Search Results (8)

Deformation predictions in high Concrete Face Rockfill Dams tend to underestimate observed settlements due to scale effect and breakage phenomena that cannot be adequately captured by laboratory tests. This paper presents a Visco-Elasto-Perfectly Plastic (VEPP) model for predicting deformations in high Concrete Face Rockfill Dams (CFRDs) that addresses these challenges incorporating explicitly key rockfill parameters like grain size and post-compaction porosity, which influence both the non-linear elastic and plastic behaviors of rockfill. The VEPP model enables deformation prediction while using standard laboratory test results. The model’s effectiveness was demonstrated through its application to the 233 m high Shuibuya Dam, the tallest CFRD in the world. The VEPP model predictions closely align with observed deformations throughout the dam’s construction, impoundment, and early operational stages. By using physically meaningful parameters, the model reduces the uncertainty associated with the empirical assessment of model parameters using back-analysis from similar projects. While the VEPP model offers improved predictive accuracy, particularly during early design phases, further advancements could be achieved by refining the creep formulation and accounting for grain size evolution during construction. This approach has the potential to optimize the design and construction of future high CFRD. Full article

Accurately predicting hydrological runoff is crucial for water resource allocation and power station scheduling. However, there is no perfect model that can accurately predict future runoff. In this paper, a daily runoff prediction method with a seasonal decomposition-based-deep gated-recurrent-unit (GRU) method (SD-GRU) is proposed. The raw data is preprocessed and then decomposed into trend, seasonal, and residual components using the seasonal decomposition algorithm. The deep GRU model is then used to predict each subcomponent, which is then integrated into the final prediction results. In particular, the hyperparameter optimization algorithm of tree-structured parzen estimators (TPE) is used to optimize the model. Moreover, this paper introduces the single machine learning model (including multiple linear regression (MLR), back propagation (BP), long short-term memory neural network (LSTM) and gate recurrent unit (GRU)) and a combination model (including seasonal decomposition–back propagation (SD-BP), seasonal decomposition–multiple linear regression (SD-MLR), along with seasonal decomposition–long-and-short-term-memory neural network (SD-LSTM), which are used as comparison models to verify the excellent prediction performance of the proposed model. Finally, a case study of the Qingjiang Shuibuya test set, which considers the period 1 January 2019 to 31 December 2019, is conducted. Case studies of the Qingjiang River show the proposed model outperformed the other models in prediction performance. The model achieved a mean absolute error (MAE) index of 38.5, a Nash-Sutcliffe efficiency (NSE) index of 0.93, and a coefficient of determination (R²) index of 0.7. In addition, compared to the comparison model, the NSE index of the proposed model increased by 19.2%, 19.2%, 16.3%, 16.3%, 2.2%, 2.2%, and 1.1%, when compared to BP, MLR, LSTM, GRU, SD-BP, SD-MLR, SD-LSTM, and SD-GRU, respectively. This research can provide an essential reference for the study of daily runoff prediction models. Full article

The flood discharge atomization of high dams involves a complex coupled flow of water and air. Small-scale model tests are typically used to predict the atomization of flood discharge. However, the accuracy of the prediction results often suffers because of the scale effect between the model and the prototype. Considering that the numerical simulation method has the advantage of not being restricted by similarity scales, this paper studies the influence of the scale effect on the atomization of flood discharge based on the principle of water-air two-phase flow. Taking the Shuibuya Hydropower Station as the research object, the distribution of the flood discharge atomized rainfall and the atomized wind speed are studied when the boundary conditions, ambient atmospheric pressure, and geometric dimensions meet similar requirements. The research results show that under the same boundary conditions, the geometric scale is the most important factor affecting flood discharge atomization. The smaller the geometric scale, the smaller the atomization wind speed and rainfall intensity obtained by the model, which means that smaller monitoring errors lead to larger prediction deviations. When the calculation model satisfies similar atmospheric pressure conditions, the atomization wind speed and rainfall obtained by the models with different geometric scales satisfy the standard exponential function relationship. By comparing with the atomized rainfall and wind speed data observed by the Shuibuya prototype, it is found that the prediction accuracy of the prototype can be greatly improved when the model satisfies a similar atmospheric pressure. Full article

Deformation monitoring is of importance to ensure the operation status of concrete-face rockfill dams (CFRD). This paper reported a novel fiber optic gyroscope (FOG) monitoring system for continuously monitoring face slab deformation of CFRD, which consisted of a permanent monitoring pipeline and a sensing vehicle. The monitoring pipeline was made of steel pipes and polyvinyl chloride polymer connectors, which was embedded in a slot of the crushing-type sidewall beneath the concrete face slab of CFRD, forming a permanent monitoring channel. The sensing vehicle was equipped with a high-precision FOG sensor. A low-pass filter was designed to eliminate the vibration noise of the angular velocities of the sensing vehicle during the monitoring process. An in situ test was carried out on the Shuibuya dam, the highest CFRD in the world. The measurements of the FOG monitoring system agreed well with traditional instrument measurements, serving as validation of the system. The FOG monitoring system has the advantages of excellent repeatability, long service life, distributed monitoring, and automatic measurement. Full article

Landslide is the second most frequent geological disaster after earthquake, which causes a large number of casualties and economic losses every year. China frequently experiences devastating landslides in mountainous areas. Interferometric Synthetic Aperture Radar (InSAR) technology has great potential for detecting potentially unstable landslides across wide areas and can monitor surface displacement of a single landslide. However traditional time series InSAR technology such as persistent scatterer interferometry (PSI) and small-baseline subset (SBAS) cannot identify enough points in mountainous areas because of dense vegetation and steep terrain. In order to improve the accuracy of landslide hazard detection and the reliability of landslide deformation monitoring in areas lacking high coherence stability point targets, this study proposes an adaptive distributed scatterer interferometric synthetic aperture radar (ADS-InSAR) method based on the spatiotemporal coherence of the distributed scatterer (DS), which automatically adjusts its detection threshold to improve the spatial distribution density and reliability of DS detection in the landslide area. After time series network modeling and deformation calculation of the ADS target, the displacement deformation of the landslide area can be accurately extracted. Shuibuya Town in Enshi Prefecture, Hubei Province, China, was used as a case study, along with 18 Sentinal-1A images acquired from March 2016 to April 2017. The ADS-InSAR method was used to obtain regional deformation data. The deformation time series was combined with hydrometeorological and related data to analyze landslide deformation. The results show that the ADS-InSAR method can effectively improve the density of DS distribution, successfully detect existing ancient landslide groups and determine multiple potential landslide areas, enabling early warning for landslide hazards. This study verifies the reliability and accuracy of ADS-InSAR for landslide disaster prevention and mitigation. Full article

Reservoirs operation alters the natural flow regime at downstream site and thus has a great impact on the design flood values. The general framework of flood regional composition and Equivalent Frequency Regional Composition (EFRC) method are currently used to calculate design floods at downstream site while considering the impact of the upstream reservoirs. However, this EFRC method deems perfect correlation between peak floods that occurred at one sub-basin and downstream site, which implicitly assumes that the rainfall and the land surface process are uniformly distributed for various sub-basins. In this study, the Conditional Expectation Regional Composition (CERC) method and Most Likely Regional Composition (MLRC) method based on copula function are proposed and developed under the flood regional composition framework. The proposed methods (i.e., CERC and MLRC) are tested and compared with the EFRC method in the Shuibuya-Geheyan-Gaobazhou cascade reservoirs located at Qingjiang River basin, a tributary of Yangtze River in China. Design flood values of the Gaobazhou reservoir site are estimated under the impact of upstream cascade reservoirs, respectively. Results show that design peak discharges at the Gaobazhou dam site have been significantly reduced due to the impact of upstream reservoir regulation. The EFRC method, not taking the actual dependence of floods occurred at various sub-basins into account; as a consequence, it yields an under-or overestimation of the risk that is associated with a given event in hydrological design. The proposed methods with stronger statistical basis can better capture the actual spatial correlation of flood events occurred at various sub-basins, and the estimated design flood values are more reasonable than the currently used EFRC method. The MLRC method is recommended for design flood estimation in the cascade reservoirs since its composition is unique and easy to implement. Full article

How to accurately determine the mechanical parameters of rockfill is one of the key issues of concrete-face rockfill dams. Parameter back-analysis using internal or external monitoring data has been proven to be an efficient way to solve this problem. However, traditional internal or external monitoring methods have limitations in efficiency and long-term monitoring. In this paper, the displacement of the Shuibuya concrete-face rockfill dam is monitored by the space-borne Interferometric Synthetic Aperture Radar (InSAR) time series method. Using the InSAR results and the finite element method, the back-analysis of the mechanical parameters of the rockfill dam is investigated, and the back-analysis results of InSAR and levelling are compared. A high correlation of 0.99 for the displacement results generated from InSAR and the levelling offers good agreement between the two methods. The agreement provides confidence that the external InSAR monitoring measurement allows producing a reliable back-analysis and captures the displacement properties of the dam. Based on the identified parameters from the InSAR results, the dam displacement is predicted. The prediction of the maximum settlement of the dam is 2.332 m by the end of 2020, according to the dam displacement characteristics, which agrees well with the results derived from the recorded internal monitoring data. Therefore, the external monitoring results from the InSAR observation can be used as a supplement for traditional monitoring methods to analyse the parameters of the dam. Full article

Settlement is one of the most important deformation characteristics of high concrete faced rockfill dams (CFRDs, >100 m). High CFRDs safety would pose a great threat to the security of people’s lives and property downstream if this kind of deformation were not to be measured correctly, as traditional monitoring approaches have limitations in terms of durability, coverage, and efficiency. It has become urgent to develop new monitoring techniques to complement or replace traditional monitoring approaches for monitoring the safety and operation status of high CFRDs. This study examines the Shuibuya Dam (up to 233.5 m in height) in China, which is currently the highest CFRD in the world. We used space-borne Interferometric Synthetic Aperture Radar (InSAR) time series to monitor the surface deformation of the Shuibuya Dam. Twenty-one ALOS PALSAR images that span the period from 28 February 2007 to 11 March 2011 were used to map the spatial and temporal deformation of the dam. A high correlation of 0.93 between the InSAR and the in-situ monitoring results confirmed the reliability of the InSAR method; the deformation history derived from InSAR is also consistent with the in-situ settlement monitoring system. In addition, the InSAR results allow continuous investigation of dam deformation over a wide area that includes the entire dam surface as well as the surrounding area, offering a clear picture continuously of the dam deformation. Full article