利用机器学习对二次处理废水和粉煤灰的混凝土力学性能进行预测建模

Rajiv K.N. , Ramalinga Reddy Y. , Shiva Kumar G , Ramaraju HK (Professor)
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引用次数: 0

摘要

本研究探讨了二级处理废水和粉煤灰在混凝土生产中的利用,重点是使用机器学习模型建模力学特性。用自来水和三种二级处理废水配制了16种混凝土混合物,不同粉煤灰比例(0 %,10 %,20 %和30 %)。对每种混合物的工作性、抗压强度、劈裂抗拉强度和抗弯强度进行了评估。采用线性回归、LASSO回归、决策树回归、随机森林回归和多层感知机五种机器学习模型对混凝土力学性能进行预测。结果表明,利用二次处理废水和粉煤灰可以有效地生产M30级混凝土,通过减少淡水使用量并将粉煤灰作为补充胶凝材料,为更可持续的混凝土生产提供了一种有前途的策略。值得注意的是,随机森林回归器对抗压强度的预测精度优于其他模型,并被证明是优化混凝土配合比设计的宝贵工具。它能够可靠地预测混凝土强度特性,确保在配合比设计配方中具有更高的准确性,这对于实现预期性能同时最大限度地减少材料浪费至关重要。从可持续发展的角度来看,在混凝土生产中使用二次处理的废水可以显著减少对淡水的需求,从而保护这一宝贵资源。加入煤燃烧的副产品粉煤灰,不仅可以提高混凝土的性能,还有助于转移垃圾填埋场的工业废物,减少对环境的影响。机器学习模型的应用,特别是随机森林回归,允许更精确和有效的混合设计,进一步促进混凝土生产的可持续性。这种方法通过减少水的使用、促进工业副产品的回收和提高混凝土制造过程的整体效率,提供了巨大的环境效益。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Predictive modelling of mechanical properties of concrete using machine learning with secondary treated waste water and fly ash
This study investigates the utilization of secondary treated wastewater and fly ash in concrete production, focusing on modelling mechanical properties using machine learning models. Sixteen concrete mixtures were prepared with tap water and three types of secondary treated wastewater, varying the fly ash proportions (0 %, 10 %, 20 %, and 30 %). Workability, compressive strength, split tensile strength, and flexural strength were assessed for each mixture. Five machine learning models Linear Regression, LASSO Regression, Decision Tree Regression, Random Forest Regression, and Multi-Layer Perceptron were used to predict concrete's mechanical properties. The results show that M30 grade concrete can be effectively produced using secondary treated wastewater and fly ash, presenting a promising strategy for more sustainable concrete production by reducing freshwater usage and incorporating fly ash as a supplementary cementitious material. Notably, the Random Forest Regressor demonstrated superior prediction accuracy for compressive strength, outperforming the other models and proving to be an invaluable tool for optimizing concrete mix designs. Its ability to reliably predict concrete strength properties ensures higher accuracy in mix design formulation, which is critical for achieving desired performance while minimizing material waste. From a sustainability perspective, using secondary treated wastewater in concrete production significantly reduces the demand for freshwater, conserving this precious resource. Incorporating fly ash, a byproduct of coal combustion, not only enhances concrete properties but also helps divert industrial waste from landfills, reducing environmental impact. The application of machine learning models, especially the Random Forest Regressor, allows for more precise and efficient mix designs, further contributing to the sustainability of concrete production. This approach offers substantial environmental benefits by reducing water usage, promoting recycling of industrial byproducts, and improving the overall efficiency of concrete manufacturing processes.
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