[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123480-en":3,"doc-seo-123480-105":30,"detail-sidebar-cat-0-en-105":92},{"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":20,"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},123480,687197207919,"Theodora","https://ap-avatar.wpscdn.com/avatar/a000253d6f5f7c60be?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779446848396160552",8,"Research & Report","Machine Learning-Based Prediction and Classification of Hygrothermal Degradation in Fibre-Reinforced Composites across Multiple Architectures and Ageing Protocols","This study investigates prediction of water uptake and mechanical performance of fibre-reinforced composites under hygrothermal aging using machine learning. Eight models are used for water absorption, while six datasets support mechanical property prediction across varied aging and manufacturing conditions. Fiber orientation, pattern number, curing, and aging temperature are identified as key drivers of model accuracy. Gaussian process regression, SVR, XGBoost, and BRT reach R2 above 0.95 with relative errors under 5%. Random forest and ANN also capture tensile, compressive, hoop, deflection and load–displacement behavior, enabling durability assessment, though generalization is limited by dataset size and diversity.","Machine Learning-Based Prediction and Classification of Hygrothermal Degradation in Fibre-Reinforced Composites across Multiple Architectures and Ageing Protocols  \nLappeenranta–Lahti University of Technology LUT  \nMaster’s Programme in Mechanical Engineering, Master’s thesis  \n2025  \nZhicheng Huang  \nExaminer(s): Assistant Professor Humberto Almeida Jr, D.Sc. (Tech.)  \nAssistant Professor Guilherme Gomes, D.Sc. (Tech.)  \nABSTRACT  \nLappeenranta–Lahti University of Technology LUTLUT School of Energy Systems  \nMechanical Engineering  \nZhicheng Huang  \nMachine Learning-Based Prediction and Classification of Hygrothermal Degradation in Fibre-Reinforced Composites across Multiple Architectures and Ageing Protocols  \nMaster’s thesis 2025  \n77 pages, 41 figures, 6 tables  \nExaminer(s): Assistant Professor Humberto Almeida Jr, D.Sc. (Tech.) and Assistant Professor Guilherme Gomes, D.Sc. (Tech.)  \nKeywords: Machine learning, Composite material, Hydrothermal aging, Filament winding pattern  \nThis study investigated the prediction of water uptake and mechanical performance of fibrereinforced composites under hygrothermal aging using machine learning. Eight models (ANN, BPNN, SVR, RF, RT, BRT, XGBoost, and GPR) were applied for water absorption prediction, while six datasets were used for mechanical property prediction under varying aging and manufacturing conditions. Key factors such as fiber orientation, pattern number, curing, and aging temperature strongly influenced the results. Models like GPR, SVR, XGBoost, and BRT achieved R2 > 0.95 and relative errors below 5%, outperforming classical Fick diffusion models. RF and ANN accurately predicted tensile, compressive, and hoop strength, deflection, and load–displacement behavior. These findings demonstrate that machine learning effectively captures complex relationships in composites under hygrothermal aging, providing a reliable tool for material design and durability assessment, though generalization is limited by the size and diversity of the datasets.  \nACKNOWLEDGEMENTS  \nAt first, I would like to express my sincere gratitude to my supervisor, Assistant Professor Humberto Almeida Jr., for his invaluable guidance and insightful advice throughout my thesis work. His profound knowledge, patient guidance, and thoughtful advice not only greatly enhanced my understanding of the discipline but also helped me cultivate critical thinking and research skills. Throughout the research process, he consistently provided constructive feedback, helping me stay focused and motivated. His passion for research and dedication to academic excellence deeply inspired me and set an example for my future academic and professional development. I am profoundly grateful for the opportunity to work under his guidance and for the trust he placed in me throughout this journey.  \nAt the same time, I would like to express my deepest gratitude to my parents, Huang Feng and Chen Chunhong, for their selfless and unconditional love. Although we are separated by thousands of miles, their unwavering support and encouragement during times of disappointment, uncertainty, and confusion have been the driving force that keeps me moving forward on this journey.  \nFinally, I would like to thank my friends—Yuxuan Guo, Zihan Ren, Jinwei Lu, Minoo Yadi, and Md Kabir—for their support and companionship. Their kindness, encouragement, and help in daily life have made this journey more manageable and truly enjoyable. I am lucky to have shared this experience with them.  \nZhicheng Huang  \n07.10.2025  \nLappeenranta Finland  \nSYMBOLS AND ABBREVIATIONS  \nAbbreviations  \nANN  \nBPNN  \nSVR  \nRF  \nRT  \nBRT  \nXGBoost  \nGPR  \nCFRP  \nGFRP  \nMAE  \nRMSE  \nR2  \nPC  \nFC  \nFRCs  \nFW  \nML  \nCNN  \nLSTM  \nArtificial Neural Network Backpropagation Neural Network Support Vector Regression  \nRandom Forest  \nRegression Tree  \nBoosted Regression Tree  \nExtreme Gradient Boosting Gaussian Process Regression Carbon Fiber Reinforced Polymer Glass Fiber Reinforc","cbCaij32o7WNMveS","https://ap.wps.com/l/cbCaij32o7WNMveS","pdf",5651949,1,77,"English","en",105,"# Abstract\n# Acknowledgements\n# Symbols and abbreviations\n# Declarations\n# 1 Introduction\n# 2 Objectives of the research\n## 2.1 Objectives\n## 2.2 Research problem and research question\n## 2.3 Research methods\n## 2.4 Scope\n## 2.5 Research gap\n# 3 Literature review\n## 3.1 Fibre-Reinforced Composites and Hygrothermal Ageing","[{\"question\":\"What does the thesis predict for fibre-reinforced composites under hygrothermal aging?\",\"answer\":\"It predicts water uptake (water absorption) and mechanical performance, including tensile, compressive, hoop strength, deflection, and load–displacement behavior.\"},{\"question\":\"Which machine learning models are used for water absorption prediction and mechanical property prediction?\",\"answer\":\"Water absorption uses eight models: ANN, BPNN, SVR, RF, RT, BRT, XGBoost, and GPR. Mechanical properties are predicted using six datasets under varying aging and manufacturing conditions.\"},{\"question\":\"What factors most strongly influence the prediction results?\",\"answer\":\"Fiber orientation, pattern number, curing conditions, and aging temperature are highlighted as key factors affecting the results.\"}]","Machine Learning-Based Prediction and Classification of Hygrothermal Degradation in Fibre-Reinforced Composites across Multiple Architectures and Ageing Protocols | PDF",1785816749,194,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":28},"machine-learning-based-prediction-and-classification-of-hygrothermal-degradation-in-fibre-reinforced-composites-across-multiple-architectures-and-ageing-protocols","",{"@graph":36,"@context":86},[37,54,69],{"@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/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-based-prediction-and-classification-of-hygrothermal-degradation-in-fibre-reinforced-composites-across-multiple-architectures-and-ageing-protocols/123480/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05","2026-08-04",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What does the thesis predict for fibre-reinforced composites under hygrothermal aging?","Question",{"text":76,"@type":77},"It predicts water uptake (water absorption) and mechanical performance, including tensile, compressive, hoop strength, deflection, and load–displacement behavior.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which machine learning models are used for water absorption prediction and mechanical property prediction?",{"text":81,"@type":77},"Water absorption uses eight models: ANN, BPNN, SVR, RF, RT, BRT, XGBoost, and GPR. Mechanical properties are predicted using six datasets under varying aging and manufacturing conditions.",{"name":83,"@type":74,"acceptedAnswer":84},"What factors most strongly influence the prediction results?",{"text":85,"@type":77},"Fiber orientation, pattern number, curing conditions, and aging temperature are highlighted as key factors affecting the results.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":46,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":46,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]