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Tetsu-to-Hagané Vol. 110 (2024), No. 15

ISIJ International
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Grid List Abstracts
ONLINE ISSN: 1883-2954
PRINT ISSN: 0021-1575
Publisher: The Iron and Steel Institute of Japan

Backnumber

  1. Vol. 110 (2024)

    1. No.15
    2. No.14
    3. No.13
    4. No.12
    5. No.11
    6. No.10
    7. No.9
    8. No.8
    9. No.7
    10. No.6
    11. No.5
    12. No.4
    13. No.3
    14. No.2
    15. No.1

  2. Vol. 109 (2023)

    1. No.12
    2. No.11
    3. No.10
    4. No.9
    5. No.8
    6. No.7
    7. No.6
    8. No.5
    9. No.4
    10. No.3
    11. No.2
    12. No.1

  3. Vol. 108 (2022)

    1. No.12
    2. No.11
    3. No.10
    4. No.9
    5. No.8
    6. No.7
    7. No.6
    8. No.5
    9. No.4
    10. No.3
    11. No.2
    12. No.1

  4. Vol. 107 (2021)

    1. No.12
    2. No.11
    3. No.10
    4. No.9
    5. No.8
    6. No.7
    7. No.6
    8. No.5
    9. No.4
    10. No.3
    11. No.2
    12. No.1

  5. Vol. 106 (2020)

    1. No.12
    2. No.11
    3. No.10
    4. No.9
    5. No.8
    6. No.7
    7. No.6
    8. No.5
    9. No.4
    10. No.3
    11. No.2
    12. No.1

  6. Vol. 105 (2019)

    1. No.12
    2. No.11
    3. No.10
    4. No.9
    5. No.8
    6. No.7
    7. No.6
    8. No.5
    9. No.4
    10. No.3
    11. No.2
    12. No.1

  7. Vol. 104 (2018)

    1. No.12
    2. No.11
    3. No.10
    4. No.9
    5. No.8
    6. No.7
    7. No.6
    8. No.5
    9. No.4
    10. No.3
    11. No.2
    12. No.1

  8. Vol. 103 (2017)

    1. No.12
    2. No.11
    3. No.10
    4. No.9
    5. No.8
    6. No.7
    7. No.6
    8. No.5
    9. No.4
    10. No.3
    11. No.2
    12. No.1

  9. Vol. 102 (2016)

    1. No.12
    2. No.11
    3. No.10
    4. No.9
    5. No.8
    6. No.7
    7. No.6
    8. No.5
    9. No.4
    10. No.3
    11. No.2
    12. No.1

  10. Vol. 101 (2015)

    1. No.12
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    3. No.10
    4. No.9
    5. No.8
    6. No.7
    7. No.6
    8. No.5
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    10. No.3
    11. No.2
    12. No.1

  11. Vol. 100 (2014)

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    3. No.10
    4. No.9
    5. No.8
    6. No.7
    7. No.6
    8. No.5
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    10. No.3
    11. No.2
    12. No.1

  12. Vol. 99 (2013)

    1. No.12
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    3. No.10
    4. No.9
    5. No.8
    6. No.7
    7. No.6
    8. No.5
    9. No.4
    10. No.3
    11. No.2
    12. No.1

  13. Vol. 98 (2012)

    1. No.12
    2. No.11
    3. No.10
    4. No.9
    5. No.8
    6. No.7
    7. No.6
    8. No.5
    9. No.4
    10. No.3
    11. No.2
    12. No.1

  14. Vol. 97 (2011)

    1. No.12
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    3. No.10
    4. No.9
    5. No.8
    6. No.7
    7. No.6
    8. No.5
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    10. No.3
    11. No.2
    12. No.1

  15. Vol. 96 (2010)

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    4. No.9
    5. No.8
    6. No.7
    7. No.6
    8. No.5
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    10. No.3
    11. No.2
    12. No.1

  16. Vol. 95 (2009)

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    3. No.10
    4. No.9
    5. No.8
    6. No.7
    7. No.6
    8. No.5
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    10. No.3
    11. No.2
    12. No.1

  17. Vol. 94 (2008)

    1. No.12
    2. No.11
    3. No.10
    4. No.9
    5. No.8
    6. No.7
    7. No.6
    8. No.5
    9. No.4
    10. No.3
    11. No.2
    12. No.1

  18. Vol. 93 (2007)

    1. No.12
    2. No.11
    3. No.10
    4. No.9
    5. No.8
    6. No.7
    7. No.6
    8. No.5
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    10. No.3
    11. No.2
    12. No.1

  19. Vol. 92 (2006)

    1. No.12
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    3. No.10
    4. No.9
    5. No.8
    6. No.7
    7. No.6
    8. No.5
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    10. No.3
    11. No.2
    12. No.1

  20. Vol. 91 (2005)

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    3. No.10
    4. No.9
    5. No.8
    6. No.7
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    8. No.5
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  21. Vol. 90 (2004)

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    4. No.9
    5. No.8
    6. No.7
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    11. No.2
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  22. Vol. 89 (2003)

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    3. No.10
    4. No.9
    5. No.8
    6. No.7
    7. No.6
    8. No.5
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    10. No.3
    11. No.2
    12. No.1

  23. Vol. 88 (2002)

    1. No.12
    2. No.11
    3. No.10
    4. No.9
    5. No.8
    6. No.7
    7. No.6
    8. No.5
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    10. No.3
    11. No.2
    12. No.1

  24. Vol. 87 (2001)

    1. No.12
    2. No.11
    3. No.10
    4. No.9
    5. No.8
    6. No.7
    7. No.6
    8. No.5
    9. No.4
    10. No.3
    11. No.2
    12. No.1

  25. Vol. 86 (2000)

    1. No.12
    2. No.11
    3. No.10
    4. No.9
    5. No.8
    6. No.7
    7. No.6
    8. No.5
    9. No.4
    10. No.3
    11. No.2
    12. No.1

  26. Vol. 85 (1999)

    1. No.12
    2. No.11
    3. No.10
    4. No.9
    5. No.8
    6. No.7
    7. No.6
    8. No.5
    9. No.4
    10. No.3
    11. No.2
    12. No.1

  27. Vol. 84 (1998)

    1. No.12
    2. No.11
    3. No.10
    4. No.9
    5. No.8
    6. No.7
    7. No.6
    8. No.5
    9. No.4
    10. No.3
    11. No.2
    12. No.1

  28. Vol. 83 (1997)

    1. No.12
    2. No.11
    3. No.10
    4. No.9
    5. No.8
    6. No.7
    7. No.6
    8. No.5
    9. No.4
    10. No.3
    11. No.2
    12. No.1

  29. Vol. 82 (1996)

    1. No.12
    2. No.11
    3. No.10
    4. No.9
    5. No.8
    6. No.7
    7. No.6
    8. No.5
    9. No.4
    10. No.3
    11. No.2
    12. No.1

  30. Vol. 81 (1995)

    1. No.12
    2. No.11
    3. No.10
    4. No.9
    5. No.8
    6. No.7
    7. No.6
    8. No.5
    9. No.4
    10. No.3
    11. No.2
    12. No.1

  31. Vol. 80 (1994)

    1. No.12
    2. No.11
    3. No.10
    4. No.9
    5. No.8
    6. No.7
    7. No.6
    8. No.5
    9. No.4
    10. No.3
    11. No.2
    12. No.1

  32. Vol. 79 (1993)

    1. No.12
    2. No.11
    3. No.10
    4. No.9
    5. No.8
    6. No.7
    7. No.6
    8. No.5
    9. No.4
    10. No.3
    11. No.2
    12. No.1

  33. Vol. 78 (1992)

    1. No.12
    2. No.11
    3. No.10
    4. No.9
    5. No.8
    6. No.7
    7. No.6
    8. No.5
    9. No.4
    10. No.3
    11. No.2
    12. No.1

  34. Vol. 77 (1991)

    1. No.12
    2. No.11
    3. No.10
    4. No.9
    5. No.8
    6. No.7
    7. No.6
    8. No.5
    9. No.4
    10. No.3
    11. No.2
    12. No.1

  35. Vol. 76 (1990)

    1. No.12
    2. No.11
    3. No.10
    4. No.9
    5. No.8
    6. No.7
    7. No.6
    8. No.5
    9. No.4
    10. No.3
    11. No.2
    12. No.1

  36. Vol. 75 (1989)

    1. No.12
    2. No.11
    3. No.10
    4. No.9
    5. No.8
    6. No.7
    7. No.6
    8. No.5
    9. No.4
    10. No.3
    11. No.2
    12. No.1

  37. Vol. 74 (1988)

    1. No.12
    2. No.11
    3. No.10
    4. No.9
    5. No.8
    6. No.7
    7. No.6
    8. No.5
    9. No.4
    10. No.3
    11. No.2
    12. No.1

  38. Vol. 73 (1987)

    1. No.16
    2. No.15
    3. No.14
    4. No.13
    5. No.12
    6. No.11
    7. No.10
    8. No.9
    9. No.8
    10. No.7
    11. No.6
    12. No.5
    13. No.4
    14. No.3
    15. No.2
    16. No.1

  39. Vol. 72 (1986)

    1. No.16
    2. No.15
    3. No.14
    4. No.13
    5. No.12
    6. No.11
    7. No.10
    8. No.9
    9. No.8
    10. No.7
    11. No.6
    12. No.5
    13. No.4
    14. No.3
    15. No.2
    16. No.1

  40. Vol. 71 (1985)

    1. No.16
    2. No.15
    3. No.14
    4. No.13
    5. No.12
    6. No.11
    7. No.10
    8. No.9
    9. No.8
    10. No.7
    11. No.6
    12. No.5
    13. No.4
    14. No.3
    15. No.2
    16. No.1

  41. Vol. 70 (1984)

    1. No.16
    2. No.15
    3. No.14
    4. No.13
    5. No.12
    6. No.11
    7. No.10
    8. No.9
    9. No.8
    10. No.7
    11. No.6
    12. No.5
    13. No.4
    14. No.3
    15. No.2
    16. No.1

  42. Vol. 69 (1983)

    1. No.16
    2. No.15
    3. No.14
    4. No.13
    5. No.12
    6. No.11
    7. No.10
    8. No.9
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    10. No.7
    11. No.6
    12. No.5
    13. No.4
    14. No.3
    15. No.2
    16. No.1

  43. Vol. 68 (1982)

    1. No.16
    2. No.15
    3. No.14
    4. No.13
    5. No.12
    6. No.11
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    8. No.9
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    10. No.7
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    16. No.1

  44. Vol. 67 (1981)

    1. No.16
    2. No.15
    3. No.14
    4. No.13
    5. No.12
    6. No.11
    7. No.10
    8. No.9
    9. No.8
    10. No.7
    11. No.6
    12. No.5
    13. No.4
    14. No.3
    15. No.2
    16. No.1

  45. Vol. 66 (1980)

    1. No.14
    2. No.13
    3. No.12
    4. No.11
    5. No.10
    6. No.9
    7. No.8
    8. No.7
    9. No.6
    10. No.5
    11. No.4
    12. No.3
    13. No.2
    14. No.1

  46. Vol. 65 (1979)

    1. No.14
    2. No.13
    3. No.12
    4. No.11
    5. No.10
    6. No.9
    7. No.8
    8. No.7
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    10. No.5
    11. No.4
    12. No.3
    13. No.2
    14. No.1

  47. Vol. 64 (1978)

    1. No.14
    2. No.13
    3. No.12
    4. No.11
    5. No.10
    6. No.9
    7. No.8
    8. No.7
    9. No.6
    10. No.5
    11. No.4
    12. No.3
    13. No.2
    14. No.1

  48. Vol. 63 (1977)

    1. No.14
    2. No.13
    3. No.12
    4. No.11
    5. No.10
    6. No.9
    7. No.8
    8. No.7
    9. No.6
    10. No.5
    11. No.4
    12. No.3
    13. No.2
    14. No.1

  49. Vol. 62 (1976)

    1. No.14
    2. No.13
    3. No.12
    4. No.11
    5. No.10
    6. No.9
    7. No.8
    8. No.7
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    10. No.5
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    14. No.1

  50. Vol. 61 (1975)

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    2. No.14
    3. No.13
    4. No.12
    5. No.11
    6. No.10
    7. No.9
    8. No.8
    9. No.7
    10. No.6
    11. No.5
    12. No.4
    13. No.3
    14. No.2
    15. No.1

  51. Vol. 60 (1974)

    1. No.14
    2. No.13
    3. No.12
    4. No.11
    5. No.10
    6. No.9
    7. No.8
    8. No.7
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    10. No.5
    11. No.4
    12. No.3
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  52. Vol. 59 (1973)

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    3. No.12
    4. No.11
    5. No.10
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  53. Vol. 58 (1972)

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    5. No.10
    6. No.9
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    8. No.7
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  54. Vol. 57 (1971)

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    3. No.12
    4. No.11
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    7. No.8
    8. No.7
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    10. No.5
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    14. No.1

  55. Vol. 56 (1970)

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  56. Vol. 55 (1969)

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  57. Vol. 54 (1968)

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    3. No.12
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  58. Vol. 53 (1967)

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  59. Vol. 52 (1966)

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  60. Vol. 51 (1965)

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    6. No.8
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  61. Vol. 50 (1964)

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  62. Vol. 49 (1963)

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  63. Vol. 48 (1962)

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  64. Vol. 47 (1961)

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  65. Vol. 46 (1960)

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    4. No.11
    5. No.10
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  66. Vol. 45 (1959)

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  67. Vol. 44 (1958)

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  68. Vol. 43 (1957)

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  69. Vol. 42 (1956)

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  70. Vol. 41 (1955)

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Tetsu-to-Hagané Vol. 110 (2024), No. 15

Preface to the Special Issue “Diagnostic, Evaluation and Data Utilization Technologies for Corrosion Deterioration of Infrastructures”

Hideki Katayama

pp. 1165-1165

DOI:

10.2355/tetsutohagane.110.1165
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Preface to the Special Issue “Diagnostic, Evaluation and Data Utilization Technologies for Corrosion Deterioration of Infrastructures”

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Composition Analysis of Corrosion Products on Steel by Machine Learning of Optical Microscopic Images

Yuki Tsuji, Kota Hirasawa, Sunao Shoji, Yuichi Kitagawa, Yasuchika Hasegawa, Koji Fushimi

pp. 1166-1178

DOI:

10.2355/tetsutohagane.TETSU-2024-056

Abstract

Analysis of the corrosion distribution and composition of corrosion products on steel surfaces using supervised machine learning of optical microscopic images was investigated. The accuracy of the artificial intelligence in evaluating the composition of iron compound reference samples was affected by the illumination intensity and surface roughness during image capture. The evaluation accuracy was high for compounds with a wide distribution of R value such as Fe2O3 and FeOOH, but low for compounds with a narrow distribution such as Fe3O4. The results of wet-dry cycling tests on weathering steel with NaCl particles on the surface showed that the transition of corrosion products during the corrosion progress can be analyzed from optical microscope images.

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Composition Analysis of Corrosion Products on Steel by Machine Learning of Optical Microscopic Images

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Relation between Surface Appearance and Corrosion Depth Distribution of Carbon Steel under Atmospheric Corrosion Environment

Yu Sugawara, Takahiro Igarashi

pp. 1179-1186

DOI:

10.2355/tetsutohagane.TETSU-2024-061

Abstract

Degradation due to atmospheric corrosion is an important problem for steel structures such as bridges. In order to maintain steel structures safely over a long period of time, there is a need for a low-cost and easy-to-use method to evaluate corrosion degradation. In this study, corrosion morphology under atmospheric corrosion environment was focused on. The relation between the surface appearance and the distribution of corrosion depth of the carbon steel specimen after atmospheric exposure was analyzed, and the prediction of corrosion morphology under rust layers by surface observation of steel specimens was examined. It was found that deeply-corroded areas were possible to be located within the dark brown regions in the rusted specimen after the atmospheric exposure. As the exposure period increased, the correspondence between the dark brown regions in the rusted specimen and deeply-corroded areas became clearer. Since the corrosion progressed more locally as the exposure period increased, it is considered that the surface appearance of the rusted specimen showed traces of the deeply-corroded areas.

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Relation between Surface Appearance and Corrosion Depth Distribution of Carbon Steel under Atmospheric Corrosion Environment

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Corrosion Risk Prediction of Carbon Steels by Hyperspectral Analysis

Hideki Katayama, Yuto Yoshida, Takaya Akashi, Mariko Kadowaki, Yoshiharu Murase, Yusuke Tsutsumi

pp. 1187-1194

DOI:

10.2355/tetsutohagane.TETSU-2024-076

Abstract

The utility of hyperspectral measurement was assessed as a means of predicting the corrosion risk of steel materials based on surface information. Carbon steels exposed to outdoor conditions in Choshi and Miyakojima were used as the test specimens. Exposure tests were conducted every six months for a duration of two and a half years. Corrosion loss was calculated by comparing the weight of specimens before exposure test and after removing corrosion products from the surface. Hyperspectral measurements were conducted on these specimens, with corrosion products identified through SAM (Spectral Angle Mapper) analysis. α-FeOOH, β-FeOOH, γ-FeOOH, and Fe3O4 were employed as reference data for SAM analysis. In both Choshi and Miyakojima test sites, γ-FeOOH was predominantly detected on the specimens after ordinary exposure tests, whereas Fe3O4 was prevalent on the specimens exposed to sheltered environments. The correlation between the proportion of each corrosion product identified through SAM analysis and the amount of corrosion change for one year was explored. α-FeOOH exhibited a positive correlation with the amount of corrosion change, whereas the amount of corrosion change tended to decrease with an increase in β-FeOOH fraction.

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Corrosion Risk Prediction of Carbon Steels by Hyperspectral Analysis

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Application of Admittance Analysis for Corrosion Rate Evaluation of Carbon Steel with Rust Layer in Electrochemical Impedance Spectroscopy

Yusaku Akimoto, Yoshinao Hoshi

pp. 1195-1202

DOI:

10.2355/tetsutohagane.TETSU-2024-059

Abstract

This paper describes an evaluation method for corrosion rate of carbon steel covered with rust layer by admittance analysis in electrochemical impedance spectroscopy. The polarization resistance, Rp, is often estimated from the impedance spectrum of carbon steel to determine the corrosion rate. The Rp can be determined from the impedance spectrum when the low frequency impedance is converted to the real axis on the Nyquist diagram. Because the impedance spectrum of carbon steel covered with rust layer often describes a part of loop in the low frequency range, it is difficult to determine the Rp by extrapolating the low frequency impedance to the real axis. In the present study, an admittance analysis was employed to determine the Rp from the admittance spectrum of carbon steel covered with rust layer. The admittance is plotted as the reciprocal of impedance on the complex plane. In this case, the Rp can be determined from the admittance spectrum when the low frequency admittance is converged to the real axis. The admittance spectrum of carbon steel with rust layer indicated that the low frequency admittance was converted to the real axis, namely, the Rp could be determined from the admittance spectrum. The corrosion rate of carbon steel with rust layer could be estimated from the Rp by admittance analysis, demonstrating that the value was corelated to that estimated from corrosion loss. The impedance and admittance simulations were performed using an equivalent circuit to discuss the time constant observed in the low frequency range.

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Application of Admittance Analysis for Corrosion Rate Evaluation of Carbon Steel with Rust Layer in Electrochemical Impedance Spectroscopy

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Year-round Monitoring of Temperature and Humidity using a Sensor Network installed in the Hokkaido Centennial Memorial Tower

Kazuhisa Azumi, Masatoshi Sakairi, Koji Fushimi, Takashi Sato, Ken Mikami, Masazumi Miura

pp. 1203-1215

DOI:

10.2355/tetsutohagane.TETSU-2024-007

Abstract

Daily and seasonal changes in temperature (T) and relative humidity (RH) were monitored using a sensor network system installed in the Hokkaido Centennial Memorial Tower, built more than 50 years ago using weathering steel, to investigate its corrosion condition. Five T-RH sensors were set at the south side wall, inside the south tower, in the semi-open central area, inside the north tower, and on the north side wall on the 4th, 14th, and 24th floors. The T changed as a function of altitude, location in the floor, season, weather, solar radiation, diurnal cycle, distance from the wall, etc. The highest T of the south wall at daytime in the winter season could rise more than 30 °C even if the outer temperature was below 0 °C due to solar radiation causing the repetition of ice or snow melting in the daytime and freezing of water at night. The change in RH and T inside the tower followed a Tomashov-type RH-T curve (high RH at low T in the morning and evening). In winter, however, T and RH distribution, i.e., high-RH (> ca.60%) area below the freezing point and low-RH area with the high-T, caused air transportation inside the tower, condensation (and freezing) in the low-T area, and drying in the high-T area. In the visual inspections, severe corrosion, such as blistering and peeling, has been observed at the bottom of the tower, where snow has accumulated, and rainwater has stayed for a long time, especially at welds and joints.

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Year-round Monitoring of Temperature and Humidity using a Sensor Network installed in the Hokkaido Centennial Memorial Tower

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Identification of Rusts Formed on Steels by Image Analysis with HSV Color Space

Hiroo Ishii, Masatoshi Sakairi

pp. 1216-1224

DOI:

10.2355/tetsutohagane.TETSU-2024-055

Abstract

Corrosion products formed on steel in various environments were identified with image analysis, Raman spectroscopy, and X-ray diffraction (XRD). The results of Raman spectroscopy showed that γ-FeOOH exhibits bright yellow color and β-FeOOH exhibits gray color. Main corrosion products composition formed on the steels by XRD analysis was γ-FeOOH. Using proper threshold of HSV color space, makes it possible to identify corrosion products formed on steel. Comparison of corrosiveness of exposed environments and formed corrosion products, α-FeOOH is preferentially formed in mild corrosive environments, while β-FeOOH was formed in severe corrosive environments.

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Identification of Rusts Formed on Steels by Image Analysis with HSV Color Space

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Color Tone of Corrosion Products and Estimation of Rust Composition by Image Processing

Kenta Fujihashi, Wataru Oshikawa

pp. 1225-1236

DOI:

10.2355/tetsutohagane.TETSU-2024-067

Abstract

Appropriate maintenance and maintenance of infrastructures that has been used for a long time is required because there is the concern that safety will deteriorate due to atmospheric corrosion. However, the cost of maintenance and management is also increasing, and there is required to save labor and improve efficiency of maintenance and management. Therefore, the purpose of this study was to easily estimate the rust composition by the image processing of image of steel surface corroded by the outdoor exposure test. The outdoor exposure test was conducted in Choshi City and those conditions were open exposure and shelter exposure for 2.5 years. The compositions of those corrosion products measured by X-ray diffraction. Exposed test pieces were photographed RAW with the digital camera. Those photographs were developed and trimmed. The obtained images were converted from RGB images to La*b* and LCh images. The histograms of hue were fitted to the Gaussian function to determine the peak position and the spread of the histogram. As a result, it was indicated that the peak position shown in the histogram of hue shifted to the low-angle side due to the increase in FeFe2O4. In addition, it was indicated that the composition ratio of α-FeOOH, β-FeOOH, γ-FeOOH and FeFe2O4 can be estimated by the shape, spread and peak position of the histogram of hue. As shown in the graphical abstract, the composition ratio of the corrosion products measured by RIR method and estimated by image processing was in good agreement.

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Color Tone of Corrosion Products and Estimation of Rust Composition by Image Processing

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Properties at Corrosion Sites Formed on Pure Iron during Wet-dry Cycling Test and on Steel during Exposure Test

Soshiro Yamazaki, Fuka Kawamura, Koki Saito, Makoto Chiba

pp. 1237-1243

DOI:

10.2355/tetsutohagane.TETSU-2024-062

Abstract

Steels are widely used as a structural material for infrastructure, such as bridges. However, some of bridges built in Japan, have passed more than 50 years, since construction. Thus, these bridges will need to be reconstruction, because atmospheric corrosion of these materials will occur in this condition. However, there are numerous bridges in Japan, and reconstruction of these bridges would need much cost. Therefore, it is necessary to establish the technique for selection of the bridges with severe damage by atmospheric corrosion. To establish a new non-destructive and non-contact techniques for identifying the sites of atmospheric corrosion occurred in steel, used as structural materials for bridges, the relation between corrosion site and color of corrosion product, surface morphology or distribution of surface potential of the pure iron and steel after corrosion test were evaluated.

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Properties at Corrosion Sites Formed on Pure Iron during Wet-dry Cycling Test and on Steel during Exposure Test

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Evaluation of Corrosion on Steel Surface Using Image Processing

Takahiro Igarashi, Yu Sugawara, Kyohei Otani, Takahito Aoyama

pp. 1244-1250

DOI:

10.2355/tetsutohagane.TETSU-2024-058

Abstract

Using two types of image processing techniques without machine learning, edge extraction processing and keypoint extraction processing, progressively corroded regions under the rust layer from images of corroded steel surfaces was extracted. We found that there was a relatively good correlation between the keypoint strength obtained from the keypoint extraction processing for HSL transformed and histogram flattened corroded surface images and the corrosion depth after rust removal.

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Evaluation of Corrosion on Steel Surface Using Image Processing

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