Journal of Zhejiang University-SCIENCE A

, Volume 19, Issue 4, pp 289–303 | Cite as

Rockfill dam compaction quality evaluation based on cloud-fuzzy model

  • Fei Wang
  • Deng-hua Zhong
  • Yu-ling Yan
  • Bing-yu Ren
  • Bin-ping Wu


The quality of compaction is key to the safety of dam construction and operation. However, because of incomplete information about the construction process and the unknown relationship between compaction quality and the factors that influence it, traditional evaluation methods such as neural networks and multivariate linear regression models fail to take uncertainty fully into account. This paper proposes a cloud-fuzzy method for assessing compaction quality by considering randomness, fuzziness, and incomplete information. The compaction parameters and material source parameters are the key parameters in the assessment of compaction quality. A five-layer neural-network model of compaction quality assessment is established that considers compacted dry density and its classification membership and probability as the criteria, and the rolling speed, rolling passes, and compacted layer thickness as alternatives. Because of uncertainties in the criteria and alternatives, the cloud-fuzzy method, in which a fuzzy neural network is extended with a cloud model to handle uncertain and fuzzy problems more effectively, is introduced to determine the compaction quality. A case study is presented to evaluate the compaction quality of a hydropower project in China. The results indicate that the cloud-fuzzy model is feasible in relation to precision and makes up for the sole focus on precision by traditional methods. The proposed method provides a triple index for understanding compaction quality, which facilitates assessment of the compaction quality of an entire dam surface.


Rockfill dam Cloud model Uncertainty Compaction quality evaluation 




施工质量对于大坝建设期及运行期的安全至关重 要。由于施工过程中的信息不完备及碾压质量与 影响因素之间的关系并不是完全确定等原因,传 统的评估方法很少考虑不确定性对施工质量的 影响。本文旨在探讨考虑不确定性影响的碾压质 量评估方法,改善施工质量评估的可信性。


1. 通过研究模糊神经网络与径向基神经网络,结 合云模型建立云-模糊模型;2. 建立施工质量三指 标体系评价方法。


1. 通过碾压质量实时监控系统和现场试坑试验获 取参数数据;2. 通过云分析,建立云-模糊模型; 3. 对比不同的模型,验证云-模糊模型的可行性; 4. 利用验证的云-模糊模型对大坝施工仓面进行 压实干密度预测;5. 计算评价体系的三指标,对 施工质量进行评估。


1. 云-模糊模型不但能在精度上满足预测要求,而 且能够综合考虑施工质量与影响因素之间的不 确定性关系;2. 云-模糊评价方法弥补了传统评价 方法仅追求精度的单一性,使得施工质量评价更 符合客观规律;3. 提出的施工质量三指标评价体 系充实了传统的评价方法,能够更客观地指导实 际工程建设。


堆石坝 云模型 不确定性 施工质量评价 

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Copyright information

© Zhejiang University and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.State Key Laboratory of Hydraulic Engineering Simulation and SafetyTianjin UniversityTianjinChina

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