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<p>PPHCS are widely utilized around the world as essential components in horizontal structural systems, playing a key role in contemporary precast concrete construction practices. In this study, thirteen full-scale prestressed hollow-core slabs with varying shear span-to-depth (a/d) ratios were tested to failure to evaluate their ultimate load-carrying capacity. An artificial neural network (ANN) model was developed to predict this capacity, utilizing a comprehensive dataset that combines detailed experimental results from tested slabs with valuable data sourced from existing literature. A total of 431 PPHC slabs were used to train and validate the ANN model, utilizing key input variables including slab length, overall depth, effective prestressing force, area of prestressing steel, Prestressing load eccentricity, shear span-to-depth ratio, concrete compressive strength, and web width. The model’s predictive performance was thoroughly evaluated using multiple statistical metrics, delivering a strong coefficient of determination (R² = 0.948), along with a low root mean square error (RMSE = 43.433) and mean absolute error (MAE = 31.03). The experimental results were evaluated against the predictions from both the ACI code and the ANN model. The analysis indicated that the proposed ANN model provides a more accurate estimate of the ultimate load capacity of PPHCS compared to the ACI code.</p>

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Structural strength prediction of prestressed precast hollow core slabs (PPHCS) using deep artificial neural network model

  • 期刊论文

  • 发表日期：

    2025年8月13日

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  • Muneer K. Saeed

摘要

PPHCS are widely utilized around the world as essential components in horizontal structural systems, playing a key role in contemporary precast concrete construction practices. In this study, thirteen full-scale prestressed hollow-core slabs with varying shear span-to-depth (a/d) ratios were tested to failure to evaluate their ultimate load-carrying capacity. An artificial neural network (ANN) model was developed to predict this capacity, utilizing a comprehensive dataset that combines detailed experimental results from tested slabs with valuable data sourced from existing literature. A total of 431 PPHC slabs were used to train and validate the ANN model, utilizing key input variables including slab length, overall depth, effective prestressing force, area of prestressing steel, Prestressing load eccentricity, shear span-to-depth ratio, concrete compressive strength, and web width. The model’s predictive performance was thoroughly evaluated using multiple statistical metrics, delivering a strong coefficient of determination (R² = 0.948), along with a low root mean square error (RMSE = 43.433) and mean absolute error (MAE = 31.03). The experimental results were evaluated against the predictions from both the ACI code and the ANN model. The analysis indicated that the proposed ANN model provides a more accurate estimate of the ultimate load capacity of PPHCS compared to the ACI code.