Publication Cover
International Journal of Architectural Heritage
Conservation, Analysis, and Restoration
Volume 18, 2024 - Issue 5
1,380
Views
2
CrossRef citations to date
0
Altmetric
Research Article

Digital Toolkit to Assist the Interpretation of Traditional Masonry Construction

ORCID Icon, ORCID Icon, ORCID Icon, &
Pages 725-739 | Received 27 Jun 2022, Accepted 14 Feb 2023, Published online: 06 Mar 2023

References

  • Acas, D. (2021). Automated segmentation of 3D point clouds of masonry walls and its applications to enhance building surveying, maintenance and repair. BEng Thesis. The University of Edinburgh, 2021.
  • Antón, D., B. Medjdoub, R. Shrahily, and J. Moyano. 2018. Accuracy evaluation of the semi-automatic 3D modeling for historical building information models. International Journal of Architectural Heritage 12 (5):790–805. doi:10.1080/15583058.2017.1415391.
  • Asteris, P. G., V. Sarhosis, A. Mohebkhah, V. Plevris, L. Papaloizou, P. Komodromos, and J. V. Lemos. 2015. Numerical modeling of historic masonry structures. In Handbook of research on seismic assessment and rehabilitation of historic structures, 213–56. Hershey, Pennsylvania, USA: IGI Global. doi:10.4018/978-1-4666-8286-3.
  • Beckman, P. 1995. Structural aspects of building conservation. London: McGraw-Hill book company.
  • Bell, D. 1997. Technical advice note 8: The historic Scotland guide to international charters. Edinburgh: HMSO.
  • Bosche, F. N., A. M. Forster, and E. Valero (2022) Digital documentation, computer vision and machine learning for masonry surveying & maintenance – Technical Paper No. 38- Historic Environment Scotland (HES)
  • Browning, H. C. 1996. The principles of architectural drafting: A sourcebook of techniques and graphic standards. New York: Watson-Guptill Publications.
  • Bruno, N., and R. Roncella. 2019. HBIM for Conservation: A New Proposal for Information Modeling. Remote Sensing 11 (15):1751. doi:10.3390/rs11151751.
  • Brunskill, R. W. 1978. Illustrated handbook of vernacular architecture. London: Faber & Faber.
  • Bryan, P., B. Blake, J. Bedford, D. Barber, and J. Mills. 2013. Metric survey specifications for cultural heritage. Swindon: English Heritage.
  • BS 7913 (2013). ‘The principles of the conservation of historic buildings’ BSi, Tunbridge Wells, UK [ original version 1998].
  • CloudCompare (version 2.12) [GPL software]. (2022). Retrieved from http://www.cloudcompare.org.
  • Dallas, R. 2003. Guide for practitioners No. 4 ‘Measured survey & Building recording’, 198. Edinburgh, UK: Historic Scotland.
  • D’Altri, A. M., V. Sarhosis, G. Milani, J. Rots, S. Cattari, S. Lagomarsino, E. Sacco, A. Tralli, G. Castellazzi, and S. de Miranda. 2020. Modeling strategies for the computational analysis of unreinforced masonry structures: Review and classification. Archives of Computational Methods in Engineering 27 (4):1153–85. doi:10.1007/s11831-019-09351-x.
  • Dezen-Kempter, E., C. K. Cogima, D. P. Vieira, P. Victor, D. C. Garcia, and M. Antonio (2018). BIM for heritage documentation – an ontology-based approach. Proceedings of the 36th eCAADe Conference, Lodz, Poland, pp. 213–22.
  • Dezen-Kempter, E., D. Lopes Mezencio, E. De Matos Miranda, D. Pico De Sá, and U. Dias. (2020). Towards a digital twin for heritage interpretation – From HBIM to AR visualization, Proceedings of CAADRIA, Bangkok, Thailand, pp. 183.191.
  • Forster, A. M. 2010a. Building conservation philosophy for masonry repair: Part 1 ‘Ethics. Structural Survey: Journal of Building Pathology & Refurbishment 28 (2):91–107. doi:10.1108/02630801011044208.
  • Forster, A. M. 2010b. Building conservation philosophy for masonry repair: Part 2 ‘Principles. Structural Survey: Journal of Building Pathology & Refurbishment 28 (3):165–88. doi:10.1108/02630801011058906.
  • Forster, A. M., and J. Douglas. 2010. Condition survey objectivity and philosophy driven masonry repair: an increased probability for project divergence? Structural Survey 28 (5):384–407. doi:10.1108/02630801011089173.
  • Harris, E. C. 2014. Archaeological stratigraphy: A paradigm for the anthropocene. Journal of Contemporary Archaeology. doi:10.1558/jca.v1i1.73.
  • Idjaton, K., X. Desquesnes, S. Treuillet, and X. Brunetaud (2021). Stone-by-stone segmentation for monitoring large historical monuments using deep neural networks. ICPR International Workshops and Challenges. ICPR 2021. Lecture Notes in Computer Science, vol 12667. Springer. 10.1007/978-3-030-68787-8_17
  • Idjaton, K., X. Desquesnes, S. Treuillet, and X. Brunetaud. 2022. Transformers with YOLO network for damage detection in limestone wall images. In Image analysis and processing. ICIAP 2022 workshops. ICIAP 2022. Lecture notes in computer science, ed. P. L. Mazzeo, E. Frontoni, S. Sclaroff, and C. Distante, Vol. 13374, pp. 302–313. Springer. doi:10.1007/978-3-031-13324-4_26.
  • Jouan, P., and P. Hallot (2019). Digital Twin: A HBIM-based methodology to support preventive conservation of historic assets through heritage significance awareness, International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences – ISPRS Archives, Volume 42, Issue 2/W15, Avila, Spain. pp. 609–15, August 19, 2019. https://doi.org/10.5194/isprs-archives-XLII-2-W15-609-2019.
  • Kumar, B. 2015. A practical guide to adopting BIM in construction projects, pp. 144. Scotland: Whittles Publishing.
  • Kwon, D., and J. Yu. 2019. Automatic damage detection of stone cultural property based on deep learning algorithm. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 42(2/W15), 639–643. doi:10.5194/isprs-archives-XLII-2-W15-639-2019.
  • Logothetis, S., A. Delinasiou, and E. Stylianidis (2015). “Building information modelling for cultural heritage: A review.” ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences II-5/W3 (September): 177–83. 10.5194/isprsannals-II-5-W3-177-2015
  • Martin, R., J. I. Murillo, and M. A. Utrero (2019) Reflexiones y criterios relativos a la creación de modelos BIM de edificios históricos. ARQUEOLOGÍA DE LA ARQUITECTURA, 18, enero-diciembre 2021, e113 Madrid/Vitoria -L: 1695-2731 10.3989/arq.arqt.2021.005
  • McWilliam, C., and C. Wilson. 1978. Lothian, except Edinburgh, Vol. 1. UK: Penguin.
  • Musicco, A., R. A. Galantucci, S. Bruno, C. Verdoscia, and F. Fatiguso. 2021. Automatic point cloud segmentation for the detection of alterations on historical buildings through an unsupervised and clustering-based machine learning approach. ISPAn 52 (2):129–36. doi:10.5194/ISPRS-ANNALS-V-2-2021-129-2021.
  • Nieto-Julián, J. E., J. Farratell, M. Bouzas Cavada, and J. Moyano. 2022. Collaborative workflow in an HBIM project for the restoration and conservation of cultural heritage. International Journal of Architectural Heritage 16:1–20.
  • Nieto Julián, J. E., and J. J. Moyano Campos. 2014. The paramental study on the model of information of historic building or HBIM project. Virtual Archaeology Review 5:73–85.
  • Ph, P. D., J. Boehm, P. Bryan, J. Still, and J. Grau-Bové. 2018. “Building information models for monitoring and simulation data in heritage buildings”. Int. Arch. Photogramm, Remote Sens. Spat. Inf. Science XLII–2 2:909–16. doi:10.5194/isprs-archives-XLII-2-909-2018.
  • RICS (2021) Professional Standards & Guidance - Survey Standard 1st ed. https://www.rics.org/globalassets/rics-website/media/qualify/home-survey-standard-nov-2020.pdf (accessed January 2023)
  • Scotland, H. E. 2012. Statement of Significance ‘Linlithgow Palace, Peel and park’. Accessed on 21/ 04/2022. https://www.historicenvironment.scot/archives-and-research/publications/publication/?publicationid=ec7b44bc-f5d5-4ad3-aa28-a78c00f8e692.
  • Scotland, H. E. 2022. ‘Listed buildings. In What is listing?’, Accessed on 18/ 11/2022. https://www.historicenvironment.scot/advice-and-support/listing-scheduling-and-designations/listed-buildings/what-is-listing/#categories-of-listing_tab.
  • UK, I. C. O. M. O. S. 1990. Guide to recording historic buildings. Accessed on 11/ 05/2022. http://www.bill-blake.co.uk/files/Download/ICOMOS%20UK%20Guide%20to%20Recording%20Hist%20Blgs.pdf.
  • Valero, E., and F. Bosché. 2020. Masonry segmentation plugin for CloudCompare, [software]. Cyberbuild Lab. The University of Edinburgh. doi:10.7488/ds/2892.
  • Valero, E., F. Bosché, and A. M. Forster. 2018a. Automatic Segmentation of 3D point clouds of rubble masonry walls, and its application to building surveying, repair and maintenance. Automation in Construction 96:29–39. doi:10.1016/j.autcon.2018.08.018.
  • Valero, E., F. Bosché, A. M. Forster, I. Beirick, L. Wilson, A. Turmel, and E. Hyslop (2020). Development of a novel tool for the segmentation of 3D point clouds of masonry walls and integration in CloudCompare, 14th International Stone Conference on deterioration and conservation of stone Gottingen, Germany.
  • Valero, E., F. Bosché, A. M. Forster, and E. Hyslop (2018b). Historic digital survey: Reality capture and automatic data processing for the interpretation and analysis of historic architectural rubble masonry Proceedings of the 11th International conference on structural analysis of historical constructions (SAHC, Peru), September. doi:10.1007/978-3-319-99441-3_41.
  • Valero, E., A. M. Forster, F. Bosché, E. Hyslop, and L. Wilson (2018c). High level-of-detail BIM and machine learning for automated masonry wall defect surveying, Proceedings of the 35th International Symposium on Automation and Robotics in Construction (ISARC), Berlin. doi:10.22260/ISARC2018/0101.
  • Valero, E., A. M. Forster, F. Bosché, E. Hyslop, L. Wilson, and A. Turmel (2019). Automated defect detection and classification in masonry walls using machine learning, Automation in Construction. 106, 10.1016/j.autcon.2019.102846
  • Wang, N., X. Zhao, P. Zhao, Y. Zhang, Z. Zou, and J. Ou. 2019. Automatic damage detection of historic masonry buildings based on mobile deep learning. Automation in Construction 103:53–66. doi:10.1016/j.autcon.2019.03.003.
  • Warland, E. G. 1929. Modern Practical Masonry, 1929. 350 pages. London: B.T Batsford.