玻璃纤维自密实混凝土（GFRSCC）是以玻璃纤维为增强材料，自密实混凝土浆体为基体的复合材料。玻璃纤维增强混凝土（GFRC）作为一种新型复合材料，在各个领域得到了广泛的应用。自密实混凝土（SCC）基体中掺入玻璃纤维，目标是为了利用自密实混凝土的高工作性能和高流动性，以及玻璃纤维混凝土的高韧性优点。

因此，本文首先对自密实混凝土水化过程及其不同阶段的工作性能开展了试验研究，发现自密实混凝土在不同粉煤灰掺量下的水化特征，验证了一种能够准确监测自密实混凝土水化过程并预测其强度发展的新指标。在此基础上，本文对玻璃纤维增强自密实混凝土的水化过程及强度发展进行了实验监测研究，分析了不同玻璃纤维含量对GFRSCC水化过程和渗透性能的影响。主要包括以下内容：

（1）基于机电阻抗法的自密实混凝土早期水化特性研究和抗压强度预测研究。基于不同粉煤灰掺量的SCC电导频谱图进行主成分分析，根据第一主成分可以简单地反映出不同粉煤灰掺量SCC早期水化的凝结过程，反映SCC的早期水化过程对智能骨料约束变化与第一主成分的关系；同时，基于主成分分析结果发现频谱特性受PZT封装层显著的影响，发现混凝土基体变化对智能骨料的环境约束有着显著的影响。根据混凝土抗压强度进行机器学习的训练以达到优化混凝土配合比的目的，利用收集公开发表的文献混凝土组成成分与抗压强度的数据集，建立多种机器学习回归模型进行训练及预测并对比分析，挑选出优化度高的机器学习模型以便于后续玻璃纤维混凝土的研究。

（2）基于机电阻抗法的玻璃纤维混凝土早期水化特性研究和渗透性能研究。基于不同玻璃纤维掺量GFRSCC的电导频谱图进行主成分分析，根据第一主成分得到早期水化时对GFRSCC中的纤维量的定量分析，观察不同纤维掺量的GFRSCC的基体变化对智能骨料的约束影响；建立基体环境对智能骨料的约束与渗透性能之间的关系，改进GFRSCC的孔隙率。通过机器学习模型得到影响玻璃纤维混凝土渗透性能的主要因素依序是养护龄期、玻璃纤维掺量、玻璃纤维长度，提出关于玻璃纤维混凝土配合比以达到降低渗透性能降低孔隙率的设计建议。

Glass fiber self-compacting concrete (GFRSCC) is a composite material with glass fiber as reinforcement material and self-compacting concrete slurry as matrix. Glass fiber reinforced concrete (GFRC), as a new composite material, has been widely used in various fields. Glass fiber is incorporated into the matrix of self-compacting concrete (SCC). The goal is to take advantage of the high working performance and fluidity of self-compacting concrete, as well as the high toughness advantages of glass fiber concrete.

Therefore, this paper first carried out an experimental study on the hydration process of self-compacting concrete and its working performance at different stages, found the hydration characteristics of self-compacting concrete under different fly ash content, and verified a new index that can accurately monitor the hydration process of self-compacting concrete and predict its strength development. On this basis, the hydration process and strength development of glass fiber reinforced self-compacting concrete were monitored, and the effects of different glass fiber contents on the hydration process and permeability of GFRSCC were analyzed. It mainly includes the following contents:

(1) Study on early hydration characteristics and compressive strength prediction of self-compacting concrete based on electromechanical impedance method. Principal component analysis was carried out based on SCC conductance spectra with different fly ash contents. According to the first principal component, the condensation process of early hydration of SCC with different fly ash contents could be simply reflected, and the relationship between the constraint change of the early hydration process of SCC and the first principal component could be reflected. Meanwhile, based on the results of principal component analysis, it is found that the spectrum characteristics are significantly affected by the PZT encapsulation layer, and it is found that the change of concrete matrix has a significant impact on the environmental constraints of smart aggregate. Machine learning training was conducted according to the concrete compressive strength to achieve the purpose of optimizing the concrete mix ratio. A variety of machine learning regression models were established for training, prediction and comparative analysis by collecting the data sets of concrete composition and compressive strength in the published literature, so as to select the machine learning models with high optimization degree to facilitate the subsequent research on glass fiber concrete.

(2) Study on early hydration characteristics and permeability of glass fiber reinforced concrete based on electromechanical impedance method. Principal component analysis was carried out based on the conductance spectra of GFRSCC with different glass fiber content. The quantitative analysis of the fiber content in GFRSCC during early hydration was obtained according to the first principal component, and the constraint effect of the matrix change of GFRSCC with different fiber content on smart aggregate was observed. To improve the porosity of GFRSCC, the relationship between the matrix environment constraint and the permeability of smart aggregate was established. Through the machine learning model, the main factors affecting the permeability of glass fiber reinforced concrete are obtained in order of curing age, glass fiber content, glass fiber length, and put forward some suggestions on the design of glass fiber reinforced concrete mix ratio to achieve the reduction of permeability and porosity.

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