{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T09:19:28Z","timestamp":1783070368660,"version":"3.54.6"},"reference-count":62,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100008081","name":"Southeast University","doi-asserted-by":"publisher","award":["RF1028623287"],"award-info":[{"award-number":["RF1028623287"]}],"id":[{"id":"10.13039\/501100008081","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Engineering Applications of Artificial Intelligence"],"published-print":{"date-parts":[[2026,8]]},"DOI":"10.1016\/j.engappai.2026.115158","type":"journal-article","created":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T12:04:27Z","timestamp":1779192267000},"page":"115158","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"P2","title":["High-fidelity generation of three-dimensional X-ray computed tomography graph for concrete under limited data based on a latent diffusion model"],"prefix":"10.1016","volume":"178","author":[{"given":"Zhantang","family":"Chen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bihan","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.engappai.2026.115158_bib1","author":"Bengio"},{"key":"10.1016\/j.engappai.2026.115158_bib2","doi-asserted-by":"crossref","DOI":"10.1016\/j.eja.2024.127254","article-title":"Machine learning based on functional principal component analysis to quantify the effects of the main drivers of wheat yields","volume":"159","author":"Bonneu","year":"2024","journal-title":"Eur. J. Agron."},{"key":"10.1016\/j.engappai.2026.115158_bib3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.pmatsci.2018.01.005","article-title":"Computational microstructure characterization and reconstruction: review of the state-of-the-art techniques","volume":"95","author":"Bostanabad","year":"2018","journal-title":"Prog. Mater. Sci."},{"key":"10.1016\/j.engappai.2026.115158_bib4","doi-asserted-by":"crossref","DOI":"10.1016\/j.petrol.2021.109590","article-title":"Reconstruction of three-dimension digital rock guided by prior information with a combination of InfoGAN and style-based GAN","volume":"208","author":"Cao","year":"2022","journal-title":"J. Petrol. Sci. Eng."},{"key":"10.1016\/j.engappai.2026.115158_bib5","series-title":"Med3D: Transfer Learning for 3D Medical Image Analysis","author":"Chen","year":"2019"},{"key":"10.1016\/j.engappai.2026.115158_bib6","series-title":"Towards Generalizable Tumor Synthesis","author":"Chen","year":"2024"},{"key":"10.1016\/j.engappai.2026.115158_bib7","article-title":"Mesoscopic pore characteristics analysis of aged bridge concrete based on X-ray computed tomography","volume":"78","author":"Cui","year":"2023","journal-title":"J. Build. Eng."},{"key":"10.1016\/j.engappai.2026.115158_bib8","doi-asserted-by":"crossref","DOI":"10.1016\/j.istruc.2025.108973","article-title":"Flexural behaviour of low-magnetic concrete beams reinforced with ribbed BFRP and\/or zero-magnetic steel bars","volume":"76","author":"Dong","year":"2025","journal-title":"Structures"},{"key":"10.1016\/j.engappai.2026.115158_bib9","series-title":"Taming Transformers for High-Resolution Image Synthesis","author":"Esser","year":"2020"},{"key":"10.1016\/j.engappai.2026.115158_bib10","doi-asserted-by":"crossref","DOI":"10.1016\/j.rineng.2026.109376","article-title":"A transfer-learning framework to alleviate data scarcity in cross-slope wind pressure modeling","volume":"29","author":"Fan","year":"2026","journal-title":"Results Eng."},{"key":"10.1016\/j.engappai.2026.115158_bib11","author":"Goodfellow"},{"key":"10.1016\/j.engappai.2026.115158_bib12","doi-asserted-by":"crossref","DOI":"10.1016\/j.aei.2026.104582","article-title":"Toward intelligent pavement maintenance: a transferable deep learning framework for cross-domain crack segmentation and UAV-based field inspection","volume":"73","author":"He","year":"2026","journal-title":"Adv. Eng. Inform."},{"key":"10.1016\/j.engappai.2026.115158_bib13","series-title":"Denoising Diffusion Probabilistic Models","author":"Ho","year":"2020"},{"key":"10.1016\/j.engappai.2026.115158_bib14","series-title":"2010 20th International Conference on Pattern Recognition","first-page":"2366","article-title":"Image quality metrics: PSNR vs. SSIM","author":"Hore","year":"2010"},{"key":"10.1016\/j.engappai.2026.115158_bib15","series-title":"Parameter-Efficient Transfer Learning for NLP","author":"Houlsby","year":"2019"},{"key":"10.1016\/j.engappai.2026.115158_bib16","author":"Hu"},{"key":"10.1016\/j.engappai.2026.115158_bib17","doi-asserted-by":"crossref","first-page":"340","DOI":"10.1016\/j.ijsolstr.2015.05.002","article-title":"3D meso-scale fracture modelling and validation of concrete based on in-situ X-ray computed tomography images using damage plasticity model","volume":"67\u201368","author":"Huang","year":"2015","journal-title":"Int. J. Solid Struct."},{"key":"10.1016\/j.engappai.2026.115158_bib18","unstructured":"Huggingface, GitHub. https:\/\/github.com\/huggingface\/diffusers. (Accessed 10.25 25)."},{"key":"10.1016\/j.engappai.2026.115158_bib19","author":"Jayasumana"},{"key":"10.1016\/j.engappai.2026.115158_bib20","doi-asserted-by":"crossref","DOI":"10.1016\/j.cemconres.2022.106892","article-title":"Pore structure characterization of cement paste by different experimental methods and its influence on permeability evaluation","volume":"159","author":"Jiang","year":"2022","journal-title":"Cement Concr. Res."},{"key":"10.1016\/j.engappai.2026.115158_bib21","doi-asserted-by":"crossref","DOI":"10.1016\/j.commatsci.2024.113441","article-title":"A comprehensive investigation on the performance of reconstruction of noncircular fiber-representative volume elements in unidirectional composites using diffusion generative models","volume":"246","author":"Jin","year":"2025","journal-title":"Comput. Mater. Sci."},{"key":"10.1016\/j.engappai.2026.115158_bib22","unstructured":"Jtimo, GitHub. https:\/\/github.com\/jtimo\/pycmg. (Accessed December.30 2021)."},{"key":"10.1016\/j.engappai.2026.115158_bib23","author":"Kench"},{"issue":"1","key":"10.1016\/j.engappai.2026.115158_bib24","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-023-34341-2","article-title":"Medical diffusion: denoising diffusion probabilistic models for 3D medical image generation","volume":"13","author":"Khader","year":"2023","journal-title":"Sci. Rep."},{"key":"10.1016\/j.engappai.2026.115158_bib25","author":"Kingma"},{"key":"10.1016\/j.engappai.2026.115158_bib26","author":"Kirillov"},{"issue":"1","key":"10.1016\/j.engappai.2026.115158_bib27","doi-asserted-by":"crossref","DOI":"10.1038\/s41524-024-01280-z","article-title":"Multi-plane denoising diffusion-based dimensionality expansion for 2D-to-3D reconstruction of microstructures with harmonized sampling","volume":"10","author":"Lee","year":"2024","journal-title":"npj Comput. Mater."},{"key":"10.1016\/j.engappai.2026.115158_bib28","doi-asserted-by":"crossref","DOI":"10.1016\/j.cma.2024.116876","article-title":"Denoising diffusion-based synthetic generation of three-dimensional (3D) anisotropic microstructures from two-dimensional (2D) micrographs","volume":"423","author":"Lee","year":"2024","journal-title":"Comput. Methods Appl. Mech. Eng."},{"key":"10.1016\/j.engappai.2026.115158_bib29","doi-asserted-by":"crossref","DOI":"10.1016\/j.cemconcomp.2023.104979","article-title":"Damage evolution and full-field 3D strain distribution in passively confined concrete","volume":"138","author":"Li","year":"2023","journal-title":"Cement Concr. Compos."},{"key":"10.1016\/j.engappai.2026.115158_bib30","doi-asserted-by":"crossref","DOI":"10.1016\/j.matdes.2025.114251","article-title":"Conditional generative AI for high-fidelity synthesis of hydrating cementitious microstructures","volume":"256","author":"Liang","year":"2025","journal-title":"Mater. Des."},{"key":"10.1016\/j.engappai.2026.115158_bib31","article-title":"Generation of cement paste microstructure using machine learning models","volume":"21","author":"Liang","year":"2025","journal-title":"Dev. Built Environ."},{"key":"10.1016\/j.engappai.2026.115158_bib32","series-title":"Efficient Algorithms for t-distributed Stochastic Neighborhood Embedding","author":"Linderman","year":"2017"},{"key":"10.1016\/j.engappai.2026.115158_bib33","doi-asserted-by":"crossref","DOI":"10.1016\/j.conbuildmat.2023.130704","article-title":"Reconstruction of the meso-scale concrete model using a deep convolutional generative adversarial network (DCGAN)","volume":"370","author":"Liu","year":"2023","journal-title":"Constr. Build. Mater."},{"key":"10.1016\/j.engappai.2026.115158_bib34","author":"Liu"},{"key":"10.1016\/j.engappai.2026.115158_bib35","doi-asserted-by":"crossref","DOI":"10.1016\/j.jclepro.2026.147599","article-title":"Synthetic data augmentation and Integrated prediction framework for low-carbon recycled concrete: leveraging CTGAN and multiple ML models stacking","volume":"543","author":"Liu","year":"2026","journal-title":"J. Clean. Prod."},{"issue":"1","key":"10.1016\/j.engappai.2026.115158_bib36","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-024-54861-9","article-title":"Microstructure reconstruction of 2D\/3D random materials via diffusion-based deep generative models","volume":"14","author":"Lyu","year":"2024","journal-title":"Sci. Rep."},{"issue":"9","key":"10.1016\/j.engappai.2026.115158_bib37","doi-asserted-by":"crossref","first-page":"867","DOI":"10.1016\/j.cemconcomp.2011.01.008","article-title":"Damage distribution and size effect in numerical concrete from lattice analyses","volume":"33","author":"Man","year":"2011","journal-title":"Cement Concr. Compos."},{"key":"10.1016\/j.engappai.2026.115158_bib38","doi-asserted-by":"crossref","DOI":"10.1016\/j.conbuildmat.2023.133570","article-title":"Methods for the modelling of concrete mesostructures: a critical review","volume":"408","author":"Ren","year":"2023","journal-title":"Constr. Build. Mater."},{"key":"10.1016\/j.engappai.2026.115158_bib39","author":"Rombach"},{"key":"10.1016\/j.engappai.2026.115158_bib40","author":"Saxena"},{"key":"10.1016\/j.engappai.2026.115158_bib41","article-title":"Mechanical properties characteristics of high strength concrete exposed to low vacuum environment","volume":"63","author":"Shangguan","year":"2023","journal-title":"J. Build. Eng."},{"key":"10.1016\/j.engappai.2026.115158_bib42","author":"Song"},{"key":"10.1016\/j.engappai.2026.115158_bib43","series-title":"Pixel Recurrent Neural Networks","author":"van den Oord","year":"2016"},{"key":"10.1016\/j.engappai.2026.115158_bib44","series-title":"Neural Discrete Representation Learning","author":"van den Oord","year":"2017"},{"key":"10.1016\/j.engappai.2026.115158_bib45","author":"Wang"},{"key":"10.1016\/j.engappai.2026.115158_bib46","article-title":"Exploration of computational formulations for wind-induced interference effects on high-rise buildings via Kolmogorov\u2013Arnold networks","volume":"24","author":"Wang","year":"2025","journal-title":"Dev. Built Environ."},{"key":"10.1016\/j.engappai.2026.115158_bib47","author":"Wei"},{"issue":"6","key":"10.1016\/j.engappai.2026.115158_bib48","doi-asserted-by":"crossref","first-page":"1083","DOI":"10.1016\/j.cemconres.2005.10.006","article-title":"Pore segmentation of cement-based materials from backscattered electron images","volume":"36","author":"Wong","year":"2006","journal-title":"Cement Concr. Res."},{"key":"10.1016\/j.engappai.2026.115158_bib49","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1016\/j.cemconcomp.2016.10.001","article-title":"In-situ X-ray computed tomography characterisation of 3D fracture evolution and image-based numerical homogenisation of concrete","volume":"75","author":"Yang","year":"2017","journal-title":"Cement Concr. Compos."},{"key":"10.1016\/j.engappai.2026.115158_bib50","doi-asserted-by":"crossref","DOI":"10.1016\/j.jenvman.2024.122173","article-title":"Efficient utilization of coral waste for internal curing material to prepare eco-friendly marine geopolymer concrete","volume":"368","author":"Yang","year":"2024","journal-title":"J. Environ. Manag."},{"key":"10.1016\/j.engappai.2026.115158_bib51","article-title":"Mechanical properties and mesoscopic damage characteristics of basalt fibre-reinforced seawater sea-sand slag-based geopolymer concrete","volume":"84","author":"Yang","year":"2024","journal-title":"J. Build. Eng."},{"key":"10.1016\/j.engappai.2026.115158_bib52","article-title":"Evolution of the microporous structure in cement hydration: a deep learning-based image translation method","volume":"94","author":"Yao","year":"2024","journal-title":"J. Build. Eng."},{"issue":"1","key":"10.1016\/j.engappai.2026.115158_bib53","first-page":"495","article-title":"Reconstructing random media","volume":"57","author":"Yeong","year":"1998","journal-title":"Phys. Rev."},{"key":"10.1016\/j.engappai.2026.115158_bib54","series-title":"LION: Latent Point Diffusion Models for 3D Shape Generation","author":"Zeng","year":"2022"},{"key":"10.1016\/j.engappai.2026.115158_bib55","doi-asserted-by":"crossref","DOI":"10.1016\/j.ijimpeng.2020.103775","article-title":"Micro CT image-based simulations of concrete under high strain rate impact using a continuum-discrete coupled model","volume":"149","author":"Zhang","year":"2021","journal-title":"Int. J. Impact Eng."},{"key":"10.1016\/j.engappai.2026.115158_bib56","author":"Zhang"},{"key":"10.1016\/j.engappai.2026.115158_bib57","article-title":"A metaheuristic-driven categorical boosting framework with interpretability for high-precision prediction of mechanical properties in corroded reinforced concrete beams","volume":"163","author":"Zhang","year":"2026","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.engappai.2026.115158_bib58","doi-asserted-by":"crossref","DOI":"10.1016\/j.cemconres.2024.107726","article-title":"3D microstructural generation from 2D images of cement paste using generative adversarial networks","volume":"187","author":"Zhao","year":"2025","journal-title":"Cement Concr. Res."},{"issue":"1","key":"10.1016\/j.engappai.2026.115158_bib59","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1007\/s11242-021-01728-6","article-title":"Digital rock reconstruction with user-defined properties using conditional generative adversarial networks","volume":"144","author":"Zheng","year":"2022","journal-title":"Transport Porous Media"},{"key":"10.1016\/j.engappai.2026.115158_bib60","doi-asserted-by":"crossref","DOI":"10.1016\/j.conbuildmat.2019.116785","article-title":"Modeling and mechanical influence of meso-scale concrete considering actual aggregate shapes","volume":"228","author":"Zhou","year":"2019","journal-title":"Constr. Build. Mater."},{"key":"10.1016\/j.engappai.2026.115158_bib61","doi-asserted-by":"crossref","DOI":"10.1016\/j.mechmat.2023.104684","article-title":"Prediction of compressive mechanical properties of three-dimensional mesoscopic aluminium foam based on deep learning method","volume":"182","author":"Zhuang","year":"2023","journal-title":"Mech. Mater."},{"key":"10.1016\/j.engappai.2026.115158_bib62","doi-asserted-by":"crossref","DOI":"10.1016\/j.ijmecsci.2024.109530","article-title":"Inverse design of functionally graded porous structures with target dynamic responses","volume":"280","author":"Zou","year":"2024","journal-title":"Int. J. Mech. Sci."}],"container-title":["Engineering Applications of Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0952197626014417?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0952197626014417?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T08:52:32Z","timestamp":1783068752000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0952197626014417"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8]]},"references-count":62,"alternative-id":["S0952197626014417"],"URL":"https:\/\/doi.org\/10.1016\/j.engappai.2026.115158","relation":{},"ISSN":["0952-1976"],"issn-type":[{"value":"0952-1976","type":"print"}],"subject":[],"published":{"date-parts":[[2026,8]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"High-fidelity generation of three-dimensional X-ray computed tomography graph for concrete under limited data based on a latent diffusion model","name":"articletitle","label":"Article Title"},{"value":"Engineering Applications of Artificial Intelligence","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.engappai.2026.115158","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":"115158"}}