{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T22:48:04Z","timestamp":1778107684504,"version":"3.51.4"},"reference-count":36,"publisher":"MDPI AG","issue":"6","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":["Algorithms"],"abstract":"<jats:p>The shallow features extracted by the traditional artificial intelligence algorithm-based damage identification methods pose low sensitivity and ignore the timing characteristics of vibration signals. Thus, this study uses the high-dimensional feature extraction advantages of convolutional neural networks (CNNs) and the time series modeling capability of long short-term memory networks (LSTM) to identify damage to long-span bridges. Firstly, the features extracted by CNN and LSTM are fused as the input of the fully connected layer to train the CNN-LSTM model. After that, the trained CNN-LSTM model is employed for damage identification. Finally, a numerical example of a large-span suspension bridge was carried out to investigate the effectiveness of the proposed method. Furthermore, the performance of CNN-LSTM and CNN under different noise levels was compared to test the feasibility of application in practical engineering. The results demonstrate the following: (1) the combination of CNN and LSTM is satisfactory with 94% of the damage localization accuracy and only 8.0% of the average relative identification error (ARIE) of damage severity identification; (2) in comparison to the CNN, the CNN-LSTM results in superior identification accuracy; the damage localization accuracy is improved by 8.13%, while the decrement of ARIE of damage severity identification is 5.20%; and (3) the proposed method is capable of resisting the influence of environmental noise and acquires an acceptable recognition effect for multi-location damage; in a database with a lower signal-to-noise ratio of 3.33, the damage localization accuracy of the CNN-LSTM model is 67.06%, and the ARIE of the damage severity identification is 31%. This work provides an innovative idea for damage identification of long-span bridges and is conducive to promote follow-up studies regarding structural condition evaluation.<\/jats:p>","DOI":"10.3390\/a14060180","type":"journal-article","created":{"date-parts":[[2021,6,7]],"date-time":"2021-06-07T22:23:00Z","timestamp":1623104580000},"page":"180","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":38,"title":["Damage Identification of Long-Span Bridges Using the Hybrid of Convolutional Neural Network and Long Short-Term Memory Network"],"prefix":"10.3390","volume":"14","author":[{"given":"Lei","family":"Fu","sequence":"first","affiliation":[{"name":"State Key Laboratory of Mountain Bridges and Tunnel Engineering, Chongqing Jiaotong University, Chongqing 400074, China"},{"name":"Guizhou Bridge Construction Group Co., Ltd., Guiyang 550000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qizhi","family":"Tang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Mountain Bridges and Tunnel Engineering, Chongqing Jiaotong University, Chongqing 400074, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peng","family":"Gao","sequence":"additional","affiliation":[{"name":"CMCU Engineering Co., Ltd., Chongqing 400039, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7494-298X","authenticated-orcid":false,"given":"Jingzhou","family":"Xin","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Mountain Bridges and Tunnel Engineering, Chongqing Jiaotong University, Chongqing 400074, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5981-4696","authenticated-orcid":false,"given":"Jianting","family":"Zhou","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Mountain Bridges and Tunnel Engineering, Chongqing Jiaotong University, Chongqing 400074, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,6,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"109319","DOI":"10.1016\/j.engstruct.2019.109319","article-title":"Relationship between shear-stress distribution and resulting acoustic-emission variation along concrete joints in prefabricated girder structures","volume":"196","author":"Liu","year":"2019","journal-title":"Eng. 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