Defect detection method of underwater bored cast-in-place pile based on optical image in borehole

Abstract In order to ensure the borehole forming of underwater bored cast-in-place pile and the overall quality of pile foundation engineering, the rapid defect detection on the borehole wall of bored cast-in-place pile and its retaining wall is of great significance. Aiming at the problem of image acquisition and defect detection of underwater complex environment in bored pile, based on the optical image acquisition technology of underwater bored pile borehole wall, this study puts forward a defect detection method suitable for underwater bored pile borehole wall. Firstly, using the principle of underwater optical imaging, an optical image acquisition equipment suitable for the borehole wall of underwater bored cast-in-place pile is developed. Combined with the working principle of the equipment and the optical image characteristics of the defect area, the borehole wall optical image correction method is constructed, which weakens the influence of close distance and inner curvature of the borehole wall on the optical imaging. Then, by constructing the underwater optical imaging model of borehole wall defects, an underwater optical image enhancement function considering the intensity of light source and the difference in optical propagation at different wavelengths is proposed. Combined with gradient operator and maximum interclass variance method, an effective identification method for borehole wall defect area of underwater bored cast-in-place pile is formed by sharpening the edge of borehole wall defect area. Finally, three characteristic parameter pointers, target frame ratio, area and aspect ratio, and special shape, are proposed to form the search and detection method of borehole wall target defect area, which can realize the accurate defect detection of borehole wall binary image. Combined with the comparative analysis of borehole wall image processing and quantitative evaluation and analysis of borehole wall defects, it is proved that this method has accurate and efficient detection ability, which can realize the intelligent detection of optical image of borehole wall defects in the underwater bored cast-in-place pile, improve the accuracy of artificial intelligence measurement of borehole wall defect area of bored cast-in-place pile, and provide more data support for the detection and maintenance of borehole wall defects of underwater bored cast-in-place pile. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Wang, Jinchao [verfasserIn] Liu, Houcheng

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Bored pile Defect detection Digital image Underwater detection Automatic identification

Anmerkung:	© Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Übergeordnetes Werk:	Enthalten in: Journal of civil structural health monitoring - Berlin : Springer, 2011, 14(2023), 1 vom: 28. Okt., Seite 189-207
Übergeordnetes Werk:	volume:14 ; year:2023 ; number:1 ; day:28 ; month:10 ; pages:189-207

Links:	Volltext

DOI / URN:	10.1007/s13349-023-00724-2

Katalog-ID:	SPR054743745

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520			\|a Abstract In order to ensure the borehole forming of underwater bored cast-in-place pile and the overall quality of pile foundation engineering, the rapid defect detection on the borehole wall of bored cast-in-place pile and its retaining wall is of great significance. Aiming at the problem of image acquisition and defect detection of underwater complex environment in bored pile, based on the optical image acquisition technology of underwater bored pile borehole wall, this study puts forward a defect detection method suitable for underwater bored pile borehole wall. Firstly, using the principle of underwater optical imaging, an optical image acquisition equipment suitable for the borehole wall of underwater bored cast-in-place pile is developed. Combined with the working principle of the equipment and the optical image characteristics of the defect area, the borehole wall optical image correction method is constructed, which weakens the influence of close distance and inner curvature of the borehole wall on the optical imaging. Then, by constructing the underwater optical imaging model of borehole wall defects, an underwater optical image enhancement function considering the intensity of light source and the difference in optical propagation at different wavelengths is proposed. Combined with gradient operator and maximum interclass variance method, an effective identification method for borehole wall defect area of underwater bored cast-in-place pile is formed by sharpening the edge of borehole wall defect area. Finally, three characteristic parameter pointers, target frame ratio, area and aspect ratio, and special shape, are proposed to form the search and detection method of borehole wall target defect area, which can realize the accurate defect detection of borehole wall binary image. Combined with the comparative analysis of borehole wall image processing and quantitative evaluation and analysis of borehole wall defects, it is proved that this method has accurate and efficient detection ability, which can realize the intelligent detection of optical image of borehole wall defects in the underwater bored cast-in-place pile, improve the accuracy of artificial intelligence measurement of borehole wall defect area of bored cast-in-place pile, and provide more data support for the detection and maintenance of borehole wall defects of underwater bored cast-in-place pile.
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allfields	10.1007/s13349-023-00724-2 doi (DE-627)SPR054743745 (SPR)s13349-023-00724-2-e DE-627 ger DE-627 rakwb eng Wang, Jinchao verfasserin aut Defect detection method of underwater bored cast-in-place pile based on optical image in borehole 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract In order to ensure the borehole forming of underwater bored cast-in-place pile and the overall quality of pile foundation engineering, the rapid defect detection on the borehole wall of bored cast-in-place pile and its retaining wall is of great significance. Aiming at the problem of image acquisition and defect detection of underwater complex environment in bored pile, based on the optical image acquisition technology of underwater bored pile borehole wall, this study puts forward a defect detection method suitable for underwater bored pile borehole wall. Firstly, using the principle of underwater optical imaging, an optical image acquisition equipment suitable for the borehole wall of underwater bored cast-in-place pile is developed. Combined with the working principle of the equipment and the optical image characteristics of the defect area, the borehole wall optical image correction method is constructed, which weakens the influence of close distance and inner curvature of the borehole wall on the optical imaging. Then, by constructing the underwater optical imaging model of borehole wall defects, an underwater optical image enhancement function considering the intensity of light source and the difference in optical propagation at different wavelengths is proposed. Combined with gradient operator and maximum interclass variance method, an effective identification method for borehole wall defect area of underwater bored cast-in-place pile is formed by sharpening the edge of borehole wall defect area. Finally, three characteristic parameter pointers, target frame ratio, area and aspect ratio, and special shape, are proposed to form the search and detection method of borehole wall target defect area, which can realize the accurate defect detection of borehole wall binary image. Combined with the comparative analysis of borehole wall image processing and quantitative evaluation and analysis of borehole wall defects, it is proved that this method has accurate and efficient detection ability, which can realize the intelligent detection of optical image of borehole wall defects in the underwater bored cast-in-place pile, improve the accuracy of artificial intelligence measurement of borehole wall defect area of bored cast-in-place pile, and provide more data support for the detection and maintenance of borehole wall defects of underwater bored cast-in-place pile. Bored pile (dpeaa)DE-He213 Defect detection (dpeaa)DE-He213 Digital image (dpeaa)DE-He213 Underwater detection (dpeaa)DE-He213 Automatic identification (dpeaa)DE-He213 Liu, Houcheng aut Enthalten in Journal of civil structural health monitoring Berlin : Springer, 2011 14(2023), 1 vom: 28. Okt., Seite 189-207 (DE-627)645092878 (DE-600)2592302-X 2190-5479 nnns volume:14 year:2023 number:1 day:28 month:10 pages:189-207 https://dx.doi.org/10.1007/s13349-023-00724-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 14 2023 1 28 10 189-207
spelling	10.1007/s13349-023-00724-2 doi (DE-627)SPR054743745 (SPR)s13349-023-00724-2-e DE-627 ger DE-627 rakwb eng Wang, Jinchao verfasserin aut Defect detection method of underwater bored cast-in-place pile based on optical image in borehole 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract In order to ensure the borehole forming of underwater bored cast-in-place pile and the overall quality of pile foundation engineering, the rapid defect detection on the borehole wall of bored cast-in-place pile and its retaining wall is of great significance. Aiming at the problem of image acquisition and defect detection of underwater complex environment in bored pile, based on the optical image acquisition technology of underwater bored pile borehole wall, this study puts forward a defect detection method suitable for underwater bored pile borehole wall. Firstly, using the principle of underwater optical imaging, an optical image acquisition equipment suitable for the borehole wall of underwater bored cast-in-place pile is developed. Combined with the working principle of the equipment and the optical image characteristics of the defect area, the borehole wall optical image correction method is constructed, which weakens the influence of close distance and inner curvature of the borehole wall on the optical imaging. Then, by constructing the underwater optical imaging model of borehole wall defects, an underwater optical image enhancement function considering the intensity of light source and the difference in optical propagation at different wavelengths is proposed. Combined with gradient operator and maximum interclass variance method, an effective identification method for borehole wall defect area of underwater bored cast-in-place pile is formed by sharpening the edge of borehole wall defect area. Finally, three characteristic parameter pointers, target frame ratio, area and aspect ratio, and special shape, are proposed to form the search and detection method of borehole wall target defect area, which can realize the accurate defect detection of borehole wall binary image. Combined with the comparative analysis of borehole wall image processing and quantitative evaluation and analysis of borehole wall defects, it is proved that this method has accurate and efficient detection ability, which can realize the intelligent detection of optical image of borehole wall defects in the underwater bored cast-in-place pile, improve the accuracy of artificial intelligence measurement of borehole wall defect area of bored cast-in-place pile, and provide more data support for the detection and maintenance of borehole wall defects of underwater bored cast-in-place pile. Bored pile (dpeaa)DE-He213 Defect detection (dpeaa)DE-He213 Digital image (dpeaa)DE-He213 Underwater detection (dpeaa)DE-He213 Automatic identification (dpeaa)DE-He213 Liu, Houcheng aut Enthalten in Journal of civil structural health monitoring Berlin : Springer, 2011 14(2023), 1 vom: 28. Okt., Seite 189-207 (DE-627)645092878 (DE-600)2592302-X 2190-5479 nnns volume:14 year:2023 number:1 day:28 month:10 pages:189-207 https://dx.doi.org/10.1007/s13349-023-00724-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 14 2023 1 28 10 189-207
allfields_unstemmed	10.1007/s13349-023-00724-2 doi (DE-627)SPR054743745 (SPR)s13349-023-00724-2-e DE-627 ger DE-627 rakwb eng Wang, Jinchao verfasserin aut Defect detection method of underwater bored cast-in-place pile based on optical image in borehole 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract In order to ensure the borehole forming of underwater bored cast-in-place pile and the overall quality of pile foundation engineering, the rapid defect detection on the borehole wall of bored cast-in-place pile and its retaining wall is of great significance. Aiming at the problem of image acquisition and defect detection of underwater complex environment in bored pile, based on the optical image acquisition technology of underwater bored pile borehole wall, this study puts forward a defect detection method suitable for underwater bored pile borehole wall. Firstly, using the principle of underwater optical imaging, an optical image acquisition equipment suitable for the borehole wall of underwater bored cast-in-place pile is developed. Combined with the working principle of the equipment and the optical image characteristics of the defect area, the borehole wall optical image correction method is constructed, which weakens the influence of close distance and inner curvature of the borehole wall on the optical imaging. Then, by constructing the underwater optical imaging model of borehole wall defects, an underwater optical image enhancement function considering the intensity of light source and the difference in optical propagation at different wavelengths is proposed. Combined with gradient operator and maximum interclass variance method, an effective identification method for borehole wall defect area of underwater bored cast-in-place pile is formed by sharpening the edge of borehole wall defect area. Finally, three characteristic parameter pointers, target frame ratio, area and aspect ratio, and special shape, are proposed to form the search and detection method of borehole wall target defect area, which can realize the accurate defect detection of borehole wall binary image. Combined with the comparative analysis of borehole wall image processing and quantitative evaluation and analysis of borehole wall defects, it is proved that this method has accurate and efficient detection ability, which can realize the intelligent detection of optical image of borehole wall defects in the underwater bored cast-in-place pile, improve the accuracy of artificial intelligence measurement of borehole wall defect area of bored cast-in-place pile, and provide more data support for the detection and maintenance of borehole wall defects of underwater bored cast-in-place pile. Bored pile (dpeaa)DE-He213 Defect detection (dpeaa)DE-He213 Digital image (dpeaa)DE-He213 Underwater detection (dpeaa)DE-He213 Automatic identification (dpeaa)DE-He213 Liu, Houcheng aut Enthalten in Journal of civil structural health monitoring Berlin : Springer, 2011 14(2023), 1 vom: 28. Okt., Seite 189-207 (DE-627)645092878 (DE-600)2592302-X 2190-5479 nnns volume:14 year:2023 number:1 day:28 month:10 pages:189-207 https://dx.doi.org/10.1007/s13349-023-00724-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 14 2023 1 28 10 189-207
allfieldsGer	10.1007/s13349-023-00724-2 doi (DE-627)SPR054743745 (SPR)s13349-023-00724-2-e DE-627 ger DE-627 rakwb eng Wang, Jinchao verfasserin aut Defect detection method of underwater bored cast-in-place pile based on optical image in borehole 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract In order to ensure the borehole forming of underwater bored cast-in-place pile and the overall quality of pile foundation engineering, the rapid defect detection on the borehole wall of bored cast-in-place pile and its retaining wall is of great significance. Aiming at the problem of image acquisition and defect detection of underwater complex environment in bored pile, based on the optical image acquisition technology of underwater bored pile borehole wall, this study puts forward a defect detection method suitable for underwater bored pile borehole wall. Firstly, using the principle of underwater optical imaging, an optical image acquisition equipment suitable for the borehole wall of underwater bored cast-in-place pile is developed. Combined with the working principle of the equipment and the optical image characteristics of the defect area, the borehole wall optical image correction method is constructed, which weakens the influence of close distance and inner curvature of the borehole wall on the optical imaging. Then, by constructing the underwater optical imaging model of borehole wall defects, an underwater optical image enhancement function considering the intensity of light source and the difference in optical propagation at different wavelengths is proposed. Combined with gradient operator and maximum interclass variance method, an effective identification method for borehole wall defect area of underwater bored cast-in-place pile is formed by sharpening the edge of borehole wall defect area. Finally, three characteristic parameter pointers, target frame ratio, area and aspect ratio, and special shape, are proposed to form the search and detection method of borehole wall target defect area, which can realize the accurate defect detection of borehole wall binary image. Combined with the comparative analysis of borehole wall image processing and quantitative evaluation and analysis of borehole wall defects, it is proved that this method has accurate and efficient detection ability, which can realize the intelligent detection of optical image of borehole wall defects in the underwater bored cast-in-place pile, improve the accuracy of artificial intelligence measurement of borehole wall defect area of bored cast-in-place pile, and provide more data support for the detection and maintenance of borehole wall defects of underwater bored cast-in-place pile. Bored pile (dpeaa)DE-He213 Defect detection (dpeaa)DE-He213 Digital image (dpeaa)DE-He213 Underwater detection (dpeaa)DE-He213 Automatic identification (dpeaa)DE-He213 Liu, Houcheng aut Enthalten in Journal of civil structural health monitoring Berlin : Springer, 2011 14(2023), 1 vom: 28. Okt., Seite 189-207 (DE-627)645092878 (DE-600)2592302-X 2190-5479 nnns volume:14 year:2023 number:1 day:28 month:10 pages:189-207 https://dx.doi.org/10.1007/s13349-023-00724-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 14 2023 1 28 10 189-207
allfieldsSound	10.1007/s13349-023-00724-2 doi (DE-627)SPR054743745 (SPR)s13349-023-00724-2-e DE-627 ger DE-627 rakwb eng Wang, Jinchao verfasserin aut Defect detection method of underwater bored cast-in-place pile based on optical image in borehole 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract In order to ensure the borehole forming of underwater bored cast-in-place pile and the overall quality of pile foundation engineering, the rapid defect detection on the borehole wall of bored cast-in-place pile and its retaining wall is of great significance. Aiming at the problem of image acquisition and defect detection of underwater complex environment in bored pile, based on the optical image acquisition technology of underwater bored pile borehole wall, this study puts forward a defect detection method suitable for underwater bored pile borehole wall. Firstly, using the principle of underwater optical imaging, an optical image acquisition equipment suitable for the borehole wall of underwater bored cast-in-place pile is developed. Combined with the working principle of the equipment and the optical image characteristics of the defect area, the borehole wall optical image correction method is constructed, which weakens the influence of close distance and inner curvature of the borehole wall on the optical imaging. Then, by constructing the underwater optical imaging model of borehole wall defects, an underwater optical image enhancement function considering the intensity of light source and the difference in optical propagation at different wavelengths is proposed. Combined with gradient operator and maximum interclass variance method, an effective identification method for borehole wall defect area of underwater bored cast-in-place pile is formed by sharpening the edge of borehole wall defect area. Finally, three characteristic parameter pointers, target frame ratio, area and aspect ratio, and special shape, are proposed to form the search and detection method of borehole wall target defect area, which can realize the accurate defect detection of borehole wall binary image. Combined with the comparative analysis of borehole wall image processing and quantitative evaluation and analysis of borehole wall defects, it is proved that this method has accurate and efficient detection ability, which can realize the intelligent detection of optical image of borehole wall defects in the underwater bored cast-in-place pile, improve the accuracy of artificial intelligence measurement of borehole wall defect area of bored cast-in-place pile, and provide more data support for the detection and maintenance of borehole wall defects of underwater bored cast-in-place pile. Bored pile (dpeaa)DE-He213 Defect detection (dpeaa)DE-He213 Digital image (dpeaa)DE-He213 Underwater detection (dpeaa)DE-He213 Automatic identification (dpeaa)DE-He213 Liu, Houcheng aut Enthalten in Journal of civil structural health monitoring Berlin : Springer, 2011 14(2023), 1 vom: 28. Okt., Seite 189-207 (DE-627)645092878 (DE-600)2592302-X 2190-5479 nnns volume:14 year:2023 number:1 day:28 month:10 pages:189-207 https://dx.doi.org/10.1007/s13349-023-00724-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 14 2023 1 28 10 189-207
language	English
source	Enthalten in Journal of civil structural health monitoring 14(2023), 1 vom: 28. Okt., Seite 189-207 volume:14 year:2023 number:1 day:28 month:10 pages:189-207
sourceStr	Enthalten in Journal of civil structural health monitoring 14(2023), 1 vom: 28. Okt., Seite 189-207 volume:14 year:2023 number:1 day:28 month:10 pages:189-207
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Bored pile Defect detection Digital image Underwater detection Automatic identification
isfreeaccess_bool	false
container_title	Journal of civil structural health monitoring
authorswithroles_txt_mv	Wang, Jinchao @@aut@@ Liu, Houcheng @@aut@@
publishDateDaySort_date	2023-10-28T00:00:00Z
hierarchy_top_id	645092878
id	SPR054743745
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Firstly, using the principle of underwater optical imaging, an optical image acquisition equipment suitable for the borehole wall of underwater bored cast-in-place pile is developed. Combined with the working principle of the equipment and the optical image characteristics of the defect area, the borehole wall optical image correction method is constructed, which weakens the influence of close distance and inner curvature of the borehole wall on the optical imaging. Then, by constructing the underwater optical imaging model of borehole wall defects, an underwater optical image enhancement function considering the intensity of light source and the difference in optical propagation at different wavelengths is proposed. Combined with gradient operator and maximum interclass variance method, an effective identification method for borehole wall defect area of underwater bored cast-in-place pile is formed by sharpening the edge of borehole wall defect area. 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author	Wang, Jinchao
spellingShingle	Wang, Jinchao misc Bored pile misc Defect detection misc Digital image misc Underwater detection misc Automatic identification Defect detection method of underwater bored cast-in-place pile based on optical image in borehole
authorStr	Wang, Jinchao
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topic_title	Defect detection method of underwater bored cast-in-place pile based on optical image in borehole Bored pile (dpeaa)DE-He213 Defect detection (dpeaa)DE-He213 Digital image (dpeaa)DE-He213 Underwater detection (dpeaa)DE-He213 Automatic identification (dpeaa)DE-He213
topic	misc Bored pile misc Defect detection misc Digital image misc Underwater detection misc Automatic identification
topic_unstemmed	misc Bored pile misc Defect detection misc Digital image misc Underwater detection misc Automatic identification
topic_browse	misc Bored pile misc Defect detection misc Digital image misc Underwater detection misc Automatic identification
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title	Defect detection method of underwater bored cast-in-place pile based on optical image in borehole
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title_full	Defect detection method of underwater bored cast-in-place pile based on optical image in borehole
author_sort	Wang, Jinchao
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author_browse	Wang, Jinchao Liu, Houcheng
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doi_str_mv	10.1007/s13349-023-00724-2
title_sort	defect detection method of underwater bored cast-in-place pile based on optical image in borehole
title_auth	Defect detection method of underwater bored cast-in-place pile based on optical image in borehole
abstract	Abstract In order to ensure the borehole forming of underwater bored cast-in-place pile and the overall quality of pile foundation engineering, the rapid defect detection on the borehole wall of bored cast-in-place pile and its retaining wall is of great significance. Aiming at the problem of image acquisition and defect detection of underwater complex environment in bored pile, based on the optical image acquisition technology of underwater bored pile borehole wall, this study puts forward a defect detection method suitable for underwater bored pile borehole wall. Firstly, using the principle of underwater optical imaging, an optical image acquisition equipment suitable for the borehole wall of underwater bored cast-in-place pile is developed. Combined with the working principle of the equipment and the optical image characteristics of the defect area, the borehole wall optical image correction method is constructed, which weakens the influence of close distance and inner curvature of the borehole wall on the optical imaging. Then, by constructing the underwater optical imaging model of borehole wall defects, an underwater optical image enhancement function considering the intensity of light source and the difference in optical propagation at different wavelengths is proposed. Combined with gradient operator and maximum interclass variance method, an effective identification method for borehole wall defect area of underwater bored cast-in-place pile is formed by sharpening the edge of borehole wall defect area. Finally, three characteristic parameter pointers, target frame ratio, area and aspect ratio, and special shape, are proposed to form the search and detection method of borehole wall target defect area, which can realize the accurate defect detection of borehole wall binary image. Combined with the comparative analysis of borehole wall image processing and quantitative evaluation and analysis of borehole wall defects, it is proved that this method has accurate and efficient detection ability, which can realize the intelligent detection of optical image of borehole wall defects in the underwater bored cast-in-place pile, improve the accuracy of artificial intelligence measurement of borehole wall defect area of bored cast-in-place pile, and provide more data support for the detection and maintenance of borehole wall defects of underwater bored cast-in-place pile. © Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
abstractGer	Abstract In order to ensure the borehole forming of underwater bored cast-in-place pile and the overall quality of pile foundation engineering, the rapid defect detection on the borehole wall of bored cast-in-place pile and its retaining wall is of great significance. Aiming at the problem of image acquisition and defect detection of underwater complex environment in bored pile, based on the optical image acquisition technology of underwater bored pile borehole wall, this study puts forward a defect detection method suitable for underwater bored pile borehole wall. Firstly, using the principle of underwater optical imaging, an optical image acquisition equipment suitable for the borehole wall of underwater bored cast-in-place pile is developed. Combined with the working principle of the equipment and the optical image characteristics of the defect area, the borehole wall optical image correction method is constructed, which weakens the influence of close distance and inner curvature of the borehole wall on the optical imaging. Then, by constructing the underwater optical imaging model of borehole wall defects, an underwater optical image enhancement function considering the intensity of light source and the difference in optical propagation at different wavelengths is proposed. Combined with gradient operator and maximum interclass variance method, an effective identification method for borehole wall defect area of underwater bored cast-in-place pile is formed by sharpening the edge of borehole wall defect area. Finally, three characteristic parameter pointers, target frame ratio, area and aspect ratio, and special shape, are proposed to form the search and detection method of borehole wall target defect area, which can realize the accurate defect detection of borehole wall binary image. Combined with the comparative analysis of borehole wall image processing and quantitative evaluation and analysis of borehole wall defects, it is proved that this method has accurate and efficient detection ability, which can realize the intelligent detection of optical image of borehole wall defects in the underwater bored cast-in-place pile, improve the accuracy of artificial intelligence measurement of borehole wall defect area of bored cast-in-place pile, and provide more data support for the detection and maintenance of borehole wall defects of underwater bored cast-in-place pile. © Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
abstract_unstemmed	Abstract In order to ensure the borehole forming of underwater bored cast-in-place pile and the overall quality of pile foundation engineering, the rapid defect detection on the borehole wall of bored cast-in-place pile and its retaining wall is of great significance. Aiming at the problem of image acquisition and defect detection of underwater complex environment in bored pile, based on the optical image acquisition technology of underwater bored pile borehole wall, this study puts forward a defect detection method suitable for underwater bored pile borehole wall. Firstly, using the principle of underwater optical imaging, an optical image acquisition equipment suitable for the borehole wall of underwater bored cast-in-place pile is developed. Combined with the working principle of the equipment and the optical image characteristics of the defect area, the borehole wall optical image correction method is constructed, which weakens the influence of close distance and inner curvature of the borehole wall on the optical imaging. Then, by constructing the underwater optical imaging model of borehole wall defects, an underwater optical image enhancement function considering the intensity of light source and the difference in optical propagation at different wavelengths is proposed. Combined with gradient operator and maximum interclass variance method, an effective identification method for borehole wall defect area of underwater bored cast-in-place pile is formed by sharpening the edge of borehole wall defect area. Finally, three characteristic parameter pointers, target frame ratio, area and aspect ratio, and special shape, are proposed to form the search and detection method of borehole wall target defect area, which can realize the accurate defect detection of borehole wall binary image. Combined with the comparative analysis of borehole wall image processing and quantitative evaluation and analysis of borehole wall defects, it is proved that this method has accurate and efficient detection ability, which can realize the intelligent detection of optical image of borehole wall defects in the underwater bored cast-in-place pile, improve the accuracy of artificial intelligence measurement of borehole wall defect area of bored cast-in-place pile, and provide more data support for the detection and maintenance of borehole wall defects of underwater bored cast-in-place pile. © Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
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container_issue	1
title_short	Defect detection method of underwater bored cast-in-place pile based on optical image in borehole
url	https://dx.doi.org/10.1007/s13349-023-00724-2
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author2	Liu, Houcheng
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doi_str	10.1007/s13349-023-00724-2
up_date	2024-07-04T02:51:34.704Z
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Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract In order to ensure the borehole forming of underwater bored cast-in-place pile and the overall quality of pile foundation engineering, the rapid defect detection on the borehole wall of bored cast-in-place pile and its retaining wall is of great significance. Aiming at the problem of image acquisition and defect detection of underwater complex environment in bored pile, based on the optical image acquisition technology of underwater bored pile borehole wall, this study puts forward a defect detection method suitable for underwater bored pile borehole wall. 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Defect detection method of underwater bored cast-in-place pile based on optical image in borehole

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