Prediction of the mechanical properties of timber members in existing structures using the dynamic modulus of elasticity and visual grading parameters. (7th March 2022)

• Record Type: Journal Article
• Authors: Arriaga, Francisco; Osuna-Sequera, Carlos; Bobadilla, Ignacio; Esteban, Miguel
• Is Part Of: Construction & building materials. Volume 322(2022)
• Publication Date: 2022-03-07
• Subjects: Bending strength -- Existing structures -- In-situ assessment -- MOE -- MOR; Building materials -- Periodicals; 624.18
• DOI: 10.1016/j.conbuildmat.2022.126512
• Languages: English
• ISSNs: 0950-0618

Abstract

Highlights: Systematized method for the evaluation of bending timber structures: wave velocity, density and VSG. Comparison between ungraded and graded timber through MOR and MOE relationships. It was verified that density estimation with the WOODEX was similar to actual density. A factor is proposed for the relationship between dynamic and static MOE based on previous experiences in pieces with similar characteristics. MOR prediction models can be improved by adding to the MOE, knotty and slope of grain. Abstract: The modulus of elasticity and bending strength of 45 structural Salzmann pine timber pieces with nominal dimensions of 150x200x5400 mm 3 from an existing 18th century structure were estimated by semi-destructive density estimation probing method (drilling chips extraction) and acoustic wave velocity (stress and ultrasound wave). Bending strength, modulus of elasticity and density were obtained according to the EN 408 European standard, and visual grading singularities were recorded. Visual grading methods are highly ineffective for existing timber structures. Sample mechanical properties show a typical profile of material from existing structures, and this was compared with the results of similar works. MOE and MOR predictive models were proposed with determination coefficients r 2 of 66–68% and 51–52%, respectively, using dynamic MOE, relative edge knot diameter and slope of grain as independent variables. MOR prediction improved when these grading parameters were included.