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dc.contributorDepartment of Mechanical Engineering-
dc.creatorTeng, S-
dc.creatorChen, G-
dc.creatorLiu, Z-
dc.creatorCheng, L-
dc.creatorSun, X-
dc.publisherMolecular Diversity Preservation International (MDPI)en_US
dc.rights© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// 4.0/).en_US
dc.rightsThe following publication Teng, S.; Chen, G.; Liu, Z.; Cheng, L.; Sun, X. Multi-Sensor and Decision-Level Fusion-Based Structural Damage Detection Using a One-Dimensional Convolutional Neural Network. Sensors 2021, 21, 3950 is available at
dc.subject1-D convolutional neural networken_US
dc.subjectAcceleration signalsen_US
dc.subjectBridge modelen_US
dc.subjectDecision-level fusionen_US
dc.subjectStructural damage detectionen_US
dc.subjectVibration experimentsen_US
dc.titleMulti-sensor and decision-level fusion-based structural damage detection using a one-dimensional convolutional neural networken_US
dc.typeJournal/Magazine Articleen_US
dcterms.abstractThis 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 16–30%, 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.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationSensors, June 2021, v. 21, no. 12, 3950-
dc.description.validate202110 bcvc-
dc.description.oaVersion of Recorden_US
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