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Machine Learning Regression Algorithms for Shear Strength Prediction of SFRC-DBs: Performance Evaluation and Comparisons

  • Research Article-Civil Engineering
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Abstract

The objective of this study is to assess the shear strength of Deep Steel Fiber Reinforced Concrete Beams without stirrups (SFRC-DBs) and to forecast the values of such shear strengths using machine learning techniques. To facilitate the design of such complex structural elements, this work generated an up-to-date database of 170 SFRC-DBs, where eleven popular machine learning regression algorithms are evaluated using five common performance metrics. This introduces a comprehensive assessment framework enabling practitioners to develop reliable and efficient data-driven applications for this task. The evaluation process included the multilayer perceptron (MLP), the linear regression, the ridge, the lasso, the elastic net, the decision tree, the random forest (RF), the gradient boosting (GB), the extreme gradient boosting (XGBoost), the AdaBoost, and the k-nearest neighbor. The results reveal that MLP, GB, RF, and XGBoost show superior variance-explaining capabilities within the dataset. Their models can explain more than 86% of the variance in the dependent variable with a Mean Absolute Error of about 25.0, Mean Absolute Percentage Error of nearly or below 20%, Root Mean Square Error of less than 44.5, and R-squared more than 85%. This interesting finding might encourage civil engineers to focus on utilizing and testing these methods for practical shear strength estimate applications, while other regression algorithms might need dataset expansion and/or data engineering to augment future forecasting capabilities.

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Data Availability Statement

Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.

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Authors and Affiliations

  1. Department of Information Technology, Faculty of Prince Al-Hussein Bin Abdallah II For Information Technology, The Hashemite University, P.O. Box 330127, Zarqa, 13133, Jordan

    Majdi Maabreh

  2. Department of Civil Engineering, Faculty of Engineering, The Hashemite University, P.O. box 330127, Zarqa, 13133, Jordan

    Ghassan Almasabha

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  1. Majdi Maabreh
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Correspondence to Majdi Maabreh.

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Maabreh, M., Almasabha, G. Machine Learning Regression Algorithms for Shear Strength Prediction of SFRC-DBs: Performance Evaluation and Comparisons. Arab J Sci Eng 49, 4711–4727 (2024). https://doi.org/10.1007/s13369-023-08176-y

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