[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123318-en":3,"doc-seo-123318-105":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":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},123318,2336464648746,"Skyler","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Machine learning models to predict the static failure of double-lap shear bolted connections","This study investigates whether machine learning models can predict the failure load and failure mode of double-lap shear bolted connections. Five algorithms are evaluated—adaptive boosting, artificial neural networks, decision trees, support vector machines with radial basis kernels, and k-nearest neighbors—using a dataset of 221 experimental and numerical tests with varying input parameters and different grades of stainless and carbon steel. To improve generalizability, k-fold cross-validation is used instead of an 80/20 split to reduce bias and limit overfitting. The models show strong performance for regression and classification. Adaptive boosting yields the best failure-load predictions, while neural networks achieve the highest accuracy for classifying failure modes. The results indicate that well-trained machine learning can outperform codified design approaches, especially when trained on diverse datasets.","This is a repository copy of Machine learning models to predict the static failure of double‐ lap shear bolted connections.  \nWhite Rose Research Online URL for this paper:  \n[https://eprints.whiterose.ac.uk/228553/](https://eprints.whiterose.ac.uk/228553/)  \nVersion: Published Version  \nArticle:  \nAlmuhanna, [H. orcid.org/0000-0001-7486-6307](H. orcid.org/0000-0001-7486-6307) , Torelli, [G. orcid.org/0000-0002-0607-](G. orcid.org/0000-0002-0607-)[ ](G. orcid.org/0000-0002-0607-)695X and Susmel, [L. orcid.org/0000-0001-7753-9176](L. orcid.org/0000-0001-7753-9176) (2025) Machine learning models to predict the static failure of double‐lap shear bolted connections. Fatigue & Fracture of Engineering Materials & Structures. ISSN 8756-758X  \n[https://doi.org/10.1111/ffe.70019](https://doi.org/10.1111/ffe.70019)  \nReuse  \nThis article is distributed under the terms of the Creative Commons Attribution (CC BY) licence. This licence allows you to distribute, remix, tweak, and build upon the work, even commercially, as long as you credit the authors for the original work. More information and the full terms of the licence here: [https://creativecommons.org/licenses/](https://creativecommons.org/licenses/)  \nTakedown  \nIf you consider content in White Rose Research Online to be in breach of UK law, please notify us by  \nemailing [eprints@whiterose.ac.uk](eprints@whiterose.ac.uk) including the URL of the record and the reason for the withdrawal request.  \n[eprints@whiterose.ac.uk](eprints@whiterose.ac.uk)[ ](eprints@whiterose.ac.uk)[https://eprints.whiterose.ac.uk/](https://eprints.whiterose.ac.uk/)  \nFatigue & Fracture of Engineering Materials & Structures  \nORIGINAL ARTICLE  OPEN ACCESS   \nMachine Learning Models to Predict the Static Failure of Double-Lap Shear Bolted Connections  \nH. Almuhanna1  | G. Torelli1 | L. Susmel2   \n1School of Mechanical, Aerospace and Civil Engineering, The University of Sheffield, Sheffield, UK | 2School of Engineering and Built Environment, Sheffield Hallam University, Sheffield, UK  \nCorrespondence: L. Susmel ([l.susmel@shu.ac.uk](l.susmel@shu.ac.uk))  \nReceived: 4 April 2025 | Revised: 10 June 2025 | Accepted: 17 June 2025  \nFunding: The authors received no specific funding for this work.  \nKeywords: adaptive boosting | artificial neural network | bolted connections | decision tree | K-nearest neighbors | machine learning | support vector machine  \nABSTRACT  \nThis study investigates the potential of machine learning models to predict the failure load and mode of double-lap shear bolted connections. Five algorithms were evaluated: adaptive boosting, artificial neural network, decision trees, support vector machines with radial basis function kernel, and k-nearest neighbors. A dataset comprising 221 experimental and numerical tests with varying input parameters, including different grades of stainless and carbon steel, was used to train the models. Unlike previous studies, the inclusion of diverse materials enabled the development of more generalizable models. To address data limitations, reduce biases associated with data split, and mitigate overfitting, k-fold cross-validation was adopted instead of the conventional 80/20 split. Results show that both regression and classification models achieved high coefficients of determination across most algorithms. Adaptive boosting delivered the most accurate failure load predictions, while artificial neural network achieved the highest accuracy in classifying failure modes. The findings highlight the potential of well-trained machine learning models to outperform traditional codified methods in accurately predicting the structural response of bolted connections, especially when trained on diverse datasets.  \n1 | Introduction  \nJoining steel frames in structures using bolted connections requires a careful design procedure illustrated in relevant design standards such as BS EN 1993-1-8 [1] or ANSI/AISC 360-22 [2] . The codified design equations considered in these s","cbCaivJV2mRyRa2n","https://ap.wps.com/l/cbCaivJV2mRyRa2n","pdf",2837268,1,16,"English","en",105,"# Introduction\n## Design standards and codified equations\n## Limitations and non-conservative cases\n## Need for improved prediction methods\n## Scope of the machine learning study\n# Summary of findings","[{\"question\":\"What failure aspects does the study aim to predict for double-lap shear bolted connections?\",\"answer\":\"The study targets both the failure load and the failure mode of double-lap shear bolted connections.\"},{\"question\":\"Which machine learning algorithms are evaluated in the paper?\",\"answer\":\"Adaptive boosting, artificial neural networks, decision trees, support vector machines with a radial basis function kernel, and k-nearest neighbors are evaluated.\"},{\"question\":\"How is the model validation designed to address data bias and overfitting?\",\"answer\":\"The study uses k-fold cross-validation instead of a conventional 80/20 data split to reduce bias from the data partition and mitigate overfitting.\"}]","Machine learning models to predict the static failure of double-lap shear bolted connections | 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