Accurate prediction of the shear strength of reinforced concrete (RC) beams remains a challenging problem due to the complex nonlinear interactions among material properties, reinforcement characteristics, and beam geometry. This study presents a data-driven artificial neural network (ANN) framework for predicting the shear strength of RC beams using a systematically curated experimental database comprising 1,977 specimens collected from published literature. The database was preprocessed to remove incomplete and duplicate records, and the optimal ANN architecture was selected using the total goodness function. Model performance was evaluated using 10-fold cross-validation together with multiple statistical metrics, including the coefficient of determination (R²), mean absolute error, root mean squared error, bias, and prediction interval. The ANN achieved R² values of 0.9968 and 0.9686 for the representative training and testing datasets, respectively, and an overall R² of 0.993, with 1,668 predictions falling within the ± 30% error criterion. Comparative evaluation with the Canadian Standards Association (CSA), American Concrete Institute 318 (ACI 318), and Eurocode 2 (EC2) design-code models demonstrated that the proposed ANN consistently achieved superior predictive accuracy and reliability. Parametric analyses further confirmed that the predicted trends agree with established reinforced concrete shear mechanics, highlighting the dominant influence of the shear span-to-depth ratio, beam geometry, and reinforcement ratio on shear resistance. The proposed framework provides an accurate and robust decision-support tool that complements conventional design-code methods for predicting the shear strength of RC beams.
The proposed framework provides accurate, robust and interpretable prediction of rock mechanical properties, demonstrating its potential for geotechnical characterization and transportation infrastructure applications.
Aman Jangir, Biswajit Acharya· Transportation Infrastructur...· 0 citations
In this paper, an ANN-based model is suggested for the prediction of shear strength of steel fiber-reinforced concrete (SFRC) beams without transverse reinforcements, and the model is developed directly using Excel software to ensure computational transparency and statistical interpretability, while remaining an empirical data-driven tool. In this study, a comprehensive database including 923 experimental data is collected, prepared and utilized to develop the neural network. Following the completion of the learning process, the ultimate weight and bias for the neural network are obtained. These parameters are employed in the Excel worksheet. With this process, the learned model is converted into a standalone and operational design tool. Sensitivity analysis, which has been performed through a step-by-step and perturbation method, revealed that the beam width (b) is the most influential parameter in the determination of shear strength (33.4%), followed by concrete compressive strength (fc, 17.1%) and effective depth (d, 15.3%). Then, a parametric study is conducted to study the effect of the shear span-to-depth ratio (a/d), longitudinal reinforcement ratio (ρ) and fiber aspect ratio (Lf/df) on the shear behavior of the members.
Majid Al‐Gburi, Asaad Almssad, A. A. Alhayani· Buildings· 0 citations
The results indicate that the governing parameters for failure mechanisms differ from those controlling shear strength, and highlight the importance of simultaneously assessing shear strength and failure mode in RC beam–column joints.
Gamze Demirtas, Muhammet Zeki Ozyurt, Omer Fatih Sancak et al.· Buildings· 0 citations
An interpretable and uncertainty-aware machine-learning framework for estimating the shear capacity of FRCM-strengthened beams enables accurate, transparent, and uncertainty-aware assessment of shear capacity in FRCM-strengthened concrete beams.
Xiangsheng Liu, G. Figueredo, G. Gordon et al.· Journal of composites for co...· 0 citations
This study proposes a data-driven surrogate modeling framework for predicting
solidification time and mold thermal stress during low-pressure die casting
(LPDC) of aluminum alloy wheels. The methodology employed an optimal Latin
hypercube design (OLHD) to sample key parameters including cooling channel
geometry and process conditions. A sequential simulation methodology combining
ProCAST and Abaqus was implemented to generate a comprehensive dataset of
solidification times and thermal stress distributions. Based on this dataset,
surrogate models were developed using Support Vector Regression, Kriging, and
Polynomial Response Surface Methodology, with their hyperparameters
automatically tuned through Bayesian Optimization (BO). The optimized models
were rigorously evaluated using four statistical metrics: Coefficient of
Determination (R2), Mean Squared Error (MSE), Mean Absolute Error (MAE), and Root
Mean Squared Error (RMSE). The evaluation results show that the BO–SVR model
demonstrated superior prediction accuracy for both output responses and
exhibited exceptional nonlinear fitting capability. This work establishes an
effective modeling approach for simultaneous quality and efficiency optimization
in wheel manufacturing.
Fan Fuhao, Yunlang Zhan, Zhenfei Zhan et al.· SAE technical paper series· 0 citations
A comprehensive data-driven framework integrating ensemble machine learning models with systematic hyperparameter sensitivity analysis and explainable artificial intelligence techniques is proposed, demonstrating that the XGB model significantly outperforms the other approaches, achieving superior accuracy and robust generalization.
Qaim Shah, Waheed Ali Khoso, Fawad Iqbal et al.· Discover Artificial Intellig...· 0 citations