{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T13:43:10Z","timestamp":1771940590613,"version":"3.50.1"},"reference-count":38,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2022,8,12]],"date-time":"2022-08-12T00:00:00Z","timestamp":1660262400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Key R&amp;D Program of Jiangsu","award":["BE2020094"],"award-info":[{"award-number":["BE2020094"]}]},{"name":"Key R&amp;D Program of Jiangsu","award":["2020YFC1511900"],"award-info":[{"award-number":["2020YFC1511900"]}]},{"name":"National Key R&amp;D Program of China","award":["BE2020094"],"award-info":[{"award-number":["BE2020094"]}]},{"name":"National Key R&amp;D Program of China","award":["2020YFC1511900"],"award-info":[{"award-number":["2020YFC1511900"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Structural health monitoring (SHM) systems have been widely applied in long-span bridges and a large amount of SHM data is continually collected. The harsh environment of sensors installed at structures causes multiple types of anomalies such as outlier, minor, missing, trend, drift, and break in the SHM data, which seriously hinders the further analysis of SHM data. In order to achieve anomaly detection from a large amount of SHM data, this paper proposes a long-short term memory (LSTM) network-based anomaly detection method. Firstly, the proposed method reduces the workload for preparing training sets. Secondly, the purpose of real-time anomaly detection can be met. Thirdly, the problem of high alarm rate can be avoided by utilizing double thresholds. To validate the effectiveness of the proposed method, a case study of finite element model simulation is firstly introduced, which illustrates the detailed implementation process. Finally, acceleration data from the SHM system of a long-span suspension bridge located in Jiangyin, China is employed. The results show that the proposed method can detect anomaly with high accuracy and identify abnormal accidents such as a ship collision quickly.<\/jats:p>","DOI":"10.3390\/s22166045","type":"journal-article","created":{"date-parts":[[2022,8,15]],"date-time":"2022-08-15T23:44:03Z","timestamp":1660607043000},"page":"6045","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["Long-Short Term Memory Network-Based Monitoring Data Anomaly Detection of a Long-Span Suspension Bridge"],"prefix":"10.3390","volume":"22","author":[{"given":"Jianliang","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Civil Engineering, Southeast University, Nanjing 211189, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9129-255X","authenticated-orcid":false,"given":"Jian","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Civil Engineering, Southeast University, Nanjing 211189, China"},{"name":"Jiangsu Key Laboratory of Engineering Mechanics, Southeast University, Nanjing 211189, China"}]},{"given":"Zhishen","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Civil Engineering, Southeast University, Nanjing 211189, China"},{"name":"Key Laboratory C&PC Structures, Ministry of Education, Southeast University, Nanjing 210096, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,12]]},"reference":[{"key":"ref_1","first-page":"714","article-title":"Damage prognosis for aerospace, civil and mechanical systems","volume":"6531","author":"Inman","year":"2005","journal-title":"Damage Progn. Aerosp. Civ. Mech. Syst."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Farrar, C.R., Czarnecki, J.J., Sohn, H., and Hemez, F.M. (2002, January 7\u201312). A review of structural health monitoring literature 1996\u20132001. Proceedings of the Third World Conference on Structural Control, Como, Italy.","DOI":"10.1117\/12.434158"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1002\/stc.1559","article-title":"SMC structural health monitoring benchmark problem using monitored data from an actual cable-stayed bridge","volume":"21","author":"Li","year":"2013","journal-title":"Struct. Control Health Monit."},{"key":"ref_4","unstructured":"Wu, Z.S., Zhang, J., and Noori, M. (2019). Fiber-Optic Sensors for Infrastructure Health Monitoring, Volume I: Introduction and Fundamental Concepts, Momentum Press. [1st ed.]."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1111\/mice.12567","article-title":"Noncontact cable force estimation with unmanned aerial vehicle and computer vision","volume":"36","author":"Tian","year":"2021","journal-title":"Comput.-Aided Civ. Infrastruct. Eng."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"549","DOI":"10.1111\/mice.12519","article-title":"Real-time crack assessment using deep neural networks with wall-climbing unmanned aerial system","volume":"35","author":"Jiang","year":"2020","journal-title":"Comput.-Aided Civ. Infrastruct. Eng."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"04016124","DOI":"10.1061\/(ASCE)BE.1943-5592.0001003","article-title":"In-service condition assessment of a long-span suspension bridge using temperature-induced strain data","volume":"22","author":"Xia","year":"2016","journal-title":"J. Bridge Eng."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"B4016014","DOI":"10.1061\/(ASCE)AS.1943-5525.0000678","article-title":"Vibration and deformation monitoring of a long-span rigid-frame bridge with distributed long-gauge sensors","volume":"30","author":"Zhang","year":"2017","journal-title":"J. Aerosp. Eng."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"703","DOI":"10.1111\/mice.12112","article-title":"Mobile impact testing for structural flexibility identification with only a single reference","volume":"30","author":"Zhang","year":"2015","journal-title":"Comput.-Aided Civ. Infrastruct. Eng."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"522","DOI":"10.1111\/j.1467-8667.2012.00802.x","article-title":"Advanced markov chain monte carlo approach for finite element calibration under uncertainty","volume":"28","author":"Zhang","year":"2013","journal-title":"Comput.-Aided Civ. Infrastruct. Eng."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10115-006-0034-6","article-title":"Finding the most unusual time series subsequence: Algorithms and applications","volume":"11","author":"Keogh","year":"2007","journal-title":"Knowl. Inf. Syst."},{"key":"ref_12","unstructured":"Ma, J., Ma, J., Perkins, S., and Perkins, S. (2003, January 20\u201324). Time-series novelty detection using one-class support vector machines. Proceedings of the International Joint Conference on Neural Networks, Portland, OR, USA."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Markus, G., and Seiichi, U. (2016). A comparative evaluation of unsupervised anomaly detection algorithms for multivariate data. PLoS ONE, 11.","DOI":"10.1371\/journal.pone.0152173"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"07061","DOI":"10.1051\/epjconf\/202024507061","article-title":"Machine learning-based anomaly detection of ganglia monitoring data in HEP Data Center","volume":"245","author":"Chen","year":"2020","journal-title":"EPJ Web Conf."},{"key":"ref_15","unstructured":"Jian, Z., Wing, Y., and Ti, Z. (2018, January 16\u201318). Anomaly detection of target dynamics based on clustering. Proceedings of the\u20142018 15th International Symposium on Pervasive Systems, Algorithms and Networks, I-SPAN 2018, Yichang, China."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1016\/j.ins.2016.10.023","article-title":"Abstracting massive data for lightweight intrusion detection in computer networks","volume":"433\u2013434","author":"Wang","year":"2018","journal-title":"Inf. Sci."},{"key":"ref_17","first-page":"93","article-title":"Data Anomaly Detection for Structural Health Monitoring of Bridges using Shapelet Transform","volume":"29","author":"Arul","year":"2022","journal-title":"Smart Struct. Syst."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1016\/0098-1354(92)80051-A","article-title":"Autoassociative neural networks","volume":"16","author":"Kramer","year":"1992","journal-title":"Comput. Chem. Eng."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1016\/j.ress.2013.02.022","article-title":"Failure diagnosis using deep belief learning based health state classification","volume":"115","author":"Tamilselvan","year":"2013","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1016\/j.jsv.2016.10.043","article-title":"Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks","volume":"388","author":"Abdeljaber","year":"2017","journal-title":"J. Sound Vib."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1111\/mice.12263","article-title":"Deep learning-based crack damage detection using convolutional neural networks","volume":"32","author":"Cha","year":"2017","journal-title":"Comput.-Aided Civ. Infrastruct. Eng."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1111\/mice.12409","article-title":"Automated pixel-level pavement crack detection on 3d asphalt surfaces with a recurrent neural network","volume":"34","author":"Zhang","year":"2018","journal-title":"Comput.-Aided Civ. Infrastruct. Eng."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1016\/j.ymssp.2018.03.027","article-title":"Bayesian operational modal analysis of jiangyin yangtze river bridge","volume":"110","author":"Brownjohn","year":"2018","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Xiang, X.Z., Lv, N., Guo, X.L., Wang, S., and Abdulmotaleb, E.S. (2018). Engineering vehicles detection based on modified faster R-CNN for power grid surveillance. Sensors, 18.","DOI":"10.3390\/s18072258"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"292","DOI":"10.3390\/s18010292","article-title":"Multi-sensor data integration using deep learning for characterization of defects in steel elements","volume":"18","author":"Grzegorz","year":"2018","journal-title":"Sensors"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"315","DOI":"10.1016\/j.eswa.2017.12.037","article-title":"An architecture for emergency event prediction using lstm recurrent neural networks","volume":"97","author":"Cortez","year":"2017","journal-title":"Expert Syst. Appl."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1016\/j.neucom.2018.05.086","article-title":"Deep sequential fusion lstm network for image description","volume":"312","author":"Tang","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.neucom.2018.04.045","article-title":"Lstm with sentence representations for document-level sentiment classification","volume":"308","author":"Rao","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Kambayashi, Y., Winiwarter, W., and Arikawa, M. (2002). Outlier detection using replicator neural networks. International Conference on Data Warehousing and Knowledge Discovery, Springer.","DOI":"10.1007\/3-540-46145-0"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.ins.2018.05.020","article-title":"DeepNAP: Deep neural anomaly pre-detection in a semiconductor fab","volume":"457\u2013458","author":"Kim","year":"2018","journal-title":"Inf. Sci."},{"key":"ref_31","unstructured":"Filonov, P., Lavrentyev, A., and Vorontsov, A. (2016). Multivariate industrial time series with cyber-attack simulation: Fault detection using an LSTM-based predictive data model. arXiv."},{"key":"ref_32","unstructured":"Tuor, A., Kaplan, S., Hutchinson, B., Nichols, N., and Robinson, S. (2017). Deep learning for unsupervised insider threat detection in structured cybersecurity data streams. arXiv."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"401","DOI":"10.1177\/1475921718757405","article-title":"Computer vision and deep learning\u2013based data anomaly detection method for structural health monitoring","volume":"18","author":"Bao","year":"2018","journal-title":"Struct. Health Monit."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"685","DOI":"10.1111\/mice.12528","article-title":"Deep learning for data anomaly detection and data compression of a long-span suspension bridge","volume":"35","author":"Ni","year":"2020","journal-title":"Comput. Civ. Infrastruct. Eng."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Nanduri, A., and Sherry, L. (2016, January 19\u201321). Anomaly detection in aircraft data using Recurrent Neural Networks (RNN). Proceedings of the 2016 Integrated Communications Navigation and Surveillance (ICNS), Herndon, VA, USA.","DOI":"10.1109\/ICNSURV.2016.7486356"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1006\/jcss.1995.1013","article-title":"On the computational power of neural nets","volume":"50","author":"Siegelmann","year":"1995","journal-title":"J. Comput. Syst. Sci."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1109\/72.279181","article-title":"Learning long-term dependencies with gradient descent is difficult","volume":"5","author":"Bengio","year":"1994","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/16\/6045\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:08:06Z","timestamp":1760141286000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/16\/6045"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,12]]},"references-count":38,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2022,8]]}},"alternative-id":["s22166045"],"URL":"https:\/\/doi.org\/10.3390\/s22166045","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,8,12]]}}}