{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,5]],"date-time":"2026-09-05T17:12:22Z","timestamp":1788628342990,"version":"build-2803163510"},"reference-count":44,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Advanced Engineering Informatics"],"published-print":{"date-parts":[[2026,11]]},"DOI":"10.1016\/j.aei.2026.104980","type":"journal-article","created":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T06:54:45Z","timestamp":1781852085000},"page":"104980","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"PA","title":["A deep transfer learning framework for dynamic cracking risk assessment of gravity dams in cold regions under extreme environmental conditions"],"prefix":"10.1016","volume":"76","author":[{"given":"Ruijie","family":"Yu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liang","family":"Pei","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yongqi","family":"Su","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jichen","family":"Tian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiang","family":"Lu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tingting","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.aei.2026.104980_b0005","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2023.106813","article-title":"The role of artificial intelligence and digital technologies in dam engineering: narrative review and outlook","volume":"126","author":"Hariri-Ardebili","year":"2023","journal-title":"Eng. Appl. Artif. Intel."},{"key":"10.1016\/j.aei.2026.104980_b0010","doi-asserted-by":"crossref","DOI":"10.1016\/j.jobe.2024.110914","article-title":"Investigation on the evolution of concrete pore structure under freeze-thaw and fatigue loads","volume":"97","author":"Gan","year":"2024","journal-title":"Journal of Building Engineering"},{"key":"10.1016\/j.aei.2026.104980_b0015","doi-asserted-by":"crossref","DOI":"10.1016\/j.rser.2024.115108","article-title":"Climate challenges for sustainable hydropower development and operational resilience: a review","volume":"209","author":"Othman","year":"2025","journal-title":"Renew. Sustain. Energy Rev."},{"key":"10.1016\/j.aei.2026.104980_b0020","doi-asserted-by":"crossref","DOI":"10.1016\/j.conbuildmat.2024.139296","article-title":"Analysis of concrete damage evolution in cold regions under combined freeze-thaw cycle-crack effect","volume":"456","author":"Wang","year":"2024","journal-title":"Constr. Build. Mater."},{"key":"10.1016\/j.aei.2026.104980_b0025","doi-asserted-by":"crossref","first-page":"487","DOI":"10.1007\/s13349-022-00553-9","article-title":"Thermal analysis of RCC dams during construction considering different ambient boundary conditions at the upstream and downstream faces","volume":"12","author":"Pouya","year":"2022","journal-title":"J. Civ. Struct. Heal. Monit."},{"key":"10.1016\/j.aei.2026.104980_b0030","doi-asserted-by":"crossref","first-page":"537","DOI":"10.1016\/j.istruc.2023.04.081","article-title":"An advanced method for evaluating the cracking risks of gravity dams during the construction period considering the uncertainty of thermodynamic parameters","volume":"53","author":"Liu","year":"2023","journal-title":"Structures"},{"key":"10.1016\/j.aei.2026.104980_b0035","doi-asserted-by":"crossref","first-page":"528","DOI":"10.1016\/j.engstruct.2013.05.037","article-title":"Seismic cracking analysis of concrete gravity dams with initial cracks using the extended finite element method","volume":"56","author":"Zhang","year":"2013","journal-title":"Eng. Struct."},{"key":"10.1016\/j.aei.2026.104980_b0040","article-title":"Numerical simulations of multiple cracks in concrete face rockfill dams coupled multi-factor during construction","volume":"22","author":"Chen","year":"2025","journal-title":"Case Stud. Constr. Mater."},{"key":"10.1016\/j.aei.2026.104980_b0045","doi-asserted-by":"crossref","DOI":"10.1002\/nme.70030","article-title":"Plastic-damage thin-layer element model for seismic failure analysis of concrete dams","volume":"126","author":"Qiu","year":"2025","journal-title":"Int. J. Numer. Meth. Eng."},{"key":"10.1016\/j.aei.2026.104980_b0050","article-title":"A risk assessment model for dam combining the probabilistic and the nonprobabilistic methods","volume":"2020","author":"Zhu","year":"2020","journal-title":"Math. Probl. Eng."},{"key":"10.1016\/j.aei.2026.104980_b0055","article-title":"Slope reliability analysis of high rockfill dams considering stress-coupled spatial variability of friction angle","volume":"265","author":"Wu","year":"2026","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"10.1016\/j.aei.2026.104980_b0060","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2025.110961","article-title":"A novel reliability method for assessing dam slope stability by incorporating intrinsic correlations of rockfill materials","volume":"259","author":"Yang","year":"2025","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"10.1016\/j.aei.2026.104980_b0065","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2023.109234","article-title":"An efficient variable selection-based Kriging model method for the reliability analysis of slopes with spatially variable soils","volume":"235","author":"Ding","year":"2023","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"10.1016\/j.aei.2026.104980_b0070","doi-asserted-by":"crossref","first-page":"4192","DOI":"10.1016\/j.jrmge.2024.04.007","article-title":"Efficient slope reliability and sensitivity analysis using quantile-based first-order second-moment method","volume":"16","author":"Yang","year":"2024","journal-title":"J. Rock Mech. Geotech. Eng."},{"key":"10.1016\/j.aei.2026.104980_b0075","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2025.111251","article-title":"Time-dependent system reliability analysis of a high concrete-faced rockfill dam by integrating multi-source monitoring information","volume":"262","author":"Peng","year":"2025","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"10.1016\/j.aei.2026.104980_b0080","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2025.129135","article-title":"Real-time classification model for anomalous sensor data in dam safety monitoring based on convolutional neural networks and transfer learning","volume":"296","author":"Tian","year":"2026","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.aei.2026.104980_b0085","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2025.111508","article-title":"Reliability analysis of structures by multiple sparse polynomial chaos expansion based adaptive metamodel","volume":"265","author":"Modak","year":"2026","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"10.1016\/j.aei.2026.104980_b0090","doi-asserted-by":"crossref","first-page":"1507","DOI":"10.1111\/mice.13141","article-title":"Inverse analysis of deformation moduli for high arch dams using the displacement reconstruction technique and multi-objective optimization","volume":"39","author":"Li","year":"2024","journal-title":"Comput. Aided Civ. Inf. Eng."},{"key":"10.1016\/j.aei.2026.104980_b0095","doi-asserted-by":"crossref","DOI":"10.1016\/j.probengmech.2024.103595","article-title":"Effects of limit state data on constructing accurate surrogate models for structural reliability analyses","volume":"76","author":"Doan","year":"2024","journal-title":"Probab. Eng. Mech."},{"key":"10.1016\/j.aei.2026.104980_b0100","doi-asserted-by":"crossref","first-page":"955","DOI":"10.1007\/s00158-018-1938-y","article-title":"Global kriging surrogate modeling for general time-variant reliability-based design optimization problems","volume":"58","author":"Hawchar","year":"2018","journal-title":"Struct. Multidiscip. Optim."},{"key":"10.1016\/j.aei.2026.104980_b0105","doi-asserted-by":"crossref","DOI":"10.1016\/j.measurement.2025.117810","article-title":"Data reconstruction leverages one-dimensional convolutional neural networks (1DCNN) combined with long short-term memory (LSTM) networks for structural health monitoring (SHM)","volume":"253","author":"Minh","year":"2025","journal-title":"Measurement"},{"key":"10.1016\/j.aei.2026.104980_b0110","doi-asserted-by":"crossref","first-page":"447","DOI":"10.1177\/14759217241239041","article-title":"An integrated deep neural network model combining 1D CNN and LSTM for structural health monitoring utilizing multisensor time-series data","volume":"24","author":"Ahmadzadeh","year":"2025","journal-title":"Struct. Health Monitoring-An Int. J."},{"key":"10.1016\/j.aei.2026.104980_b0115","doi-asserted-by":"crossref","DOI":"10.1016\/j.aei.2025.104209","article-title":"Cross-domain adaptive digital twin modelling for dam structures using graph attention networks","volume":"70","author":"Tian","year":"2026","journal-title":"Adv. Eng. Inf."},{"key":"10.1016\/j.aei.2026.104980_b0120","doi-asserted-by":"crossref","DOI":"10.1016\/j.autcon.2025.106079","article-title":"Digital twin model for analyzing deformation and seepage in high earth-rock dams","volume":"173","author":"Tian","year":"2025","journal-title":"Autom. Constr."},{"key":"10.1016\/j.aei.2026.104980_b0125","doi-asserted-by":"crossref","DOI":"10.1016\/j.autcon.2024.105440","article-title":"CNN-Transformer hybrid network for concrete dam crack patrol inspection","volume":"163","author":"Li","year":"2024","journal-title":"Autom. Constr."},{"key":"10.1016\/j.aei.2026.104980_b0130","doi-asserted-by":"crossref","DOI":"10.1016\/j.engstruct.2025.121426","article-title":"A digital twin method for real-time analysis of structural deformation and failure for high arch dams","volume":"345","author":"Tian","year":"2025","journal-title":"Eng. Struct."},{"key":"10.1016\/j.aei.2026.104980_b0135","doi-asserted-by":"crossref","DOI":"10.1016\/j.aei.2025.104194","article-title":"A few-shot modelling method for early-stage safety monitoring of high earth-rock dams based on multi-source domain adaptation","volume":"70","author":"Tian","year":"2026","journal-title":"Adv. Eng. Inf."},{"key":"10.1016\/j.aei.2026.104980_b0140","doi-asserted-by":"crossref","DOI":"10.1016\/j.jobe.2024.108628","article-title":"Deterioration of interfacial bonding performance between metal mortar and concrete under the coupling effect of freeze-thaw cycle and abrasion","volume":"85","author":"Su","year":"2024","journal-title":"Journal of Building Engineering"},{"key":"10.1016\/j.aei.2026.104980_b0145","article-title":"Degradation of dynamic elastic modulus of concrete under periodic temperature-humidity action","volume":"13","author":"Chen","year":"2020","journal-title":"Materials"},{"key":"10.1016\/j.aei.2026.104980_b0150","doi-asserted-by":"crossref","first-page":"770","DOI":"10.1177\/14759217211009780","article-title":"A new dam structural response estimation paradigm powered by deep learning and transfer learning techniques","volume":"21","author":"Li","year":"2022","journal-title":"Struct. Health Monitoring-An Int. J."},{"key":"10.1016\/j.aei.2026.104980_b0155","doi-asserted-by":"crossref","first-page":"3071","DOI":"10.1109\/TPAMI.2018.2868685","article-title":"Transferable representation learning with deep adaptation networks","volume":"41","author":"Long","year":"2018","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.aei.2026.104980_b0160","doi-asserted-by":"crossref","first-page":"264","DOI":"10.1109\/TNNLS.2021.3093468","article-title":"Rethinking maximum mean discrepancy for visual domain adaptation","volume":"34","author":"Wang","year":"2021","journal-title":"IEEE Trans. Neural Networks Learn. Syst."},{"key":"10.1016\/j.aei.2026.104980_b0165","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2022.109088","article-title":"Unsupervised domain adaptation via deep conditional adaptation network","volume":"134","author":"Ge","year":"2023","journal-title":"Pattern Recogn."},{"key":"10.1016\/j.aei.2026.104980_b0170","doi-asserted-by":"crossref","first-page":"1309","DOI":"10.1617\/s11527-010-9702-9","article-title":"Effect of freeze\u2013thaw cycles on the stress\u2013strain curves of unconfined and confined concrete","volume":"44","author":"Duan","year":"2011","journal-title":"Mater. Struct."},{"key":"10.1016\/j.aei.2026.104980_b0175","doi-asserted-by":"crossref","first-page":"2955","DOI":"10.3390\/ma18132955","article-title":"Comprehensive assessment and freeze\u2013thaw durability prediction of wet-sprayed concrete for cold-region tunnels","volume":"18","author":"Wang","year":"2025","journal-title":"Materials"},{"key":"10.1016\/j.aei.2026.104980_b0180","first-page":"62","article-title":"Mechanical property deterioration model for concrete in environment with freezing-thawing","volume":"26","author":"Jinpeng","year":"2009","journal-title":"J. Arch. Civil Eng."},{"key":"10.1016\/j.aei.2026.104980_b0185","doi-asserted-by":"crossref","first-page":"3347","DOI":"10.1177\/01423312211018305","article-title":"Hydraulic-seasonal-time-based state space model for displacement monitoring of high concrete dams","volume":"43","author":"Wang","year":"2021","journal-title":"Trans. Inst. Meas. Control"},{"key":"10.1016\/j.aei.2026.104980_b0190","doi-asserted-by":"crossref","first-page":"425","DOI":"10.1016\/j.tust.2018.09.027","article-title":"Mechanical parameter inversion in tunnel engineering using support vector regression optimized by multi-strategy artificial fish swarm algorithm","volume":"83","author":"Zhuang","year":"2019","journal-title":"Tunn. Undergr. Space Technol."},{"key":"10.1016\/j.aei.2026.104980_b0195","doi-asserted-by":"crossref","first-page":"1367","DOI":"10.1007\/s11440-022-01495-8","article-title":"Displacement prediction of Jiuxianping landslide using gated recurrent unit (GRU) networks","volume":"17","author":"Zhang","year":"2022","journal-title":"Acta Geotech."},{"key":"10.1016\/j.aei.2026.104980_b0200","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/j.gr.2022.10.004","article-title":"Efficient time-variant reliability analysis of Bazimen landslide in the Three Gorges Reservoir Area using XGBoost and LightGBM algorithms","volume":"123","author":"Zhang","year":"2023","journal-title":"Gondw. Res."},{"key":"10.1016\/j.aei.2026.104980_b0205","doi-asserted-by":"crossref","DOI":"10.1016\/j.petrol.2021.109520","article-title":"An optimized XGBoost method for predicting reservoir porosity using petrophysical logs","volume":"208","author":"Pan","year":"2022","journal-title":"J. Petrol. Sci. Eng."},{"key":"10.1016\/j.aei.2026.104980_b0210","doi-asserted-by":"crossref","first-page":"1235","DOI":"10.1162\/neco_a_01199","article-title":"A Review of recurrent neural networks: LSTM cells and network architectures","volume":"31","author":"Yu","year":"2019","journal-title":"Neural Comput."},{"key":"10.1016\/j.aei.2026.104980_b0215","article-title":"Multisource information fusion model for deformation safety monitoring of earth and rock dams based on deep graph feature fusion","author":"Tian","year":"2024","journal-title":"Struct. Health Monitoring-An Int. J."},{"key":"10.1016\/j.aei.2026.104980_b0220","doi-asserted-by":"crossref","first-page":"3848","DOI":"10.1109\/TITS.2019.2935152","article-title":"T-GCN: a temporal graph convolutional network for traffic prediction","volume":"21","author":"Zhao","year":"2020","journal-title":"IEEE Trans. Intell. Transp. Syst."}],"container-title":["Advanced Engineering Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1474034626006725?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1474034626006725?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,9,5]],"date-time":"2026-09-05T16:37:48Z","timestamp":1788626268000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1474034626006725"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,11]]},"references-count":44,"alternative-id":["S1474034626006725"],"URL":"https:\/\/doi.org\/10.1016\/j.aei.2026.104980","relation":{},"ISSN":["1474-0346"],"issn-type":[{"value":"1474-0346","type":"print"}],"subject":[],"published":{"date-parts":[[2026,11]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"A deep transfer learning framework for dynamic cracking risk assessment of gravity dams in cold regions under extreme environmental conditions","name":"articletitle","label":"Article Title"},{"value":"Advanced Engineering Informatics","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.aei.2026.104980","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"104980"}}