Early-age shrinkage of concrete can initiate pre-mature cracking, which can compromise the durability of concrete structures. Monitoring capillary pressure, the leading cause of concrete shrinkage, and understanding its evolution is crucial for the performance-based design of concrete, particularly at early-stages when it is more prone to cracking. This study deploys an innovative multi-scale experimental program using high-capacity tensiometers to monitor the capillary pressure up to 2000 kPa. This allowed investigating the effects of key design parameters, including the water-to-cement ratio, GGBS, SRA, and measurement depth, on the capillary pressure evolution in concrete. A new robust deep neural network model was developed to conduct extensive numerical experiments to predict the capillary pressure evolution of diverse mixtures. The net effect of multi-parameters on the capillary pressure can be investigated with this model, providing insights into the optimum design of more durable concrete mixtures with the lowest capillary pressure evolution, and guiding the implementation of appropriate cost-effective shrinkage-mitigating strategies.

中文翻译：

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用于优化早期混凝土毛细管压力演变的新型多尺度实验方法和深度学习模型

混凝土的早期收缩会引发过早开裂，从而损害混凝土结构的耐久性。监测毛细管压力（混凝土收缩的主要原因）并了解其演变对于基于性能的混凝土设计至关重要，特别是在混凝土更容易开裂的早期阶段。这项研究部署了一个创新的多尺度实验计划，使用大容量张力计来监测高达 2000 kPa 的毛细管压力。这样可以研究关键设计参数（包括水灰比、GGBS、SRA 和测量深度）对混凝土毛细管压力演变的影响。开发了一种新的鲁棒深度神经网络模型来进行广泛的数值实验，以预测不同混合物的毛细管压力演变。可以使用该模型研究多参数对毛细管压力的净影响，为具有最低毛细管压力演变的更耐用混凝土混合物的优化设计提供见解，并指导实施适当的具有成本效益的收缩缓解策略。