{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,6]],"date-time":"2026-04-06T19:02:09Z","timestamp":1775502129094,"version":"3.50.1"},"reference-count":55,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Advanced Engineering Informatics"],"published-print":{"date-parts":[[2026,9]]},"DOI":"10.1016\/j.aei.2026.104666","type":"journal-article","created":{"date-parts":[[2026,4,6]],"date-time":"2026-04-06T13:07:40Z","timestamp":1775480860000},"page":"104666","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"PA","title":["Graph attention networks enhanced predictive modeling for penetration-explosion damage in concrete structures"],"prefix":"10.1016","volume":"74","author":[{"given":"Chenyu","family":"Gao","sequence":"first","affiliation":[]},{"given":"Junbo","family":"Yan","sequence":"additional","affiliation":[]},{"given":"Yan","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Fan","family":"Bai","sequence":"additional","affiliation":[]},{"given":"Fenglei","family":"Huang","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.aei.2026.104666_b0005","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.engstruct.2016.04.035","article-title":"Influence of cylindrical charge orientation on the blast response of high strength concrete panels","volume":"149","author":"Adhikary","year":"2017","journal-title":"Eng. Struct."},{"key":"10.1016\/j.aei.2026.104666_b0010","doi-asserted-by":"crossref","DOI":"10.1016\/j.ijimpeng.2023.104595","article-title":"Failure mode and stress wave propagation in concrete target subjected to a projectile penetration followed by charge explosion: experimental and numerical investigation","volume":"177","author":"Yang","year":"2023","journal-title":"Int. J. Impact Eng."},{"key":"10.1016\/j.aei.2026.104666_b0015","doi-asserted-by":"crossref","DOI":"10.1016\/j.aei.2020.101192","article-title":"A methodology of risk assessment, management, and coping actions for nuclear power plant (NPP) hit by high-explosive warheads","volume":"46","author":"Ornai","year":"2020","journal-title":"Adv. Eng. Inf."},{"key":"10.1016\/j.aei.2026.104666_b0020","doi-asserted-by":"crossref","first-page":"2921","DOI":"10.1007\/s11831-020-09483-5","article-title":"Performance assessment of concrete and steel material models in LS-DYNA for enhanced numerical simulation, A state of the art review","volume":"28","author":"Abedini","year":"2021","journal-title":"Arch. Comput. Method E"},{"key":"10.1016\/j.aei.2026.104666_b0025","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2023.109683","article-title":"Probabilistic capacity models and fragility estimate for NRC and UHSC panels subjected to contact blast","volume":"242","author":"Bhuyan","year":"2024","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"10.1016\/j.aei.2026.104666_b0030","doi-asserted-by":"crossref","DOI":"10.1016\/j.engstruct.2025.121093","article-title":"Spalling damage on steel reinforced concrete structures under deep embedded explosion","volume":"343","author":"Gao","year":"2025","journal-title":"Eng. Struct."},{"key":"10.1016\/j.aei.2026.104666_b0035","doi-asserted-by":"crossref","DOI":"10.1017\/dce.2023.2","article-title":"Modeling the wall shear stress in large-eddy simulation using graph neural networks","volume":"4","author":"Dupuy","year":"2023","journal-title":"DCE"},{"key":"10.1016\/j.aei.2026.104666_b0040","first-page":"1026","article-title":"An finite element analysis surrogate model with boundary oriented graph embedding approach for rapid design","volume":"10","author":"Fu","year":"2023","journal-title":"J. Comput. Des. Eng."},{"key":"10.1016\/j.aei.2026.104666_b0045","doi-asserted-by":"crossref","DOI":"10.1016\/j.jcp.2024.112866","article-title":"A finite element-inspired hypergraph neural network: application to fluid dynamics simulations","volume":"504","author":"Gao","year":"2024","journal-title":"J. Comput. Phys."},{"key":"10.1016\/j.aei.2026.104666_b0050","doi-asserted-by":"crossref","first-page":"418","DOI":"10.1016\/j.aei.2009.06.005","article-title":"Modeling blast wave propagation using artificial neural network methods","volume":"23","author":"Flood","year":"2009","journal-title":"Adv. Eng. Inf."},{"key":"10.1016\/j.aei.2026.104666_b0055","doi-asserted-by":"crossref","first-page":"525","DOI":"10.1007\/s00466-019-01740-0","article-title":"Prediction of aerodynamic flow fields using convolutional neural networks","volume":"64","author":"Bhatnagar","year":"2019","journal-title":"Comput. Mech."},{"key":"10.1016\/j.aei.2026.104666_b0060","doi-asserted-by":"crossref","first-page":"315","DOI":"10.1007\/s10462-024-10931-y","article-title":"A review of graph neural network applications in mechanics-related domains","volume":"57","author":"Zhao","year":"2024","journal-title":"Artif. Intell. Rev."},{"key":"10.1016\/j.aei.2026.104666_b0065","unstructured":"H. Salehinejad, S. Sankar, J. Barfett, E. Colak, S. Valaee, Recent advances in recurrent neural networks, arXiv Preprint arXiv:1801.01078 (2017)."},{"key":"10.1016\/j.aei.2026.104666_b0070","doi-asserted-by":"crossref","DOI":"10.1016\/j.ymssp.2019.106292","article-title":"Impact load identification of nonlinear structures using deep recurrent neural network","volume":"133","author":"Zhou","year":"2019","journal-title":"Mech. Syst. Sig. Process."},{"key":"10.1016\/j.aei.2026.104666_b0075","doi-asserted-by":"crossref","unstructured":"G. Liang, P. Tiwari, S. Nowaczyk, S. Byttner, Higher-order Spatio-temporal Physics-incorporated Graph Neural Network for Multivariate Time Series Imputation, (2024).","DOI":"10.1145\/3627673.3679775"},{"key":"10.1016\/j.aei.2026.104666_b0080","doi-asserted-by":"crossref","DOI":"10.1016\/j.engstruct.2025.121267","article-title":"Physics-informed graph neural networks: predicting the composite mechanical behavior of clay-infilled concrete structures with morphed honeycomb configurations","volume":"343","author":"Wang","year":"2025","journal-title":"Eng. Struct."},{"key":"10.1016\/j.aei.2026.104666_b0085","doi-asserted-by":"crossref","DOI":"10.1016\/j.compstruc.2023.107188","article-title":"Machine learning prediction of structural dynamic responses using graph neural networks","volume":"289","author":"Li","year":"2023","journal-title":"Comput. Struct."},{"key":"10.1016\/j.aei.2026.104666_b0090","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2023.109639","article-title":"Machine learning prediction of BLEVE loading with graph neural networks","volume":"241","author":"Li","year":"2024","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"10.1016\/j.aei.2026.104666_b0095","unstructured":"P. Veli\u010dkovi\u0107, G. Cucurull, A. Casanova, A. Romero, P. Lio, Y. Bengio, Graph attention networks, arXiv Preprint arXiv:1710.10903 (2017)."},{"key":"10.1016\/j.aei.2026.104666_b0100","doi-asserted-by":"crossref","DOI":"10.1016\/j.aei.2025.103887","article-title":"Deep fusion of fragmented process knowledge for process route planning","volume":"69","author":"Zhang","year":"2026","journal-title":"Adv. Eng. Inf."},{"key":"10.1016\/j.aei.2026.104666_b0105","unstructured":"Q. Liu, W. Zhu, X. Jia, F. Ma, Y. Gao, Fluid Simulation System Based on Graph Neural Network, (2022)."},{"key":"10.1016\/j.aei.2026.104666_b0110","doi-asserted-by":"crossref","DOI":"10.1115\/1.4052195","article-title":"SuperMeshing: a new deep learning architecture for increasing the mesh density of physical fields in metal forming numerical simulation","volume":"89","author":"Xu","year":"2022","journal-title":"J. Appl. Mech."},{"key":"10.1016\/j.aei.2026.104666_b0115","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2024.110777","article-title":"Physics_GNN: towards physics-informed graph neural network for the real-time simulation of obstructed gas explosion","volume":"256","author":"Shi","year":"2025","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"10.1016\/j.aei.2026.104666_b0120","first-page":"1","article-title":"Numerical simulation of damping performance of elastically supported underground arch structures subject to penetration and explosion","volume":"2022","author":"Yuan","year":"2022","journal-title":"Adv. Civ. Eng."},{"key":"10.1016\/j.aei.2026.104666_b0125","doi-asserted-by":"crossref","first-page":"447","DOI":"10.1016\/j.conbuildmat.2016.12.216","article-title":"Numerical study of ultra-high performance concrete under non-deformable projectile penetration","volume":"135","author":"Liu","year":"2017","journal-title":"Constr. Build. Mater."},{"key":"10.1016\/j.aei.2026.104666_b0130","doi-asserted-by":"crossref","DOI":"10.1016\/j.conbuildmat.2025.141397","article-title":"Thermal and dynamic response of hybrid fiber-reinforced concrete to fire exposure: experimental and computational approaches","volume":"478","author":"Ali","year":"2025","journal-title":"Constr. Build. Mater."},{"key":"10.1016\/j.aei.2026.104666_b0135","article-title":"Coupled effects of thermal exposure and high strain rate on co2 emissions of concrete structures: a comparative study of AI-driven emission signatures","volume":"48","author":"Ali","year":"2025","journal-title":"Mater. Today Commun."},{"key":"10.1016\/j.aei.2026.104666_b0140","doi-asserted-by":"crossref","DOI":"10.1016\/j.aei.2025.103924","article-title":"Intelligent detection method for debonding and voids in concrete-filled steel\/aluminum tubular structures based on impact acoustics and unsupervised learning","volume":"69","author":"An","year":"2026","journal-title":"Adv. Eng. Inf."},{"key":"10.1016\/j.aei.2026.104666_b0145","doi-asserted-by":"crossref","DOI":"10.1016\/j.aei.2025.103991","article-title":"From optimization to network: a low-rank and sparse-aware deep unfolding framework for infrared small target detection","volume":"69","author":"Li","year":"2026","journal-title":"Adv. Eng. Inf."},{"key":"10.1016\/j.aei.2026.104666_b0150","doi-asserted-by":"crossref","DOI":"10.1016\/j.ijimpeng.2024.105125","article-title":"A hybrid data-driven machine learning framework for predicting the impact resistance of composite armor","volume":"195","author":"Zhu","year":"2025","journal-title":"Int. J. Impact Eng"},{"key":"10.1016\/j.aei.2026.104666_b0155","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2024.108932","article-title":"Maximum displacement prediction model for steel beams with hexagonal web openings under impact loading based on artificial neural networks","volume":"136","author":"Chen","year":"2024","journal-title":"Eng. Appl. Artif. Intel."},{"key":"10.1016\/j.aei.2026.104666_b0160","article-title":"Interpretable machine-learning models for maximum displacements of RC beams under impact loading predictions","author":"Lai","year":"2023","journal-title":"Eng. Struct."},{"key":"10.1016\/j.aei.2026.104666_b0165","doi-asserted-by":"crossref","DOI":"10.1016\/j.ijimpeng.2025.105483","article-title":"Machine learning model incorporating domain knowledge for predicting maximum deflection of reinforced concrete beams under low-velocity impact","volume":"207","author":"Ahn","year":"2026","journal-title":"Int. J. Impact Eng."},{"key":"10.1016\/j.aei.2026.104666_b0170","article-title":"Genetic programming-based algorithms application in modeling the compressive strength of steel fiber-reinforced concrete exposed to elevated temperatures","volume":"15","author":"Ali","year":"2024","journal-title":"Compos. Part C: Open Access"},{"key":"10.1016\/j.aei.2026.104666_b0175","article-title":"Unveiling the combined thermal and high strain rate effects on compressive behavior of steel fiber-reinforced concrete: a novel predictive approach","volume":"22","author":"Ali","year":"2025","journal-title":"Case Stud. Constr. Mater."},{"key":"10.1016\/j.aei.2026.104666_b0180","doi-asserted-by":"crossref","unstructured":"M. Ali, M.A. Rusho, L. Chen, D.M.T. Cruz, Advancing Structural Safety: Genetic Programming Approaches to Steel Fiber-Reinforced Concrete (SFRC) Blast Response Prediction, in: 2025 17th International Conference on Computer and Automation Engineering (ICCAE), IEEE, Perth, Australia, 2025: pp. 183\u2013187.","DOI":"10.1109\/ICCAE64891.2025.10980530"},{"key":"10.1016\/j.aei.2026.104666_b0185","article-title":"Artificial neural network (ANN) model for predicting blast-induced tunnel response in steel fiber reinforced concrete (SFRC) structures","volume":"23","author":"Ali","year":"2025","journal-title":"Case Stud. Constr. Mater."},{"key":"10.1016\/j.aei.2026.104666_b0190","doi-asserted-by":"crossref","DOI":"10.1016\/j.aei.2025.103986","article-title":"Rapid prediction of 3D global residual stress field in metal workpieces manufactured by directed energy deposition using graph attention networks","volume":"69","author":"Li","year":"2026","journal-title":"Adv. Eng. Inf."},{"key":"10.1016\/j.aei.2026.104666_b0195","unstructured":"T. Pfaff, M. Fortunato, A. Sanchez-Gonzalez, P.W. Battaglia, Learning Mesh-Based Simulation with Graph Networks, (2021)."},{"key":"10.1016\/j.aei.2026.104666_b0200","doi-asserted-by":"crossref","DOI":"10.1016\/j.ijimpeng.2024.105123","article-title":"A graph network-based learnable simulator for spatial-temporal prediction of rigid projectile penetration","volume":"195","author":"Li","year":"2025","journal-title":"Int. J. Impact Eng."},{"key":"10.1016\/j.aei.2026.104666_b0205","doi-asserted-by":"crossref","DOI":"10.1016\/j.engstruct.2025.120505","article-title":"Self-adaptive graph neural network for predicting blast-induced damage in RC columns across multiple scenarios","volume":"337","author":"Peng","year":"2025","journal-title":"Eng. Struct."},{"key":"10.1016\/j.aei.2026.104666_b0210","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1504\/IJCAET.2008.021254","article-title":"ANSYS and LS-DYNA used for structural analysis","volume":"1","author":"Liu","year":"2008","journal-title":"Int. J. Comput. Aided Eng. Technol."},{"key":"10.1016\/j.aei.2026.104666_b0215","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1016\/j.engstruct.2018.06.098","article-title":"Experimental and numerical studies of ultra-high performance concrete targets against high-velocity projectile impacts","volume":"173","author":"Liu","year":"2018","journal-title":"Eng. Struct."},{"key":"10.1016\/j.aei.2026.104666_b0220","series-title":"Proceedings of the Fourteenth International LS-DYNA User Conference","article-title":"LS-DYNA structured ALE (S-ALE) solver","author":"Chen","year":"2016"},{"key":"10.1016\/j.aei.2026.104666_b0225","doi-asserted-by":"crossref","first-page":"2768","DOI":"10.3390\/ma12172768","article-title":"Application of PTFE\/Al reactive materials for double-layered liner shaped charge","volume":"12","author":"Wang","year":"2019","journal-title":"Materials"},{"key":"10.1016\/j.aei.2026.104666_b0230","unstructured":"L.-D. LSTC, Keyword User\u2019s Manual vol, II: Livermore Software Technology Corporation (LSTC) (2007)."},{"key":"10.1016\/j.aei.2026.104666_b0235","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1016\/j.ijimpeng.2018.12.005","article-title":"Penetration behavior of reactive liner shaped charge jet impacting thick steel plates","volume":"126","author":"Guo","year":"2019","journal-title":"Int. J. Impact Eng."},{"key":"10.1016\/j.aei.2026.104666_b0240","doi-asserted-by":"crossref","DOI":"10.1016\/j.ijimpeng.2023.104591","article-title":"Research on damage effect of penetration and explosion integration based on volume filling method","volume":"177","author":"Wei","year":"2023","journal-title":"Int. J. Impact Eng."},{"key":"10.1016\/j.aei.2026.104666_b0245","doi-asserted-by":"crossref","first-page":"847","DOI":"10.1016\/S0734-743X(97)00023-7","article-title":"A plasticity concrete material model for DYNA3D","volume":"19","author":"Malvar","year":"1997","journal-title":"Int. J. Impact Eng."},{"key":"10.1016\/j.aei.2026.104666_b0250","unstructured":"J.E. Crawford, Y. Wu, H.-J. Choi, J.M. Magallanes, S. Lan, Use and validation of the release iii K&C concrete material model in ls-dyna, (2011)."},{"key":"10.1016\/j.aei.2026.104666_b0255","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2024.110665","article-title":"Probabilistic modelling of steel column response to far-field detonations","volume":"255","author":"Gangolu","year":"2025","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"10.1016\/j.aei.2026.104666_b0260","article-title":"Dynamic response of UHPFRC beams with different concrete strength under blast loading (in Chinese)","volume":"46","author":"Zhang","year":"2025","journal-title":"Acta Armamentarii"},{"key":"10.1016\/j.aei.2026.104666_b0265","doi-asserted-by":"crossref","DOI":"10.1016\/j.engstruct.2022.113958","article-title":"Investigation of ultra-high performance concrete slabs under contact explosions with a calibrated K&C model","volume":"255","author":"Liu","year":"2022","journal-title":"Eng. Struct."},{"key":"10.1016\/j.aei.2026.104666_b0270","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1016\/j.ijimpeng.2018.09.005","article-title":"Thick plain concrete targets subjected to high speed penetration of 30CrMnSiNi2A steel projectiles: tests and analyses","volume":"122","author":"Feng","year":"2018","journal-title":"Int. J. Impact Eng."},{"key":"10.1016\/j.aei.2026.104666_b0275","doi-asserted-by":"crossref","first-page":"3188","DOI":"10.1002\/suco.202400076","article-title":"Numerical analysis of the local damage of RC beams under close\u2010in explosions based on the calibrated Karagozian & Case (K&C) concrete model","volume":"25","author":"Xu","year":"2024","journal-title":"Struct. Concr."}],"container-title":["Advanced Engineering Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1474034626003587?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1474034626003587?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,4,6]],"date-time":"2026-04-06T18:00:20Z","timestamp":1775498420000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1474034626003587"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9]]},"references-count":55,"alternative-id":["S1474034626003587"],"URL":"https:\/\/doi.org\/10.1016\/j.aei.2026.104666","relation":{},"ISSN":["1474-0346"],"issn-type":[{"value":"1474-0346","type":"print"}],"subject":[],"published":{"date-parts":[[2026,9]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Graph attention networks enhanced predictive modeling for penetration-explosion damage in concrete structures","name":"articletitle","label":"Article Title"},{"value":"Advanced Engineering Informatics","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.aei.2026.104666","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":"104666"}}