{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T21:28:18Z","timestamp":1784150898429,"version":"3.55.0"},"reference-count":44,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2021,6,8]],"date-time":"2021-06-08T00:00:00Z","timestamp":1623110400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This paper presents a novel approach to substantially improve the detection accuracy of structural damage via a one-dimensional convolutional neural network (1-D CNN) and a decision-level fusion strategy. As structural damage usually induces changes in the dynamic responses of a structure, a CNN can effectively extract structural damage information from the vibration signals and classify them into the corresponding damage categories. However, it is difficult to build a large-scale sensor system in practical engineering; the collected vibration signals are usually non-synchronous and contain incomplete structure information, resulting in some evident errors in the decision stage of the CNN. In this study, the acceleration signals of multiple acquisition points were obtained, and the signals of each acquisition point were used to train a 1-D CNN, and their performances were evaluated by using the corresponding testing samples. Subsequently, the prediction results of all CNNs were fused (decision-level fusion) to obtain the integrated detection results. This method was validated using both numerical and experimental models and compared with a control experiment (data-level fusion) in which all the acceleration signals were used to train a CNN. The results confirmed that: by fusing the prediction results of multiple CNN models, the detection accuracy was significantly improved; for the numerical and experimental models, the detection accuracy was 10% and 16\u201330%, respectively, higher than that of the control experiment. It was demonstrated that: training a CNN using the acceleration signals of each acquisition point and making its own decision (the CNN output) and then fusing these decisions could effectively improve the accuracy of damage detection of the CNN.<\/jats:p>","DOI":"10.3390\/s21123950","type":"journal-article","created":{"date-parts":[[2021,6,8]],"date-time":"2021-06-08T21:16:58Z","timestamp":1623187018000},"page":"3950","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":70,"title":["Multi-Sensor and Decision-Level Fusion-Based Structural Damage Detection Using a One-Dimensional Convolutional Neural Network"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1703-3362","authenticated-orcid":false,"given":"Shuai","family":"Teng","sequence":"first","affiliation":[{"name":"School of Civil and Transportation Engineering, Guangdong University of Technology, Guangzhou 510006, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7232-9583","authenticated-orcid":false,"given":"Gongfa","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Civil and Transportation Engineering, Guangdong University of Technology, Guangzhou 510006, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zongchao","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Civil and Transportation Engineering, Guangdong University of Technology, Guangzhou 510006, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6110-8099","authenticated-orcid":false,"given":"Li","family":"Cheng","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, The Hong Kong Polytechnic University, Hong Kong 999077, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoli","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Civil and Transportation Engineering, Guangdong University of Technology, Guangzhou 510006, China"},{"name":"Guangzhou Municipal Engineering Testing Co., Ltd., Guangzhou 510520, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,6,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2198","DOI":"10.1016\/j.ymssp.2006.10.002","article-title":"Development in vibration-based structural damage detection technique","volume":"21","author":"Yan","year":"2007","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"e2416","DOI":"10.1002\/stc.2416","article-title":"Recent progress and future trends on damage identification methods for bridge structures","volume":"26","author":"An","year":"2019","journal-title":"Struct. Control Health Monit."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1016\/0022-460X(91)90595-B","article-title":"Damage detection from changes in curvature mode shapes","volume":"145","author":"Pandey","year":"1991","journal-title":"J. Sound Vib."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"4123","DOI":"10.1016\/j.jsv.2014.04.056","article-title":"Modal flexibility-based damage detection of cantilever beam-type structures using baseline modification","volume":"333","author":"Sung","year":"2014","journal-title":"J. Sound Vib."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1101","DOI":"10.1006\/jsvi.2001.4092","article-title":"Multiple Damage Location with Flexibility Curvature and Relative Frequency Change for Beam Structures","volume":"253","author":"Lu","year":"2002","journal-title":"J. Sound Vib."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Teng, S., Chen, G., Liu, G., Lv, J., and Cui, F. (2019). Modal Strain Energy-Based Structural Damage Detection Using Convolutional Neural Networks. Appl. Sci., 9.","DOI":"10.3390\/app9163376"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"347","DOI":"10.1111\/mice.12122","article-title":"Structural Damage Detection Using Modal Strain Energy and Hybrid Multiobjective Optimization","volume":"30","author":"Cha","year":"2015","journal-title":"Comput. Aided Civ. Infrastruct. Eng."},{"key":"ref_8","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. Aided Civ. Infrastruct. Eng."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"e1852","DOI":"10.1002\/stc.1852","article-title":"Completely contactless structural health monitoring of real-life structures using cameras and computer vision","volume":"24","author":"Khuc","year":"2017","journal-title":"Struct. Control Health Monit."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"916","DOI":"10.1016\/j.jsv.2006.05.007","article-title":"A Kalman-filter based time-domain analysis for structural damage diagnosis with noisy signals","volume":"297","author":"Feng","year":"2006","journal-title":"J. Sound Vib."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"302","DOI":"10.1177\/1475921716639587","article-title":"A machine-learning approach for structural damage detection using least square support vector machine based on a new combinational kernel function","volume":"15","author":"Ghiasi","year":"2016","journal-title":"Struct. Health Monit."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"403","DOI":"10.1016\/S0263-8223(03)00023-0","article-title":"Vibration-based damage detection for composite structures using wavelet transform and neural network identification","volume":"60","author":"Yam","year":"2003","journal-title":"Compos. Struct."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1122","DOI":"10.1016\/j.eswa.2007.08.008","article-title":"Damage detection of truss bridge joints using Artificial Neural Networks","volume":"35","author":"Mehrjoo","year":"2008","journal-title":"Expert Syst. Appl."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"416","DOI":"10.1016\/j.compstruc.2007.02.021","article-title":"Seismic damage identification in buildings using neural networks and modal data","volume":"86","author":"Gonzalez","year":"2008","journal-title":"Comput. Struct."},{"key":"ref_15","first-page":"789384","article-title":"Bridge Damage Severity Quantification Using Multipoint Acceleration Measurement and Artificial Neural Networks","volume":"2015","author":"Chun","year":"2015","journal-title":"Shock Vib."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1556","DOI":"10.1016\/j.ymssp.2009.12.008","article-title":"Damage classification and estimation in experimental structures using time series analysis and pattern recognition","volume":"24","author":"Lautour","year":"2010","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.istruc.2021.01.010","article-title":"Damage identification by wavelet analysis of modal rotation differences","volume":"30","author":"Katunin","year":"2021","journal-title":"Structures"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1001","DOI":"10.1260\/1369-4332.13.6.1001","article-title":"Dynamic-Based Damage Identification Using Neural Network Ensembles and Damage Index Method","volume":"13","author":"Dackermann","year":"2010","journal-title":"Adv. Struct. Eng."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Zhong, K., Teng, S., Liu, G., Chen, G., and Cui, F. (2020). Structural Damage Features Extracted by Convolutional Neural Networks from Mode Shapes. Appl. Sci., 10.","DOI":"10.3390\/app10124247"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1025","DOI":"10.1111\/mice.12313","article-title":"Structural Damage Detection with Automatic Feature extraction through Deep Learning","volume":"32","author":"Lin","year":"2017","journal-title":"Comput. Aided Civ. Infrastruct. Eng."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Teng, S., Liu, Z., Chen, G., and Cheng, L. (2021). Concrete Crack Detection Based on Well-Known Feature Extractor Model and the YOLO_v2 Network. Appl. Sci., 11.","DOI":"10.3390\/app11020813"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1203","DOI":"10.1016\/S0141-0296(02)00054-8","article-title":"Shake table testing of a base isolated model","volume":"24","author":"Yi","year":"2002","journal-title":"Eng. Struct."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1177\/1475921718804132","article-title":"A novel deep learning-based method for damage identification of smart building structures","volume":"18","author":"Yu","year":"2019","journal-title":"Struct. Health Monit."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"8136","DOI":"10.1109\/TIE.2018.2886789","article-title":"Fault Detection and Severity Identification of Ball Bearings by Online Condition Monitoring","volume":"66","author":"Abdeljaber","year":"2019","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"8760","DOI":"10.1109\/TIE.2018.2833045","article-title":"Real-Time Fault Detection and Identification for MMC using 1D Convolutional Neural Networks","volume":"66","author":"Kiranyaz","year":"2018","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_26","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_27","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1016\/j.jsv.2018.03.008","article-title":"Wireless and real-time structural damage detection: A novel decentralized method for wireless sensor networks","volume":"424","author":"Avci","year":"2018","journal-title":"J. Sound Vib."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"822","DOI":"10.1111\/mice.12447","article-title":"Vibration-based structural state identification by a 1-dimensional convolutional neural network","volume":"34","author":"Zhang","year":"2019","journal-title":"Comput. Aided Civ. Infrastruct. Eng."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1016\/j.ejrad.2006.11.029","article-title":"CT-MR image data fusion for computer assisted navigated neurosurgery of temporal bone tumors","volume":"62","author":"Nemec","year":"2007","journal-title":"Eur. J. Radiol."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"619","DOI":"10.1016\/j.apgeog.2011.07.010","article-title":"Image data fusion for the remote sensing of freshwater environments","volume":"32","author":"Ashraf","year":"2011","journal-title":"Appl. Geogr."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"e2296","DOI":"10.1002\/stc.2296","article-title":"Convolutional neural network-based data anomaly detection method using multiple information for structural health monitoring","volume":"26","author":"Tang","year":"2019","journal-title":"Struct. Control Health Monit."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1007\/s13349-013-0070-3","article-title":"A multi-stage data-fusion procedure for damage detection of linear systems based on modal strain energy","volume":"4","author":"Ernesto","year":"2014","journal-title":"J. Civil Struct. Health Monit."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"945","DOI":"10.1007\/s11012-019-01052-w","article-title":"Structural damage detection using convolutional neural networks combining strain energy and dynamic response","volume":"55","author":"Teng","year":"2019","journal-title":"Meccanica"},{"key":"ref_34","unstructured":"Huo, Z., Zhang, Y., and Shu, L. (2018, January 5\u20137). Bearing Fault Diagnosis using Multi-sensor Fusion based on weighted D-S Evidence Theory. Proceedings of the 2018 18th International Conference on Mechatronics-Mechatronika (ME), Brno, Czech Republic."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"2042006","DOI":"10.1142\/S0219455420420067","article-title":"A two-step drive-by bridge damage detection using Dual Kalman Filter","volume":"20","author":"Li","year":"2020","journal-title":"Int. J. Struct. Stab. Dyn."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"694","DOI":"10.1002\/stc.1712","article-title":"An algorithm based on two-step Kalman filter for intelligent structural damage detection","volume":"22","author":"Ying","year":"2015","journal-title":"Struct. Control Health Monit."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1007\/s13349-013-0054-3","article-title":"Application of substructural damage identification using adaptive Kalman filter","volume":"4","author":"Xing","year":"2013","journal-title":"J. Civ. Struct. Health Monit."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"e1961","DOI":"10.1002\/stc.1961","article-title":"Online structural damage identification technique using constrained dual extended Kalman filter","volume":"24","author":"Sen","year":"2017","journal-title":"Struct. Control Health Monit."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"428","DOI":"10.1016\/j.measurement.2016.04.016","article-title":"Moving-window extended Kalman filter for structural damage detection with unknown process and measurement noises","volume":"88","author":"Lai","year":"2016","journal-title":"Measurement"},{"key":"ref_40","first-page":"04015012","article-title":"Novel Unscented Kalman Filter for Health Assessment of Structural Systems with Unknown Input","volume":"141","author":"Haldar","year":"2015","journal-title":"J. Eng. Mech."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"2607","DOI":"10.1109\/TIM.2020.2981220","article-title":"Entropy Measures in Machine Fault Diagnosis: Insights and Applications","volume":"69","author":"Huo","year":"2020","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_42","first-page":"5687837","article-title":"Reconstructed Phase Space-Based Damage Detection Using a Single Sensor for Beam-Like Structure Subjected to a Moving Mass","volume":"2017","author":"Nie","year":"2017","journal-title":"Shock Vib."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Scherer, D., M\u00fcller, A., and Behnke, S. (2010, January 15\u201318). Evaluation of Pooling Operations in Convolutional Architectures for Object Recognition. Proceedings of the International Conference on Artificial Neural Networks, Thessaloniki, Greece.","DOI":"10.1007\/978-3-642-15825-4_10"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"e2424","DOI":"10.1002\/stc.2424","article-title":"Improved Kalman filter damage detection approach based on lp regularization","volume":"26","author":"Huang","year":"2019","journal-title":"Struct. Control Health Monit."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/12\/3950\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:12:02Z","timestamp":1760163122000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/12\/3950"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6,8]]},"references-count":44,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2021,6]]}},"alternative-id":["s21123950"],"URL":"https:\/\/doi.org\/10.3390\/s21123950","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,6,8]]}}}