- -

Improving Cultural Heritage conservation: LSTM neural networks to effectively processing end-user's maintenance requests

RiuNet: Repositorio Institucional de la Universidad Politécnica de Valencia

Compartir/Enviar a

Citas

Estadísticas

  • Estadisticas de Uso

Improving Cultural Heritage conservation: LSTM neural networks to effectively processing end-user's maintenance requests

Mostrar el registro sencillo del ítem

Ficheros en el ítem

Thumbnail
Nombre: DOrazioBernardiniDi ...
Tamaño: 1.896Mb
Formato: PDF
Descripción: Versión editorial
Abrir
dc.contributor.author D'Orazio, Marco es_ES
dc.contributor.author Bernardini, Gabriele es_ES
dc.contributor.author Di Giuseppe, Elisa es_ES
dc.date.accessioned 2023-04-26T06:33:04Z
dc.date.available 2023-04-26T06:33:04Z
dc.date.issued 2023-04-04
dc.identifier.uri http://hdl.handle.net/10251/192955
dc.description.abstract [EN] Preventive conservation of cultural heritage can avoid or minimize future damage, deterioration, loss and consequently, any invasive intervention. Recently, Machine Learning methods were proposed to support preventive conservation and maintenance plans, based on their ability to predict the future state of the built heritage by collected data. Several data sources were used, such as structural data and images depicting the evolution of the deterioration state, but till now textual information, exchanged by people living or working in historical buildings to require maintenance interventions, was not used to support conservation programmes. This work proposes a method to support preventive conservation programs based on the analysis of data collected into CMMS (computer maintenance management software). In a Cultural Heritage building in Italy, hosting a University Campus, data about end-user s maintenance requests collected for 34 months were analysed, and LSTM neural networks were trained to predict the category of each request. Results show a prediction accuracy of 96.6%, thus demonstrating the potentialities of this approach in dynamically adapting the maintenance program to emerging issues. es_ES
dc.language Inglés es_ES
dc.publisher Universitat Politècnica de València es_ES
dc.relation.ispartof VITRUVIO - International Journal of Architectural Technology and Sustainability es_ES
dc.rights Reconocimiento - No comercial (by-nc) es_ES
dc.subject Cultural heritage es_ES
dc.subject Preventive conservation es_ES
dc.subject Maintenance es_ES
dc.subject NLP es_ES
dc.subject Neural Networks es_ES
dc.title Improving Cultural Heritage conservation: LSTM neural networks to effectively processing end-user's maintenance requests es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.4995/vitruvio-ijats.2023.18811
dc.rights.accessRights Abierto es_ES
dc.description.bibliographicCitation D'orazio, M.; Bernardini, G.; Di Giuseppe, E. (2023). Improving Cultural Heritage conservation: LSTM neural networks to effectively processing end-user's maintenance requests. VITRUVIO - International Journal of Architectural Technology and Sustainability. 8:18-29. https://doi.org/10.4995/vitruvio-ijats.2023.18811 es_ES
dc.description.accrualMethod OJS es_ES
dc.relation.publisherversion https://doi.org/10.4995/vitruvio-ijats.2023.18811 es_ES
dc.description.upvformatpinicio 18 es_ES
dc.description.upvformatpfin 29 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 8 es_ES
dc.identifier.eissn 2444-9091
dc.relation.pasarela OJS\18811 es_ES
dc.description.references Baek, S., Jung, W. and Han, S.H. (2021), “A critical review of text-based research in construction: Data source, analysis method, and implications”, Automation in Construction, Elsevier B.V., Vol. 132 No. August, p. 103915. https://doi.org/10.1016/j.autcon.2021.103915 es_ES
dc.description.references Balen, K. Van. (2015), “Preventive Conservation of Historic Buildings”, Restoration of Buildings and Monuments, Vol. 21 No. 2–3, pp. 99–104. https://doi.org/10.1515/rbm-2015-0008 es_ES
dc.description.references Balen, K. van and Vandesande, A. (2018), Innovative Built Heritage Models: Edited Contributions to the International Conference on Innovative Built Heritage Models and Preventive Systems (CHANGES 2017), February 6-8, 2017, Leuven, Belgium, CRC Press, available at: https://books.google.it/books?id=tCxKDwAAQBAJ es_ES
dc.description.references Borgarino, M.P. (2015), “Managing Built Cultural Heritage , from condition assessment to risk assessment from condition assessment to risk assessment”, ISBP2015. es_ES
dc.description.references Bortolini, R. and Forcada, N. (2020), “Analysis of building maintenance requests using a text mining approach: building services evaluation”, Building Research & Information, Vol. 48 No. 2, pp. 207–217. https://doi.org/10.1080/09613218.2019.1609291 es_ES
dc.description.references Burak Gunay, H., Shen, W. and Newsham, G. (2019), “Data analytics to improve building performance: A critical review”, Automation in Construction, Elsevier, Vol. 97, pp. 96–109. https://doi.org/10.1016/j.autcon.2018.10.020 es_ES
dc.description.references Carman, J. (2019), “What sustaining heritage really does”, VITRUVIO - International Journal of Architectural Technology and Sustainability, Vol. 4 No. 1, p. 1. https://doi.org/10.4995/vitruvio-ijats.2019.11772 es_ES
dc.description.references Çınar, Z.M., Abdussalam Nuhu, A., Zeeshan, Q., Korhan, O., Asmael, M. and Safaei, B. (2020), “Machine Learning in Predictive Maintenance towards Sustainable Smart Manufacturing in Industry 4.0”, Sustainability, Vol. 12 No. 19, p. 8211. https://doi.org/10.3390/su12198211 es_ES
dc.description.references D’Orazio, M., Di Giuseppe, E. and Bernardini, G. (2022), “Automatic detection of maintenance requests: Comparison of Human Manual Annotation and Sentiment Analysis techniques”, Automation in Construction, Vol. 134, p. 104068. https://doi.org/10.1016/j.autcon.2021.104068 es_ES
dc.description.references De Gregorio, S. (2019). “The rehabilitation of buildings. Reflections on construction systems for the environmental sustainability of interventions”, VITRUVIO - International Journal of Architectural Technology and Sustainability, 4(2), p. 47. https://doi.org/10.4995/vitruvio-ijats.2019.12634 es_ES
dc.description.references Della Torre, S. (2010). “Preventiva, integrata, programmata: le logiche coevolutive della conservazione”, Pensare la prevenzione. Manufatti, usi, ambienti: atti del XXVI convegno Scienza e Beni culturali (Bressanone 13-16 Luglio 2010), pp. 70–81. es_ES
dc.description.references Du, M., Li, F., Zheng, G. and Srikumar, V. (2017), “DeepLog: Anomaly detection and diagnosis from system logs through deep learning”, Proceedings of the ACM Conference on Computer and Communications Security, pp. 1285–1298. https://doi.org/10.1145/3133956.3134015 es_ES
dc.description.references Dzulkifli, N., Sarbini, N.N., Ibrahim, I.S., Abidin, N.I., Yahaya, F.M. and Nik Azizan, N.Z. (2021), “Review on maintenance issues toward building maintenance management best practices”, Journal of Building Engineering, Elsevier Ltd, Vol. 44 No. July, p. 102985. https://doi.org/10.1016/j.jobe.2021.102985 es_ES
dc.description.references Eken, E., Taşcı, B. and Gustafsson, C. (2019), “An evaluation of decision-making process on maintenance of built cultural heritage: The case of Visby, Sweden”, Cities, Vol. 94 No. June, pp. 24–32. https://doi.org/10.1016/j.cities.2019.05.030 es_ES
dc.description.references El-Dash, K. (2007), “Assessing Human Resource Management in Construction Projects in Kuwait”, Journal of Asian Architecture and Building Engineering, Vol. 6 No. 1, pp. 65–71. https://doi.org/10.3130/jaabe.6.65 es_ES
dc.description.references Ferreira, C., Silva, A., de Brito, J., Dias, I.S. and Flores-Colen, I. (2021), “The impact of imperfect maintenance actions on the degradation of buildings’ envelope components”, Journal of Building Engineering, Elsevier Ltd, Vol. 33 No. June 2020, p. 101571. https://doi.org/10.1016/j.jobe.2020.101571 es_ES
dc.description.references Gonçalves, P., Araújo, M., Benevenuto, F. and Cha, M. (2013), “Comparing and combining sentiment analysis methods”, COSN 2013 - Proceedings of the 2013 Conference on Online Social Networks, pp. 27–37. https://doi.org/10.1145/2512938.2512951 es_ES
dc.description.references Gunay, H.B., Shen, W. and Yang, C. (2019), “Text-mining building maintenance work orders for component fault frequency”, Building Research & Information, Taylor & Francis, Vol. 47 No. 5, pp. 518–533. https://doi.org/10.1080/09613218.2018.1459004 es_ES
dc.description.references Gutjahr, W.J. and Reiter, P. (2010), “Bi-objective project portfolio selection and staff assignment under uncertainty”, Optimization, Taylor & Francis, Vol. 59 No. 3, pp. 417–445. https://doi.org/10.1080/02331931003700699 es_ES
dc.description.references Hong, T., Koo, C., Kim, J., Lee, M. and Jeong, K. (2015), “A review on sustainable construction management strategies for monitoring, diagnosing, and retrofitting the building’s dynamic energy performance: Focused on the operation and maintenance phase”, Applied Energy, Elsevier Ltd, Vol. 155, pp. 671–707. https://doi.org/10.1016/j.apenergy.2015.06.043 es_ES
dc.description.references Kim, Y., Bang, S., Sohn, J. and Kim, H. (2022), “Question answering method for infrastructure damage information retrieval from textual data using bidirectional encoder representations from transformers”, Automation in Construction, Elsevier B.V., Vol. 134 No. October 2021, p. 104061. https://doi.org/10.1016/j.autcon.2021.104061 es_ES
dc.description.references Marocco, M. and Garofolo, I. (2021), “Operational text-mining methods for enhancing building maintenance management”, Building Research and Information. https://doi.org/10.1080/09613218.2021.1953368 es_ES
dc.description.references McArthur, J.J., Shahbazi, N., Fok, R., Raghubar, C., Bortoluzzi, B. and An, A. (2018), “Machine learning and BIM visualization for maintenance issue classification and enhanced data collection”, Advanced Engineering Informatics, Elsevier, Vol. 38 No. October 2017, pp. 101–112. https://doi.org/10.1016/j.aei.2018.06.007 es_ES
dc.description.references Mo, Y., Zhao, D., Du, J., Syal, M., Aziz, A. and Li, H. (2020), “Automated staff assignment for building maintenance using natural language processing”, Automation in Construction, Vol. 113, p. 103150. https://doi.org/10.1016/j.autcon.2020.103150 es_ES
dc.description.references Pishdad-Bozorgi, P., Gao, X., Eastman, C. and Self, A.P. (2018), “Planning and developing facility management-enabled building information model (FM-enabled BIM)”, Automation in Construction, Elsevier, Vol. 87 No. October 2017, pp. 22–38. https://doi.org/10.1016/j.autcon.2017.12.004 es_ES
dc.description.references Ribeiro, F.N., Araújo, M., Gonçalves, P., André Gonçalves, M. and Benevenuto, F. (2016), “SentiBench - a benchmark comparison of state-of-the-practice sentiment analysis methods”, EPJ Data Science, Ribeiro et al., Vol. 5 No. 1, pp. 1–29. https://doi.org/10.1140/epjds/s13688-016-0085-1 es_ES
dc.description.references Sheu, S.H., Chang, C.C., Chen, Y.L. and George Zhang, Z. (2015), “Optimal preventive maintenance and repair policies for multi-state systems”, Reliability Engineering and System Safety, Vol. 140, pp. 78–87. https://doi.org/10.1016/j.ress.2015.03.029 es_ES
dc.description.references Sourav Das Adhikari, Steve Hoffman and Britta Lietke. (2019), Six Emerging Trends in Facilities Management Sourcing. McKinsey & Company. es_ES
dc.description.references Villafranca Jiménez, M.M. and Gutiérrez-Carrillo, M.L. (2019), “The Alhambra master plan (2007-2020) as a strategic model of preventive conservation of cultural heritage”, VITRUVIO - International Journal of Architectural Technology and Sustainability, Vol. 4 No. 2, p. 59. https://doi.org/10.4995/vitruvio-ijats.2019.12642 es_ES
dc.description.references Wu, M.C. and Sun, S.H. (2006), “A project scheduling and staff assignment model considering learning effect”, International Journal of Advanced Manufacturing Technology, Vol. 28 No. 11–12, pp. 1190–1195. https://doi.org/10.1007/s00170-004-2465-0 es_ES
dc.description.references Yan, H., Yang, N., Peng, Y. and Ren, Y. (2020), “Data mining in the construction industry: Present status, opportunities, and future trends”, Automation in Construction, Elsevier, Vol. 119 No. May, p. 103331. https://doi.org/10.1016/j.autcon.2020.103331 es_ES
dc.description.references Yang, C., Shen, W., Chen, Q. and Gunay, B. (2018), “A practical solution for HVAC prognostics: Failure mode and effects analysis in building maintenance”, Journal of Building Engineering, Vol. 15 No. October 2017, pp. 26–32. https://doi.org/10.1016/j.jobe.2017.10.013 es_ES
dc.description.references Žižka, J., Dařena, F. and Svoboda, A. (2019), Text Mining with Machine Learning, Text Mining with Machine Learning, available at: https://doi.org/10.1201/9780429469275 es_ES


Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro sencillo del ítem