用7万组数据对比多种模型，嵌入式神经网络预测混凝土强度最准。
Comparative Assessment of Concrete Compressive Strength Prediction at Industry Scale Using Embedding-based Neural Networks, Transformers, and Traditional Machine Learning Approaches
- 用嵌入式神经网络捕捉材料混合变量间的复杂关系。
- 28天强度预测误差低至约2.5%，接近实验室测试精度。
- 适合建筑质量控制、智能施工决策系统研发者参考。
混凝土是全球使用最广泛的建筑材料，但其抗压强度的可靠预测因材料异质性、配比多变及现场环境敏感而困难。人工智能的发展使数据驱动建模成为可能，支持施工质量控制中的自动化决策。本研究基于约7万条抗压强度测试记录的工业级数据集，评估并比较了线性回归、决策树、随机森林、基于Transformer的神经网络以及嵌入式神经网络等多种预测方法。模型纳入关键配合比与施工变量，如水胶比、胶凝材料用量、坍落度、含气量、温度及浇筑条件。结果表明，嵌入式神经网络在所有模型中表现最优，28天强度预测平均误差约为2.5%。该精度与常规实验室测试波动相当，证明嵌入式学习框架具备实现大规模施工中自动化、数据驱动质量控制与决策支持的潜力。
原文摘要 · Abstract (English)
Concrete is the most widely used construction material worldwide; however, reliable prediction of compressive strength remains challenging due to material heterogeneity, variable mix proportions, and sensitivity to field and environmental conditions. Recent advances in artificial intelligence enable data-driven modeling frameworks capable of supporting automated decision-making in construction quality control. This study leverages an industry-scale dataset consisting of approximately 70,000 compressive strength test records to evaluate and compare multiple predictive approaches, including linear regression, decision trees, random forests, transformer-based neural networks, and embedding-based neural networks. The models incorporate key mixture design and placement variables such as water cement ratio, cementitious material content, slump, air content, temperature, and placement conditions. Results indicate that the embedding-based neural network consistently outperforms traditional machine learning and transformer-based models, achieving a mean 28-day prediction error of approximately 2.5%. This level of accuracy is comparable to routine laboratory testing variability, demonstrating the potential of embedding-based learning frameworks to enable automated, data-driven quality control and decision support in large-scale construction operations.
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