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<p>Predicting concrete compressive strength with limited data remains a critical challenge in civil engineering. This study proposes a novel framework integrating Model-Agnostic Meta-Learning (MAML) with SHAP (Shapley Additive Explanations) to improve predictive accuracy and interpretability in data-scarce scenarios. Unlike conventional machine learning models that require extensive data, the MAML-based approach enables rapid adaptation to new tasks using minimal samples, offering robust generalization in few-shot learning contexts. The proposed pipeline includes structured preprocessing, normalization, a neural network-based meta-learning core, and SHAP-based feature attribution. A curated dataset of 430 samples was used, focusing on 28-day compressive strength, with input features including cement, water, aggregates, admixtures, and age. Compared to standard models like XGBoost and Random Forest, the MAML framework achieved superior performance, with MAE = 3.56 MPa, RMSE = 5.55 MPa, and R<sup>2</sup> = 0.913. SHAP analysis revealed nonlinear interactions and dominant factors like water-cement ratio, curing age, and aggregate content. Statistical validation via the Wilcoxon Signed-Rank Test confirmed the significance of the model’s improvements (p &lt; 0.05). Furthermore, SHAP insights closely align with domain knowledge and mix design principles, enhancing model transparency for practical application. This work demonstrates the applicability of meta-learning in civil engineering and provides a scalable, interpretable solution for strength prediction in real-world, data-limited conditions. </p>

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Few-shot meta-learning for concrete strength prediction: a model-agnostic approach with SHAP analysis

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年9月1日

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  • Mayaz Uddin Gazi,
  • Md. Titumir Hasan,
  • Ponkaj Debnath

摘要

Predicting concrete compressive strength with limited data remains a critical challenge in civil engineering. This study proposes a novel framework integrating Model-Agnostic Meta-Learning (MAML) with SHAP (Shapley Additive Explanations) to improve predictive accuracy and interpretability in data-scarce scenarios. Unlike conventional machine learning models that require extensive data, the MAML-based approach enables rapid adaptation to new tasks using minimal samples, offering robust generalization in few-shot learning contexts. The proposed pipeline includes structured preprocessing, normalization, a neural network-based meta-learning core, and SHAP-based feature attribution. A curated dataset of 430 samples was used, focusing on 28-day compressive strength, with input features including cement, water, aggregates, admixtures, and age. Compared to standard models like XGBoost and Random Forest, the MAML framework achieved superior performance, with MAE = 3.56 MPa, RMSE = 5.55 MPa, and R2 = 0.913. SHAP analysis revealed nonlinear interactions and dominant factors like water-cement ratio, curing age, and aggregate content. Statistical validation via the Wilcoxon Signed-Rank Test confirmed the significance of the model’s improvements (p < 0.05). Furthermore, SHAP insights closely align with domain knowledge and mix design principles, enhancing model transparency for practical application. This work demonstrates the applicability of meta-learning in civil engineering and provides a scalable, interpretable solution for strength prediction in real-world, data-limited conditions.