This paper is divided into two parts, with the first part examining the influence of recycled brick aggregate (BA) ratios on physical properties of blended coarse aggregate (BNA). These properties include apparent density (SSD), water absorption (WA), and crushing value (CV). The compressive strength of recycled concrete (RAC) prepared with BNA was tested. The tested results show that when SSD decreases from 2700 kg/m to 2000 kg/m, the compressive strength of the corresponding RAC decreases by 40.5 %. Moreover, when the WA increases from 5 % to 10 %, the compressive strength of the concrete decreases by 15.9 %. Furthermore, as the CV rises from 9 % to 37 %, the compressive strength of the concrete decreases by 38.4 %. And the second part explores the application of machine learning models to predict the concrete compressive strength, incorporating parameters such as BA replacement rate, SSD, WA, CV, water-to-cement ratio (W/C), and blended coarse aggregate to cement ratio (BNA/C). To improve model accuracy, we integrated data from our own experimental tests with an additional 128 datasets sourced from literature, resulting in a combined dataset of 133 entries for training and testing the machine learning models. Three machine learning models were proposed and compared: the BP neural network, GA-optimized BP (GA-BP) neural network, and convolutional neural network (CNN) model. Compared to the other two models, the CNN model exhibits the highest prediction accuracy and generalization ability, as evidence by its training accuracy of 0.969 and test accuracy of 0.927. The CNN model proved capable of facilitating intelligent RAC mix design in engineering applications. A case study was presented to illustrate the utilization of the CNN model in predicting the W/C and BNA/C ratios for 11 different BA ratios, ensuring the targeted RAC strength is achieved with specific type of BNA.

中文翻译：

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使用机器学习方法研究再生混凝土性能和预测


本文分为两个部分，第一部分研究再生砖骨料（BA）配比对混合粗骨料（BNA）物理性能的影响。这些特性包括表观密度 (SSD)、吸水率 (WA) 和压碎值 (CV)。测试了用BNA制备的再生混凝土（RAC）的抗压强度。测试结果表明，当SSD从2700 kg/m降低到2000 kg/m时，相应RAC的抗压强度降低了40.5%。此外，当WA从5%增加到10%时，混凝土的抗压强度下降了15.9%。此外，随着 CV 从 9% 上升到 37%，混凝土的抗压强度下降了 38.4%。第二部分探讨了应用机器学习模型来预测混凝土抗压强度，其中结合了 BA 替代率、SSD、WA、CV、水灰比 (W/C) 以及粗骨料与水泥的混合等参数比率（BNA/C）。为了提高模型准确性，我们将自己的实验测试数据与来自文献的另外 128 个数据集集成，形成了包含 133 个条目的组合数据集，用于训练和测试机器学习模型。提出并比较了三种机器学习模型：BP神经网络、GA优化BP（GA-BP）神经网络和卷积神经网络（CNN）模型。与其他两个模型相比，CNN 模型表现出最高的预测精度和泛化能力，其训练精度为 0.969，测试精度为 0.927。事实证明，CNN 模型能够促进工程应用中的智能 RAC 混合设计。 提出了一个案例研究来说明如何利用 CNN 模型预测 11 种不同 BA 比率的 W/C 和 BNA/C 比率，确保使用特定类型的 BNA 实现目标 RAC 强度。