基于互信息和MiniRocket网络的CFST脱空识别

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Abstract In order to improve the efficiency and accuracy of concrete filled steel tube（CFST） void detection， an intelligent recognition method based on FFT (Fast Fourier Transform), MI (Mutual Information) and MiniRocket neural network is proposed in this paper. First, the time domain signal of the CFST percussion wave to be measured is converted to the frequency domain signal using FFT. Secondly, MI is used to establish the correlation between the frequency domain signal and the void state, and the top 30 features with the largest correlation are extracted to establish the data set, which avoids complex mathematical operations and redundant information. A MiniRocket deep learning network is established, and by using fewer parameters and smaller feature sizes improving the speed and accuracy of classification. Finally, the noise robustness of the model is investigated and compared with other algorithms, feature extraction methods and recognition methods. The results show that the proposed method achieves 100 % average prediction accuracy in 100 repetitions of the experiment for different void depths and void widths. At high SNR, this method is less affected. In addition, compared with other algorithms, feature extraction methods and recognition methods, this method has better prediction performance. Therefore, the proposed method has great application potential in the actual intelligent void identification of CFST in the future.

Key words： concrete filled steel tube（CFST） void knocking sound waves mutual information deep learning

Received: 11 May 2023 Published: 28 April 2024

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	Articles by authors
	QIN Yue1
	XIE Kaizhong1
	2
	3
	GUO Xiao1
	2
	3
	WANG Hongwei4
	WANG Qiuyang1
	PENG Jiawang1

Cite this article:

QIN Yue1,XIE Kaizhong1,2, et al. Identification of CFST voids based on mutual information and MiniRocket network[J]. JOURNAL OF VIBRATION AND SHOCK, 2024, 43(8): 202-212.

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http://jvs.sjtu.edu.cn/EN/ OR http://jvs.sjtu.edu.cn/EN/Y2024/V43/I8/202

[1] 侯剑岭,许维炳,陈彦江,等.大跨度异型钢管混凝土拱桥典型病害分析及支座脱空影响研究[J].振动与冲击,2020,39(18):161-168+187. HOU Jianling, XU Weibing, CHEN Yanjiang, et al. Typical diseases of long-span concrete filled steel tube arch bridges and the effects of the void of supports[J]. Journal of Vibration and Shock, 2020, 39(18): 161-168+187. [2] 薛俊青,陈宝春,BRISEGHELLA Bruno.脱粘钢管混凝土单圆管短柱偏压试验[J].建筑结构学报,2009,30(S2):237-241. XUE Junqing, CHEN Baochun, BRISEGHELLA Bruno. Experiment on debonding in concrete-filled steel single tube columns subjected to eccentrically loading[J]. Journal of Building Structures, 2009, 30(2): 237-241. [3] Han L H, Ye Y, Liao F Y. Effects of core concrete initial imperfection on performance of eccentrically loaded CFST columns[J]. Journal of Structural Engineering, 2016, 142(12): 04016132. Liao F Y, Han L H, He S H. Behavior of CFST short column and beam with initial concrete imperfection: Experiments[J]. Journal of Constructional Steel Research, 2011, 67(12): 1922-1935. [4] Liao F Y, Han L H, Tao Z. Behaviour of CFST stub columns with initial concrete imperfection: analysis and calculations[J]. Thin-Walled Structures, 2013, 70: 57-69. [5] 叶勇,韩林海,陶忠.脱空对圆钢管混凝土受剪性能的影响分析[J].工程力学,2016,33(S1):62-66+71. YE Yong, HAN Linhai, TAO Zhong. Effects of gaps on the behaviour of circular CFST members under shear[J]. Engineering Mechanics, 2016, 33: 62-66+71. [6] Guo C, Lu Z. Effect of circumferential gap on dynamic performance of CFST arch bridges[J]. Journal of Bridge Engineering, 2021, 26(2): 04020121. [7] 廖飞宇,韩浩,王宇航.带环向脱空缺陷的钢管混凝土构件在压弯扭复合受力作用下的滞回性能研究[J].土木工程学报,2019,52(07):57-68+80. LIAO Feiyu, HAN Hao, WANG Yuhang. Cyclic behaviour of concrete-filled steel tubular (CFST) members with circumferential gap under combined compression-bending-torsion load[J]. China Civil Engineering Journal, 2019, 7: 57-68+80. [8] 张伟杰,廖飞宇,李威.带圆弓形脱空缺陷的钢管混凝土构件在压弯扭复合受力作用下的滞回性能试验研究[J].工程力学,2019,36(12):121-133. ZHANG Weijie，LIAO Feiyu，LI Wei. Experimental study on the cyclic behavior of concrete-filled steel tubular (CFST) members with circular-segment gaps under combined comperssion-bending-torsion loading[J]. Engineering Mechanics, 2019, 36(12): 121-133. [9] Ho J C M, Ou X L, Li C W, et al. Uni-axial behaviour of expansive CFST and DSCFST stub columns[J]. Engineering Structures, 2021, 237: 112193. [10] Jiang Y, Abu-Hamdeh N H, Bantan R A R, et al. Influence of upstream angled ramp on fuel mixing of hydrogen jet at supersonic cross flow[J]. Aerospace Science and Technology, 2021, 119: 107099. [11] Xu B, Zhang T, Song G, et al. Active interface debonding detection of a concrete-filled steel tube with piezoelectric technologies using wavelet packet analysis[J]. Mechanical Systems and Signal Processing, 2013, 36(1): 7-17. [12] Gong S, Feng X, Zhang G, et al. Distributed detection of internal cavities in concrete-filled steel tube arch bridge elements[J]. Structural Health Monitoring, 2023, 22(1): 657-671. [13] Zhang J, Li Y, Du G, et al. Damage detection of L-shaped concrete filled steel tube (L-CFST) columns under cyclic loading using embedded piezoceramic transducers[J]. Sensors, 2018, 18(7): 2171. [14] Du G, Li Z, Song G. A PVDF-based sensor for internal stress monitoring of a concrete-filled steel tubular (CFST) column subject to impact loads[J]. Sensors, 2018, 18(6): 1682. [15] Guo S, Zhang L, Chen S, et al. Ultrasonic transducers from thermal sprayed lead-free piezoelectric ceramic coatings for in-situ structural monitoring for pipelines[J]. Smart Materials and Structures, 2019, 28(7): 075031. [16] Duan C, Zhang H, Li Z, et al. FBG smart bolts and their application in power grids[J]. IEEE Transactions on Instrumentation and Measurement, 2019, 69(5): 2515-2521. [17] Kumar R, Ismail M, Zhao W, et al. Damage detection of wind turbine system based on signal processing approach: A critical review[J]. Clean Technologies and Environmental Policy, 2021, 23(2): 561-580. [18] Huang J, Liu J, Gong H, et al. A comprehensive review of loosening detection methods for threaded fasteners[J]. Mechanical Systems and Signal Processing, 2022, 168: 108652. [19] Kim S J. Damage detection in composite under in-plane load using tap test[J]. Journal of Mechanical Science and Technology, 2015, 29(1): 199-207. [20] Wang F, Song G. A novel percussion-based method for multi-bolt looseness detection using one-dimensional memory augmented convolutional long short-term memory networks[J]. Mechanical Systems and Signal Processing, 2021, 161: 107955. [21] Chen L, Xiong H, Sang X, et al. An innovative deep neural network–based approach for internal cavity detection of timber columns using percussion sound[J]. Structural Health Monitoring, 2022, 21(3): 1251-1265. [22] Chen D, Montano V, Huo L, et al. Detection of subsurface voids in concrete-filled steel tubular (CFST) structure using percussion approach[J]. Construction and Building Materials, 2020, 262: 119761. [23] Wang F, Song G. Looseness detection in cup-lock scaffolds using percussion-based method[J]. Automation in Construction, 2020, 118: 103266. [24] Yuan R, Lv Y, Kong Q, et al. Percussion-based bolt looseness monitoring using intrinsic multiscale entropy analysis and BP neural network[J]. Smart Materials and Structures, 2019, 28(12): 125001. [25] Wang F, Song G. Bolt-looseness detection by a new percussion-based method using multifractal analysis and gradient boosting decision tree[J]. Structural Health Monitoring, 2020, 19(6): 2023-2032. [26] 吴杰,徐洪俊,张其林.模态识别的Bayesian TDD-FFT法及其应用[J].振动与冲击,2019,38(15):142-148+171. WU Jie ,XU Hongjun ,ZHANG Qilin . Bayesian TDD-FFT method for modal identification and its application[J]. Journal of Vibration and Shock, 2019, 38(15): 142-148. [27] Hoque N, Bhattacharyya D K, Kalita J K. MIFS-ND: A mutual information-based feature selection method[J]. Expert Systems with Applications, 2014, 41(14): 6371-6385. [28] 刘超,孔兵,杜国王,等.高阶互信息最大化与伪标签指导的深度聚类[J].浙江大学学报(工学版),2023,57(02):299-309. LIU Chao, KONG Bing, DU Guowang, et al. Deep clustering via high-order mutual information maximization and pseudo-label guidance[J]. Journal of ZheJiang University (Engineering Science), 57(2): 299-309. [29] 赵洪科,吴李康,李徵,等.基于深度神经网络结构的互联网金融市场动态预测[J].计算机研究与发展,2019,56(08):1621-1631. ZHAO Hongke, WU Likang, LI Zhi, et al. Predicting the dynamics in Internet finance based on deep neural network structure[J]. Journal of Computer Research and Development, 2019, 56(8): 1621-1631. [30] 苍岩,罗顺元,乔玉龙.基于深层神经网络的猪声音分类[J].农业工程学报,2020,36(09):195-204. YAN Cang,LUO Shunyuan , QIAO Yulong . Classification of pig sounds based on deep neural network[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2020, 36(9): 195-204. [31] Dempster A, Petitjean F, Webb G I. ROCKET: exceptionally fast and accurate time series classification using random convolutional kernels[J]. Data Mining and Knowledge Discovery, 2020, 34(5): 1454-1495. [32] Dempster A, Schmidt D F, Webb G I. Minirocket: A very fast (almost) deterministic transform for time series classification[C]//Proceedings of the 27th ACM SIGKDD conference on knowledge discovery & data mining. 2021: 248-257. [33] Cheng H, Wang F, Huo L, 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. [34] Chen D, Montano V, Huo L, et al. Depth detection of subsurface voids in concrete-filled steel tubular (CFST) structure using percussion and decision tree[J]. Measurement, 2020, 163: 107869. [35] Chen D, Shen Z, Huo L, et al. Percussion-based quasi real-time void detection for concrete-filled steel tubular structures using dense learned features[J]. Engineering Structures, 2023, 274: 115197.