ABSTRACT

An orthogonal test method was used to do sensibility analysis on the compressive strength and splitting strength of hybrid fiber-reinforced recycled aggregate concrete (HyFRAC). And a prediction model of compressive strength of HyFRAC based on Convolutional Neural Network (CNN) was proposed. The results show the ratio of recycled brick aggregate (RBA) to recycled concrete aggregate (RCA) has been proved the greatest influence on the compressive strength and splitting tensile strength of HyFRAC, followed by the water reducing agent content, finally the ratio of glass fiber (GF) to polypropylene fiber (PF). When RBA/RCA = 2/8, GF/PF = 7/3, and water reducing agent content is 0%, the compressive strength and splitting tensile strength of HyFRAC are the highest. According to JGJ/T10-2011, when RBA/RCA ≤ 6/4 and water reducing agent content ≥ 0.4%, the HyFRAC slump meets the 50m pumping height requirement; when RBA/RCA ≤ 4/6 and water reducing agent content ≥ 0.6%, the HyFRAC slump meets the 100m pumping height requirement. Compared to back propagation (BP) neural network model and multiple linear regression model, CNN model is more efficient in estimating the compressive strength of HyFRAC. The average relative errors and max relative errors of CNN model are 1.98% and 4.12%, respectively.

摘要

采用正交试验方法对混合纤维增强再生骨料混凝土（HyFRAC）的抗压强度和劈裂强度进行敏感性分析。并提出了一种基于卷积神经网络（CNN）的HyFRAC抗压强度预测模型。结果表明，再生砖骨料（RBA）与再生混凝土骨料（RCA）的比例对HyFRAC的抗压强度和劈拉强度影响最大，其次是减水剂含量，最后是玻璃纤维的比例。 (GF) 改成聚丙烯纤维 (PF)。当RBA/RCA=2/8、GF/PF=7/3、减水剂含量为0%时，HyFRAC的抗压强度和劈拉强度最高。根据JGJ/T10-2011，当RBA/RCA≤6/4，减水剂含量≥0.4%时，HyFRAC坍落度满足50m抽水高度要求；当RBA/RCA≤4/6，减水剂含量≥0.6%时，HyFRAC坍落度满足100m抽水高度要求。与反向传播（BP）神经网络模型和多元线性回归模型相比，CNN模型在估计HyFRAC的抗压强度方面更有效。CNN模型的平均相对误差和最大相对误差分别为1.98%和4.12%。