The main objective of this study is to present a method of determining viscoelastic deformation monitoring index of a Rollercompacted concrete (RCC) gravity dam in an alpine region. By focusing on a modified deformation monitoring model considering frost heave and back analyzed mechanical parameters of the dam, the working state of viscoelasticity for the dam is illustrated followed by an investigation and designation of adverse load cases using orthogonal test method. Water pressure component is then calculated by finite element method, while temperature, time effect, and frost heave components are obtained through deformation statistical model considering frost heave. The viscoelastic deformation monitoring index is eventually determined by small probability and maximum entropy methods. The results show that (a) with the abnormal probability 1% the dam deformation monitoring index for small probability and maximum entropy methods is 23.703 mm and 22.981 mm, respectively; thus the maximum measured displacement of the dam is less than deformation monitoring index, which indicates that the dam is currently in a state of safety operation and (b) the obtained deformation monitoring index using orthogonal test method is more accurate due to the full consideration of more random factors; the method gained from this study will likely be of use to diagnose the working state for those RCC dams in alpine regions.

No abstract

Unsteady flow induced by hydropower stations exerts a significant impact on the water level in multi-approach channels, which directly threatens the safe passage of ships. In this study, a one-dimensional and a two-dimensional hydrodynamic model are adopted to simulate the water level fluctuations at the entrance of multi-approach channels and the lower lock head of a ship lift with consideration of initial water surface elevation, base flow, flow amplitude, regulation time, and locations of hydropower stations, unfavorable conditions are successfully identified; and the fluctuations at the approach channel entrance and the lower lock head of a ship lift under single-peak and double-peak regulating mode are analyzed considering the flow regulating of the Gezhouba Hydropower Station (GHS), thus, the water level oscillation process in the multi-approach channels is presented. Results show that the largest wave amplitude in the multi-approach channels manifests under unfavorable conditions including lower initial water surface elevation, smaller base flow, larger flow variation, and shorter regulation time; and water level fluctuation in the multi-approach channel is primarily induced by flow amplitude and net flow between the Three Gorges Hydropower Station (TGHS) and the GHS, with consideration of the counter-regulation process of the GHS. This research contributes to providing a reference for a similar large-scale cascade hydropower station regarding regulation and control of navigation conditions.

Water transparency is commonly used to indicate the combined effect of hydrodynamics and the aquatic environment on water quality throughout a river network. However, how water transparency responds to these indicators still needs to be explored, especially their complicated nonlinear relationship; thus, this study represents an analysis of the Suzhou civil river network. Using an artificial neural network (ANN) hydrological model and a multiple linear model (MLR) with in-situ data between 2013–2019, we investigated the Suzhou River’s sensitivity to the six factors and water transparency, which including flow velocity and data from five categories of water-quality monitoring data: total suspended matter (TSS), water temperature (TE), dissolved oxygen (DO), chlorophyll (Chl) and chemical oxygen demand (COD). The results suggest that the ANN model can achieve better performance than the MLR model. Furthermore, results also show a well-established correlation between enhanced hydrodynamics and improved water transparency when the flow velocity ranged from 0.22 to 0.45 m/s. Overall, COD is a vital factor for the SD prediction because including the COD can see a notable improvement in the ANN model (with a correlation coefficient of 0.918). This study demonstrates that the ANN model with hydrodynamic and water quality parameters can achieve a better prediction of water transparency than other discussed models for a coastal plain urban river network.