SummaryMonitoring data collected during dam construction are important in complete series of monitoring data. These data play a significant role in dam safety monitoring and the analysis of structural conditions. The traditional statistical model of the deformation of a concrete face rockfill dam (CFRD) with filling height and time factors is associated with serious multicollinearity issues during the construction phase. This study uses the Longbeiwan CFRD as an
| INTRODUCTIONA concrete face rockfill dam (CFRD) is a type of dam that uses rockfill as the support structure and an upstream surface concrete face as the anti-seepage structure. CFRDs are effective because of their adaptability to poor topographical, geological, and climatic conditions. In addition, they have excellent safety features and economic efficiency. Currently, CFRDs are one of the most commonly used and cost competitive dam types. [1,2] Deformation and seepage control are two key technical problems in CFRD construction. Deformation (such as that of the surface, interior, and foundation of dams) and various joint deformations can be monitored using various technologies. [3] For example, the horizontal displacement of the dam surface can be monitored by line of sight or torsion, whereas the surface settlement (vertical displacement) of the dam can be monitored using the geometric method. Moreover, the horizontal displacement of the rockfill body can be monitored by a meter or inclinometer, whereas theThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Abstract. Since the traditional rough set theory can easily result in information loss during attribute discretization and its attribute reduction is too complex, the fuzzy rough set theory and the golden section method are introduced for dam health diagnosis. With attribute fuzzification replacing attribute discretization, and attribute significance as a condition of attribute reduction, the dam health rough set diagnosis model is improved. Next, the improved dam health rough set diagnosis model is applied to a practical project. Results show that the improved attribute reduction put forward in this paper can more fully demonstrate factors influencing uncertainty of the dam health status. The diagnosis results, while more reasonably reflecting the dam's practical health status, can provide a new research path for dam health diagnosis.
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