Abstract

The void content of aggregates (VCoA) considerably influences the mechanical response of asphalt concrete; the rapid prediction of VCoA can aid in designing high-performance concrete. To predict VCoA, the size and shape parameters of aggregate particles are measured using image analysis processing, and the results are used as inputs in a neural network model. Void content data is non-linear and interlacing; therefore, the Elman and back propagation neural networks are used to predict VCoA owing to their non-linear mapping abilities. The correlation coefficient in the data set and maximum prediction error are used to compare the robustness of the two models. Results indicate that the Elman neural network model can obtain a low prediction error and stronger correlation between predicted and simulated values when choosing the appropriate number of hidden layer nodes. Considering complex shapes of practical aggregates, the predicted VCoA using the Elman neural network model was corrected and verified via experiments; the results show that the error between the predicted VCoA after the correction and the actual VCoA is within 1%, which meets the accuracy requirements of engineering application. Thus, combining imaging technology and the neural network is an effective approach to predict VCoA.

• HIGHLIGHTS

• Void content can be predicted by the particle size and particle morphological parameters and the deviation between the predictive value and the true value was within 1%.

• The rapid prediction model of void content based on the Elman neural network was established.

• EDEM simulation software was used to construct abnormally-shaped aggregate particles and to carry out void content simulation experiments.

摘要

骨料的空隙率 (VCoA) 显着影响沥青混凝土的力学响应；VCoA 的快速预测有助于设计高性能混凝土。为了预测 VCoA，使用图像分析处理测量聚集颗粒的尺寸和形状参数，并将结果用作神经网络模型的输入。空内容数据是非线性和交错的；因此，Elman 和反向传播神经网络由于其非线性映射能力而被用于预测 VCoA。数据集中的相关系数和最大预测误差用于比较两个模型的鲁棒性。结果表明，Elman神经网络模型在选择合适的隐藏层节点数时，可以得到较低的预测误差和较强的预测值与模拟值相关性。考虑到实际聚集体的复杂形状，对使用Elman神经网络模型预测的VCoA进行了修正和实验验证；结果表明，修正后的预测VCoA与实际VCoA的误差在1%以内，满足工程应用的精度要求。因此，结合成像技术和神经网络是预测 VCoA 的有效方法。结果表明，修正后的预测VCoA与实际VCoA的误差在1%以内，满足工程应用的精度要求。因此，结合成像技术和神经网络是预测 VCoA 的有效方法。结果表明，修正后的预测VCoA与实际VCoA的误差在1%以内，满足工程应用的精度要求。因此，结合成像技术和神经网络是预测 VCoA 的有效方法。

• 强调

• 空隙率可以通过粒径和颗粒形态参数来预测，预测值与真实值的偏差在1%以内。

• 建立了基于Elman神经网络的空隙率快速预测模型。

• 采用EDEM模拟软件构建异形骨料颗粒并进行空隙率模拟实验。