Li Pengfei, Wang Xiaoyan, Cao Hanbo
Department of Harbor, Waterway, and Coastal Engineering, Chongqing Jiaotong University, Chongqing 400074, China.
Materials (Basel). 2023 Jun 25;16(13):4585. doi: 10.3390/ma16134585.
The mix design of UHPC has always been based on a large number of experiments; in order to reduce the number of repeated experiments, in this study, silica fume (SF), fly ash (FA), and limestone powder (LP) were used as the raw materials to conduct 15 groups of experiments to determine the particle size distribution (PSD) properties of UHPC. A model of multi-component hydration based on the SF, FA, and LP pozzolanic reactions was devised to quantify the rate and total heat release during the hydration process. Additionally, a microscopic pore development model, which was based on the accumulation of hydration products, was established to measure the effect of these products on the particle-packing properties. Utilizing this model, a UHPC strength prediction technique was formulated to precisely forecast the compressive strength based on a restricted experimental data set. The applicability of this prediction method was verified using 15 sets of existing experimental data along with the data collected from 4 research articles. The results show that the prediction method can predict the strength values of different mix proportions with an accuracy rate of over 80%.
超高性能混凝土（UHPC）的配合比设计一直基于大量实验；为了减少重复实验的次数，在本研究中，以硅灰（SF）、粉煤灰（FA）和石灰石粉（LP）为原料进行了15组实验，以确定UHPC的粒度分布（PSD）特性。设计了一个基于SF、FA和LP火山灰反应的多组分水化模型，以量化水化过程中的反应速率和总热释放量。此外，建立了一个基于水化产物堆积的微观孔隙发展模型，以测量这些产物对颗粒堆积性能的影响。利用该模型，制定了一种UHPC强度预测技术，以基于有限的实验数据集精确预测抗压强度。使用15组现有实验数据以及从4篇研究文章中收集的数据验证了该预测方法的适用性。结果表明，该预测方法能够以超过80%的准确率预测不同配合比的强度值。
