Assessment of Flexural Capacity Prediction Models for Glulam Beams of Nigerian Wood Species

Abstract:

This study is undertaken to provide a viable alternative to destructive test assessment which is prohibitively costly and makes it difficult to fully capture the bending strength distribution of timber. The bending strength of glued laminated Nigerian wood specie which from previous research have clearly not been sufficiently studied were carefully evaluated under controlled conditions. Five wood species—Afara (Terminalia superba), Akomu (Pycnanthus angolensis), Melina (Gmelina arborea), Iroko (Milicia excelsa), and Omo cedar (Stereospermum accuminatissmum)—were tested for physical and mechanical properties at a mean moisture content level of 13%. Using standard procedures, density was determined for the wood species and found to be within low and medium densities. The glulam beams were produced using Phenol resorcinol formaldehyde, Urea formaldehyde, and Polyurethane adhesives. Furthermore, the bending strength of the beams was assessed parallel and perpendicular to the glue line using central point loading. Based on the strength assessment, four variables were evaluated as determinants for bending strength. These were wood density, wood species, adhesive type, and load direction. Analysis of variance was conducted to evaluate the significance of these variables as bending strength determinants in the beams. Furthermore, a stepwise regression method was used to develop the models, from which three models emerged with density, wood species, and load direction as predictors. Finally, these models were validated using an in-sample technique by splitting the data into a validation group and a training group in a ratio of 20% to 80%, respectively. The Pearson correlation between the predicted and experimental data was 0.847, 0.917, and 0.916 in the validation group and 0.824, 0.875, and 0.877 in the training group for models 1, 2, and 3, respectively. Higher correlation with the experimental data was found in the validation group than in the training group, thereby validating the models. These models are recommended for use because of the simplicity and efficiency of prediction.

• Periodical: Materials Science Forum (Volume 1162)
• Pages: 95-109
• DOI: https://doi.org/10.4028/p-2Q7Hlh
• Online since: October 2025
• Authors: Olurotimi Olusegun Ekundayo*, Chinwuba Arum, Jacob Mayowa Owoyemi
• Keywords: Bending Strength, Glulam, Prediction, Step Wise Regression

References

[1] Ali, R. (2020). Predictive Modeling: Types, Benefits, and Algorithms. Retrieved from Oracle Netsuite: https://www. netsuite. Com/portal/resource/articles/financial management/predictive-modeling. html.

[2] Hernandez, R., Bender, D. A., Richburg, B. A., and Kline, K. S. (1992). Probabilistic Modeling of Glued-Laminated Timber Beams. Wood and fiber science, 294-306.

[3] Reinprecht, L., Ciglian, D., Iždinský, J., &Sedliačik, J. (2022). Effect of Primary Spruce Lamella Aging on the Bending Characteristics of Glulam Beams. Applied Sciences, 12(24), 12872.

DOI: 10.3390/app122412872

[4] Owoyemi, J.M., Olaniran, S.O., and Aliyu, D.I., (2013). Effect of density on the natural resistance of ten selected Nigerian wood species to subterranean termites. Pro Lingo (1).

[5] Alayande, T. A., Ede, A. N., Aguwa, J. I., Ofuyatan, O. M., Oyebisi, S. O., &Oluwafemi, J. O. (2019, December). Reliability Assessment of the Nigerian Timber–An Environmental Sustainability Approach in the 21st Century. In Journal of Physics: Conference Series (Vol. 1378, No. 3, p.032053). IOP Publishing.

DOI: 10.1088/1742-6596/1378/3/032053

[6] Ekundayo, O. O., Arum, C., &Owoyemi, J. M. (2020). Forest Product Industry and Engineered Wood Products: The Nigerian Experience. Journal of Applied Sciences and Environmental Management, 25(1), 93-97.

DOI: 10.4314/jasem.v25i1.14

[7] Camú, C. T., & Aicher, S. (2018, August). A stochastic finite element model for glulam beams of hardwoods. In World Conference on Timber Engineering, Seul.

[8] Frese, M., &Blaß, H. J. (2009). Bending strength of spruce glulam. European Journal of Wood and Wood Products, 67(3), 277-286.

DOI: 10.1007/s00107-009-0316-2

[9] Lee, J. J., Park, J. S., Kim, K. M., and Oh, J. K. (2005). Prediction of Bending Properties for Structural Glulam using Optimized Distributions of Knot Characteristics and Laminar MOE. Journal of wood science, 51(6), 640-647.

DOI: 10.1007/s10086-005-0704-0

[10] Gao, Y., Wu, Y., Zhu, X., Zhu, L., Yu, Z., & Wu, Y. (2015). Numerical analysis of the bending properties of cathay poplar glulam. Materials, 8(10), 7059-7073.

DOI: 10.3390/ma8105362

[11] Kashaboina, M. (2021, June 26). Predictive Analytics — Model Predictions And Their Interpretability Challenges. Towards Data Science. Retrieved January 12, 2023, from https://towardsdatascience.com/predictive-analytics-model-predictions-and-their-interpretability-challenges-acbb8ff44b3f

[12] Frese, M., &Blaß, H. J. (2007). Characteristic bending strength of beech glulam. Materials and structures, 40(1), 3-13.

DOI: 10.1617/s11527-006-9117-9

[13] Timbolmas, C., Bravo, R., Rescalvo, F. J., &Gallego, A. (2022). Development of an analytical model to predict the bending behavior of composite glulam beams in tension and compression. Journal of Building Engineering, 45, 103471.

DOI: 10.1016/j.jobe.2021.103471

[14] Gholamy, A., Kreinovich, V., & Kosheleva, O. (2018). Why 70/30 or 80/20 relation between training and testing sets: A pedagogical explanation. International Journal of Intelligent Technologies and Applied Statistics, 11(2), 105-111.

[15] Frake (2021): retrieved from https://www.tropix.cirad

[16] Blomberg, J., Persson, B., and Blomberg A. (2005). Effects of Semi-isostatic Densification of Wood on the Variation in Strength Properties with Density. Wood Science and Technology, 39(5), 339-350.

DOI: 10.1007/s00226-005-0290-8

[17] Burdurlu, E., Kilic, M., Ilce, A. C., and Uzunkavak, O. (2007). The Effects of Ply Organization and Loading Direction on Bending Strength and Modulus of Elasticity in Laminated Veneer Lumber (LVL) Obtained from Beech (Fagusorientalis L.) and Lombardy Poplar (Populusnigra L.). Construction and Building Materials, 21(8), 1720-1725

DOI: 10.1016/j.conbuildmat.2005.05.002

[18] Kılıç, M. (2011). The effects of the force loading direction on bending strength and modulus of elasticity in laminated veneer lumber (LVL). BioResources, 6(3), 2805-2817.

DOI: 10.15376/biores.6.3.2805-2817

[19] Schober, P., Boer, C., & Schwarte, L. A. (2018). Correlation coefficients: appropriate use and interpretation. Anesthesia & Analgesia, 126(5), 1763-1768. https://doi.org/10.1213/ane. 0000000000002864

DOI: 10.1213/ane.0000000000002864

[20] Bal, B. C., and Bektaş, İ. (2012). The Effects of Wood Species, Load Direction, and Adhesives on Bending Properties of Laminated Veneer Lumber. Bioresources, 7(3), 3104-3112.

DOI: 10.15376/biores.7.3.3104-3112

[21] Komariah, R. N., Hadi, Y. S., Massijaya, M. Y., and Suryana, J. (2015). Physical-Mechanical Properties of Glued Laminated Timber Made from Tropical Small-Diameter Logs Grown in Indonesia. Journal of the Korean Wood Science and Technology, 43(2), 156-167.

DOI: 10.5658/wood.2015.43.2.156