{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T22:29:55Z","timestamp":1775341795679,"version":"3.50.1"},"reference-count":51,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2025,10,14]],"date-time":"2025-10-14T00:00:00Z","timestamp":1760400000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Buildings"],"abstract":"<jats:p>Monitoring the condition of existing structures remains one of the most pressing challenges within the construction industry. Structural health monitoring (SHM) techniques have proven increasingly effective in this regard; however, maintaining and archiving complete lifecycle data for such structures remains costly. Data acquisition is particularly critical, as the SHM system relies upon this information to analyse and evaluate structural behaviour. Nonetheless, a range of challenges\u2014such as environmental influences, sensor malfunction, and transmission failures\u2014can lead to data corruption or loss. These issues compromise the reliability of the dataset, necessitating either data reconstruction or additional measurement campaigns, both of which are resource-intensive. This study proposes the use of a long short-term memory (LSTM) network to reconstruct missing or corrupted data. A complete dataset collected from an actual construction project is employed to train the network. Data loss scenarios are then simulated, including single-channel (loss from one sensor) and multi-channel (loss from multiple sensors) cases. The trained LSTM model is subsequently applied to reconstruct the missing data. A case study on a real bridge demonstrates that the reconstructed data show strong agreement with the original measurements in both the time and frequency domains. These findings indicate that the proposed approach has the potential to support engineers in conserving resources by reducing the need for costly and time-consuming additional measurement interventions.<\/jats:p>","DOI":"10.3390\/buildings15203702","type":"journal-article","created":{"date-parts":[[2025,10,14]],"date-time":"2025-10-14T13:41:29Z","timestamp":1760449289000},"page":"3702","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Deep Learning-Based Reconstruction of Vibration Sensor Data for Structural Health Monitoring: A Case Study"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8101-4698","authenticated-orcid":false,"given":"Thuc V.","family":"Ngo","sequence":"first","affiliation":[{"name":"Urban Infrastructure Faculty, Mien Tay Construction University, Vinh Long 85100, Vietnam"}]},{"given":"Nga T. T.","family":"Nguyen","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, University of Transport Technology, Hanoi 11407, Vietnam"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1536-2149","authenticated-orcid":false,"given":"Jos\u00e9 C.","family":"Matos","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering, ISISE, ARISE, University of Minho, 4800-058 Guimar\u00e3es, Portugal"}]},{"given":"Huyen T.","family":"Dang","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering, ISISE, ARISE, University of Minho, 4800-058 Guimar\u00e3es, 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, ISISE, ARISE, University of Minho, 4800-058 Guimar\u00e3es, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2025,10,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Nilnoree, S., Taparugssanagorn, A., Kaemarungsi, K., and Mizutani, T. 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