Ultrasonic pulse velocity for mechanical properties determination of wood

References

• Agostinelli, F., Hoffman, M., Sadowski, P. and Baldi, P. (2014) Learning activation functions to improve deep neural networks. arXiv preprint arXiv:1412.6830.
   Google Scholar
• Batista, F. G., De Melo, R. R., De Medeiros, D. T., Dos Santos Oliveira, A. G., Freitas, C. B. A., Da Silva, E. D. G. and Pimenta, A. S. (2020) Longitudinal variation of wood quality in the five forest species from Caatinga. Revista Brasileira de Ciências Agrárias, 15(4), 1–9.
   Web of Science ®Google Scholar
• Beall, F. C. (2002) Overview of the use of ultrasonic technologies in research on wood properties. Wood Science and Technology, 36(3), 197–212.
   Web of Science ®Google Scholar
• Burges, C. J. (1998) A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery, 2(2), 121–167.
   Web of Science ®Google Scholar
• Chen, Z. Q., Karlsson, B., Lundqvist, S. O., García Gil, M. R., Olsson, L. and Wu, H. X. (2015) Estimating solid wood properties using Pilodyn and acoustic velocity on standing trees of Norway spruce. Annals of Forest Science, 72(4), 499–508.
   Web of Science ®Google Scholar
• Daryan, A. S., Palizi, S. and Farhoudi, N. (2019) Optimization of plastic analysis of moment frames using modified dolphin echolocation algorithm. Advances in Structural Engineering, 22(11), 2504–2516.
   Web of Science ®Google Scholar
• Daryan, A. S., Salari, M., Palizi, S. and Farhoudi, N. (2023, February) Size and Layout Optimum Design of Frames with Steel Plate Shear Walls by Metaheuristic Optimization Algorithms. In Structures Vol. 48 (Elsevier), pp. 657–668.
   Google Scholar
• Demirkir, C., Özsahin, Ş, Aydin, I. and Colakoglu, G. (2013) Optimization of some panel manufacturing parameters for the best bonding strength of plywood. International Journal of Adhesion and Adhesives, 46, 14–20.
   Web of Science ®Google Scholar
• de Oliveira, F. G. R. and Sales, A. (2006) Relationship between density and ultrasonic velocity in Brazilian tropical woods. Bioresource Technology, 97(18), 2443–2446.
   PubMed Web of Science ®Google Scholar
• Esteban, L. G., Fernández, F. G. and de Palacios, P. (2009) MOE prediction in Abies pinsapo Boiss. timber: Application of an artificial neural network using non-destructive testing. Computers & Structures, 87(21-22), 1360–1365.
   Web of Science ®Google Scholar
• Fathi, H., Nasir, V. and Kazemirad, S. (2020) Prediction of the mechanical properties of wood using guided wave propagation and machine learning. Construction and Building Materials, 262, 120848.
   Web of Science ®Google Scholar
• García, S., Luengo, J. and Herrera, F. (2015) Data Preprocessing in Data Mining Vol. 72 (Cham, Switzerland: Springer International Publishing), (pp. 59-139).
   Google Scholar
• Güntekin, E., Aydin, T. Y. and Niemz, P. (2015) Prediction of compression properties in three orthotropic directions for some important Turkish wood species using ultrasound. BioResources, 10(4), 7252–7262.
   Web of Science ®Google Scholar
• Ilic, J. (2003) Dynamic MOE of 55 species using small wood beams. Holz als Roh- und Werkstoff, 61(3), 167–172.
   Google Scholar
• Keunecke, D., Sonderegger, W., Pereteanu, K., Lüthi, T. and Niemz, P. (2007) Determination of Young’s and shear moduli of common yew and Norway spruce by means of ultrasonic waves. Wood Science and Technology, 41, 309–327.
   Web of Science ®Google Scholar
• Ozyhar, T., Hering, S., Sanabria, S. J. and Niemz, P. (2013) Determining moisture-dependent elastic characteristics of beech wood by means of ultrasonic waves. Wood Science and Technology, 47, 329–341.
   Web of Science ®Google Scholar
• Palizi, S. and Saedi Daryan, A. (2020) Plastic analysis of braced frames by application of metaheuristic optimization algorithms. International Journal of Steel Structures, 20(4), 1135–1150.
   Web of Science ®Google Scholar
• Palizi, S. and Saedi Daryan, A. (2022) Critical temperature evaluation of moment frames by means of plastic analysis theory and genetic algorithm. Iranian Journal of Science and Technology, Transactions of Civil Engineering, 46(2), 843–856.
   Web of Science ®Google Scholar
• Palizi, S. and Toufigh, V. (2022) Bond strength prediction of timber-FRP under standard and acidic/alkaline environmental conditions based on gene expression programming. European Journal of Wood and Wood Products, 80(6), 1457–1471.
   Web of Science ®Google Scholar
• Rakotovololonalimanana, H., Chaix, G., Brancheriau, L., Ramamonjisoa, L., Ramananantoandro, T. and Thevenon, M. F. (2015) A novel method to correct for wood MOE ultrasonics and NIRS measurements on increment cores in Liquidambar styraciflua L. Annals of Forest Science, 72(6), 753–761.
   Web of Science ®Google Scholar
• Ruder, S. (2016) An overview of gradient descent optimization algorithms. arXiv preprint arXiv:1609.04747.
   Google Scholar
• Rumelhart, D. E., Hinton, G. E. and Williams, R. J. (1985) Learning Internal Representations by Error Propagation (California Univ San Diego La Jolla Inst for Cognitive Science).
   Google Scholar
• Saedi Daryan, A. and Palizi, S. (2020) New plastic analysis procedure for collapse prediction of braced frames by means of genetic algorithm. Journal of Structural Engineering, 146(1), 04019168.
   Web of Science ®Google Scholar
• Saedi Daryan, A., Salari, M., Farhoudi, N. and Palizi, S. (2021) Seismic design optimization of steel frames with steel shear wall system using modified Dolphin algorithm. International Journal of Steel Structures, 21(3), 771–786.
   Web of Science ®Google Scholar
• Sakai, H., Minamisawa, A. and Takagi, K. (1990) Effect of moisture content on ultrasonic velocity and attenuation in woods. Ultrasonics, 28(6), 382–385.
   Web of Science ®Google Scholar
• Samui, P. (2008) Support vector machine applied to settlement of shallow foundations on cohesionless soils. Computers and Geotechnics, 35(3), 419–427.
   Web of Science ®Google Scholar
• Smola, A. J. and Schölkopf, B. (1998) A tutorial on support vector regression. Rep No. NC2-TR-1998-030. NeuroCOLT2 Technical Report Series.
   Google Scholar
• Sonderegger, W., Mandallaz, D. and Niemz, P. (2008) An investigation of the influence of selected factors on the properties of spruce wood. Wood Science and Technology, 42, 281–298.
   Web of Science ®Google Scholar
• Tiryaki, S. and Hamzaçebi, C. (2014) Predicting modulus of rupture (MOR) and modulus of elasticity (MOE) of heat treated woods by artificial neural networks. Measurement, 49, 266–274.
   Web of Science ®Google Scholar
• Toufigh, V. and Palizi, S. (2022) Performance evaluation of slag-based concrete at elevated temperatures by a novel machine learning approach. Construction and Building Materials, 358, 1–15.
   Web of Science ®Google Scholar
• Unterwieser, H. and Schickhofer, G. (2011) Influence of moisture content of wood on sound velocity and dynamic MOE of natural frequency- and ultrasonic runtime measurement. European Journal of Wood and Wood Products, 69(2), 171–181.
   Web of Science ®Google Scholar
• Uyanık, G. K. and Güler, N. (2013) A study on multiple linear regression analysis. Procedia - Social and Behavioral Sciences, 106, 234–240.
   Google Scholar
• Uysal, M. and Tanyildizi, H. (2012) Estimation of compressive strength of self compacting concrete containing polypropylene fiber and mineral additives exposed to high temperature using artificial neural network. Construction and Building Materials, 27(1), 404–414.
   Web of Science ®Google Scholar
• Vapnik, V. N. (1999) An overview of statistical learning theory. IEEE Transactions on Neural Networks, 10(5), 988–999.
   PubMed Web of Science ®Google Scholar
• Yang, J. L. and Evans, R. (2003) Prediction of MOE of eucalypt wood from microfibril angle and density. Holz als Roh-und Werkstoff, 61, 449–452.
   Google Scholar
• Yilmaz Aydin, T. and Ozveren, A. (2019) Effects of moisture content on elastic constants of fir wood. European Journal of Wood and Wood Products, 77(1), 63–70.
   Web of Science ®Google Scholar