{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,18]],"date-time":"2025-11-18T05:15:27Z","timestamp":1763442927132,"version":"3.45.0"},"reference-count":49,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2025,11,15]],"date-time":"2025-11-15T00:00:00Z","timestamp":1763164800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100014657","name":"University of Transport and Communications","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100014657","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Applied Sciences"],"abstract":"<jats:p>Urban railway bridges are critical components of modern transportation networks. Dynamic loads and harsh environments put urban railway bridges at high risk of damage. Conventional vibration-based damage detection approaches often fail to provide sufficient accuracy and robustness under complex urban conditions. To address this limitation, this study introduces a hybrid deep learning framework that integrates a one-dimensional convolutional neural network (1D-CNN) and a recurrent neural network (RNN) for automatic damage detection using Linear Variable Differential Transformer (LVDT) displacement data. Start with the calibration of a finite element model (FEM) of the target bridge, achieved through updating the model parameters to align with field-acquired LVDT data, thereby establishing a robust and reliable baseline representation of the structure\u2019s behaviour. Subsequently, a series of failure and damage scenarios is introduced within the FEM, and the associated dynamic displacement responses are generated to construct a comprehensive synthetic training dataset. These time-series responses serve as input for training a hybrid deep learning architecture, which integrates a one-dimensional convolutional neural network (1D-CNN) for automated feature extraction with a recurrent neural network (RNN) designed to capture the temporal dependencies inherent in the structural response data. Results show rapid convergence and minimal error in single-damage cases, and robust performance in multi-damage conditions on a dataset exceeding 5 million samples; the model attains a mean absolute error of \u22483.2% for damage severity and an average localisation error of &lt;0.7 m. The findings highlight the effectiveness of combining numerical simulation with advanced data-driven approaches to provide a practical, data-efficient, and scalable solution for structural health monitoring in the urban railway context.<\/jats:p>","DOI":"10.3390\/app152212132","type":"journal-article","created":{"date-parts":[[2025,11,17]],"date-time":"2025-11-17T10:24:58Z","timestamp":1763375098000},"page":"12132","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Hybrid Deep Learning Framework for Damage Detection in Urban Railway Bridges Based on Linear Variable Differential Transformer Data"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5103-6642","authenticated-orcid":false,"given":"Nhung T. C.","family":"Nguyen","sequence":"first","affiliation":[{"name":"Faculty of Civil Engineering, University of Transport and Communications, Hanoi 100000, Vietnam"}]},{"given":"Hoang N.","family":"Bui","sequence":"additional","affiliation":[{"name":"School of Engineering and Applied Sciences, State University of New York at Buffalo, New York, NY 14360, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1536-2149","authenticated-orcid":false,"given":"Jose C.","family":"Matos","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering, University of Minho, 4800-058 Guimaraes, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3341-3034","authenticated-orcid":false,"given":"Son N.","family":"Dang","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering, University of Minho, 4800-058 Guimaraes, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2025,11,15]]},"reference":[{"key":"ref_1","first-page":"48","article-title":"Structural Health Monitoring and Material Safety with Multispectral Technique: A Review","volume":"3","author":"Huang","year":"2022","journal-title":"J. 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