Prediction of wood density and compressive strength based on combined nondestructive testing technology
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(1.College of Civil Engineering, Taiyuan University of Technology, Taiyuan 030024, China; 2.Postdoctoral Research Station of Shanxi Construction Investment Group Co. Ltd., Taiyuan 030032, China; 3.Shanxi Ancient Architecture and Painted Murals Protection Research Institute, Taiyuan 030012, China)

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TJ765.4

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    Abstract:

    To realize nondestructive testing of ancient wood components, we proposed linear prediction formulas for wood density and compressive strength based on combined testing technology. The ultrasonic wave velocity and compressive strength of 12 kinds of wood in different grain directions, as well as the impedance ratio in radial and oblique directions, were measured using the ultrasonic-needle resistance instrument combined detection technology. The regression coefficients in the prediction formulas were fitted, and then the prediction formulas were applied to the prediction of the density and compressive strength of two kinds of new wood and a kind of ancient wood. Results show that the ultrasonic wave velocity and impedance ratio of wood were positively correlated with wood density and compressive strength, and the goodness of fit of the linear relationship obtained by multiple regression method was significantly improved when the ultrasonic-needle resistance instrument combined detection technology was adopted. When predicting the density of new and ancient wood, only needle resistance instrument technology achieved accurate prediction. The prediction error of new wood was less than 5%, and that of ancient wood was less than 1%. When predicting the compressive strength of new and ancient wood, the prediction error of single detection technology was large, while the prediction effect of combined detection method was better. The prediction error of new wood was less than 5%, and that of ancient wood was less than 9%. The tests verify the feasibility of using ultrasonic needle resistance instrument combined detection technology to predict wood density and compressive strength. The results can provide important technical support for the health monitoring of ancient building wood components.

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History
  • Received:May 19,2022
  • Revised:
  • Adopted:
  • Online: October 10,2023
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