Rockfill dam compaction quality evaluation based on cloud-fuzzy model

Abstract

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.