| Response of hygroscopicity to heat treatment and its relation to durability of thermally modified wood T Li, D Cheng, S Avramidis, MEP Wålinder, D Zhou Construction and Building Materials 144, 671-676, 2017 | 75 | 2017 |
| Radio frequency vacuum drying of wood. I. Mathematical model A Koumoutsakos, S Avramidis, SG Hatzikiriakos Drying Technology 19 (1), 65-84, 2001 | 68 | 2001 |
| Radio frequency vacuum drying of wood. II. Experimental model evaluation A Koumoutsakos, S Avramidis, SG Hatzikiriakos Drying technology 19 (1), 85-98, 2001 | 59 | 2001 |
| Application of near-infrared spectroscopy for moisture-based sorting of green hem-fir timber K Watanabe, SD Mansfield, S Avramidis Journal of wood science 57, 288-294, 2011 | 58 | 2011 |
| Behaviour of solid wood and bound water as a function of moisture content. A proton magnetic resonance study CD Araujo, S Avramidis, AL MacKay Walter de Gruyter, Berlin/New York 48 (1), 69-74, 1994 | 58 | 1994 |
| The effect of resin content and face-to-core ratio on some properties of oriented strand board S Avramidis, LA Smith Holzforschung 43 (2), 131-133, 1989 | 54 | 1989 |
| Predicting wood thermal conductivity using artificial neural networks S Avramidis, L Iliadis Wood and Fiber Science, 682-690, 2005 | 53 | 2005 |
| Multiphysics modeling of vacuum drying of wood S sandoval Torres, W Jomaa, JR Puiggali, S Avramidis Applied Mathematical Modelling 35 (10), 5006-5016, 2011 | 51 | 2011 |
| Neural network prediction of bending strength and stiffness in western hemlock (Tsuga heterophylla Raf.) SD Mansfield, L Iliadis, S Avramidis Walter de Gruyter 61 (6), 707-716, 2007 | 49 | 2007 |
| Classification of thermally treated wood using machine learning techniques V Nasir, S Nourian, S Avramidis, J Cool Wood Science and Technology 53, 275-288, 2019 | 48 | 2019 |
| Prediction of timber kiln drying rates by neural networks H Wu, S Avramidis Drying Technology 24 (12), 1541-1545, 2006 | 48 | 2006 |
| On the permeability of main wood species in China F Bao, J Lu, S Avramidis Walter de Gruyter 53 (4), 350-354, 1999 | 48 | 1999 |
| Prediction of physical and mechanical properties of thermally modified wood based on color change evaluated by means of “group method of data handling”(GMDH) neural network V Nasir, S Nourian, S Avramidis, J Cool Holzforschung 73 (4), 381-392, 2019 | 46 | 2019 |
| The basics of sorption S Avramidis Proceedings of international conference of COST action E 8, 1-16, 1997 | 44 | 1997 |
| Drying characteristics of thick lumber in a laboratory radio-frequeocy/vacuum dryer S Avramidis, F Liu Drying technology 12 (8), 1963-1981, 1994 | 44 | 1994 |
| Analysis of the wood sorption isotherm using clustering theory ID Hartley, S Avramidis Walter de Gruyter, Berlin/New York 47 (2), 163-167, 1993 | 43 | 1993 |
| Non-destructive measurement of moisture distribution in wood during drying using digital X-ray microscopy K Watanabe, Y Saito, S Avramidis, S Shida Drying technology 26 (5), 590-595, 2008 | 42 | 2008 |
| Wood-water sorption isotherm prediction with artificial neural networks: a preliminary study S Avramidis, L Iliadis Walter de Gruyter 59 (3), 336-341, 2005 | 41 | 2005 |
| An irreversible thermodynamics model for unsteady-state nonisothermal moisture diffusion in wood S Avramidis, SG Hatzikiriakos, JF Siau Wood Science and Technology 28 (5), 349-358, 1994 | 41 | 1994 |
| On the loss factor of wood during radio frequency heating B Zhou, S Avramidis Wood science and technology 33 (4), 299-310, 1999 | 40 | 1999 |
