Login / Signup

Correction Factors for Sclerometric Test Results in the Technical Assessment of Timber Structural Elements under Diverse Conditions.

Justyna Jaskowska-LemańskaDaniel WałachMonika Górka
Published in: Materials (Basel, Switzerland) (2023)
Research on existing wooden structures relies on non-destructive and semi-destructive techniques. One of the methods enabling the estimation of the physico-mechanical characteristics of wood in building structures based on established correlational relationships is the sclerometric method. The challenge in utilizing these known correlational relationships is the lack of data regarding the impact of frequently occurring factors in objects on sclerometric test results. This paper presents the influence of selected factors on the results of sclerometric tests, such as temperature, the direction of testing in relation to annual growth rings, and the physical orientation of the measuring device. The research was conducted on pine, spruce, and fir elements, each subjected exclusively to the influence of one of these factors. The study indicates that these factors should not be overlooked in assessing technical conditions using sclerometric testing methods. The impact of temperature on sclerometric test results is relatively small; a change in temperature of 10 °C results in an average test outcome change of approximately 3%. Conversely, changing the orientation of the measuring device from horizontal to vertical can alter the test result by up to 10%. The direction of testing relative to the annual increments of wood also has a significant impact on the test results, but incorporating this factor into practice seems to be quite difficult, and in the case of elements with substantial cross-sections, it is also not required. The obtained results enable the application of established correlational relationships in the structural analysis of wooden elements for which access is challenging, especially under temperature conditions different from the reference, 20 °C.
Keyphrases
  • healthcare
  • primary care
  • high resolution
  • mental health
  • physical activity
  • mass spectrometry
  • machine learning