This paper describes a new predictive model for preventive conservation of buildings. It allows for multiscenarios  of several hazards, assessments of environmental risks, and the use level of buildings together with cultural val-ues of  monuments.  This  modeling  approach  is  based  on  fuzzy  logic  and  geographic  information  system  available  to  organizations  dedicated  to  the  restoration and rehabilitation in Spain. This system has a transversal development that includes  urban,  architectural,  cultural  heritage  value, and  the  analysis  of  environmental  and  sociodemographic  situations around  the  monuments.  This  new  tool allows for decision making based on scientific criteria and minimizes risklosses of cultural asset

Cagigas Muñiz, Daniel

Dobran, Flavio (Coordinador)

Formisano, A. (Coordinador)

Indirli, M. (Coordinador)

Macías Bernal, Juan Manuel

Martín, J. M.

Ortiz, P.

Ortiz, R.

Paolella, G. (Coordinador)

Prieto, A.J.

Romero, A.

English

Prieto Ibáñez, Andrés José

idUS. Depósito de Investigación Universidad de Sevilla

Resilience and Sustainability of Cities in Hazardous EnvironmentsGVES, Napoli – New York, ISBN 978-88-903183-1-3A New Tool Based on Artificial Intelligence and GIS for Preventive Conservation of Heritage BuildingsD. Cagigas-Muñiz1, J.M. Martín2, A.J. Prieto3, A. Romero1, R. Ortiz2J. M Macías-Bernal4, P. Ortiz2,*1 Department of Computer Architecture and Technology, ETSIUniversity of Seville, Seville, Spain2Department of Physical, Chemical and Natural SystemsPablo de Olavide University, Seville, Spain3Institute of Architecture and Urbanism, Austral University of Chile, Valdivia, Chile4Department of Architectural Construction II, ETSI, University of Seville, Seville, SpainAbstract. This paper describes a new predictive model for preventive conserva-tion of buildings. It allows for multi-scenarios of several hazards, assessmentsof environmental risks, and the use level of buildings together with cultural val-ues of monuments. This modeling approach is based on fuzzy logic and geo-graphic information system available to organizations dedicated to the restora-tion and rehabilitation in Spain. This system has a transversal development that includes urban, architectural, cultural heritage value, and the analysis of envi-ronmental and socio-demographic situations around the monuments. This new tool allows for decision-making based on scientific criteria and minimizes risklosses of cultural assets.Keywords: Hazard, vulnerability, preventive conservation, artificial intelli-gence, GIS, heritage building.1. IntroductionA monument is more than just the construction itself [1]; it is a part of the local identi-ty and a source of memory of historical events [2]. National governments and Euro-pean institutions increasingly recognise the importance of the conservation of cultural assets. The service life prediction of the materials and components of the built herit-age should achieve their maximum permanence in order to avoid possible failures of  buildings and future extremely costly interventions [3]. Fuzzy logic, introduced byZadeh in 1965 [4], has been successfully applied in the construction area and this kindof models is especially interesting when the problems modelled are subject to uncer-*Corresponding author: mportcal@upo.es326Resilience and Sustainability of Cities in Hazardous EnvironmentsGVES, Napoli – New York, ISBN 978-88-903183-1-3tainties. In this sense, fuzzy models present some advantages: (i) ability to model naturally systems that other models find vague and difficult to describe, (ii) ability to tolerate accurate and inaccurate data, (iii) ability for incorporating input informationbased on human observations, and (iv) ability to include the expertise of professionals [5].The Geographical Information System (GIS) is a tool widely used in hazard evalu-ations [6], in assessments of flood risks when integrated with satellite remote sensing systems [7], and in combination with chemical techniques to study hazards from pol-lutants [8]. This paper presents a new tool based on Artificial Intelligence (AI) and includes GIS for environmental variables (GIS maps) that can be easily supplied by stakeholders and focused on the preventive conservation of heritage buildings. These methodologies are able to deal with the uncertainty associated with the performance loss of heritage buildings. 2. MethodologyThe Art-Risk 3 model is a tool for the preventive conservation of cultural heritage that is based on a quantitative method able to deal with the uncertainty associated with the building’s degradation process. Art-Risk 3 was developed by Art-Risk project funded by Spanish National Ministry and European Union. This modeling system is support-ed by 21 input parameters, nine involving vulnerability [9], one related to the build-ing’s maintenance, five static-structural risk parameters [10], four environmental riskparameters, and two naturals risk parameters (Table 1). These variables are involved in the functionality degradation of heritage buildings, their external risks and theirown vulnerabilities, and provide a sequenced classification of priority actions for the conservation of the homogeneous architectural heritage.Several parameters of the model pertain to the location of buildings, such as AR1 (geological location), AR16 (average rainfall), AR17 (rain intensity), AR18 (thermal stress), and AR19 (freeze danger). The ArcGIS ArcMap software from ESRI was employed to build GIS Map, where each hazard was determined utilizing a relative scale based on five values (Table 1). The remaining parameters are introduced manu-ally by stakeholders (professional expert, users, practitioners, etc.). A total of 17 pro-fessionals with expertise from Spain, Portugal and Chile worked on the design of the model.In general terms, the fuzzy expert systems are structured in four stages: “fuzzifica-tion”, in which input values, subject to certain imprecision and subjectivity, are repre-sented by fuzzy sets; knowledge base; “inference” stage, in which fuzzy rules are defined such as modus ponens propositional inference rules (IF “fuzzy proposal” AND “fuzzy proposal” THEN “fuzzy proposal”); and “defuzzification”, which is used to generate specific output values. The core of a fuzzy system is the knowledge basecomprised of two components: The database and the rule base. This step is the princi-pal part of a fuzzy expert system that combines the facts derived from the fuzzifica-tion process with the rule base generated previously and carried out in the modelling process [10]. Finally, the defuzzification stage is used to obtain a (crisp) value repre-senting the fuzzy information produced by the inference. Based on this output modelit is possible to obtain a priority ranking of maintenance activities related to the func-327Resilience and Sustainability of Cities in Hazardous EnvironmentsGVES, Napoli – New York, ISBN 978-88-903183-1-3tionality, risks, and vulnerabilities of a set of built heritage with homogeneous charac-teristics. Table 1. Input variables of the Art-Risk 3 model.ID Variables CategoriesQuantitative valuation (very low/medium/very high)Descriptive valuation of the input parametersAR1 Geological locationVulnerability1.0/3.0/5.0Very favourable/acceptable/very unfavourable ground conditionsAR2 Built context 1.0/3.0/5.0Buildings without or between complex constructions around it.AR3 Constructive system 1.0/3.0/5.0Uniform or heterogeneous charac-teristics of constructive system.AR9 Roof design 1.0/3.0/5.0Fast/normal/complex and slow evacuation of water.AR10 Conservation 1.0/3.0/5.0Optimal/normal/neglected state of conservationAR4 Population growthAnthropic Vulnerability1.0/3.0/5.0Population growth greater than 15%/0%/less than 5%.AR5 Heritage value 1.0/3.0/5.0 Great/average/low historical valueAR6 Furniture value 1.0/3.0/5.0 High/average/low furniture valueAR7 Occupancy 1.0/3.0/5.0High/media/low activity in the buildingAR8 Maintenance Maintenance 1.0/3.0/5.0Good/average/bad building's maintenanceAR11 VentilationStatic-Structural risks1.0/3.0/5.0Natural cross-ventilation in all or only in some areas.AR12 Facilities 1.0/3.0/5.0All/some facilities are in use or they are not ready to be used.AR13 Overloads 1.0/3.0/5.0Live load below/equal/higher than the original level.AR14 Risk of fire1.0/3.0/5.0Low/medium/high fire load in relation with combustible struc-ture.AR15 Structural modification 1.0/3.0/5.0Apparently/symmetric and bal-anced/disorderly modification.AR16 Average RainfallEnvironmental risks1.0/3.0/5.0< 600 mmm/600 mm < 1000 mm/> 1000 mmAR17 Raindrop impact(Torrenciality index) 1.0/3.0/5.0 < 7/8 < 9/> 10AR18 Thermal stress(Thermal amplitude) 1.0/3.0/5.0 < 6/6<10/<10AR19 Frozen damage(days below 0) 1.0/3.0/5.0< 1 day/5 days < 20 days/> 60 daysAR20 Seismic risk(acceleration) Natural risks 1.0/3.0/5.0 < 0.04 g/0.08 g < 0.12 g/>0.16 gAR21 Flooding(return period) 1.0/3.0/5.0 Never/100 years/10 years328Resilience and Sustainability of Cities in Hazardous EnvironmentsGVES, Napoli – New York, ISBN 978-88-903183-1-33. Requirements, Design, and System ArchitectureThe Art-Risk tool has three important design requirements. It must be usable, accessi-ble, and multiplatform. The usability is important because it is intended to be man-aged by the end users and is therefore required to conform to ISO 9241-11 standard which defines usability as “the extent to which a product can be used by specific users to achieve specific goals with effectiveness, efficiency and satisfaction in a specific context of use” [11]. As such, a user group evaluates the usability of the tool by per-forming several tasks and measures whether these tasks can be completed or not (ef-fectiveness) on time in an efficient manner. Satisfaction for each task is measured using the Single Ease Question (SEQ) questionnaire. There is also a final question-naire to test the overall satisfaction level using the System Usability Scale (SUS) standard questionnaire.Accessibility is also a mandatory requirement according to the EU Directive 2016/2102 of the European Parliament on the accessibility of the websites and mobile applications of public sector bodies [12]. In this case the TAW online service is used. TAW is a mature and online tool with technical reference Web Accessibility Guide-lines (WCAG 2.0) of W3C. It allows checking the accessibility of a certain URL. It generates a summary report based on the analyzed page with information about the result of the review [13]. Art-Risk is based on the standard web technology as is de-scribed below. The multiplatform characteristic is very relevant in Art-Risk. The tool can be used not only in desktop computers, but also in mobile devices. This facilitatesthe fieldwork and it speeds up large building evaluations and comparisons. Therefore, the user interface (front-end) of the Art-Risk tool must be responsive. Bootstrap and HTML5 technology are used to implement the user interface part.Art-Risk architecture can be divided into two parts: A user interface or front-end and an AI engine or back-end. The Xfuzzy 3.3 tool models the Art-Risk AI engine. Xfuzzy 3.3 has a final synthesis stage that is divided into tools generating several high-level languages descriptions for software or hardware implementations. Its aim is to generate a system representation that could be used externally [14]. Art-Risk uses C language description of Xfuzzy 3.3 to generate a CGI or program (back-end) that is invoked by the user interface (front-end). On the home page of Art-Risk [15] there are available 2.0 and 3.0 versions of this tool. The 2.0 version is based on a previous AI model, whereas the 3.0 version is an ongoing prototype version that adds some automatic input variables based on GIS maps that can provide some characteristics related to building’s thermal stress, rain-fall, or geothechnics. Here the automatic means that the users need only insert the building coordinates and the system will provide values for those variables associated with the stored GIS maps.4. ConclusionsThe prototype method described in this paper aims at evaluating the functional degra-dation conditions of heritage constructions. This methodology considers the conse-329Resilience and Sustainability of Cities in Hazardous EnvironmentsGVES, Napoli – New York, ISBN 978-88-903183-1-3quences of natural, environmental, static-structural, and intrinsic and anthropogenic vulnerability conditions on the functional service life of cultural heritage (given by risks, vulnerability, and functional service life indices - Art-Risk 3).The Art-Risk 3 methodology should be very useful in the management and organi-zation of preventive maintenance-oriented activities of buildings. This approach can provide some guidance regarding the risks, vulnerabilities, and performance of build-ings that should be carefully analysed in order to minimize the degradation of cultural heritage and their risk of failure. The expert system of the model is able to simulatehuman reasoning to study relations between vulnerability and risk factors of buildings through a fuzzy sets theory. Moreover, the utility of this system can increase with the users’ inputs and can be upgraded and improved.The described GIS + fuzzy methodology can be applied to different cultural herit-age buildings, and can be adjusted to diverse environments in Europe and elsewhere.The model should enable building owners, users, public administrations, and private companies to use this open-access software to manage better the conditions of build-ings.References1. UNESCO (1972). Convention concerning the protection of the World cultural and natural heritage. United Nations Educational, Scientific and Cultural Organization, Paris.2. Silva, H.E., Henriques, F.M.A. (2015). Preventive conservation of historic buildings in temperature climates. The importance of a risk-based analysis on the decision- making process. Energy Buildings 107, 26–36.3. Anoop, M.B., Raghuprasad, B.K., Balaji Rao, K. A. (2012).  Refined methodology for du-rability-based service life estimation of reinforced concrete structural elements consideringfuzzy and random uncertainties. Comput. Aided Civil Infrastructure Eng. 27 (3), 170–186.4. Zadeh, L. (1965). Fuzzy sets. Inf Control 8 (3), 338-353.5. Silva, A., de Brito, J., Gaspar, P. L. (2016). Methodologies for service life prediction of buildings. With a focus on façade claddings. Green Energy and Technology, Springer,New York. DOI 978-3-319-33288-8.6. Indirli, M., Razafindrakoto, H., Romanelli, F., Puglisi, C., Lanzoni, L., Milani, E. (2011). Hazard Evaluation in Valparaiso: The MAR VASTO Project. Pure and Applied Geophys-ics 168(3-4), 543-82.7. Tralli, D.M., Blom, R.G., Zlotnicki, V., Donnellan, A., Evans, D.L. (2005). Satellite re-mote sensing of earthquake, volcano, flood, landslide and coastal inundation hazards.ISPRS J. Photogramm. Remote Sens 59, 185–198.8. Ortiz, R., Ortiz, P., Vázquez, M.A., Martín, J.M. (2017). Integration of georeferenced in-formed system and digital image analysis to assess the effect of cars pollution on historical buildings. Construction and Building Materials 1, 1-17. http://dx.doi.org/10.1016/ j.conbuildmat.2017.02.0300950-0618/9. Ortiz, R., Ortiz, P. (2016). Vulnerability index: A new approach for preventive conserva-tion of monuments. International Journal of Architectural Heritage 10 (8), 1078-1100.10. Prieto, A. J., Macías-Bernal, J. M., Chávez, M. J., Alejandre, F. J. (2017). Fuzzy modeling of the functional service life of architectural heritage buildings. Journal of Performance of Constructed Facilities 31, 04017041. DOI 10.1061/ (ASCE)CF.1943-5509.0001021.330Resilience and Sustainability of Cities in Hazardous EnvironmentsGVES, Napoli – New York, ISBN 978-88-903183-1-311. Bevan, N., Carter, J., Harker, S. (2015).  Iso 9241-11 revised: What have we learnt about usability since 1998? In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer.12. EUR-Lex homepage for Directive (EU) 2016/2102. https://eur-lex.europa.eu/eli/dir/2016/2102/oj13. TAW homepage. https://www.tawdis.net/14. Xfuzzy 3.3 homepage. http://www2.imse-cnm.csic.es/Xfuzzy/Xfuzzy_3.3/index.html15. Art-Risk tools homepage, https://www.upo.es/investiga/art-risk-service/art-risk2 and https://www.upo.es/investiga/art-risk-service/art-risk3331

A new tool based on artificial intelligence and GIS for preventive conservation of heritage buildings

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A new tool based on artificial intelligence and GIS for preventive conservation of heritage buildings

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