随着我国城镇化的加速导致建筑垃圾大量产生，同时传统水泥生产能耗高、碳排放大，难以满足可持续发展的需求。在此背景下，地聚合物再生混凝土（Geopolymer Recycled Aggregate Concrete，GRAC）应运而生。以偏高岭土、粉煤灰与矿渣为胶凝材料，以处理后的废弃混凝土为再生骨料，制备GRAC。这种新型材料不仅实现了建筑垃圾的资源化利用，还利用地聚合物代替水泥，降低了环境污染。

       本文首先以杂质少和性能好的偏高岭土为基础材料，掺入不同比例的矿渣和粉煤灰，改变水玻璃模数和Na₂O当量进行正交试验。根据试验确定的水玻璃模数和Na₂O当量的用量进行不加偏高岭土的矿渣-粉煤灰基地聚合物胶砂试验，改变矿渣和粉煤灰的比例、水玻璃模数和Na₂O当量来研究对凝结时间、力学性能和干燥收缩性能的变化，并根据现有的混凝土收缩模型建立地聚合物干燥收缩模型。对比分析两种地聚合物的性能和成本后，决定采用以上两种地聚合物和再生骨料相结合的GRAC进行试验，随后根据三种预测模型分析试验结果，选择误差小的一组进行GRAC的28d强度预测，得出如下结论：

（1）偏高岭土基地聚合物胶砂块正交试验中，对凝结时间和力学性能影响最大的是矿物掺合料，其次是Na₂O当量和水玻璃模数。综合考虑下，Na₂O当量为12%以及水玻璃模数为1.2时，满足工作性能的同时符合强度要求。

（2）矿渣-粉煤灰基地聚合物胶砂块试验根据前节的试验结果，确定Na₂O当量以及水玻璃模数范围，改变矿渣和粉煤灰的比例。根据单因素试验结果可知矿渣掺量的增加使强度提升，同时凝结时间缩短；Na₂O当量增加会使强度先上升后下降，凝结时间缩短；水玻璃模数的增加会使强度先上升后下降，凝结时间延长。

（3）矿渣-粉煤灰基地聚合物胶砂干燥收缩试验中，干燥收缩率随矿渣掺量和水玻璃模数的升高而升高，但是水玻璃模数对干燥收缩影响不明显，Na₂O当量的增加使得干燥收缩率先升后降。基于现有的CEB-FIP模型，建立了适用于矿渣掺量为10%~30%、Na₂O当量为10%~14%以及水玻璃模数为1.1~1.3范围内的干燥收缩预测模型。

（4）对比分析两种地聚合物发现，偏高岭土基地聚合物力学性能更优越、杂质少且早期强度高，但是其资源有限导致成本较高。矿渣-粉煤灰基地聚合物成本低、长期性能好以及对环境友好，但是早期强度较低以及成分相对复杂，不同来源的原料可能会对研究结果产生影响，因此两种地聚合物各有利弊。

（5）选择上述试验中的两种地聚合物进行GRAC试验，宏观方面进行抗压、抗折和劈拉强度试验并设置水泥为对照组。发现GRAC强度均高于再生混凝土，随着再生骨料的掺入力学性能下降，30%矿渣掺入时强度最高。细观方面进行显微硬度试验，再生骨料的掺入使得界面过渡区宽度增大，单掺矿渣对界面的改善效果最佳。

（6）在对CNN、LSTM和RF神经网络模型进行对比分析后发现，LSTM网络的均方根误差（RMSE）和平均绝对误差（MAE）相对CNN和RF模型更低，且决定系数（R²）更高，其预测曲线与实际值曲线更为接近。所以用LSTM神经网络建立的28d的GRAC抗压强度预测模型具有较高的准确性。

      With the acceleration of urbanization in China, a large amount of construction waste is produced. At the same time, traditional cement production has high energy consumption and large carbon emissions, which is difficult to meet the needs of sustainable development. In this context, Geopolymer Recycled Aggregate Concrete ( GRAC ) came into being. GRAC was prepared by using metakaolin, fly ash and slag as cementitious materials and treated waste concrete as recycled aggregate. This new material not only realizes the resource utilization of construction waste, but also uses geopolymer instead of cement to reduce environmental pollution.

      In this paper, firstly, metakaolin with less impurities and good performance was used as the basic material, and different proportions of slag and fly ash were added to change the water glass modulus and Na₂O equivalent for orthogonal test. According to the amount of water glass modulus and Na₂O equivalent determined by the test, the slag-fly ash geopolymer mortar test without metakaolin was carried out. The ratio of slag and fly ash, water glass modulus and Na₂O equivalent were changed to study the setting time, mechanical properties and drying shrinkage. The change of performance, and the drying shrinkage model of geopolymer was established based on the existing concrete shrinkage model. After comparing and analyzing the performance and cost of the two geopolymers, it was decided to use the GRAC combined with the above two geopolymers and recycled aggregates for testing. Then, the test results were analyzed according to the three prediction models, and a group with a small error was selected to predict the 28d strength of GRAC. The following conclusions are drawn :

In the orthogonal test of metakaolin-based geopolymer mortar block, the mineral admixture has the greatest influence on the setting time and mechanical properties, followed by Na₂O equivalent and water glass modulus. Under comprehensive consideration, when the Na₂O equivalent is 12 % and the water glass modulus is 1.2, it meets the working performance and meets the strength requirements.

(2) Slag-fly ash geopolymer mortar block test.According to the test results of the previous section, the range of Na₂O equivalent and the range of water glass moduluswere determined, and the ratio of slag and fly ash was changed. According to the results of single factor test, the increase of slag content improves the strength and shortens the setting time. The increase of Na₂O equivalent will increase the strength first and then decrease, and the setting time will be shortened. The increase of water glass modulus will make the strength increase first and then decrease, and the setting time will be prolonged.

(3) In the drying shrinkage test of slag-fly ash based geopolymer mortar, the drying shrinkage rate increases with the increase of slag content and water glass modulus, but the effect of water glass modulus on drying shrinkage is not obvious. The increase of Na₂O equivalent makes the drying shrinkage increase first and then decrease. Based on the existing CEB-FIP model, a drying shrinkage prediction model suitable for slag content of 10 % ~ 30 %, Na₂O equivalent of 10 % ~ 14 % and water glass modulus of 1.1 ~ 1.3 was established.

(4) Comparing and analyzing the two kinds of geopolymers, it is found that the metakaolin-based geopolymer has better mechanical properties, less impurities and high early strength, but its limited resources lead to higher costs. The slag-fly ash based geopolymer has low cost, good long-term performance and is environmentally friendly, but the early strength is low and the composition is relatively complex. Different sources of raw materials may affect the research results. Therefore, the two geopolymers have their own advantages and disadvantages.

(5) Two kinds of geopolymers in the above tests were selected for GRAC test, and the compressive, flexural and splitting tensile strength tests were carried out on the macro aspect and the cement was set as the control group. It is found that the strength of GRAC is higher than that of recycled concrete. With the decrease of mechanical properties of recycled aggregate, the strength is the highest when 30 % slag is added. The micro-hardness test was carried out on the microscopic aspect. The incorporation of recycled aggregate increased the width of the interfacial transition zone, and the improvement effect of single slag on the interface was the best.

(6) After comparing and analyzing the CNN, LSTM and RF neural network models, it is found that the root mean square error (RMSE) and mean absolute error (MAE) of the LSTM network are lower than those of the CNN and RF models, and the determination coefficient (R²) is higher, and the prediction curve is closer to the actual value curve. Therefore, the prediction model of compressive strength of 28d GRAC established by LSTM neural network has high accuracy.

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