Ahmad Ayaz, Farooq Furqan, Niewiadomski Pawel, Ostrowski Krzysztof, Akbar Arslan, Aslam Fahid, Alyousef Rayed
Department of Civil Engineering, Abbottabad Campus, COMSATS University Islamabad, Islamabad 22060, Pakistan.
Faculty of Civil Engineering, Wroclaw University of Science and Technology, Wybrzeże Wyspiańskiego 27, 50-370 Wroclaw, Poland.
Materials (Basel). 2021 Feb 8;14(4):794. doi: 10.3390/ma14040794.
Machine learning techniques are widely used algorithms for predicting the mechanical properties of concrete. This study is based on the comparison of algorithms between individuals and ensemble approaches, such as bagging. Optimization for bagging is done by making 20 sub-models to depict the accurate one. Variables like cement content, fine and coarse aggregate, water, binder-to-water ratio, fly-ash, and superplasticizer are used for modeling. Model performance is evaluated by various statistical indicators like mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE). Individual algorithms show a moderate bias result. However, the ensemble model gives a better result with R = 0.911 compared to the decision tree (DT) and gene expression programming (GEP). K-fold cross-validation confirms the model's accuracy and is done by R, MAE, MSE, and RMSE. Statistical checks reveal that the decision tree with ensemble provides 25%, 121%, and 49% enhancement for errors like MAE, MSE, and RMSE between the target and outcome response.
机器学习技术是预测混凝土力学性能的广泛使用的算法。本研究基于个体方法与集成方法（如装袋法）之间的算法比较。通过制作20个亚模型来描绘准确的模型，从而对装袋法进行优化。使用水泥含量、细骨料和粗骨料、水、水胶比、粉煤灰和高效减水剂等变量进行建模。通过平均绝对误差（MAE）、均方误差（MSE）和均方根误差（RMSE）等各种统计指标评估模型性能。个体算法显示出中等偏差结果。然而，与决策树（DT）和基因表达编程（GEP）相比，集成模型给出了更好的结果，R值为0.911。K折交叉验证证实了模型的准确性，并通过R、MAE、MSE和RMSE进行。统计检验表明，带有集成的决策树在目标响应和结果响应之间的MAE、MSE和RMSE等误差方面提高了25%、121%和49%。
