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CLC number: TV31

On-line Access: 2018-04-04

Received: 2016-11-30

Revision Accepted: 2017-05-25

Crosschecked: 2018-03-07

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Citations:  Bibtex RefMan EndNote GB/T7714


Bin-ping Wu


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Journal of Zhejiang University SCIENCE A 2018 Vol.19 No.4 P.289-303


Rockfill dam compaction quality evaluation based on cloud-fuzzy model

Author(s):  Fei Wang, Deng-hua Zhong, Yu-ling Yan, Bing-yu Ren, Bin-ping Wu

Affiliation(s):  State Key Laboratory of Hydraulic Engineering Simulation and Safety, Tianjin University, Tianjin 300072, China

Corresponding email(s):   wubinping@tju.edu.cn

Key Words:  Rockfill dam, Cloud model, Uncertainty, Compaction quality evaluation

Fei Wang, Deng-hua Zhong, Yu-ling Yan, Bing-yu Ren, Bin-ping Wu. Rockfill dam compaction quality evaluation based on cloud-fuzzy model[J]. Journal of Zhejiang University Science A, 2018, 19(4): 289-303.

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author="Fei Wang, Deng-hua Zhong, Yu-ling Yan, Bing-yu Ren, Bin-ping Wu",
journal="Journal of Zhejiang University Science A",
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%T Rockfill dam compaction quality evaluation based on cloud-fuzzy model
%A Fei Wang
%A Deng-hua Zhong
%A Yu-ling Yan
%A Bing-yu Ren
%A Bin-ping Wu
%J Journal of Zhejiang University SCIENCE A
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%P 289-303
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%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A1600753

T1 - Rockfill dam compaction quality evaluation based on cloud-fuzzy model
A1 - Fei Wang
A1 - Deng-hua Zhong
A1 - Yu-ling Yan
A1 - Bing-yu Ren
A1 - Bin-ping Wu
J0 - Journal of Zhejiang University Science A
VL - 19
IS - 4
SP - 289
EP - 303
%@ 1673-565X
Y1 - 2018
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.A1600753

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.

This manuscript proposed a cloud-fuzzy evaluation method to assess the compaction quality of rock-fill dam by taking into randomness, fuzziness and incomplete information into consideration. Compared to other traditional method, this proposed method provided a triple index to understand and evaluate the compaction quality. The results can be of great useful for rock-fill dam compaction quality evaluation.


创新点: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.
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.
结论: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.


Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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