Divos Presentation Ndt2009 Id

1. Instrument supported tree evaluation in Hungary Ferenc Divos University of West Hungary Jozsef Bodig wood NDT Laboratory Beijing, 16th International Wood NDT Symposium October 12-14, 2009

2. Content Visual tree evaluation Acoustic test, single path Acoustic tomography Acoustic root detection Pulling test Seedling test Conclusions

3. Human case Tree case 1st step: visual investigation 2nd step can be: - X-ray - Ultrasonic - CT, or MR - ……………… Arborist and patient 2nd step: NDT - acoustic tomography - pulling test - root detection - tree mechanics

4. 1. Visual tree evaluation Goal: tree related risk reduction. Requirement: well trained expert. Expert focusing to...

5. ... holes

6. ... openings, truntcations

7. ... fungi fruits, wounds

8. ... crone structure, tree shape

9. ... soil conditions, environment

10. Failure type 1.

11. Failure type 2.

12. Accident in Budapest, 2008

14. 2. Acoustic defect detection Single path (2 sensors)

15. Principle of decay detection Sound propagation in an intact and in a decayed tree.

16. Principle of decay detection

17. Principle of decay detection

18. FAKOPP Microsecond Timer Measurement perpendicular to the grain

20. Evaluation of measurement results The relative velocity change (RVC) is a measure of the defect size. If RVC is lower than 90% of the velocity in an intact tree, the tree contains an internal defect.

21. Sound propagation Oak disk, grid size is 2 by 2 cm, time resolution is 20 s.

22. The setup for wave mapping

30. 3. Acoustictomography by using more sensors

31. Intact poplar trunk

32. Decayed poplar trunk

33. FAKOPP 2D Acoustic Tomograph

48. FAKOPP 2D Acoustic Tomograph

49. Linden and Nut tree

52. 4. Acoustic Root Detection

53. Signal attenuation in soil is high, high sensitivity sensor is required Amplitude (mV) Distance (m)‏

54. P-vawe velocity in soil is 300 m/s Distance (mm)‏ Propagation time (ms)‏ Velocity in root is 3000 m/s

55. Theoretical wavefront around a tree

56. Soil velocity depends on depth

57. Setup for acoustic root detection

59. High sensitivity (10V/g) soil sensors

61. Velocity distribution around a tree

66. Root depth is measured by a steel bar.

71. Limitation of acoustic root detection Large rocks, fence, concrete roads guidesthe sound propagation. Technique is working well in parks and forest, but limited in urban area.

72. 5. Pulling test

73. Generalized Inclination - tipping load curve From Koch, 1989

75. Inclination sensor

76. Force sensor

77. Force display

78. Loading device

79. Pulling test in progress

80. Fitted curve and extrapolated maximum load

81. Evaluation Generalizedinclination curve:  = 1/3 tan(p/73,85) +0,00005 p2- 0,0009 p where: : inclination in degree p: force in % of maximum load.

83. Inclination (degree) Force (N)

84. Inclination (degree) Force (N)

85. Results of pulling test

86. Limitations of pulling test Uncertain factors: wind speed, drag factor, curve extrapolation,.. Load is not static, but dynamic. Trees are falling down not only in storms, but sometimes in wind silent condition.

87. Paulownia tomentosa trees in the yard of city the hall Kecskemét, the city of Zoltan Kodály

88. Kecskemét, city hall

93. Mechanical model for tree stability evaluation

95. Green wood material compression strength can be predicted by MOE. A stress wave velocity in fiber direction, together with the nominal density of the tree, provides the dynamic MOE and compression strength.

96. Tree stiffness determination and strength prediction MOE=V2 Higher velocity: higher MOE higher strength longer fibers low microfibril angle

97. 6. Ultrasonic seedling test

98. Shear type sensor

99. Sensor attached to seedling

100. Sensor attached to seedling

101. Testing oak seedling

103. Recently acoustic test becoming a standard tree evaluation technique in Hungary.

104. Acoustic technique is a reliable tool for tree trunk evaluation.

105. Acoustic root detection technique is approved in the practice.