Experimental Research on Detection and Evaluation for Fatigue Cracks of Steel Bridge Based on ACFM Technique

WANG Xianqiang; YANG Yi; LIU Duo; ZHANG Jiandong; CHEN Chunlin

doi:10.13204/j.gyjzG21030817

Volume 53 Issue 8

Aug. 2023

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WANG Xianqiang, YANG Yi, LIU Duo, ZHANG Jiandong, CHEN Chunlin. Experimental Research on Detection and Evaluation for Fatigue Cracks of Steel Bridge Based on ACFM Technique[J]. INDUSTRIAL CONSTRUCTION, 2023, 53(8): 102-106. doi: 10.13204/j.gyjzG21030817

Citation:

WANG Xianqiang, YANG Yi, LIU Duo, ZHANG Jiandong, CHEN Chunlin. Experimental Research on Detection and Evaluation for Fatigue Cracks of Steel Bridge Based on ACFM Technique[J]. INDUSTRIAL CONSTRUCTION, 2023, 53(8): 102-106. doi: 10.13204/j.gyjzG21030817

Citation:

WANG Xianqiang, YANG Yi, LIU Duo, ZHANG Jiandong, CHEN Chunlin. Experimental Research on Detection and Evaluation for Fatigue Cracks of Steel Bridge Based on ACFM Technique[J]. INDUSTRIAL CONSTRUCTION, 2023, 53(8): 102-106. doi: 10.13204/j.gyjzG21030817

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Experimental Research on Detection and Evaluation for Fatigue Cracks of Steel Bridge Based on ACFM Technique

doi: 10.13204/j.gyjzG21030817

1. State Key Laboratory of Safety, Durability and Health Operation of Long-Span Bridges, JSTI Group, Nanjing 211112, China;
2. School of Civil Engineering, Southeast University, Nanjing 211189, China;
3. School of Civil Engineering, Hohai University, Nanjing 210024, China

Received Date: 2021-03-08
Available Online: 2023-10-17

Abstract

Abstract

The coating of steel bridge has a significant impact and interference on fatigue crack detection. In order to improve the detection efficiency and evaluation accuracy of fatigue cracks, the experimental research on Alternating Current Field Measurement (ACFM) technique was carried out to realize the rapid scanning and quantitative evaluation of fatigue cracks without removing the coating. Firstly, aiming at the coating interference on fatigue crack detection, the influence of typical coating systems on identification and evaluation of steel bridge fatigue crack was explored. Secondly, considering the shielding effect of existing coating on fatigue cracks, the sensitivity of ACFM to the detection of hidden fatigue cracks was verified. Finally, detection and evaluation accuracy of ACFM were analyzed through the test for comparative specimens and painted specimens. The results showed that ACFM could accurately identify and quantitatively evaluate fatigue cracks under the coating. The crack detection ratio reached 100%, and the quantitative evaluation accuracy of crack length reached more than 95%. The influence of coating system type and coating shadowing effect on ACFM detection for fatigue cracks was not obvious. ACFM technology showed a good detection capacity for fatigue cracks with different lengths, and could realize effective detection and accurate evaluation of fatigue cracks in different states during fatigue crack propagation.
- steel bridge,
- fatigue crack,
- coating,
- ACFM,
- damage identification,
- quantitative evaluation

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References(22)

References

[1]	李朝阳,付晓鹏.开孔及焊接对钢桥面板疲劳性能的劣化效应研究[J].桥梁建设,2020,50(增刊1):8-13.
[2]	张清华,李俊,袁道云,等.深圳至中山跨江通道钢桥面板结构疲劳试验研究[J].土木工程学报,2020,53(11):102-115.
[3]	段兰,王春生,翟慕赛,等.基于声发射技术的钢桥面板疲劳损伤监测与评估[J].交通运输工程学报,2020,20(1):60-73.
[4]	李伟,袁新安,陈国明,等.基于ACFM的隔水管表面裂纹链式阵列检测探头设计与试验研究[J].机械工程学报,2017,53(8):8-15.
[5]	周兆明,张佳,谷翠琳.基于交流电磁场裂纹缺陷识别仿真研究[J].中国安全生产科学技术,2018,14(12):146-151.
[6]	袁新安,李伟,李文艳,等.交流电磁场裂纹实时判定与评估方法[J].无损检测,2019,41(4):7-11 ,57.
[7]	郑玲慧,任尚坤,王景林.ACFM技术的表面裂纹识别和尺寸反演算法研究[J].测控技术,2020,39(5):80-85 ,106.
[8]	袁新安,李伟,齐昌超,等.基于交流电磁场的水下结构物裂纹尺寸及剖面高精度评估方法[J].中国石油大学学报(自然科学版),2020,44(6):109-115.
[9]	陈晨,孙宏达,文青山,等.基于ACFM法的蒸压釜釜齿裂纹检测及优化研究[J].热加工工艺,2021,50(2):36-40 ,46.
[10]	吴泽民,戴光,周国强.基础不均匀沉降下球罐支柱角焊缝的ACFM检测[J].化工机械,2020,47(1):107-111.
[11]	冷建成,赵瑞金,周国强,等.ACFM技术及其在钻修机械平台无损检测中的应用[J].无损检测,2013,35(5):47-51.
[12]	赵建明,邢艳东,李伟,等.基于ACFM的轨道缺陷检测系统设计和试验[J].无损检测,2019,41(10):63-66.
[13]	葛玖浩,杨晨开,胡宝旺,等.交流电磁场检测技术钢轨表面裂纹高速检测研究[J].机械工程学报,2021,57(18):66-74.
[14]	ROWSHANDEL H, NICHOLSON G L, SHEN J L, et al. Characterisation of clustered cracks using an ACFM sensor and application of an artificial neural network[J]. NDT & E International,2018,98:80-88.
[15]	李伟,张展,张永军,等.基于交流电磁场的连续油管缺陷的在线检测[J].无损检测,2020,42(1):17-22.
[16]	周兆明,张露露,杨克龙.基于ACFM焊缝体积型和簇群型缺陷检测仿真分析[J].测试技术学报,2019,33(1):1-8.
[17]	王景林,任尚坤,张丹,等.基于ACFM检测技术的表面裂纹特征评价方法研究[J].中国测试,2019,45(1):40-46.
[18]	袁新安,李伟,殷晓康,等.基于ACFM的奥氏体不锈钢不规则裂纹可视化重构方法研究[J].机械工程学报,2020,56(10):27-33.
[19]	AKBARI K A, SADEGHI S H H. Determination of crack depth profile in cylindrical metallic structures, using alternating current field measurement data[J]. Journal of Nondestructive Evaluation, 2019, 38:1-8.
[20]	PASADAS D J,RIBEIRO A L,ROCHA T,et al. 2D surface defect images applying Tikhonov regularized inversion and ECT[J]. NDT & E International,2016,80:48-57.
[21]	李伟,杨伟超,袁新安,等.基于ACFM的油管缺陷在线检测实验平台设计与测试[J].实验技术与管理,2017,34(2):136-139.
[22]	孙长保,胡春阳,董艳冲.基于交流电磁场检测技术的裂纹缺陷信号识别方法[J].无损检测,2018,40(7):54-59.