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<p>The swift progression of three-dimensional (3D) concrete printing has opened the door to the creation of innovative materials, such as fiber-reinforced concrete, making it essential to develop accurate models for predicting their mechanical properties. Accurately estimating the compressive strength (CS) of such materials is required for optimizing mix designs and ensuring structural performance. In this context, the present study is the first to systematically investigate and compare three different support vector regression (SVR) kernels, namely, linear SVR (L-SVR), polynomial SVR (Poly-SVR), and radial basis function SVR (RBF-SVR), for predicting the compressive strength of normal, high, and ultra-high strength 3D-printed fiber-reinforced concrete (3DPFRC). A model was developed and validated using a dataset of 278 samples collected from the literature. In addition to SVR models, Artificial Neural Networks (ANN) and Gradient Boosting Machines (GBM) were developed for benchmarking purposes. Results from statistical evaluation revealed that the ANN model achieved the highest predictive accuracy overall, outperforming the L-SVR, Poly-SVR, and GBM models. Among the SVR-based models, the RBF-SVR demonstrated superior performance, showing competitive accuracy with the ANN while outperforming both the other SVR variants (L-SVR and Poly-SVR) as well as the GBM model in terms of coefficient of determination (R²) value and error metrics. All models achieved R² scores ranging between 0.94 and 0.86 for the training datasets and 0.93 and 0.72 in the testing datasets. The RBF-SVR model, in particular, offered an excellent balance between accuracy, training speed, and reliability, making it a strong and efficient alternative to more complex models. Additionally, SHapley Additive exPlanations (SHAP) analysis identified the water-to-cement ratio, silica fume content, and cement volume as the most influential parameters affecting compressive strength.</p>

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Machine learning prediction of compressive strength in 3d printed fiber reinforced concrete using support vector regression and artificial neural networks with shapley additive explanations

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月25日

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  • Mohamed Abdellatief,
  • Ahmed M. Saqr

摘要

The swift progression of three-dimensional (3D) concrete printing has opened the door to the creation of innovative materials, such as fiber-reinforced concrete, making it essential to develop accurate models for predicting their mechanical properties. Accurately estimating the compressive strength (CS) of such materials is required for optimizing mix designs and ensuring structural performance. In this context, the present study is the first to systematically investigate and compare three different support vector regression (SVR) kernels, namely, linear SVR (L-SVR), polynomial SVR (Poly-SVR), and radial basis function SVR (RBF-SVR), for predicting the compressive strength of normal, high, and ultra-high strength 3D-printed fiber-reinforced concrete (3DPFRC). A model was developed and validated using a dataset of 278 samples collected from the literature. In addition to SVR models, Artificial Neural Networks (ANN) and Gradient Boosting Machines (GBM) were developed for benchmarking purposes. Results from statistical evaluation revealed that the ANN model achieved the highest predictive accuracy overall, outperforming the L-SVR, Poly-SVR, and GBM models. Among the SVR-based models, the RBF-SVR demonstrated superior performance, showing competitive accuracy with the ANN while outperforming both the other SVR variants (L-SVR and Poly-SVR) as well as the GBM model in terms of coefficient of determination (R²) value and error metrics. All models achieved R² scores ranging between 0.94 and 0.86 for the training datasets and 0.93 and 0.72 in the testing datasets. The RBF-SVR model, in particular, offered an excellent balance between accuracy, training speed, and reliability, making it a strong and efficient alternative to more complex models. Additionally, SHapley Additive exPlanations (SHAP) analysis identified the water-to-cement ratio, silica fume content, and cement volume as the most influential parameters affecting compressive strength.