期刊
AUTOMATION IN CONSTRUCTION
卷 145, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.autcon.2022.104663
关键词
Intelligent compaction; Automated evaluation; Compaction quality; Highway construction; PSO-BP-NN; Correlation model
This paper presents a monitoring and evaluation method based on intelligent compaction (IC) and artificial neural networks (ANNs) to assess the compaction quality of a highway subgrade. Field compaction tests were conducted at various roller speeds and vibratory modes, and a particle-swarm-optimization optimized back-propagation neural network (PSO-BP-NN) model was used to predict the compactness. The integrated model demonstrated high prediction accuracy and can be applied for engineering practice and facilitate IC applications.
The compaction quality of a highway subgrade is influenced by the compaction parameters and soil properties. Current practice lacks reliable models to assess this multivariate situation. This paper describes a monitoring and evaluation method based on intelligent compaction (IC) and artificial neural networks (ANNs) to assess the compaction quality of a highway subgrade. Field compaction tests were conducted at various roller speeds and vibratory modes. The compactness was predicted using a particle-swarm-optimization optimized back -propagation neural network (PSO-BP-NN) model. The model was integrated into the IC system to automatically evaluate compaction quality. It also demonstrated the advantage of high prediction accuracy with multiple input variables of operation parameters and soil gradation. By efficiently controlling subgrade compaction, the model can be employed for engineering practice and facilitate IC applications.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据