[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121147-en":3,"doc-seo-121147-105":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},121147,1099514068035,"Ezra","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Database and optimized machine learning prediction of the deteriorated response of corroded reinforced concrete beams","Research presents an extensive database aggregating 54 experimental programs with 804 test specimens and 45 input parameters, addressing chloride-induced corrosion and its impact on the deteriorated mechanical response of corroded reinforced concrete beams. Multiple machine learning models are evaluated to identify the most accurate predictors for five response variables: residual ultimate moment capacity, residual capacity factor, yield load, yield displacement, and ultimate displacement. Existing analytical methods are included for benchmarking. Optimized ensemble tree-based models, including gradient-boosting regression trees and random forests, consistently deliver superior predictive performance, and top models are packaged into a Python application for bending-failure response prediction from new inputs.","Database and optimized machine learning prediction of the deteriorated response of corroded reinforced concrete beams  \nCitation for published version (APA):  \nMatthews, B. , Palermo, A. , Logan, T. , & Scott, A. (2024) . Database and optimized machine learning prediction of the deteriorated response of corroded reinforced concrete beams. Developments in the Built Environment, 19, Article 100527. [https://doi.org/10.1016/j.dibe.2024.100527](https://doi.org/10.1016/j.dibe.2024.100527)  \nDocument license:  \nCC BY  \nDOI:  \n10.1016/j.dibe.2024.100527  \nDocument status and date:  \nPublished: 01/10/2024  \nDocument Version:  \nPublisher’s PDF, also known as Version of Record (includes final page, issue and volume numbers)  \nPlease check the document version of this publication:  \n• A submitted manuscript is the version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website.  \n• The final author version and the galley proof are versions of the publication after peer review.  \n• The final published version features the final layout of the paper including the volume, issue and page numbers.  \nLink to publication  \nGeneral rights  \nCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.  \n• Users may download and print one copy of any publication from the public portal for the purpose of private study or research.  \n• You may not further distribute the material or use it for any profit-making activity or commercial gain  \n• You may freely distribute the URL identifying the publication in the public portal.  \nIf the publication is distributed under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license above, please follow below link for the End User Agreement:  \n[www.tue.nl/taverne](www.tue.nl/taverne)  \nTake down policy  \nIf you believe that this document breaches copyright please contact us at:  \n[openaccess@tue.nl](openaccess@tue.nl)  \nproviding details and we will investigate your claim.  \nDownload date: 01. Feb. 2025  \nDevelopments in the Built Environment 19 (2024) 100527  \nContents lists available at ScienceDirect  \nDevelopments in the Built Environment  \njournal [homepage: www.sciencedirect.com/journal/developments-in-the-built-environment](homepage: www.sciencedirect.com/journal/developments-in-the-built-environment)  \n| Database and optimized machine learning prediction of the deteriorated response of corroded reinforced concrete beams |  |  |\n| --- | --- | --- |\n| Benjamin Matthewsa,*, Alessandro Palermo b, Tom Logan c, Allan Scott c\u003Cbr>a Department of the Built Environment, Eindhoven University of Technology, Eindhoven, Netherlands b Department of Structural Engineering, University of California San Diego, San Diego, USA\u003Cbr>c Department of Civil and Natural Resources Engineering, University of Canterbury, Christchurch, New Zealand |  |  |\n| A R T I C L E I N F O\u003Cbr>Original content: Monotonic Flexural Testing of Corroded Reinforced Concrete Beams Database (Reference data)\u003Cbr>Monotonic Tensile Testing of Corroded Reinforcing Steel Bars Database (Reference data)\u003Cbr>Keywords:\u003Cbr>Corrosion Reinforced concrete Beams\u003Cbr>Machine learning Regression Optimization | A B S T R A C T\u003Cbr>This research introduces an extensive database aggregating 54 experimental programs with 804 test specimens and 45 input parameters, investigating the implications of chloride-induced corrosion on the deteriorated mechanical response of corroded reinforced concrete beams. Several machine learning models are explored to determine the highest perf","cbCaic5i5CbJroO8","https://ap.wps.com/l/cbCaic5i5CbJroO8","pdf",12221775,1,18,"English","en",105,"# Introduction\n## Background and problem statement\n# Database and experimental coverage\n## Programs, specimens, and input parameters\n# Machine learning prediction framework\n## Model comparison and optimization\n# Benchmarking with analytical approaches\n## Verification against existing methods\n# Results and deployment\n## Best models and Python-based application","[{\"question\":\"What is the main goal of the database and machine learning study?\",\"answer\":\"The study builds a large experimental database and evaluates optimized machine learning models to predict the deteriorated mechanical response of chloride-corroded reinforced concrete beams.\"},{\"question\":\"Which response variables can the models predict?\",\"answer\":\"The models target five key variables: residual ultimate moment capacity, residual capacity factor, yield load, yield displacement, and ultimate displacement.\"},{\"question\":\"How do the machine learning models compare with existing analytical approaches?\",\"answer\":\"Optimized machine learning models significantly outperform conventional analytical methods and achieve high predictive accuracy, with ensemble tree-based methods producing the best results.\"}]","Database and optimized machine learning prediction of the deteriorated response of corroded reinforced concrete beams | 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