of (NH4)2CO3, 40min Ion exchange resin with deionised water, 30min Biological poultice Π. ΑΒ57, 5min • Wet micro-blasting method • Spherical particles of CaCO3 d<80μm, • Function pressure 0.5bar, • Proportion ofCaCO3/water: 1/3, • dnozzle 12mm • working distance 50cm

35. Kn P. ΑΒ57, 5min Kn3c Ion exchange resin with deionized water, 40min Ion exchange resin with deionized water, 10min Kn3b Kn2a Ion exchange resin with solution of (NH4)2CO3, 20min Kn1a Ion exchange resin with solution of(NH4)2CO3, 10min Kn1b Ion exchange resin with deionized water, 20min Kn2b Double application of Ion exchange resin with deionized water, 2x20min Double application of Ion exchange resin with solution of(NH4)2CO3, 2x10min Kn2c Kn1c Kn2d Wet micro-blasting method In situ application of pilot cleaning interventions – Monument scale - NAM Relief marble architectural surfaces, washed out surfaces, north orientation

36. Ion exchange resin with solution of (NH4)2CO3, 10min Ion exchange resin with solution of (NH4)2CO3, 40min Ion exchange resin with deionized water, 60min Ion exchange resin with deionized water, 10min Ion exchange resin with deionized water, 20min Kke Wet micro-blasting method Ion exchange resin with solution of (NH4)2CO3, 20min Ion exchange resin with deionized water, 30min P. ΑΒ57, 5+ 15 min Kke2a Kke3a1 Kke1a Kke3a2 Kke2b Kke3b kkeg51 Kke1b kkee3ba In situ application of pilot cleaning interventions – Monument scale - NAM Relief marble architectural surfaces of capital, east orientation

37. SEM-EDS: chemical & mineralogical composition Digital Image Processing of SEM images: fracturing of the surface Shape factor (a roughness factor) stratification, total crust width, patina, macro-crystalline gypsum layer width, micro-crystalline gypsum layer width Fracture Density Patina preservation index Friability index Preservation index of gypsum layer Assessment of Cleaning Interventions: Techniques & Parameters Applying, after cleaning the same experimental techniques that were applied before cleaning, a methodological approach for cleaning assessment is compiled. Comparison of the marble surfaces physico-chemical characteristics before & after cleaning, along with recording the variations of the corresponding critical parameters, makes feasible the recommendation of the best cleaning according to the examined case.

38. Laser Profilometry: texture & roughness assessment Roughness Rq (μm) Surface area, (ratio of actual to projected area) Colorimetry CIELab color space: evaluation of color modifications L, Luminosity total colour difference ΔE difference in red-green a* difference in blue-yellow b* Assessment of Cleaning Interventions: Techniques & Parameters

39. Assessment Criteria & Critical Assessment Parameters of Cleaning Interventions – Experimental Techniques Cleaning Assessment Criteria Chemical-mineralogical composition of the surfaces-stratification Color Texture, Morphology & Surface Cohesion - Surface Microstructure Surface Preservation State – Decay Susceptibility – Durability Preservation of Patina, Preservation of Authentic Material, Roughness, Rq, Ratio of actual to projected area - Surface area Total Color Difference, ΔΕ Removal of Black Depositions Fracture Density Scanning Electron Microscopy with Energy Dispersive X-ray Spectroscopy Colorimetry Digital Image Analysis of Scanning Electron Microscopy Images Colorimetry Laser Profilometry Experimental Techniques for Measuring Critical Assessment Parameters of Cleaning Critical Assessment Parameters of Cleaning

40. Monitoring of Surface Preservation State – Durability of Marble • Decay patterns distribution on the building are mainly controlled by material location, orientation, protection from rain-wash, atmospheric conditions and pollution. • However, the long-term aesthetical and structural properties of marble are closely related to the lateral and vertical distribution of particulate matter and salts-gypsum, as well as to the bonding of the calcite grains in the matrix; factors that are strongly affected by cleaning. Therefore there is an urgent need for a tool to interrelate information – data, between space and physical-chemical characteristics of building materials-marble, taking into account their variation over time. The suggested methodological tool is a GIS platform

41. Materials Mapping in GIS, Façade, National Archaeological Museum Working scale: building’s facade • materials mapping performed using GIS based on the acquired data by NDT and in-lab analytical techniques. • The area extend of each investigated material was calculated by the means of GIS • Historic plaster area was 248.56m2, whereas new plaster area was 13.63m2 Acropolis of Athens

42. Materials Mapping in GIS, Façade detail, National Archaeological Museum Working scale: building’s facade • Digital decay mapping performed using GIS based on the acquired data by NDT and in-lab analytical techniques. • Brown color depicts areas of coating total detachment and intense fracturing total area on west façade: 25.56m2 • Blue color represents the areas of coating loose interface to the substrate total area on west façade: 219.72m2 Acropolis of Athens

43. Façade detail - National Archaeological Museum • material type • applied cleaning method • application details • application area • cost Acropolis of Athens Working scale: building’s facade

44. GIS thematic maps for decay & pilot conservation interventions Recording & ascribing attributes to features Attribute db, (physical-chemical data, indexes of building material preservation state, before and after conservation) GIS db, (topological characteristics like area, perimeter, adjacency, etc) Relational Data Base

45. Decay thematic map for the capital surface, along with RDBs for both front & side anthemia surfaces Spatial Classification of Decay. Different Physical-chemical characteristics & spatial properties RDB RDB

46. Pilot conservation interventions’ thematic map for the capital surface, along with the RDB of the front anthemia surface Spatial Classification of Conservation Interventions RDB

47. GIS analysis using Boolean and logical operations on decay thematic map for the capital surface Spatial entity in compliance with the combined expression central area of the anthemia relief • roughness ≥ 7, • fracture density ≥35.3 Which is the entity that comply with: RDB

48. ANALYSIS & OPERATIONAL TOOLS RELATIONAL DATABASE (ATTRIBUTES) GIS SPATIAL DATA Suggested Information Management System Using the continuous process of GIS platform datasets concerning building pathology & conservation interventions are recorded, correlated, distributed & attributed to space in different working scales during different time periods Support on decision making for cleaning assessment using Computational Intelligence

49. Results-Crust Combined Results K-means, Neural Results in k-Means

50. Results-Washed-out Results in k-Means Combined Results K-means, Neural