Large bridges, operating in harsh environments, generate a diverse array of anomalous data via their monitoring systems. In service time, the bridges may also be affected by extreme events such as earthquakes or ship collisions, resulting in data that exhibit similarities to anomalous data. This similarity complicates differentiation and compromises the accuracy of data analysis and early warning. This article proposes an extreme event and anomaly detection method based on a hybrid attention mechanism hierarchical network, which preserves the high-order tensor information of sensors. The designed Sensor-Spatial-Channel Hybrid Attention Hierarchical Network (SSCHA-HNet) conducts multilevel and multidimensional analyses: the sensor attention block constructs intersensor relationships, the spatial attention block captures long-range dependencies, and the channel attention block dynamically adjusts the representation of global and local features within feature maps. Additionally, it leverages features across different levels to execute hierarchical detection tasks. The proposed method is verified through actual structural health monitoring data and simulation data from a cable-stayed long-span bridge under conditions such as earthquakes and ship collisions. The results demonstrate that the approach can identify extreme event data mixed with diverse patterns of anomalous data efficiently and accurately.
JianXZhongHXiaY, et al. Faulty data detection and classification for bridge structural health monitoring via statistical and deep-learning approach. Struct Control Health Monit2021; 28: e2824.
2.
LamHFYinT.Statistical detection of multiple cracks on thin plates utilizing dynamic response. Eng Struct2010; 32: 3145–3152.
3.
HuangKYuenK-V.Hierarchical outlier detection approach for online distributed structural identification. Struct Control Health Monit2020; 27: e2623.
4.
YanW-JRenW-XHuangT-L.Statistic structural damage detection based on the closed-form of element modal strain energy sensitivity. Mech Syst Signal Process2012; 28: 183–194.
5.
YuenK-VHuangK.Real-time substructural identification by boundary force modeling. Struct Control Health Monit2018; 25: e2151.
6.
BaoYChenZWeiS, et al. The state of the art of data science and engineering in structural health monitoring. Engineering2019; 5: 234–242.
7.
ChandolaVBanerjeeAKumarV.Anomaly detection: a survey. ACM Comput Surv2009; 41: 1–58.
8.
PimentelMACliftonDACliftonL, et al. A review of novelty detection. Signal Process2014; 99: 215–249.
9.
KullaaJ.Distinguishing between sensor fault, structural damage, and environmental or operational effects in structural health monitoring. Mech Syst Signal Process2011; 25: 2976–2989.
10.
NiFZhangJNooriMN.Deep learning for data anomaly detection and data compression of a long-span suspension bridge. Comput Aid Civ Infrastruct Eng2020; 35: 685–700.
11.
MaoJWangHSpencerBF.Toward data anomaly detection for automated structural health monitoring: exploiting generative adversarial nets and autoencoders. Struct Health Monit2021; 20: 1609–1626.
12.
BaoYTangZLiH, et al. Computer vision and deep learning–based data anomaly detection method for structural health monitoring. Struct Health Monit2019; 18: 401–421.
13.
TangZChenZBaoY, et al. Convolutional neural network-based data anomaly detection method using multiple information for structural health monitoring. Struct Control Health Monit2019; 26: e2296.
14.
LiuGNiuYZhaoW, et al. Data anomaly detection for structural health monitoring using a combination network of GANomaly and CNN. Smart Struct Syst2022; 39: 195–206.
15.
PanQBaoYLiH.Transfer learning-based data anomaly detection for structural health monitoring. Struct Health Monit2023; 22: 3077–3091.
16.
KwonTHParkSHParkSI, et al. Building information modeling-based bridge health monitoring for anomaly detection under complex loading conditions using artificial neural networks. J Civil Struct Health Monit2021; 11: 1301–1319.
17.
LiuGLiLZhangL, et al. Sensor faults classification for SHM systems using deep learning-based method with Tsfresh features. Smart Mater Struct2020; 29: 075005.
18.
SaybaniMR.Anomaly detection and prediction of sensors faults in a refinery using data mining techniques and fuzzy logic. Sci Res Essays; 6. Epub ahead of print 16November2011. DOI: 10.5897/SRE11.333.
19.
ArulMKareemA.Data anomaly detection for structural health monitoring of bridges using shapelet transform. Epub ahead of print 18October2021. DOI: 10.48550/arXiv.2009.00470.
20.
ZhangJZhangJWuZ.Long-short term memory network-based monitoring data anomaly detection of a long-span suspension bridge. Sensors2022; 22: 6045.
21.
PerolTGharbiMDenolleM.Convolutional neural network for earthquake detection and location. Sci Adv2018; 4: e1700578.
22.
MengXDinhTNOwenJS, et al. Application of GeoSHM system in monitoring extreme wind events at the forth road bridge. Remote Sens2019; 11: 2799.
23.
GuoJTangZZhangC, et al. An interpretable deep learning method for identifying extreme events under faulty data interference. Appl Sci2023; 13: 5659.
24.
ZhangYWangXDingZ, et al. Anomaly detection of sensor faults and extreme events based on support vector data description. Struct Control Health Monit2022; 29: e3047.
25.
BahdanauDChoKBengioY.Neural machine translation by jointly learning to align and translate. Comput Sci. Epub ahead of print 2014. DOI: 10.48550/arXiv.1409.0473.
26.
LiuGGuoJ.Bidirectional LSTM with attention mechanism and convolutional layer for text classification. Neurocomputing2019; 337: 325–338.
27.
SongSLanCXingJ, et al. An end-to-end spatio-temporal attention model for human action recognition from skeleton data. In: Proceedings of the AAAI conference on artificial intelligence, San Francisco, CA, 2017, Vol. 31.No.1.
28.
ZhangPXueJLanC, et al. Adding attentiveness to the neurons in recurrent neural networks. In: Proceedings of the European conference on computer vision (ECCV), Munich, Germany, 2018, pp. 135–151.
29.
YanXHuSMaoY, et al. Deep multi-view learning methods: a review. Neurocomputing2021; 448: 106–129.
30.
ChanWJaitlyNLeQ, et al. Listen, attend and spell: a neural network for large vocabulary conversational speech recognition. In: 2016 IEEE international conference on acoustics, speech and signal processing (ICASSP). Shanghai: IEEE, pp. 4960–4964.
31.
HuJShenLSunG. Squeeze-and-excitation networks. In; Proceedings of the IEEE conference on computer vision and pattern recognition, Salt Lake City, UT, 2018, pp. 7132–7141.
32.
IttiLKochCNieburE.A model of saliency-based visual attention for rapid scene analysis. IEEE Trans Pattern Anal Machine Intell1998; 20: 1254–1259.
33.
OlshausenBAAndersonCHEssenDV.A neurobiological model of visual attention and invariant pattern recognition based on dynamic routing of information. J Neurosci1993; 13: 4700–4719.
34.
GuoM-HXuT-XLiuJ-J, et al. Attention mechanisms in computer vision: a survey. Comp Visual Media2022; 8: 331–368.
35.
WangQWuBZhuP, et al. ECA-Net: Efficient channel attention for deep convolutional neural networks. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, Seattle, WA, 2020, pp. 11534–11542.
36.
LiXWangWHuX, et al. Selective kernel networks. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, Long Beach, CA, 2019, pp. 510–519.
37.
MnihVHeessNGravesA, et al. Recurrent Models of Visual Attention. In: Advances in neural information processing systems. Curran Associates, Inc., 2014.
LiuYChenXWangZ, et al. Deep learning for pixel-level image fusion: Recent advances and future prospects. Inform Fusion2018; 42: 158–173.
40.
HuangZWangXHuangL, et al. Ccnet: criss-cross attention for semantic segmentation. In: Proceedings of the IEEE/CVF international conference on computer vision, Seoul, South Korea, 2019, pp. 603–612.
41.
ShaYHaoH.Nonlinear finite element analysis of barge collision with a single bridge pier. Eng Struct2012; 41: 63–76.
