Studies have been conducted to predict the compressive strength of ternary-blended concrete using regression models, such as support vector regression (SVR), random forest (RF), and artificial neural networks. In particular, deep neural networks (DNNs) are one of the most effective nonlinear regression models for predicting compressive strength based on the intricate relationships among constituent materials. However, because the DNN depends on training data, previous studies presented different varieties of ternary-blended concrete, suggesting that appropriate training is required. In addition, the analysis of optimal hyperparameter processes to ensure model performance has not been conducted extensively. This study established appropriate DNN models tuned with hyperparameters to effectively predict the compressive strength of ternary-blended concrete. The dataset used in this study was a set of 775 on-site mix proportions of ternary-blended concrete provided by a ready-mix concrete company in South Korea. Water, cement, fine aggregate, coarse aggregate, fly ash, blast furnace slag, curing temperature, and curing humidity were the inputs, and the compressive strength was set as the output. The basic statistical characteristics of the data used and the performance evaluation (mean square error [MSE] and mean absolute error [MAE]) of 15 models with hidden layers and units as variables were analyzed to determine the network architecture. Based on the model with the selected structure, hyperparameter tuning (batch size, dropout, and batch normalization) was applied to improve the performance of the DNN model. The advanced DNN model exhibited 18% lower MAE losses and 27% lower MSE losses than the conventional DNN model. In addition, based on the MAE losses and MSE losses, the advanced DNN model showed 4% and 12% lower errors than those of SVR, and 11% and 15% lower errors than those of RF, indicating that the DNN model with hyperparameter tuning performed better than the other models.

已经进行了使用回归模型预测三元混合混凝土抗压强度的研究，例如支持向量回归 (SVR)、随机森林 (RF) 和人工神经网络。特别是，深度神经网络(DNN) 是最有效的非线性回归模型之一用于根据组成材料之间的复杂关系预测抗压强度。然而，由于 DNN 依赖于训练数据，之前的研究提出了不同种类的三元混合混凝土，这表明需要进行适当的训练。此外，用于确保模型性能的最佳超参数过程的分析尚未广泛进行。本研究建立了适当的 DNN 模型，该模型使用超参数进行调整，以有效预测三元混合混凝土的抗压强度。本研究中使用的数据集是由韩国一家预拌混凝土公司提供的一组 775 种现场配合比的三元混合混凝土。输入水、水泥、细骨料、粗骨料、粉煤灰、高炉矿渣、养护温度和养护湿度，并将抗压强度设置为输出。所用数据的基本统计特征和性能评估（均方误差 [MSE] 和分析了以隐藏层和单元为变量的 15 个模型的平均绝对误差 [MAE]）以确定网络架构。基于具有所选结构的模型，应用超参数调整（批量大小、丢失和批量归一化）来提高 DNN 模型的性能。与传统 DNN 模型相比，高级 DNN 模型的 MAE 损失降低了 18%，MSE 损失降低了 27%。此外，基于 MAE 损失和 MSE 损失，高级 DNN 模型的误差比 SVR 低 4% 和 12%，比 RF 低 11% 和 15%，表明具有超参数调优的 DNN 模型表现优于其他模型。