Comparing Machine Learning and Deep Learning Approaches to Predict the Seismic Response of Slab– Column Connections

Comparing Machine Learning and Deep Learning Approaches to Predict the Seismic Response of Slab– Column Connections | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Comparing Machine Learning and Deep Learning Approaches to Predict the Seismic Response of Slab– Column Connections Mahmoud A. El-Mandouh, Hassan Youssef, M. S. Elborlsy, Mostafa A. Ebied This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8903807/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract Slab-column joints constitute the most sensitive elements of flat plate structures during seismic forces since they are likely to be faced with a brittle failure. This paper examines the predictive accuracy of an extensive Machine Learning (ML) and Deep Learning (DL) solutions for predicting seismic performance of slab-column connections, such as punching moment (M) and Drift Ratio (dr). The ML models under study are Ridge Regression (RR), Linear Regression (LR), Lasso Regression (Lasso R), Elastic Net (EN), Support Vector Regression (SVR), Gradient Boosting (GB), random Forest (RF), and Extreme Gradient Boosting (XGBoost). The models of DL that were analyzed are Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and a hybrid version that incorporates CNN and LSTM (CNN-LSTM). The analysis shows that in (M) prediction, the performance of GB was the best with a coefficient of determination (R²) of 0.870850, a Root Mean Square Error (RMSE) of 0.378246, and a Mean Absolute Error (MAE) of 0.282686. In the DL models, CNN had the best accuracy using R² of 0.827864, MAE of 0.335228, and RMSE of 0.436680. RF was better than other ML models in dr prediction with an R² of 0.565014, MAE of 0.440262, and RMSE of 0.619478. Once again, CNN performed better than the rest of the DL models, with an R² of 0.470568, MAE of 0.513140, and RMSE of 0.683429. Physical sciences/Engineering Physical sciences/Mathematics and computing Structural engineering Punching moment prediction Drift ratio estimation Machine Learning Deep Learning Hybrid model CNN-LSTM Gradient Boosting Random Forest XGBoost algorithm. Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 19 Mar, 2026 Reviews received at journal 12 Mar, 2026 Reviews received at journal 06 Mar, 2026 Reviewers agreed at journal 02 Mar, 2026 Reviews received at journal 24 Feb, 2026 Reviewers agreed at journal 24 Feb, 2026 Reviewers agreed at journal 24 Feb, 2026 Reviewers invited by journal 24 Feb, 2026 Editor assigned by journal 24 Feb, 2026 Editor invited by journal 23 Feb, 2026 Submission checks completed at journal 21 Feb, 2026 First submitted to journal 21 Feb, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8903807","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":596663532,"identity":"4253c262-78e2-46e7-9e1e-8e8970bddb69","order_by":0,"name":"Mahmoud A. 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This paper examines the predictive accuracy of an extensive Machine Learning (ML) and Deep Learning (DL) solutions for predicting seismic performance of slab-column connections, such as punching moment (M) and Drift Ratio (dr). The ML models under study are Ridge Regression (RR), Linear Regression (LR), Lasso Regression (Lasso R), Elastic Net (EN), Support Vector Regression (SVR), Gradient Boosting (GB), random Forest (RF), and Extreme Gradient Boosting (XGBoost). The models of DL that were analyzed are Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and a hybrid version that incorporates CNN and LSTM (CNN-LSTM). The analysis shows that in (M) prediction, the performance of GB was the best with a coefficient of determination (R\u0026sup2;) of 0.870850, a Root Mean Square Error (RMSE) of 0.378246, and a Mean Absolute Error (MAE) of 0.282686. In the DL models, CNN had the best accuracy using R\u0026sup2; of 0.827864, MAE of 0.335228, and RMSE of 0.436680. RF was better than other ML models in dr prediction with an R\u0026sup2; of 0.565014, MAE of 0.440262, and RMSE of 0.619478. Once again, CNN performed better than the rest of the DL models, with an R\u0026sup2; of 0.470568, MAE of 0.513140, and RMSE of 0.683429.\u003c/p\u003e","manuscriptTitle":"Comparing Machine Learning and Deep Learning Approaches to Predict the Seismic Response of Slab– Column Connections","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-25 17:37:14","doi":"10.21203/rs.3.rs-8903807/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-19T09:57:35+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-12T09:24:57+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-06T20:42:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"172219511346576361972133216866339903581","date":"2026-03-02T09:44:35+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-25T04:53:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"219043244919194930128731522234824732157","date":"2026-02-25T02:13:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"43223445916204099793894332131117030350","date":"2026-02-24T18:24:21+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-24T07:05:20+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-24T06:44:05+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-24T04:07:15+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-21T09:27:56+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-02-21T09:19:47+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"31260478-3082-4f82-a503-4166f8bddb00","owner":[],"postedDate":"February 25th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":63486250,"name":"Physical sciences/Engineering"},{"id":63486251,"name":"Physical sciences/Mathematics and computing"}],"tags":[],"updatedAt":"2026-04-24T11:10:30+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-25 17:37:14","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8903807","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8903807","identity":"rs-8903807","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}