Effectiveness and subjectivity of visual inspection as a method to assess bendi...

• Author(s): Sousa, Hélder S. ; Branco, Jorge M. ; Lourenço, Paulo B.
• Date: 2013
• Persistent ID: http://hdl.handle.net/1822/25894
• Origin: RepositóriUM - Universidade do Minho
• Subject(s): Chestnut timber; Visual inspection; Visual strength classes; Variation analysis; Subjectivity evaluation
• Document Type: Article
• Language: English

Description

Besides the difficulty of assessing an existing timber structure on site, the efficiency and accuracy of visual inspection is often compromised by its subjective nature inherited by the level of expertise of the inspector. This often leads to conservative predictions of the mechanical properties, even with the use of specific visual grading norms. The main objectives of this work are to assess the effectiveness of visual inspection as to define different classes of strength and stiffness and to provide a statistical analysis on its subjectivity. For that aim, visual inspection using Italian standard UNI 11119:2004 and bending tests of 20 old chestnut beams (Castanea sativa Mill.) at different scales, were carried out. Comparisons and effectiveness of visual inspection is analyzed within and between different scales, and also regarding different level of expertise of inspectors. The results evidence similar percentages of segments classified with higher and lower visual grades and proved to be a good qualitative indicator of bending strength between sawn beams. For the most experienced inspectors, an overall 42% accuracy was found with a better differentiation between visual classes, whereas lower level inspectors scored approximately 5% less. Lower level inspectors evidenced higher concentration of values around a higher mean for each class denoting a more conservative approach. Regardless of the inspector level, knot size was the main limiting visual parameter with higher influence in small scales of the timber elements. When studying the characterization of a single knot, coefficients of variation of 15.7% and 21.8% were found for measuring the minimum and maximum diameter. Bayesian probability networks were considered as to individually assess the accuracy in stiffness prediction of different level of inspectors, and by combination of their information, evidencing that parallel combination for prior information may allow the increase in visual inspection accuracy.