Transparent AI for Structural Engineering: Explainable Shear Strength Prediction in SFRC Beams Without Transverse Reinforcement

Transparent AI for Structural Engineering: Explainable Shear Strength Prediction in SFRC Beams Without Transverse Reinforcement | 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 Transparent AI for Structural Engineering: Explainable Shear Strength Prediction in SFRC Beams Without Transverse Reinforcement Esayies A. Wassie, Habtamu A. Tadesse, Leule M. Hailemariam This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6582890/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 Predicting the shear strength of steel fiber-reinforced concrete beams is crucial for the design and assessment of concrete structures incorporating steel fibers. Traditional empirical methods, developed from limited datasets, often attempt to provide dependable predictions of SFRC shear strength. Moreover, traditional methods fail to catch the complex non-linear behaviour of steel fiber reinforced concrete beams. Machine learning models can analyze large datasets and uncover complex relationships, offering a promising alternative. The main challenge with high-accuracy ML models lies in their lack of interpretability, as they often behave as black-box systems, making it difficult for engineers to understand how the model reaches its predictions. To overcome this, Shapley Additive exPlanations (SHAP) and partial dependence plot (PDP), a technique designed to provide transparency in machine learning models, were used to build an explainable ML model for predicting the shear strength of SFRC beams. The experimental & existing shear design models were compared to the developed ML model to evaluate the effectiveness of the model. The results indicated that the XGBoost (R² = 0.988) model made predictions very close to the experimentally observed shear strengths, offering better and more unbiased performance compared to existing models. The SHAP analysis revealed that the most influential factors affecting shear strength predictions were beam width and effective depth, followed by concrete strength and longitudinal reinforcement ratio. Steel fiber factor and the shear-span to effective depth ratio were also significant contributors. By employing XGBoost and integrating SHAP to explain the model’s decisions, this study provides an accurate and interpretable method for predicting shear strength in SFRC beams. Artificial Intelligence Explainability Steel Fiber Reinforced Concrete (SFRC) Machine Learning SHapley Additive exPlanations (SHAP) 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. 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-6582890","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":456475991,"identity":"642617a3-2a61-4e9e-bd7d-e0f42725d5d1","order_by":0,"name":"Esayies A. 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Traditional empirical methods, developed from limited datasets, often attempt to provide dependable predictions of SFRC shear strength. Moreover, traditional methods fail to catch the complex non-linear behaviour of steel fiber reinforced concrete beams. Machine learning models can analyze large datasets and uncover complex relationships, offering a promising alternative. The main challenge with high-accuracy ML models lies in their lack of interpretability, as they often behave as black-box systems, making it difficult for engineers to understand how the model reaches its predictions. To overcome this, Shapley Additive exPlanations (SHAP) and partial dependence plot (PDP), a technique designed to provide transparency in machine learning models, were used to build an explainable ML model for predicting the shear strength of SFRC beams. The experimental \u0026 existing shear design models were compared to the developed ML model to evaluate the effectiveness of the model. The results indicated that the XGBoost (R² = 0.988) model made predictions very close to the experimentally observed shear strengths, offering better and more unbiased performance compared to existing models. The SHAP analysis revealed that the most influential factors affecting shear strength predictions were beam width and effective depth, followed by concrete strength and longitudinal reinforcement ratio. Steel fiber factor and the shear-span to effective depth ratio were also significant contributors. 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