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<p>This research investigates the application of hybrid artificial neural network (ANN) models that are optimized using Grey Wolf Optimization (GWO), Particle Swarm Optimization (PSO), and Artificial Bee Colony (ABC) algorithms to predict the punching shear capacity (PSC) of flat slabs made of recycled aggregate concrete (RAC). To ensure robust model development, a database of 101 experimental specimens was compiled, processed, and divided into training (70%) and testing (30%) datasets. Regression error characteristic (REC) curves, Taylor diagrams, and statistical indices (R2, RMSE, MAE, WMAPE, WI, and LMI) were used to extensively assess the model’s accuracy, external validation, and uncertainty analysis. ANN-ABC has the best predictive value, as it delivered the lowest values of errors and R<sup>2</sup> of 0.9593 (training) and 0.9527 (testing). The robustness of its analysis was also supported by narrow uncertainty bounds, favourable rankings across all evaluation criteria, and analysis of the REC curve (AUC = 0.755 during training and 0.563 during testing). On the contrary, ANN-PSO worked with moderate accuracy, whereas ANN-GWO worked with the lowest accuracy. The sensitivity analysis showed that effective depth, reinforcement area, and water-to-cement ratio were the most sensitive parameters that control PSC behavior. Unlike previous PSC prediction studies that relied mainly on tree-based or kernel-based ML methods, this work is the first to benchmark swarm-intelligence-optimized ANNs (ANN-ABC, ANN-PSO, ANN-GWO). This hybridization improves predictive accuracy while ensuring robustness and interpretability for RAC structural applications.</p>

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Metaheuristic-optimized neural networks for punching shear capacity prediction in recycled aggregate concrete RC flat slabs

  • 期刊论文

  • 发表日期：

    2025年10月15日

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  • Albaraa Alasskar,
  • Shambhu Sharan Mishra

摘要

This research investigates the application of hybrid artificial neural network (ANN) models that are optimized using Grey Wolf Optimization (GWO), Particle Swarm Optimization (PSO), and Artificial Bee Colony (ABC) algorithms to predict the punching shear capacity (PSC) of flat slabs made of recycled aggregate concrete (RAC). To ensure robust model development, a database of 101 experimental specimens was compiled, processed, and divided into training (70%) and testing (30%) datasets. Regression error characteristic (REC) curves, Taylor diagrams, and statistical indices (R2, RMSE, MAE, WMAPE, WI, and LMI) were used to extensively assess the model’s accuracy, external validation, and uncertainty analysis. ANN-ABC has the best predictive value, as it delivered the lowest values of errors and R2 of 0.9593 (training) and 0.9527 (testing). The robustness of its analysis was also supported by narrow uncertainty bounds, favourable rankings across all evaluation criteria, and analysis of the REC curve (AUC = 0.755 during training and 0.563 during testing). On the contrary, ANN-PSO worked with moderate accuracy, whereas ANN-GWO worked with the lowest accuracy. The sensitivity analysis showed that effective depth, reinforcement area, and water-to-cement ratio were the most sensitive parameters that control PSC behavior. Unlike previous PSC prediction studies that relied mainly on tree-based or kernel-based ML methods, this work is the first to benchmark swarm-intelligence-optimized ANNs (ANN-ABC, ANN-PSO, ANN-GWO). This hybridization improves predictive accuracy while ensuring robustness and interpretability for RAC structural applications.