Toward the automatic deflection analysis in historic timber slabs by combining 3D point clouds and machine learning approaches

Authors

DOI:

https://doi.org/10.3989/ic.7096

Keywords:

diagnostics, laser scanner, artificial intelligence, timber floors, point cloud

Abstract


In the field of Cultural Heritage, 3D point clouds are pivotal for representing scenes with high resolution and accuracy. Although traditionally used for planimetries and computational modelling, this study explores their potential in diagnostics. A novel methodology is proposed that leverages 3D point clouds to evaluate deflection in historic timber slabs. By combining Artificial Intelligence with advanced 3D point cloud techniques, an approach is developed for detecting beam deflections with high precision. Specifically, a multi-resolution Random Forest classifier identifies beams and their faces, while deflection is computed using connected component and minimum bounding rectangle algorithms. Applied to timber slabs in the Nuestra Señora convent in Ávila. This methodology demonstrates promising results, achieving 99% accuracy in beam detection and a high degree of automation in deflection assessment. This approach offers significant advancements in the diagnostic capabilities of 3D point clouds in heritage conservation.

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References

K. Mirzaei et al., "3D point cloud data processing with machine learning for construction and infrastructure applications: A comprehensive review", Advanced Engineering Informatics, vol. 51, Jan. 2022. https://doi.org/10.1016/j.aei.2021.101501

Q. Wang and M. K. Kim, "Applications of 3D point cloud data in the construction industry: A fifteen-year review from 2004 to 2018," Advanced Engineering Informatics, vol. 39, pp. 306-319, Jan. 2019. https://doi.org/10.1016/j.aei.2019.02.007

L. J. Sánchez-Aparicio et al., "Detection of damage in heritage constructions based on 3D point clouds: A systematic review," Journal of Building Engineering, vol. 77, Oct. 2023. https://doi.org/10.1016/j.jobe.2023.107440

L. J. Sánchez-Aparicio, S. Del Pozo, L. F. Ramos, A. Arce, and F. M. Fernandes, "Heritage site preservation with combined radiometric and geometric analysis of TLS data," Automation in Construction, vol. 85, pp. 24-39, Jan. 2018. https://doi.org/10.1016/j.autcon.2017.09.023

E. Valero, A. Forster, F. Bosché, E. Hyslop, L. Wilson, and A. Turmel, "Automated defect detection and classification in ashlar masonry walls using machine learning," Automation in Construction, vol. 106, Oct. 2019. https://doi.org/10.1016/j.autcon.2019.102846

A. Masiero and D. Costantino, "TLS for detecting small damages on a building façade," The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. XLII-2/W11, pp. 831-836, May 2019. https://doi.org/10.5194/isprs-archives-XLII-2-W11-831-2019

S. Yang, M. Hou, and S. Li, "Three-dimensional point cloud semantic segmentation for cultural heritage: A comprehensive review," Remote Sensing (Basel), vol. 15, no. 3, Feb. 2023. https://doi.org/10.3390/rs15030548

S. Teruggi, E. Grilli, M. Russo, F. Fassi, and F. Remondino, "A hierarchical machine learning approach for multi-level and multi-resolution 3D point cloud classification," Remote Sensing, vol. 12, no. 16, p. 2598, Aug. 2020. https://doi.org/10.3390/rs12162598

P. Villanueva Llauradó et al., "A comparative study between a static and a mobile laser scanner for the digitalization of inner spaces in historical constructions," in Proc. REHABEND Int. Conf. Construction Pathology, Rehabilitation Technology and Heritage Management, 2022, pp. 2492-2498.

L. J. Sánchez-Aparicio et al., "Evaluation of a SLAM-based point cloud for deflection analysis in historic timber floors," The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. XLVIII-M-2-2023, pp. 1411-1418, Jun. 2023. https://doi.org/10.5194/isprs-archives-XLVIII-M-2-2023-1411-2023

K. Zhang, S. Teruggi, Y. Ding, and F. Fassi, "A multilevel multiresolution machine learning classification approach: A generalization test on Chinese heritage architecture," Heritage, vol. 5, no. 4, pp. 3970-3992, Dec. 2022. https://doi.org/10.3390/heritage5040204

D. Billi et al., "Machine learning and deep learning for the built heritage analysis: Laser scanning and UAV-based surveying applications on a complex spatial grid structure," Remote Sensing (Basel), vol. 15, no. 8, Apr. 2023. https://doi.org/10.3390/rs15081961

M. Russo, E. Grilli, F. Remondino, S. Teruggi, and F. Fassi, "Machine learning for cultural heritage classification," in Representation Challenges: Augmented Reality and Artificial Intelligence in Cultural Heritage and Innovative Design Domain, Milan, Italy: FrancoAngeli, 2021.

M. Pal, "Random forest classifier for remote sensing classification," International Journal of Remote Sensing, vol. 26, no. 1, pp. 217-222, Jan. 2005. https://doi.org/10.1080/01431160412331269698

Ministerio de Fomento, Documento Básico SE-M. Seguridad estructural. Madera, 2019.

L. J. Sánchez-Aparicio, R. Santamaría-Maestro, P. Sanz-Honrado, P. Villanueva-Llauradó, J. R. Aira-Zunzunegui, and D. González-Aguilera, "A holistic solution for supporting the diagnosis of historic constructions from 3D point clouds," Remote Sensing (Basel), vol. 17, no. 12, p. 2018, Jun. 2025. https://doi.org/10.3390/rs17122018

F. Lasheras Merino, "Patología de la construcción madera," in Tratado Técnico Jurídico de la Edificación y el Urbanismo. Tomo I. Patología de la Construcción y Técnicas de Intervención, Thomson Reuters Aranzadi, 2009, pp. 789-850. [Online]. Available: http://oa.upm.es/53437/1/L032009TCXIMadera.pdf

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Published

2025-12-30

How to Cite

Sanz-Honrado, P., Santamaria-Maestro, R., Aira-Zunzunegui, J. R. ., Villanueva Llauradó, P., & Sánchez-Aparicio, L. J. (2025). Toward the automatic deflection analysis in historic timber slabs by combining 3D point clouds and machine learning approaches. Informes De La Construcción, 77(580), 7096. https://doi.org/10.3989/ic.7096

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Section

Research Articles

Funding data

Comunidad de Madrid
Grant numbers APOYO-JOVENES-21-RCDT1L-85- SL9E1R

Ministerio de Ciencia e Innovación
Grant numbers CAS21/00557;PID2022-140071OB-C21