PSI - Issue 78

Angelo Aloisio et al. / Procedia Structural Integrity 78 (2026) 25–32

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[4] V Ban˜o, F Arriaga, A Soila´n, and M Guaita. Prediction of bending load capacity of timber beams using a finite element method simulation of knots and grain deviation. Biosystems engineering , 109(4):241–249, 2011. [5] Christopher M Bishop and Nasser M Nasrabadi. Pattern recognition and machine learning , volume 4. Springer, 2006. [6] L. Breiman. Random forests. Machine Learning , 45(1):5–32, 2001. [7] Alberto Cavalli, Daniele Cibecchini, Marco Togni, and He´lder S Sousa. A review on the mechanical properties of aged wood and salvaged timber. Construction and Building Materials , 114:681–687, 2016. [8] Nitesh V Chawla, Kevin W Bowyer, Lawrence O Hall, and W Philip Kegelmeyer. Smote: synthetic minority over-sampling technique. Journal of artificial intelligence research , 16:321–357, 2002. [9] Tianqi Chen and Carlos Guestrin. Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining , pages 785–794, 2016. [10] J. S. Cramer. The origins of logistic regression. Working Paper 2002-119 / 4, Tinbergen Institute, December 2002. [11] Keith I. Crews and Colin MacKenzie. Development of grading rules for re-cycled timber used in structural applications. In World Conference on Timber Engineering . World Conference on Timber Engineering (WCTE), 2008. [12] H. Cruz, D. Yeomans, E. Tsakanika, N. Macchioni, A. Jorissen, M. Touza, M. Mannucci, and P.B. Lourenc¸o. Guidelines for on-site assessment of historic timber structures. International Journal of Architectural Heritage , 9(3):277–289, 2015. [13] EN 338:2016. Structural timber — strength classes. EN338 , 2016. [14] RH Falk, D DeVisser, JR Plume, and Kenneth J Fridley. E ff ect of drilled holes on the bending strength of large dimension douglas-fir lumber. Forest products journal , 53(5):55–60, 2003. [15] Jochen Ko¨hler, John Dalsgaard Sørensen, and Michael Havbro Faber. Probabilistic modeling of timber structures. Structural safety , 29(4):255–267, 2007. [16] Daniel F Llana, Guillermo ´In˜iguez-Gonza´lez, Mitja Plos, and Goran Turk. Grading of recovered norway spruce ( Picea abies ) timber for structural purposes. Construction and Building Materials , 398:132440, 2023. [17] A.R. Marin˜o, R.M.E. Ferna´ndez, and R.C. Ferna´ndez. Ana´lisis comparativo de la densidad de la madera pinus sylvestris l. mediante la utilizacio´n del resisto´grafo. Revista CIS-Madera , 9:60–70, 2002. [18] R.D. Mart´ınez, J.-A. Balmori, D.F. Llana, and I. Bobadilla. Wood density determination by drilling chips extraction in ten softwood and hardwood species. Forests , 11(4), 2020. [19] M.J. Morales-Conde and J.S. Machado. Evaluation of cross-sectional variation of timber bending modulus of elasticity by stress waves. Construc tion and Building Materials , 134:617–625, 2017. [20] C. Osuna-Sequera, D.F. Llana, M. Esteban, and F. Arriaga. Improving density estimation in large cross-section timber from existing structures optimizing the number of non-destructive measurements. Construction and Building Materials , 211:199–206, 2019. [21] C. Osuna-Sequera, D.F. Llana, G. ´In˜iguez Gonza´lez, and F. Arriaga. The influence of cross-section variation on bending sti ff ness assessment in existing timber structures. Engineering Structures , 204, 2020. [22] Dag Pasquale Pasca, Angelo Aloisio, Yuri De Santis, Hauke Burkart, and Audun Øvrum. Visual-based classification models for grading reclaimed structural timber for reuse: A theoretical, numerical and experimental investigation. Engineering Structures , 322:119218, 2025. [23] Fabian Pedregosa, Gae¨l Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, et al. Scikit-learn: Machine learning in python. the Journal of Machine Learning Research , 12:2825–2830, 2011. [24] prNS 3691-3:2023. Evaluation of reclaimed timber: Part 3 - visual strength grading. prNS3691 , 2024. [25] A. L. Smith and S. J. Hicks. Design of floors for vibration: A new approach. Technical report, Ascot, UK: Steel Construction Institute., 2009. [26] Vladimir N Vapnik. The nature of statistical learning theory . Springer Science & Business Media, 1995.

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