Javed Muhammad Faisal, Fawad Muhammad, Lodhi Rida, Najeh Taoufik, Gamil Yaser
Department of Civil Engineering, Ghulam Ishaq Khan Institute of Engineering Sciences and Technology, Topi, Swabi, 23640, Pakistan.
Silesian University of Technology Poland, Gliwice, Poland.
Sci Rep. 2024 Apr 10;14(1):8381. doi: 10.1038/s41598-024-57896-0.
Preplaced aggregate concrete (PAC) also known as two-stage concrete (TSC) is widely used in construction engineering for various applications. To produce PAC, a mixture of Portland cement, sand, and admixtures is injected into a mold subsequent to the deposition of coarse aggregate. This process complicates the prediction of compressive strength (CS), demanding thorough investigation. Consequently, the emphasis of this study is on enhancing the comprehension of PAC compressive strength using machine learning models. Thirteen models are evaluated with 261 data points and eleven input variables. The result depicts that xgboost demonstrates exceptional accuracy with a correlation coefficient of 0.9791 and a normalized coefficient of determination (R) of 0.9583. Moreover, Gradient boosting (GB) and Cat boost (CB) also perform well due to its robust performance. In addition, Adaboost, Voting regressor, and Random forest yield precise predictions with low mean absolute error (MAE) and root mean square error (RMSE) values. The sensitivity analysis (SA) reveals the significant impact of key input parameters on overall model sensitivity. Notably, gravel takes the lead with a substantial 44.7% contribution, followed by sand at 19.5%, cement at 15.6%, and Fly ash and GGBS at 5.9% and 5.1%, respectively. The best fit model i.e., XG-Boost model, was employed for SHAP analysis to assess the relative importance of contributing attributes and optimize input variables. The SHAP analysis unveiled the water-to-binder (W/B) ratio, superplasticizer, and gravel as the most significant factors influencing the CS of PAC. Furthermore, graphical user interface (GUI) have been developed for practical applications in predicting concrete strength. This simplifies the process and offers a valuable tool for leveraging the model's potential in the field of civil engineering. This comprehensive evaluation provides valuable insights to researchers and practitioners, empowering them to make informed choices in predicting PAC compressive strength in construction projects. By enhancing the reliability and applicability of predictive models, this study contributes to the field of preplaced aggregate concrete strength prediction.
预填集料混凝土（PAC）也称为两阶段混凝土（TSC），在建筑工程中有广泛的应用。为了生产PAC，在粗集料沉积之后，将波特兰水泥、沙子和外加剂的混合物注入模具中。这个过程使得抗压强度（CS）的预测变得复杂，需要进行深入研究。因此，本研究的重点是使用机器学习模型来提高对PAC抗压强度的理解。使用261个数据点和11个输入变量对13个模型进行了评估。结果表明，XGBoost表现出卓越的准确性，相关系数为0.9791，归一化决定系数（R）为0.9583。此外，梯度提升（GB）和Catboost（CB）由于其强大的性能也表现良好。此外，Adaboost、投票回归器和随机森林以低平均绝对误差（MAE）和均方根误差（RMSE）值产生精确的预测。敏感性分析（SA）揭示了关键输入参数对整体模型敏感性的重大影响。值得注意的是，砾石的贡献最大，为44.7%，其次是沙子，为19.5%，水泥为15.6%，粉煤灰和粒化高炉矿渣分别为5.9%和5.1%。采用最佳拟合模型即XG-Boost模型进行SHAP分析，以评估贡献属性的相对重要性并优化输入变量。SHAP分析揭示了水胶比（W/B）、高效减水剂和砾石是影响PAC抗压强度的最重要因素。此外，还开发了图形用户界面（GUI）用于预测混凝土强度的实际应用。这简化了过程，并为在土木工程领域利用该模型的潜力提供了一个有价值的工具。这种全面的评估为研究人员和从业者提供了有价值的见解，使他们能够在建筑项目中预测PAC抗压强度时做出明智的选择。通过提高预测模型的可靠性和适用性，本研究为预填集料混凝土强度预测领域做出了贡献。
