{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T16:25:58Z","timestamp":1762359958947,"version":"build-2065373602"},"reference-count":30,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T00:00:00Z","timestamp":1762300800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Future Internet"],"abstract":"<jats:p>Cultural heritage conservation faces escalating challenges from environmental threats and resource constraints, necessitating innovative preservation strategies that balance predictive accuracy with interpretability. This study presents a hybrid neuro-fuzzy framework addressing critical gaps in heritage conservation practice through sequential integration of feedforward neural networks (FF-NNs) and Mamdani-type fuzzy inference systems (MFISs). The system processes multi-sensor data (temperature, vibration, pressure) through a two-stage architecture: an FF-NN for pattern recognition and an MFIS for interpretable decision-making. Evaluation on 1000 synthetic heritage building monitoring samples (70% training, 30% testing) demonstrates mean accuracy of 94.3% (\u00b10.62%), precision of 92.3% (\u00b10.78%), and recall of 90.3% (\u00b10.70%) across five independent runs. Feature importance analysis reveals temperature as the dominant fault detection driver (60.6% variance contribution), followed by pressure (36.7%), while vibration contributes negatively (\u22122.8%). The hybrid architecture overcomes the accuracy\u2013interpretability trade-off inherent in standalone approaches: while the FF-NN achieves superior fault detection, the MFIS provides transparent maintenance recommendations essential for conservation professional validation. However, comparative analysis reveals that rigid fuzzy rule structures constrain detection capabilities for borderline cases, reducing recall from 96% (standalone FF-NN) to 47% (hybrid system) in fault-dominant scenarios. This limitation highlights the need for adaptive fuzzy integration mechanisms in safety-critical heritage applications.<\/jats:p>","DOI":"10.3390\/fi17110510","type":"journal-article","created":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T16:09:47Z","timestamp":1762358987000},"page":"510","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["AI-Based Proactive Maintenance for Cultural Heritage Conservation: A Hybrid Neuro-Fuzzy Approach"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7583-725X","authenticated-orcid":false,"given":"Otilia Elena","family":"Dragomir","sequence":"first","affiliation":[{"name":"Automation, Computer Science and Electrical Engineering Department, Valahia University of T\u00e2rgovi\u0219te, 13 Aleea Sinaia Street, 130004 Targoviste, Romania"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9245-5605","authenticated-orcid":false,"given":"Florin","family":"Dragomir","sequence":"additional","affiliation":[{"name":"Automation, Computer Science and Electrical Engineering Department, Valahia University of T\u00e2rgovi\u0219te, 13 Aleea Sinaia Street, 130004 Targoviste, Romania"}]}],"member":"1968","published-online":{"date-parts":[[2025,11,5]]},"reference":[{"key":"ref_1","unstructured":"UNESCO (2023). Cultural Heritage and Climate Change: A Review of Impacts and Adaptation Measures, UNESCO World Heritage Centre."},{"key":"ref_2","unstructured":"ICOMOS (2022). The Future of Our Pasts: Engaging Cultural Heritage in Climate Action, International Council on Monuments and Sites."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Dragomir, O.E., and Dragomir, F. (2023). Application of Scheduling Techniques for Load-Shifting in Smart Homes with Renewable-Energy-Sources Integration. Buildings, 13.","DOI":"10.3390\/buildings13010134"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"338","DOI":"10.1016\/S0019-9958(65)90241-X","article-title":"Fuzzy sets","volume":"8","author":"Zadeh","year":"1965","journal-title":"Inf. Control."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2787","DOI":"10.1016\/j.comnet.2010.05.010","article-title":"The Internet of Things: A survey","volume":"54","author":"Atzori","year":"2010","journal-title":"Comput. Netw."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Laohaviraphap, N., and Waroonkun, T. (2024). Integrating Artificial Intelligence and the Internet of Things in Cultural Heritage Preservation: A Systematic Review of Risk Management and Environmental Monitoring Strategies. Buildings, 14.","DOI":"10.3390\/buildings14123979"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Doebling, S.W., Farrar, C.R., Prime, M.B., and Shevitz, D.W. (1996). Damage Identification and Health Monitoring of Structural and Mechanical Systems from Changes in Their Vibration Characteristics: A Literature Review, Los Alamos National Lab.","DOI":"10.2172\/249299"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1038\/s42256-019-0048-x","article-title":"Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead","volume":"1","author":"Rudin","year":"2019","journal-title":"Nat. Mach. Intell."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Ribeiro, M.T., Singh, S., and Guestrin, C. (2016, January 13\u201317). \u201cWhy Should I Trust You?\u201d Explaining the Predictions of Any Classifier. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939778"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1145\/3233231","article-title":"The mythos of model interpretability","volume":"61","author":"Lipton","year":"2018","journal-title":"Commun. ACM"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Pacifico, M.G., Marchiano, G., De Medici, S., and Novellino, A. (2024, January 25\u201328). Application of dynamic and AI approaches for predictive maintenance. Proceedings of the 2024 9th International Conference on Smart and Sustainable Technologies (SpliTech), Split, Croatia.","DOI":"10.23919\/SpliTech61897.2024.10612544"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Casillo, M., Colace, F., Gupta, B., Lorusso, A., Marongiu, F., and Santaniello, D. (2022, January 20\u201324). A Deep Learning Approach to Protecting Cultural Heritage Buildings Through IoT-Based Systems. Proceedings of the International Conference Smart Computing and Communications, Helsinki, Finland.","DOI":"10.1109\/SMARTCOMP55677.2022.00063"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"3815","DOI":"10.1007\/s12065-024-00959-y","article-title":"Revolutionizing cultural heritage preservation: An innovative IoT-based framework for protecting historical buildings","volume":"17","author":"Casillo","year":"2024","journal-title":"Evol. Intell."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Lucchi, E. (2025). Digital twins, artificial intelligence and immersive technologies for heritage preservation and cultural tourism in smart cities. Digital Twin, Blockchain, and Sensor Networks in the Healthy and Mobile City, Springer.","DOI":"10.1016\/B978-0-443-34174-8.00026-0"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"543","DOI":"10.1080\/19648189.2012.676365","article-title":"Application of digital techniques in monument preservation","volume":"16","author":"Brunetaud","year":"2012","journal-title":"Eur. J. Environ. Civ. Eng."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"566","DOI":"10.1016\/j.future.2017.06.030","article-title":"An energy-efficient internet of things (IoT) architecture for preventive conservation of cultural heritage","volume":"81","author":"Perles","year":"2018","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"109295","DOI":"10.1016\/j.jobe.2024.109295","article-title":"Analysis of structural integrity through the combination of non-destructive testing techniques in heritage inspections: The study case of San Segundo's Hermitage (\u00c1vila, Spain)","volume":"89","author":"Solla","year":"2024","journal-title":"J. Build. Eng."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Zhang, Q., Wu, Y.N., and Zhu, S.C. (2018, January 18\u201323). Interpretable convolutional neural networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00920"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3631130","article-title":"Deep learning for identifying Iran\u2019s cultural heritage buildings in need of conservation using image classification and grad-cam","volume":"17","author":"Bahrami","year":"2024","journal-title":"ACM J. Comput. Cult. Herit."},{"key":"ref_21","first-page":"225","article-title":"Cultural heritage reuse applying fuzzy expert knowledge and machine learning: Venice's fortresses case study","volume":"12","author":"Camatti","year":"2025","journal-title":"Reg. Stud. Reg. Sci."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"139","DOI":"10.5194\/isprs-archives-XLVIII-4-W10-2024-139-2024","article-title":"Bridging past and present: Cutting-edge technologies for predictive conservation of built cultural heritage","volume":"48","author":"Montuori","year":"2024","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1108\/IJBPA-03-2022-0040","article-title":"Remote sensing to assess the risk for cultural heritage: Forecasting potential collapses due to rainfall in historic fortifications","volume":"42","author":"Moreno","year":"2024","journal-title":"Int. J. Build. Pathol. Adapt."},{"key":"ref_24","unstructured":"ASHRAE (2020). ASHRAE Handbook\u2014HVAC Applications: Chapter 24\u2014Museums, Galleries, Archives, and Libraries, American Society of Heating, Refrigerating and Air-Conditioning Engineers."},{"key":"ref_25","unstructured":"(2016). Mechanical Vibration\u2014Measurement and Evaluation of Machine Vibration\u2014Part 1: General Guidelines (Standard No. ISO 20816-1:2016)."},{"key":"ref_26","unstructured":"(2015). Information and Documentation\u2014Document Storage Requirements for Archive and Library Materials (Standard No. ISO 11799:2015)."},{"key":"ref_27","first-page":"45","article-title":"Machine Learning Advances in Technology Applications: Cultural Heritage Tourism Trends in Experience Design","volume":"16","author":"Deng","year":"2025","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"ref_28","unstructured":"Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep Learning, MIT Press. Chapter 11: Practical Methodology."},{"key":"ref_29","unstructured":"Glorot, X., and Bengio, Y. (2010, January 13\u201315). Understanding the difficulty of training deep feedforward neural networks. Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS), Sardinia, Italy."},{"key":"ref_30","first-page":"271","article-title":"Adaptive Neuro-Fuzzy inference system for mid term prognostic error stabilization","volume":"3","author":"Dragomir","year":"2008","journal-title":"Int. J. Comput. Commun. Control."}],"container-title":["Future Internet"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-5903\/17\/11\/510\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T16:21:09Z","timestamp":1762359669000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-5903\/17\/11\/510"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,5]]},"references-count":30,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2025,11]]}},"alternative-id":["fi17110510"],"URL":"https:\/\/doi.org\/10.3390\/fi17110510","relation":{},"ISSN":["1999-5903"],"issn-type":[{"value":"1999-5903","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,5]]}}}