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This work presents a data-driven strategy that combines Digital Image Correlation (DIC) measurements with machine learning algorithms to determine where to place a single-point strain gauge. The approach uses DIC-derived strain fields to preserve the essential sparsity structure in the strain maps, enabling more informative monitoring with fewer gauges.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/use-of-digital-image-correlation-and-machine-learning-for-the-optimal-strain-placement-in-a-full-scale-composite-tidal-turbine-blade/127367/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/use-of-digital-image-correlation-and-machine-learning-for-the-optimal-strain-placement-in-a-full-scale-composite-tidal-turbine-blade/127367.png","ImageObject",300,407,{"name":92,"@type":93},"Genevieve","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-09-21","2026-08-05",true,{"@type":102,"interactionType":103,"userInteractionCount":24},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"Why combine DIC with machine learning for strain gauge placement?","Question",{"text":112,"@type":113},"DIC captures dense deformation data over the surface, but practical strain-gauge deployment still requires deciding how many points to measure and where. The method uses DIC information to guide machine learning selection of optimal gauge locations.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"What testing facility and loading modes were used to collect the data?",{"text":117,"@type":113},"Data were collected at FastBlade, which supports testing large slender structures under static and fatigue loads. The setup included three actuators with long static holding and fatigue cycling while recording DIC images.",{"name":119,"@type":110,"acceptedAnswer":120},"How does the approach define “optimal” strain placement?",{"text":121,"@type":113},"Optimal placement is defined as selecting gauge points that keep most of the sparsity contained in the strain map revealed by the DIC, preserving the key information with fewer measurement points.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},127367,1785938532,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":24,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":81,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":129,"read_time":39},962085564549,"https://ap-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45","Edinburgh Research Explorer  \nUse of Digital Image Correlation and Machine Learning for the Optimal Strain Placement in a Full Scale Composite Tidal Turbine Blade  \nCitation for published version:  \nMcLoughlin, J, Munko, M, Valdivia Camacho, MA, Cuthill, F & Lopez Dubon, S 2023, Use of Digital Image Correlation and Machine Learning for the Optimal Strain Placement in a Full Scale Composite Tidal Turbine Blade. in BSSM 17th International Conference on Advances in Experimental Mechanics. British Society for Strain Measurement, 17th International Conference on Advances in Experimental Mechanics, Glasgow, United Kingdom, 31/08/23 . \u003C[https://www.bssm.org/media/qjyhimyp/compensation-of-apparent-strain-data](https://www.bssm.org/media/qjyhimyp/compensation-of-apparent-strain-data)due-to-temperature-gradients-in-a-full-scale-mechanical-test-of-a-composite-tidal-turbine-blade. pdf>  \nLink:  \nLink to publication record in Edinburgh Research Explorer  \nPublished In:  \nBSSM 17th International Conference on Advances in Experimental Mechanics  \nGeneral rights  \nCopyright for the publications made accessible via the Edinburgh Research Explorer is retained by the author(s) and / or other copyright owners and it is a condition of accessing these publications that users recognise and abide by the legal requirements associated with these rights.  \nTake down policy  \nThe University of Edinburgh has made every reasonable effort to ensure that Edinburgh Research Explorer content complies with UK legislation. If you believe that the public display of this file breaches copyright please [contact openaccess@ed.ac.uk](contact openaccess@ed.ac.uk) providing details, and we will remove access to the work immediately and investigate your claim.  \nDownload date: 24. Nov. 2025  \nUse of Digital Image Correlation and Machine Learning for the Optimal Strain Placement in a Full Scale Composite Tidal Turbine Blade  \nJ. McLoughlin1, M.J. Munko 1, M.A. Valdivia Camacho1, F. Cuthill 1, S. Lopez Dubon1a  \n1 School of Engineering, The University of Edinburgh, The King’s Buildings, Mayfield Road, Edinburgh EH9 3JL, Scotland  \nCorresponding [author](author a: Sergio.LDubon@ed.ac.uk)[ a](author a: Sergio.LDubon@ed.ac.uk)[: Sergio.LDubon@ed.ac.uk](author a: Sergio.LDubon@ed.ac.uk)  \nAbstract. The testing and health monitoring of large structures represents many challenges , how to get as much information as possible from the specimen under analysis with a limited number of sensors. In this work, we use a data-driven approach using information from a Digital Image Correlation (DIC) system mixed with machine learning algorithms (MLA) to decide the optimal location of single-point strain gauges.  \nIntroduction  \nNowadays, technologies such as DIC allow for capturing vast information that reveals the deformation and stresses of small and large structures on almost all their surface. Compared with traditional strain gauges, it offers a huge benefit. Nevertheless , DIC requires specific setup and environmental conditions (i.e. , light) that hinder their implementation outside of controlled environments (i. e. , laboratories) , and then the use of stain gauges is necessary. Still, the optimal number of measurement points and their location remains open. To address this , we used the information from DIC in an MLA to select the points that keep most of the sparsity contained in the stain map revealed by the DIC.  \nTest Data and Setup  \nThe data was collected at FastBlade , a testing centre that allows for testing large slender structures (2-14 meters) under either static or fatigue loads, as shown in Figure 1. The facility utilises a unique Digital Displacement® Pumps system, which incorporates regenerative pumping and digital displacement hydraulics, enabling accelerated testing (up to 1Hz) with high loads (up to 1MN) [1] . The DIC images were collected and processed using MatchID® [2] software for a clamping test, two static and one fatigue test. The test was performed using ","cbCaimUtVkfw1YKq","https://ap.wps.com/l/cbCaimUtVkfw1YKq","pdf",375075,"English","# Introduction\n# Test Data and Setup\n## DIC imaging and processing\n# Blade data\n# Method and Results\n## Digital Image Correlation","[{\"question\":\"Why combine DIC with machine learning for strain gauge placement?\",\"answer\":\"DIC captures dense deformation data over the surface, but practical strain-gauge deployment still requires deciding how many points to measure and where. The method uses DIC information to guide machine learning selection of optimal gauge locations.\"},{\"question\":\"What testing facility and loading modes were used to collect the data?\",\"answer\":\"Data were collected at FastBlade, which supports testing large slender structures under static and fatigue loads. The setup included three actuators with long static holding and fatigue cycling while recording DIC images.\"},{\"question\":\"How does the approach define “optimal” strain placement?\",\"answer\":\"Optimal placement is defined as selecting gauge points that keep most of the sparsity contained in the strain map revealed by the DIC, preserving the key information with fewer measurement points.\"}]","Use of Digital Image Correlation and Machine Learning for the Optimal Strain Placement in a Full Scale Composite Tidal Turbine Blade | PDF"]