| Citation: |
WU Zhi-shen, HOU Shi-tong, HUANG Xi, HUANG Huang. DEVELOPMENT OF MOBILE PRECISION DETECTION TECHNOLOGY FOR REINFORCED CONCRETE STRUCTURES[J]. Engineering Mechanics, 2024, 41(1): 1-16. DOI: 10.6052/j.issn.1000-4750.2023.07.ST02
|
Non-destructive testing (NDT) technology is a type of testing technology that evaluates and measures material properties without causing damage to the material or its structural performance. Despite the advancements in NDT technology, the detection of complex internal damages at multiple levels remains a significant challenge in the field. The objective of this study is to review and analyze the development, classification and challenges of NDT technology by combining domestic and international research results. On this basis, the study introduces a set of research results on a comprehensive and precise inspection system developed by the author's research team, which covers macro to mesoscopic levels and internal structures. For macroscopic surface identification and quantitative mesoscopic identification of apparent defects, the author’s team has developed visual detection technology. This technology involves rapid panoramic image stitching of spatial reference points and sub-pixel-level disease segmentation, as well as centimeter-level positioning methods for apparent defects. Additionally, the team proposed an artificial intelligence algorithm capable of simultaneously identifying multi-size micro-meso cracks ranging from 0.05 to 0.2 mm, and authenticating panoramic images. Regarding structural internal damage identification, the author’s team pioneered a new principle of intelligent variable-frequency acoustic hammering scanning, enabling precise identification of various types of damage. The team also established the theoretical method of damage detection and assessment using adaptive excitation distribution of mobile acoustic hammering, artificial intelligence algorithms for acoustic wave and acoustic image features, and key technologies for intelligent equipment. Experimental verification has shown that the maximum depth of crack detection is up to 40 mm with a width of 0.05 mm, the maximum depth of delamination detection is up to 400 mm and the minimum recognition range is 50 mm.
| [1] |
MAIERHOFER C, REINHARDT H W, DOBMANN G. Non-destructive evaluation of reinforced concrete structures [M]. Amsterdam: Elsevier, 2010.
|
| [2] |
United States. Federal Highway Administration;Turner-Fairbank Highway Research Center. Development of phased-array ultrasonic testing acceptability criteria (Phase I) [R]. United States: Office of Infrastructure Research and Development, 2014.
|
| [3] |
NEUBAUER K, BULLARD E, BLUNT R. Collection of data with unmanned aerial systems (UAS) for bridge inspection and construction inspection [R]. United States: Office of Infrastructure Research and Development, 2021.
|
| [4] |
国土交通省. 道路橋定期点検要領[R]. 日本: 国土交通省道路局, 2014.
Ministry of Land, Infrastructure, Transport and Tourism (MLIT). Guidelines for regular inspection of road bridges [R]. Japan: Ministry of Land, Infrastructure, Transport and Tourism, 2014. (in Japanese
|
| [5] |
国土交通省. 新技術利用のガイドライン(案)[R]. 日本: 国土交通省, 2019.
Ministry of Land Infrastructure, Transport and Tourism (MLIT). Guidelines for the utilization of new technology (draft) [R] Japan: Ministry of Land, Infrastructure, Transport and Tourism, 2019. (in Japanese
|
| [6] |
国土交通省. 点検支援技術性能カタログ(案)[R]. 日本: 国土交通省, 2019.
Ministry of Land, Infrastructure, Transport and tourism (MLIT). Inspection support technical performance catalog (draft) [R] Japan: Ministry of Land, Infrastructure, Transport and Tourism, 2019. (in Japanese
|
| [7] |
GB 50204−1992, 混凝土结构工程施工及验收规范[S]. 辽宁: 辽宁科学技术出版社, 1992.
GB 50204−1992, Code for construction and acceptance of concrete structures [S]. Liaoning: Liaoning Science and Technology Press, 1992. (in Chinese)
|
| [8] |
国家建筑工程质量监督检验中心. 混凝土无损检测技术[M]. 北京: 中国建材工业出版社, 1996.
National Building Engineering Quality Supervision and Inspection Center. Non-destructive testing techniques for concrete [M]. Beijing: China Building Materials Industry Press, 1996. (in Chinese)
|
| [9] |
T/CECS 964−2021, 摆锤敲入法检测钢材屈服强度技术规程[S]. 北京: 中国建筑工业出版社, 2021.
T/CECS 964−2021, Technical specifications for testing yield strength of steel by pendulum bob knocking-in resistance method [S]. Beijing: China Architecture & Building Press, 2021. (in Chinese)
|
| [10] |
JGJ/T 23−2011, 回弹法检测混凝土抗压强度技术规程[S]. 北京: 中国建筑工业出版社, 2011.
JGJ/T 23−2011, Technical specification for inspecting of concrete compressive strength by rebound method [S]. Beijing: China Architecture & Building Press, 2011. (in Chinese)
|
| [11] |
T/CECS G: J50-01−2019, 桥梁混凝土结构无损检测技术规程[S]. 北京: 人民交通出版社股份有限公司, 2019.
T/CECS G: J50-01−2019, Technical specification for non-destructive testing of bridge concrete structure [S]. Beijing: People's Communications Publishing House Co., Ltd, 2019. (in Chinese)
|
| [12] |
DB53/T 1034−2021, 公路隧道隐蔽工程无损检测技术规程[S]. 北京: 人民交通出版社股份有限公司, 2021.
DB53/T 1034−2021, Technical specifications for non-destructive testing of concealed works in highway tunnels [S]. Beijing: People's Communications Publishing House Co., Ltd, 2021. (in Chinese)
|
| [13] |
GB/T 43143−2023, 无损检测 声发射检测 混凝土结构活动裂缝分类的检测方法[S]北京: 中国标准出版社, 2023.
GB/T 43143−2023, Non-destructive testing—Acoustic emission testing—Test method for classification of active cracks in concrete structures [S]. Beijing: Standards Press of China, 2023. (in Chinese)
|
| [14] |
GB/T 41055−2021, 预应力钢筒混凝土管无损检测(远场涡流电磁法)技术要求[S]. 北京: 中国标准出版社, 2021.
GB/T 41055−2021, Technical requirements for nondestructive testing (electromagnetic method based on remote field eddy current) on prestressed concrete cylinder pipe [S]. Beijing: Standards Press of China, 2021. (in Chinese)
|
| [15] |
FUJII H, YAMASHITA A, ASAMA H. Defect detection with estimation of material condition using ensemble learning for hammering test [C]// Proceedings of 2016 IEEE International Conference on Robotics and Automation (ICRA) Stockholm. Stockholm, Sweden: IEEE, 2016: 3847 − 3854.
|
| [16] |
SANSALONE M J, STREETT W B. Impact-echo: Nondestructive evaluation of concrete and masonry [M]. New York: Bullbrier Press, 1997: 123 − 134.
|
| [17] |
TOSTI F, FERRANTE C. Using ground penetrating radar methods to investigate reinforced concrete structures [J]. Surveys in Geophysics, 2020, 41(3): 485 − 530. doi: 10.1007/s10712-019-09565-5
|
| [18] |
MA Y, ELSEIFI M A, DHAKAL N, et al. Non-destructive detection of asphalt concrete stripping damage using ground penetrating radar [J]. Transportation Research Record:Journal of the Transportation Research Board, 2021, 2675(10): 938 − 947. doi: 10.1177/03611981211014199
|
| [19] |
陈必港. 机器视觉测量技术在既有公路隧道拱顶沉降监测中的应用[J]. 福建交通科技, 2021(7): 91 − 94
CHEN Bigang. Application of machine vision measurement technology in the monitoring of existing highway tunnel vault settlement [J]. Fujian Transportation Technology, 2021(7): 91 − 94. (in Chinese)
|
| [20] |
邵新星, 黄金珂, 员方, 等. 基于视觉的桥梁挠度测量方法与研究进展[J]. 实验力学, 2021, 36(1): 29 − 42.
SHAO Xinxing, HUANG Jinke, YUAN Fang, et al. Measurement method and Recent progress of vision-based deflection measurement of bridges [J]. Journal of Experimental Mechanics, 2021, 36(1): 29 − 42. (in Chinese)
|
| [21] |
朱前坤, 崔德鹏, 杜永峰. 基于网络摄像机的桥梁挠度非接触识别[J]. 工程力学, 2022, 39(6): 146 − 155 doi: 10.6052/j.issn.1000-4750.2021.03.0221
ZHU Qiankun, CUI Depeng, DU Yongfeng. Non-contact identification of bridge deflection based on network camera [J]. Engineering Mechanics, 2022, 39(6): 146 − 155.(in Chinese) doi: 10.6052/j.issn.1000-4750.2021.03.0221
|
| [22] |
WEI K, YUAN F, SHAO X X, et al. High-speed multi-camera 3D DIC measurement of the deformation of cassette structure with large shaking table [J]. Mechanical Systems and Signal Processing, 2022, 177: 109273. doi: 10.1016/j.ymssp.2022.109273
|
| [23] |
修晟, 张愿, 单伽锃. 基于视觉和振动监测数据融合的结构动态位移识别及其试验验证[J]. 工程力学, 2023, 40(11): 90 − 98 doi: 10.6052/j.issn.1000-4750.2022.01.0106
XIU Cheng, ZHANG Yuan, SHAN Jiazeng. Vision and vibration data fusion-based structural dynamic displacement measurement with test validation [J]. Engineering Mechanics, 2023, 40(11): 90 − 98.(in Chinese) doi: 10.6052/j.issn.1000-4750.2022.01.0106
|
| [24] |
WU T, HOU S T, SUN W H, et al. Visual measurement method for three-dimensional shape of underwater bridge piers considering multirefraction correction [J]. Automation in Construction, 2023, 146: 104706. doi: 10.1016/j.autcon.2022.104706
|
| [25] |
谢文高, 张怡孝, 刘爱荣, 等. 基于水下机器人与数字图像技术的混凝土结构表面裂缝检测方法[J]. 工程力学, 2022, 39(Suppl): 64 − 70 doi: 10.6052/j.issn.1000-4750.2021.05.S010
XIE Wengao, ZHANG Yixiao, LIU Airong, et al. Method for concrete surface cracking detection based on rov and digital image technology [J]. Engineering Mechanics, 2022, 39(Suppl): 64 − 70.(in Chinese) doi: 10.6052/j.issn.1000-4750.2021.05.S010
|
| [26] |
LI S Y, ZHAO X F, ZHOU G Y. Automatic pixel‐level multiple damage detection of concrete structure using fully convolutional network [J]. Computer-Aided Civil and Infrastructure Engineering, 2019, 34(7): 616 − 634. doi: 10.1111/mice.12433
|
| [27] |
CHA Y J, CHOI W, BÜYÜKÖZTÜRK O. Deep learning‐based crack damage detection using convolutional neural networks [J]. Computer-Aided Civil and Infrastructure Engineering, 2017, 32(5): 361 − 378. doi: 10.1111/mice.12263
|
| [28] |
本特集号担当委員. PC構造物の非破壊検査技術に関するアンケート, コンクリート構造診断士に聞く [J]. プレストレストコンクリート, 2014, 56(6): 75 − 83.
Survey on non-destructive testing techniques for PC (prestressed concrete) structures, asking concrete structure diagnosticians [J]. JPCI, 2014, 56(6): 75 − 83. (in Japanese
|
| [29] |
ACI Committee. Report on practices for evaluation of concrete in existing massive structures for service conditions [R]. United States: ACI, 2018.
|
| [30] |
MEHRABI A, FARHANGDOUST S. NDT methods applicable to health monitoring of ABC closure joints [R]. United States: Accelerated Bridge Construction University Transportation Center (ABC-UTC), 2019.
|
| [31] |
BRIGANTE M, SUMBATYAN M A. Acoustic methods for the nondestructive testing of concrete: A review of foreign publications in the experimental field [J]. Russian Journal of Nondestructive Testing, 2013, 49(2): 100 − 111. doi: 10.1134/S1061830913020034
|
| [32] |
LIN S B, MENG D W, CHOI H, et al. Laboratory assessment of nine methods for nondestructive evaluation of concrete bridge decks with overlays [J]. Construction and Building Materials, 2018, 188: 966 − 982. doi: 10.1016/j.conbuildmat.2018.08.127
|
| [33] |
LIN S B, AZARI H, MENG D W, et al. Nondestructive evaluation of concrete bridge decks with overlays [R]. McLean: Office of Infrastructure Research and Development, 2021.
|
| [34] |
HUANG X, HUANG H, WU Z S. Development of an automated hammer testing system for concrete structures [J]. International Journal of Sustainable Materials and Structural Systems, 2022, 6(1): 32 − 45. doi: 10.1504/IJSMSS.2022.131229
|
| [35] |
BAJAJ R, RANAWEERA S L, AGRAWAL D P. GPS: Location-tracking technology [J]. Computer, 2002, 35(4): 92 − 94. doi: 10.1109/MC.2002.993780
|
| [36] |
DECESARE N J, SQUIRES J R, KOLBE J A. Effect of forest canopy on GPS-based movement data [J]. Wildlife Society Bulletin, 2005, 33(3): 935 − 941. doi: 10.2193/0091-7648(2005)33[935:EOFCOG]2.0.CO;2
|
| [37] |
BRAASCH M S. Isolation of GPS multipath and receiver tracking errors [J]. Navigation, 1994, 41(4): 415 − 435. doi: 10.1002/j.2161-4296.1994.tb01888.x
|
| [38] |
MORAVEC H P. Rover visual obstacle avoidance [C]// Proceedings of the 7th International Joint Conference on Artificial Intelligence. Vancouver: William Kaufmann, 1981: 785 − 790.
|
| [39] |
HARRIS C, STEPHENS M. A combined corner and edge detector [C]// Proceedings of the 4th Alvey Vision Conference. Manchester: Alvey Vision Club, 1988: 147 − 151.
|
| [40] |
LOWE D G. Distinctive image features from scale-invariant keypoints [J]. International Journal of Computer Vision, 2004, 60(2): 91 − 110. doi: 10.1023/B:VISI.0000029664.99615.94
|
| [41] |
RUBLEE E, RABAUD V, KONOLIGE K, et al. ORB: An efficient alternative to SIFT or SURF [C]// Proceedings of 2011 International conference on computer vision. Barcelona: IEEE, 2011: 2564 − 2571.
|
| [42] |
ZARAGOZA J, CHIN T J, BROWN M S, et al. As-projective-as-possible image stitching with moving DLT [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014, 36(7): 1285 − 1298. doi: 10.1109/TPAMI.2013.247
|
| [43] |
CHEN L C, ZHU Y K, PAPANDREOU G, et al. Encoder-Decoder with atrous separable convolution for semantic image segmentation [C]// Proceedings of the 15th European Conference on Computer Vision. Munich: Springer, 2018: 833 − 851.
|
| [44] |
FU H, MENG D, LI W, et al. Bridge crack semanticsegmentation based on improved Deeplabv3+ [J]. Journal of Marine Science and Engineering, 2021, 9(6): 671.
|
| [45] |
DANG L M, WANG H, LI Y, et al. Deep learning-based masonry crack segmentation and real-life crack length measurement [J]. Construction and Building Materials, 2022, 359: 129438.
|
| [46] |
BIANCHI E, HEBDON M. Development of extendable open-source structural inspection datasets [J]. Journal of Computing in Civil Engineering, 2022, 36(6): 04022039.
|
| [47] |
KIM H, KIM C. Deep-learning-based classification of point clouds for bridge inspection [J]. Remote Sensing, 2020, 12(22): 3757.
|
| [48] |
丁威, 马亥波, 舒江鹏, 等. 基于残差网络的混凝土结构病害分类识别研究[J]. 建筑科学与工程学报, 2022, 39(4): 127 − 136. doi: 10.19815/j.jace.2021.10112
DING Wei, MA Haibo, SHU Jiangpeng, et al. Research on classification and recognition of concrete structure diseases based on residual network [J]. Journal of Architecture and Civil Engineering, 2022, 39(4): 127 − 136. (in Chinese) doi: 10.19815/j.jace.2021.10112
|
| [49] |
倪有豪, 陆欢, 季超, 等. 基于语义分割的桥梁锈蚀病害识别对比分析[J]. 东南大学学报(自然科学版), 2023, 53(2): 201 − 209.
NI Youhao, LU Huan, JI Chao, et al. Comparative analysis on bridge corrosion damage detection based on semantic segmentation [J]. Journal of Southeast University (Natural Science Edition), 2023, 53(2): 201 − 209. (in Chinese)
|
| [50] |
阮小丽, 王波, 吴巨峰, 等. 基于深度学习的钢筋混凝土桥梁掉块露筋病害识别[J]. 世界桥梁, 2020, 48(6): 88 − 92.
RUAN Xiaoli, WANG Bo, WU Jufeng, et al. Identification of spalled concrete and exposed reinforcement in reinforced concrete bridge based on deep learning [J]. World Bridges, 2020, 48(6): 88 − 92. (in Chinese)
|
| [51] |
CHEN L C, PAPANDREOU G, SCHROFF F, et al. Rethinking atrous convolution for semantic image segmentation [J]. arXiv preprint arXiv: 1706.05587, 2017.
|
| [52] |
WU X W, SAHOO D, HOI S C H. Recent advances in deep learning for object detection [J]. Neurocomputing, 2020, 396: 39 − 64. doi: 10.1016/j.neucom.2020.01.085
|
| [53] |
CANNY J. A computational approach to edge detection [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1986, 8(6): 679 − 698. doi: 10.1109/TPAMI.1986.4767851
|
| [54] |
KITTLER J. On the accuracy of the Sobel edge detector [J]. Image and Vision Computing, 1983, 1(1): 37 − 42 doi: 10.1016/0262-8856(83)90006-9
|
| [55] |
LI L, WU H, ZHU X, et al. Multi-scale dense convolutional encoder-decoder network for crack detection [J]. IEEE Transactions on Industrial Electronics, 2018, 65(2): 1945 − 1953
|
| [56] |
吴贺贺, 王安红, 王海东. 基于Faster R-CNN的隧道图像裂缝检测[J]. 太原科技大学学报, 2019, 40(3): 165 − 168.
WU Hehe, WANG Anhong, WANG Haidong. Crack of tunnel detection based on faster R-CNN [J]. Journal of Taiyuan University of Science and Technology, 2019, 40(3): 165 − 168. (in Chinese)
|
| [57] |
JI J S, WU L J, CHEN Z C, et al. Automated pixel-level surface crack detection using U-Net [C]// Proceedings of the 12th International Conference on Multi-disciplinary Trends in Artificial Intelligence. Hanoi: Springer, 2018: 69 − 78.
|
| [58] |
QIN X B, ZHANG Z C, HUANG C Y, et al. U2-Net: Going deeper with nested U-structure for salient object detection [J]. Pattern Recognition, 2020, 106: 107404. doi: 10.1016/j.patcog.2020.107404
|
| [59] |
DHANACHANDRA N, MANGLEM K, CHANU Y J. Image segmentation using K -means clustering algorithm and subtractive clustering algorithm [J]. Procedia Computer Science, 2015, 54: 764 − 771. doi: 10.1016/j.procs.2015.06.090
|
| [60] |
HUANG X, HUANG H, WU Z S. Development of a variable-frequency hammering method using acoustic features for damage-type identification [J]. Applied Sciences, 2023, 13(3): 1329. doi: 10.3390/app13031329
|
| [61] |
HUANG H, HUANG X, WU Z S, et al. Adaptive excitation frequency matching and acoustic-feature-based automatic hammering inspection system [J]. International Journal of Structural Stability and Dynamics, 2023.
|
| [62] |
ALHEBRAWI M N, HUANG H, WU Z S. Artificial intelligence enhanced automatic identification for concrete cracks using acoustic impact hammer testing [J]. Journal of Civil Structural Health Monitoring, 2023, 13(2/3): 469 − 484.
|
| [63] |
CHENG H, WANG F R, HUO L S, et al. Detection of sand deposition in pipeline using percussion, voice recognition, and support vector machine [J]. Structural Health Monitoring, 2020, 19(6): 2075 − 2090. doi: 10.1177/1475921720918890
|
| [1] | LUO Yang-jun, WU Xiao-xiang, DENG Zi-chen. STUDY ON STRESS BASED TOPOLOGY OPTIMIZATION FOR REINFORCED CONCRETE STRUCTURES[J]. Engineering Mechanics, 2013, 30(6): 22-29. DOI: 10.6052/j.issn.1000-4750.2012.02.0090 |
| [2] | TANG Guo-bin, WANG Tong-ning, XIANG Yi-qiang. CRACK MODEL OF REINFORCED CONCRETE BASED ON STRAIN DECOMPOSITION METHOD[J]. Engineering Mechanics, 2013, 30(3): 174-180. DOI: 10.6052/j.issn.1000-4750.2011.09.0639 |
| [3] | XING Guo-hua, WU Tao, LIU Bo-quan. STUDY ON CRACK RESISTANCE OF BEAM-COLUMN JOINTS IN REINFORCED CONCRETE FRAME STRUCTURES[J]. Engineering Mechanics, 2011, 28(3): 163-169. |
| [4] | WANG Guang-yong, HAN Lin-hai, YU Hong-xia. FIRE PERFORMANCE OF REINFORCED CONCRETE BEAM-COLUMN PLANE JOINTS[J]. Engineering Mechanics, 2010, 27(12): 164-173. |
| [5] | CUI Jian-yu, SUN Jian-gang, SONG Yu-pu, Watanabe Fumio. MODELING OF LOAD-RESISTANCE MECHANISMS OF REINFORCED CONCRETE KNEE JOINTS[J]. Engineering Mechanics, 2010, 27(7): 148-153. |
| [6] | LONG Yu-chuan, ZHANG Chu-han, ZHOU Yuan-de. EMBEDDED SLIP MODEL FOR ANALYZING REINFORCED CONCRETE STRUCTURES[J]. Engineering Mechanics, 2007, 24(增Ⅰ): 41-045. |
| [7] | LIU Xia, YI Wei-jian, SHEN Pu-sheng. TOPOLOGY OPTIMIZATION OF STRUT-AND-TIE MODELS IN DEEP REINFORCED CONCRETE BEAMS[J]. Engineering Mechanics, 2006, 23(9): 93-97. |
| [8] | BAO Yi-wang, HUANG Peng-fei, YAO Yan, HAN Yan-fei. THERMAL STRESS ANALYSIS OF REINFORCED CONCRETE[J]. Engineering Mechanics, 2006, 23(4): 93-98. |
| [9] | WU Jin, WU Sheng-xing. RELIABILITY ASSESSMENT OF CONCRETE STRUCTURES WITH CORRODED REINFORCEMENT[J]. Engineering Mechanics, 2005, 22(1): 118-122. |
| [10] | Xu Maobo, Liu Xila. RELIABILITY ANALYSIS OF REINFORCED CONCRETE BEAMS CONSIDERING HUMAN ERRORS[J]. Engineering Mechanics, 1992, 9(2): 52-58. |
| 1. |
牛虎利, 李泉金, 杨佳俊, 张嘉钰. 基于图像处理的核电高强钢筋连接套筒内螺纹螺距快速测量方法. 机床与液压. 2025(13)
| |
| 2. |
周祥. 建筑工程材料检测试验检测技术问题研究. 实验室检测. 2025(12)
| |
| 3. |
喻有彪. 铁路扩能改造中桥梁检测方法及结果分析. 铁道建筑技术. 2025(07)
| |
| 4. |
李艺. 基于道路桥梁混凝土现场施工的检测技术. 水泥. 2025(03): 83-85 .
| |
| 5. |
白玉石，龚恩成. 钢板混凝土结构中锚固与搭接的优化技术和分析. 建筑技术开发. 2024(06): 11-13 .
|
Supported by: Beijing Renhe Information Technology Co.,Ltd. 
DownLoad: