A deep learning approach to concrete water-cement ratio prediction
A deep learning approach to concrete water-cement ratio prediction
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混凝土水灰比预测的深度学习方法
DOI：
10.1016/j.rinma.2022.100300
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发表时间：
2022
影响因子：
--
通讯作者：
Bello S
中科院分区：
文献类型：
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作者：
Bello S
混凝土是一种多用途的建筑材料，但其含水量对其质量有很大影响。然而，使用试验和错误的方法来确定混凝土配合比的最佳用水量导致混凝土结构质量差，这些混凝土结构通常最终作为建筑垃圾被填埋，从而威胁环境安全。本文开发了深度神经网络来预测正常混凝土配合比所需的水。从认证/领先实验室获得的标准数据样本被输入深度学习模型（多层前馈神经网络），以自动校准混凝土含水量的混合功率，从而提高水控制精度。我们将数据随机分为70%，15%和15%，分别用于训练，验证和测试模型。开发的DNN模型受到相关统计指标的影响，并与随机森林，梯度提升机和支持向量机进行了基准测试。与其他混凝土水预测模型相比，DNN模型获得的性能指标具有最高的可靠性。
Concrete is a versatile construction material, but the water content can greatly influence its quality. However, using the trials and error method to determine the optimum water for the concrete mix results in poor quality concrete structures, which often end up in landfills as construction wastes, thus threatening environmental safety. This paper develops deep neural networks to predict the required water for a normal concrete mix. Standard data samples obtained from certified/leading laboratories were fed into a deep learning model (multilayers feedforward neural network) to automate the calibration of mixing power of the concrete water content for improved water control accuracy. We randomly split the data into 70%, 15% and 15%, respectively, to train, validate and test the model. The developed DNN model was subjected to relevant statistical metrics and benchmarked against the random forest, gradient boosting machines, and support vector machines. The performance indices obtained by the DNN model have the highest reliability compared to other models for concrete water prediction.