Cube compressive strength at 7 and 28 days.

This research study aims to understand the application of Artificial Neural Networks (ANNs) to forecast the Self-Compacting Recycled Coarse Aggregate Concrete (SCRCAC) compressive strength. From different literature, 602 available data sets from SCRCAC mix designs are collected, and the data are rearranged, reconstructed, trained and tested for the ANN model development. The models were established using seven input variables: the mass of cementitious content, water, natural coarse aggregate content, natural fine aggregate content, recycled coarse aggregate content, chemical admixture and mineral admixture used in the SCRCAC mix designs. Two normalization techniques are used for data normalization to visualize the data distribution. For each normalization technique, three transfer functions are used for modelling. In total, six different types of models were run in MATLAB and used to estimate the 28th day SCRCAC compressive strength. Normalization technique 2 performs better than 1 and TANSING is the best transfer function. The best k-fold cross-validation fold is k = 7. The coefficient of determination for predicted and actual compressive strength is 0.78 for training and 0.86 for testing. The impact of the number of neurons and layers on the model was performed. Inputs from standards are used to forecast the 28th day compressive strength. Apart from ANN, Machine Learning (ML) techniques like random forest, extra trees, extreme boosting and light gradient boosting techniques are adopted to predict the 28th day compressive strength of SCRCAC. Compared to ML, ANN prediction shows better results in terms of sensitive analysis. The study also extended to determine 28th day compressive strength from experimental work and compared it with 28th day compressive strength from ANN best model. Standard and ANN mix designs have similar fresh and hardened properties. The average compressive strength from ANN model and experimental results are 39.067 and 38.36 MPa, respectively with correlation coefficient is 1. It appears that ANN can validly predict the compressive strength of concrete.

本研究旨在探究人工神经网络（Artificial Neural Networks, ANNs）在预测自密实再生粗骨料混凝土（Self-Compacting Recycled Coarse Aggregate Concrete, SCRCAC）抗压强度中的应用。研究从现有文献中收集得到602条SCRCAC配合比的有效数据集，随后对数据进行整理、重构，用于人工神经网络模型的开发与测试。模型采用7项输入变量，分别为SCRCAC配合比中的胶凝材料用量、用水量、天然粗骨料用量、天然细骨料用量、再生粗骨料用量、化学外加剂用量及矿物外加剂用量。本研究采用两种标准化方法对数据进行处理以可视化数据分布，针对每种标准化方法，分别使用3种传递函数开展建模。总计在MATLAB中构建并运行了6种不同的模型，用于预估SCRCAC的28天抗压强度。结果表明，标准化方法2的表现优于方法1，且TANSING为最优传递函数；最优的k折交叉验证折数为k=7。训练集上预测值与实际抗压强度的决定系数为0.78，测试集则为0.86。本研究还探究了神经元数量与网络层数对模型性能的影响。基于标准配合比参数开展28天抗压强度预测。除人工神经网络外，本研究还采用了随机森林、极端随机树、极限梯度提升以及轻量梯度提升等机器学习（Machine Learning, ML）技术，用于预测SCRCAC的28天抗压强度。敏感性分析结果显示，相较于其他机器学习技术，人工神经网络的预测效果更优。本研究通过试验实测得到了28天抗压强度，并将其与最优人工神经网络模型的预测结果进行对比。标准配合比与人工神经网络配合比的新拌及硬化性能相近。人工神经网络模型与试验所得的平均抗压强度分别为39.067 MPa与38.36 MPa，二者的相关系数为1。由此可见，人工神经网络可有效预测混凝土的抗压强度。