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Performance Evaluation of Soft Computing for Modeling the Strength Properties of Waste Substitute Green Concrete
Sustainability ( IF 3.3 ) Pub Date : 2021-03-06 , DOI: 10.3390/su13052867
Muhammad Izhar Shah , Muhammad Nasir Amin , Kaffayatullah Khan , Muhammad Sohaib Khan Niazi , Fahid Aslam , Rayed Alyousef , Muhammad Faisal Javed , Amir Mosavi

The waste disposal crisis and development of various types of concrete simulated by the construction industry has encouraged further research to safely utilize the wastes and develop accurate predictive models for estimation of concrete properties. In the present study, sugarcane bagasse ash (SCBA), a by-product from the agricultural industry, was processed and used in the production of green concrete. An advanced variant of machine learning, i.e., multi expression programming (MEP), was then used to develop predictive models for modeling the mechanical properties of SCBA substitute concrete. The most significant parameters, i.e., water-to-cement ratio, SCBA replacement percentage, amount of cement, and quantity of coarse and fine aggregate, were used as modeling inputs. The MEP models were developed and trained by the data acquired from the literature; furthermore, the modeling outcome was validated through laboratory obtained results. The accuracy of the models was then assessed by statistical criteria. The results revealed a good approximation capacity of the trained MEP models with correlation coefficient above 0.9 and root means squared error (RMSE) value below 3.5 MPa. The results of cross-validation confirmed a generalized outcome and the resolved modeling overfitting. The parametric study has reflected the effect of inputs in the modeling process. Hence, the MEP-based modeling followed by validation with laboratory results, cross-validation, and parametric study could be an effective approach for accurate modeling of the concrete properties.

中文翻译:

软计算在替代废弃绿色混凝土强度特性建模中的性能评估

废物处置危机和建筑业模拟的各种类型混凝土的发展鼓励了进一步的研究以安全地利用废物,并开发出准确的预测模型来估计混凝土的性能。在本研究中,对来自农业的副产品甘蔗渣(SCBA)进行了加工,并用于生产绿色混凝土。然后,使用了机器学习的高级变体,即多表达式编程(MEP),来开发用于对SCBA替代混凝土的力学性能进行建模的预测模型。最重要的参数,即水灰比,SCBA替代百分比,水泥量以及粗骨料和细骨料的数量,被用作模型输入。MEP模型是通过从文献中获得的数据开发和训练的;此外,通过实验室获得的结果验证了建模结果。然后通过统计标准评估模型的准确性。结果表明,经过训练的MEP模型具有良好的近似能力,相关系数高于0.9,均方根误差(RMSE)值低于3.5 MPa。交叉验证的结果确认了通用结果和已解决的建模过度拟合。参数研究已反映了建模过程中输入的影响。因此,基于MEP的建模以及随后的实验室结果验证,交叉验证和参数研究可能是对混凝土特性进行精确建模的有效方法。通过实验室获得的结果验证了建模结果。然后通过统计标准评估模型的准确性。结果表明,经过训练的MEP模型具有良好的近似能力,相关系数高于0.9,均方根误差(RMSE)值低于3.5 MPa。交叉验证的结果确认了通用结果和已解决的建模过度拟合。参数研究已反映了建模过程中输入的影响。因此,基于MEP的建模以及随后的实验室结果验证,交叉验证和参数研究可能是对混凝土特性进行精确建模的有效方法。通过实验室获得的结果验证了建模结果。然后通过统计标准评估模型的准确性。结果表明,经过训练的MEP模型具有良好的近似能力,相关系数高于0.9,均方根误差(RMSE)值低于3.5 MPa。交叉验证的结果确认了通用结果和已解决的建模过度拟合。参数研究已反映了建模过程中输入的影响。因此,基于MEP的建模以及随后的实验室结果验证,交叉验证和参数研究可能是对混凝土特性进行精确建模的有效方法。结果表明,经过训练的MEP模型具有良好的近似能力,相关系数高于0.9,均方根误差(RMSE)值低于3.5 MPa。交叉验证的结果确认了通用结果和已解决的建模过度拟合。参数研究已反映了建模过程中输入的影响。因此,基于MEP的建模以及随后的实验室结果验证,交叉验证和参数研究可能是对混凝土特性进行精确建模的有效方法。结果表明,经过训练的MEP模型具有良好的近似能力,相关系数高于0.9,均方根误差(RMSE)值低于3.5 MPa。交叉验证的结果确认了通用结果和已解决的建模过度拟合。参数研究已反映了建模过程中输入的影响。因此,基于MEP的建模以及随后的实验室结果验证,交叉验证和参数研究可能是对混凝土特性进行精确建模的有效方法。
更新日期:2021-03-07
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