{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,3]],"date-time":"2025-12-03T01:00:46Z","timestamp":1764723646172,"version":"3.46.0"},"reference-count":25,"publisher":"Springer Science and Business Media LLC","issue":"16","license":[{"start":{"date-parts":[[2025,11,17]],"date-time":"2025-11-17T00:00:00Z","timestamp":1763337600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,11,17]],"date-time":"2025-11-17T00:00:00Z","timestamp":1763337600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["QNXM20250019"],"award-info":[{"award-number":["QNXM20250019"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["SIViP"],"published-print":{"date-parts":[[2025,12]]},"DOI":"10.1007\/s11760-025-04903-0","type":"journal-article","created":{"date-parts":[[2025,11,17]],"date-time":"2025-11-17T14:34:30Z","timestamp":1763390070000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Damage localization based on MSCNN-Transformer-BiGRU algorithm"],"prefix":"10.1007","volume":"19","author":[{"given":"Shuzhen","family":"Yang","sequence":"first","affiliation":[]},{"given":"Zhongliang","family":"Zou","sequence":"additional","affiliation":[]},{"given":"Bo","family":"Song","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,11,17]]},"reference":[{"key":"4903_CR1","doi-asserted-by":"publisher","first-page":"549","DOI":"10.1016\/j.ymssp.2017.08.037","volume":"101","author":"M Krishnan","year":"2018","unstructured":"Krishnan, M., Bhowmik, B., Hazra, B., et al.: Real time damage detection using recursive principal components and time varying auto-regressive modeling. Mech. Syst. Signal. Process. 101, 549\u2013574 (2018)","journal-title":"Mech. Syst. Signal. Process."},{"issue":"3","key":"4903_CR2","doi-asserted-by":"publisher","first-page":"e2488","DOI":"10.1002\/stc.2488","volume":"27","author":"SO Sajedi","year":"2020","unstructured":"Sajedi, S.O., Liang, X.: A data-driven framework for near real-time and robust damage diagnosis of building structures. Struct. Control Health Monit. 27(3), e2488\u2013e2481 (2020)","journal-title":"Struct. Control Health Monit."},{"issue":"3","key":"4903_CR3","doi-asserted-by":"publisher","first-page":"207","DOI":"10.1111\/j.1467-8667.2010.00687.x","volume":"26","author":"R Jafarkhani","year":"2011","unstructured":"Jafarkhani, R., Masri, S.F.: Finite element model updating using evolutionary strategy for damage detection. Comput-Aided Civ. Infrastruct. Eng. 26(3), 207\u2013224 (2011)","journal-title":"Comput-Aided Civ. Infrastruct. Eng."},{"key":"4903_CR4","doi-asserted-by":"publisher","first-page":"112146","DOI":"10.1016\/j.ymssp.2024.112146","volume":"224","author":"Z Ding","year":"2025","unstructured":"Ding, Z., Kuok, S., Lei, Y., et al.: Clustering driven incremental learning surrogate model-assisted evolution for structural condition assessment. Mech. Syst. Signal. Proc. 224, 112146\u2013112141 (2025)","journal-title":"Mech. Syst. Signal. Proc."},{"issue":"9","key":"4903_CR5","first-page":"174","volume":"10","author":"S Rostami","year":"2023","unstructured":"Rostami, S., Mashhadi, J., Hajizadeh, A.: Damage detection of plate using the wavelet-based contourlet transform method. J. Struct. Constr. Eng. 10(9), 174\u2013192 (2023)","journal-title":"J. Struct. Constr. Eng."},{"key":"4903_CR6","doi-asserted-by":"crossref","unstructured":"Malathy, R.B.: A hybrid modified artificial bee colony and extended Kalman filter algorithm for structural system identification. Asian J. Civ. Eng. 25(1), 385\u2013396 (2024)","DOI":"10.1007\/s42107-023-00782-3"},{"key":"4903_CR7","unstructured":"Rogers, T.J.: Towards Bayesian system identification: with application to SHM of offshore structures. PhD Thesis, University of Sheffield, UK. (2019)"},{"key":"4903_CR8","doi-asserted-by":"crossref","unstructured":"Lei, Y., Li, J., Hao, H.: Structural damage identification based on physics-guided deep learning: Applications to large-scale structures. Struct. Health Monit. 14759217251353399, 1\u201321 (2025)","DOI":"10.1177\/14759217251353399"},{"key":"4903_CR9","doi-asserted-by":"publisher","first-page":"113435","DOI":"10.1016\/j.measurement.2023.113435","volume":"220","author":"LL Fu","year":"2023","unstructured":"Fu, L.L., Yang, J.S., Li, S., et al.: Artificial neural network-based damage detection of composite material using laser ultrasonic technology. Meas. 220, 113435 (2023)","journal-title":"Meas"},{"key":"4903_CR10","doi-asserted-by":"publisher","first-page":"154","DOI":"10.1016\/j.jsv.2016.10.043","volume":"388","author":"O Abdeljaber","year":"2017","unstructured":"Abdeljaber, O., Avci, O., Kiranyaz, S., et al.: Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks. J. Sound Vib. 388, 154\u2013170 (2017)","journal-title":"J. Sound Vib."},{"key":"4903_CR11","doi-asserted-by":"publisher","first-page":"128731","DOI":"10.1016\/j.eswa.2025.128731","volume":"293","author":"S Shi","year":"2025","unstructured":"Shi, S., Du, D., Mercan, O., et al.: Contrastive and self-supervised learning for open-set damage classification in structural health monitoring with incomplete and imbalanced vibration data. Expert Syst. Appl. 293, 128731\u2013128731 (2025)","journal-title":"Expert Syst. Appl."},{"key":"4903_CR12","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1016\/j.patrec.2022.09.004","volume":"162","author":"X Feng","year":"2022","unstructured":"Feng, X., Zhou, S., Zhu, Z., et al.: Local to global feature learning for salient object detection. Pattern Recognit. Lett. 162, 81\u201388 (2022)","journal-title":"Pattern Recognit. Lett."},{"key":"4903_CR13","first-page":"435","volume":"30","author":"S Kanai","year":"2017","unstructured":"Kanai, S., Fujiwara, Y., Iwamura, S.: Preventing gradient explosions in gated recurrent units. Adv. Neural Inf. Process. Syst. 30, 435\u2013444 (2017)","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"4903_CR14","unstructured":"Talathi, S.S., Vartak, A.: Improving performance of recurrent neural network with relu nonlinearity. arXiv preprint arXiv, 1511.03771 (2015)"},{"key":"4903_CR15","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., et al.: Attention is all you need. Adv. Neural Inf. Process. Syst. 30, 1\u201311 (2017)"},{"issue":"10","key":"4903_CR16","doi-asserted-by":"publisher","first-page":"2299","DOI":"10.3390\/electronics12102299","volume":"12","author":"KAA Fuad","year":"2023","unstructured":"Fuad, K.A.A., Chen, L.: A survey on sparsity exploration in transformer-based accelerators. Electron. 12(10), 2299 (2023)","journal-title":"Electron"},{"key":"4903_CR17","first-page":"1736","volume":"33","author":"X Li","year":"2020","unstructured":"Li, X., Cooper Stickland, A., Tang, Y., et al.: Deep transformers with latent depth. Adv. Neural Inf. Process. Syst. 33, 1736\u20131746 (2020)","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"4903_CR18","doi-asserted-by":"publisher","first-page":"895","DOI":"10.1016\/j.procs.2018.04.298","volume":"131","author":"G Shen","year":"2018","unstructured":"Shen, G., Tan, Q., Zhang, H., et al.: Deep learning with gated recurrent unit networks for financial sequence predictions. Procedia Comput. Sci. 131, 895\u2013903 (2018)","journal-title":"Procedia Comput. Sci."},{"key":"4903_CR19","doi-asserted-by":"publisher","first-page":"107627","DOI":"10.1016\/j.ijepes.2021.107627","volume":"137","author":"D Li","year":"2022","unstructured":"Li, D., Sun, G., Miao, S., et al.: A short-term electric load forecast method based on improved sequence-to-sequence GRU with adaptive temporal dependence. Int. J. Electr. Power Energy Syst. 137, 107627 (2022)","journal-title":"Int. J. Electr. Power Energy Syst."},{"key":"4903_CR20","first-page":"24261","volume":"34","author":"IO Tolstikhin","year":"2021","unstructured":"Tolstikhin, I.O., Houlsby, N., Kolesnikov, A., et al.: Mlp-mixer: An all-mlp architecture for vision. Adv. Neural Inf. Process. Syst. 34, 24261\u201324272 (2021)","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"4903_CR21","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., et al.: Deep residual learning for image recognition. In: IEEE conference on computer vision and pattern recognition, Las Vegas, United States, Jun. 26 - Jul 01, 2016, paper no. 16541111, pp. 770\u2013778. New York: IEEE","DOI":"10.1109\/CVPR.2016.90"},{"key":"4903_CR22","unstructured":"Chung, J., Gulcehre, C., Cho, K.H., et al.: Empirical evaluation of gated recurrent neural networks on sequence modeling. ArXiv Preprint arXiv, 1412.3555, 1\u20139, (2014)"},{"key":"4903_CR23","unstructured":"Paszke, A., Gross, S., Massa, F., et al.: Pytorch: An imperative style, high-performance deep learning library. NeurIPS. 32, 1\u201312, (2019)"},{"key":"4903_CR24","unstructured":"Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. arXiv preprint arXiv, 1412.6980 (2014)"},{"key":"4903_CR25","doi-asserted-by":"crossref","unstructured":"Avci, O., Abdeljaber, O., Kiranyaz, S., et al.: A new benchmark problem for structural damage detection: Bolt loosening tests on a large-scale laboratory structure. In: SEM Conference and Exposition on Structural Dynamics, Orlando, Florida, USA, February, 8\u201311, 2021, paper no. Volume 2, pp. 15\u201322. Bethel: SEM.","DOI":"10.1007\/978-3-030-77143-0_2"}],"container-title":["Signal, Image and Video Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-025-04903-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11760-025-04903-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-025-04903-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,3]],"date-time":"2025-12-03T00:55:51Z","timestamp":1764723351000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11760-025-04903-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,17]]},"references-count":25,"journal-issue":{"issue":"16","published-print":{"date-parts":[[2025,12]]}},"alternative-id":["4903"],"URL":"https:\/\/doi.org\/10.1007\/s11760-025-04903-0","relation":{},"ISSN":["1863-1703","1863-1711"],"issn-type":[{"type":"print","value":"1863-1703"},{"type":"electronic","value":"1863-1711"}],"subject":[],"published":{"date-parts":[[2025,11,17]]},"assertion":[{"value":"13 August 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 October 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 October 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 November 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"1366"}}