[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119598-en":3,"doc-seo-119598-105":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},119598,687197207919,"Theodora","https://ap-avatar.wpscdn.com/avatar/a000253d6f5f7c60be?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779446848396160552",8,"Research & Report","Prediction of FRP RC Columns Using AI and Machine Learning","Fiber-reinforced polymers (FRP) are increasingly used as a replacement for steel bars in concrete columns, providing axial load-carrying contribution similar to conventional reinforcement. Existing design codes restrict FRP bars in columns because comprehensive predictive models remain insufficient. This study evaluates physical prediction models at the ultimate limit state and develops AI/ML predictors. A dataset of 88 short, concentrically loaded FRP-RC column samples trains and tests ANN and Extreme Gradient Boosting models, supported by SHAP sensitivity analysis to identify influential parameters.","Prediction of FRP RC columns using AI and machine learning  \nAalaa Shakir  \nRwayda Kh. S. Al-Hamd Farid Abed  \nThis presentation was delivered at the International Conference on Digital Frontiers in Buildings and Infrastructure, Delft, Netherlands, 11-13 June 2025.  \nShakir, A. , Al-Hamd, R. Kh.S. & Abed, F. (2025) 'Prediction ofFRPRC columns using AI and machine learning', Paper presented at International Conference on Digital Frontiers in Buildings and Infrastructure, Delft, Netherlands, 11-13 June 2025.  \nTakedown policy  \nIf you believe that this document breaches copyright please contact [repository@abertay.ac.uk](repository@abertay.ac.uk) providing relevant details, so we can investigate your claim.  \nPrediction ofFRP RC Columns using AI and Machine  \nLearning  \nAalaa Shakir1,a , Rwayda Kh. S. Al Hamd2,b and Farid Abed1,c  \n1 Department of Civil Engineering, American University of Sharjah, UAE  \n2 Abertay University School of Applied Sciences Dundee, Dundee, UK  \n[E-mail:](E-mail: a g00104950@aus.edu)[ a](E-mail: a g00104950@aus.edu)[ g00104950@aus.edu](E-mail: a g00104950@aus.edu), [b](b r.al-hamd@abertay.ac.uk)[ r.al-hamd@abertay.ac.uk](b r.al-hamd@abertay.ac.uk),  \n[c](cfabed@aus.edu)[fabed@aus.edu](cfabed@aus.edu)  \nAbstract. In recent years, fiber-reinforced polymers (FRP) have been gaining attention as a replacement for steel bars in concrete columns. Like steel reinforcement, FRP contributes to the axial load-carrying capacity. Multiple equations were proposed to understand the load-carrying capacity of FRP-reinforced concrete columns. However, existing design codes limit the use ofFRP bars in columns since comprehensive predictive models are lacking. To address this gap, artificial intelligence (AI) and machine learning (ML) methods provide a powerful alternative by capturing nonlinear relationships between key structural parameters and column capacity. The study aims to check the reliability of the most well-known physical models that predict the effect and contribution ofFRP bars to the overall capacity of columns at the ultimate limit state. The key parameters included in the data to be considered in this study are the column’s cross-sectional area, the column length, the compressive strength of the concrete, the elastic modulus of GFRP bars, and both longitudinal and transverse GFRP reinforcement ratios. A comprehensive dataset of tested FRP-RC was collected from existing literature to train, validate, and test machine learning models. Ann and Extreme Gradient Boosting ML algorithms are explored to determine the most accurate predictive model. Feature Sensitivity analysis is implemented using Shapley Additive explanations (SHAP) method to understand the machine learning models in the study, with focus on which variables influence the ML models the most or the least. The outcome of this research will contribute to ongoing discussions on using FRP reinforcement in compression members, where existing gapsin design methodologies will be addressed and potentially influencing future structural codes.  \nKeywords: GFRP, Machine Learning, Fiber reinforced polymer bars.  \n1 Introduction  \nAt the moment, the most common design codes that feature FRP-reinforced concrete do not account for the FRP rebar in the axial loading capacity offlexural members [1] . This reduces the potential for this reinforced concrete to be utilized under robust load conditions. Even though no standard methodology exists to account for FRP rebar in  \naxial compression, research on this topic is expanding [2-4] . However, limitations in understanding the underlying relationships between key properties of FRP-RC elements under axial loading limit possible new findings. This is where ML and AI would present a promising solution to this limitation. Many attempts have been made to produce physical models that include the contribution ofFRP reinforcement [5] . However, the predictions were not very accurate.  \nWith sufficient experimental data, AI pe","cbCaiprho5wMUSVl","https://ap.wps.com/l/cbCaiprho5wMUSVl","pdf",899233,1,11,"English","en",105,"# Introduction\n# Experimental database\n## Data collection","[{\"question\":\"Why are FRP-reinforced concrete columns limited in current design codes?\",\"answer\":\"Design codes limit FRP bar use because comprehensive predictive models are lacking to accurately represent column axial load capacity and related behavior.\"},{\"question\":\"What dataset and loading conditions are used to build the AI/ML models?\",\"answer\":\"The study collects 88 short FRP-RC column samples from existing literature, all subjected to concentric loading, with experimental axial load capacity recorded for each specimen.\"},{\"question\":\"Which machine learning algorithms are explored, and how is model interpretability handled?\",\"answer\":\"ANN and Extreme Gradient Boosting are tested to obtain accurate predictions, while SHAP (Shapley Additive explanations) is used for feature sensitivity analysis to determine variable influence.\"}]","Prediction of FRP RC Columns Using AI and Machine Learning | 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are FRP-reinforced concrete columns limited in current design codes?","Question",{"text":75,"@type":76},"Design codes limit FRP bar use because comprehensive predictive models are lacking to accurately represent column axial load capacity and related behavior.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What dataset and loading conditions are used to build the AI/ML models?",{"text":80,"@type":76},"The study collects 88 short FRP-RC column samples from existing literature, all subjected to concentric loading, with experimental axial load capacity recorded for each specimen.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning algorithms are explored, and how is model interpretability handled?",{"text":84,"@type":76},"ANN and Extreme Gradient Boosting are tested to obtain accurate predictions, while SHAP (Shapley Additive explanations) is used for feature sensitivity analysis to determine variable 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