Synergizing FEM with AI: Leveraging Dense Neural Networks, Random Forests, and SHAP for Enhanced Feature Importance Analysis in Long-Term Arch Dam Displacement Prediction

Synergizing FEM with AI: Leveraging Dense Neural Networks, Random Forests, and SHAP for Enhanced Feature Importance Analysis in Long-Term Arch Dam Displacement Prediction | 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 Research Article Synergizing FEM with AI: Leveraging Dense Neural Networks, Random Forests, and SHAP for Enhanced Feature Importance Analysis in Long-Term Arch Dam Displacement Prediction AM Babaadi, H Mirzabozorg, K Baharan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5922355/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study combines advanced machine learning techniques with finite element modeling to analyze the structural behavior and displacement of a super-high concrete arch dam. By integrating finite element analysis, dense neural networks, and random forest approaches, the research determines the relative importance of factors influencing dam displacement over long-term operations. The study utilizes SHAP (SHapley Additive exPlanations) values to interpret the results, providing deeper insights into how each factor contributes to the model's predictions. The findings highlight lake level as the most significant factor, followed by thermal effects and creep, demonstrating the potential of this integrated approach for enhancing structural health monitoring of large infrastructure projects. Feature Importance Arch Dam Dense Neural Network Random Forest CatBoost Shap Creep Long-term Operation Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. 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