[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117451-en":3,"doc-seo-117451-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},117451,1374391975076,"Riley","https://ap-avatar.wpscdn.com/avatar/14000253ca4ec9f6853?x-image-process=image/resize,m_fixed,w_180,h_180&k=1783305029341752051",6,"Technology","Muographic Image Upsampling with Machine Learning for Built Infrastructure Applications","The civil engineering industry requires innovative non-destructive evaluation methods for ageing infrastructure, especially reinforced concrete elements where existing techniques cannot reliably image deep features. Muography enables three-dimensional density mapping by detecting cosmic-ray muon interactions, but limited muon flux leads to long acquisition times, noisy reconstructions, and difficult interpretation. A two-model deep learning workflow was developed using cWGAN-GP for predictive upsampling and a second cWGAN-GP for semantic segmentation, improving perceptual quality and noise metrics while quantifying rebar and tendon duct features and mitigating z-plane smearing artifacts using Geant4-simulated data.","Article  \nMuographic Image Upsampling with Machine Learning for Built Infrastructure Applications  \nWilliam O’Donnell 1,2, *, David Mahon 1,2, Guangliang Yang 1,2 and Simon Gardner 1  \nAcademic Editors: Tommaso Dorigo, Roberto Ruiz de Austri Bazan, Jose Salt and Pietro Vischia  \nReceived: 30 January 2025  \nRevised: 21 February 2025  \nAccepted: 14 March 2025  \nPublished: 18 March 2025  \nCitation: O’Donnell, W.; Mahon, D.; Yang, G.; Gardner, S. Muographic Image Upsampling with Machine Learning for Built Infrastructure Applications. Particles 2025, 8, 33 .  \n[https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)particles8010033  \nCopyright: © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://creativecommons.org/](https://creativecommons.org/)[ ](https://creativecommons.org/)[licenses/by/4.0/](licenses/by/4.0/)) .  \n1 School of Physics & Astronomy, University of Glasgow, Kelvin Building, University Avenue, Glasgow G12 8QQ, UK  \n2 Lynkeos Technology Ltd., University of Glasgow, No. 11 The Square, Glasgow G12 8QQ, UK  \n* [Correspondence: w.odonnell.1@research.gla.ac.uk](Correspondence: w.odonnell.1@research.gla.ac.uk)  \nAbstract: The civil engineering industry faces a critical need for innovative non-destructive evaluation methods, particularly for ageing critical infrastructure, such as bridges, where current techniques fall short. Muography, a non-invasive imaging technique, constructs three-dimensional density maps by detecting the interactions of naturally occurring cosmicray muons within the scanned volume. Cosmic-ray muons offer both deep penetration capabilities due to their high momenta and inherent safety due to their natural source. However, the technology’s reliance on this natural source results in a constrained muon flux, leading to prolonged acquisition times, noisy reconstructions, and challenges in image interpretation. To address these limitations, we developed a two-model deep learning approach. First, we employed a conditional Wasserstein Generative Adversarial Network with Gradient Penalty (cWGAN-GP) to perform predictive upsampling of undersampledmuography images. Using the Structural Similarity Index Measure (SSIM), 1-day sampled images were able to match the perceptual qualities of a 21-day image, while the Peak Signal-to-Noise Ratio (PSNR) indicated a noise improvement to that of 31 days worth of sampling. A second cWGAN-GP model, trained for semantic segmentation, was developed to quantitatively assess the upsampling model’s impact on each of the features within the concrete samples. This model was able to achieve segmentation of rebar grids and tendon ducts embedded in the concrete, with respective Dice–Sørensen accuracy coefficients of 0.8174 and 0.8663 . This model also revealed an unexpected capability to mitigate—and in some cases entirely remove—z-plane smearing artifacts caused by the muography’s inherent inverse imaging problem. Both models were trained on a comprehensive dataset generated through Geant4 Monte Carlo simulations designed to reflect realistic civil infrastructure scenarios. Our results demonstrate significant improvements in both acquisition speed and image quality, marking a substantial step toward making muography more practical for reinforced concrete infrastructure monitoring applications.  \nKeywords: muography; non-destructive testing; machine learning; image processing; upsampling; semantic segmentation; simulation; cosmic rays  \n1. Introduction  \nIt has been widely established that the growing quantity of reinforced-concrete-based infrastructure nearing the end of its original intended service life poses a significant challenge. Current reflection-based near-surface detection techniques—Ground-p Penetrating Radar (GPR) and ultrasonic echo measurements—are limited in scenarios with high concrete thickness ","cbCais7DMLeEjIJy","https://ap.wps.com/l/cbCais7DMLeEjIJy","pdf",4703450,1,21,"English","en",105,"# Introduction\n## Non-destructive evaluation needs for reinforced concrete infrastructure\n## Limits of existing near-surface detection and radiation-protected X-ray imaging\n## Muon scattering tomography and existing progress\n## Remaining challenges in acquisition time and image artifacts","[{\"question\":\"What problem does the document address in civil infrastructure monitoring?\",\"answer\":\"Conventional non-destructive testing struggles to inspect deep reinforced-concrete structures, limiting reliable detection of rebar grids, tendon ducts, and internal defects.\"},{\"question\":\"How does muography work and why is it challenging?\",\"answer\":\"Muography constructs 3D density maps by detecting interactions of naturally occurring cosmic-ray muons. Its natural, low muon flux causes prolonged acquisition and leads to noisy reconstructions and interpretation issues.\"},{\"question\":\"What machine learning approach is proposed to improve muographic images?\",\"answer\":\"The study uses two cWGAN-GP models: one for predictive upsampling of undersampled images and another trained for semantic segmentation to quantify embedded concrete features and reduce z-plane smearing artifacts.\"}]","Muographic Image Upsampling with Machine Learning for Built Infrastructure Applications | PDF",1785675927,53,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"muographic-image-upsampling-with-machine-learning-for-built-infrastructure-applications","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/technology/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/muographic-image-upsampling-with-machine-learning-for-built-infrastructure-applications/117451/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the document address in civil infrastructure monitoring?","Question",{"text":75,"@type":76},"Conventional non-destructive testing struggles to inspect deep reinforced-concrete structures, limiting reliable detection of rebar grids, tendon ducts, and internal defects.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does muography work and why is it challenging?",{"text":80,"@type":76},"Muography constructs 3D density maps by detecting interactions of naturally occurring cosmic-ray muons. Its natural, low muon flux causes prolonged acquisition and leads to noisy reconstructions and interpretation issues.",{"name":82,"@type":73,"acceptedAnswer":83},"What machine learning approach is proposed to improve muographic images?",{"text":84,"@type":76},"The study uses two cWGAN-GP models: one for predictive upsampling of undersampled images and another trained for semantic segmentation to quantify embedded concrete features and reduce z-plane smearing artifacts.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,113,118,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":111,"slug":112},50,"technology",{"id":114,"doc_module":4,"doc_module_name":46,"category_name":115,"show_sort_weight":116,"slug":117},7,"Healthcare",40,"healthcare",{"id":119,"doc_module":4,"doc_module_name":46,"category_name":120,"show_sort_weight":121,"slug":122},8,"Research & Report",30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]