Dam Operation Research Articles

Study on the Characteristics and Evolution Laws of Seepage Damage in Red Mud Tailings Dams

Seepage damage is a significant factor leading to red mud tailings dam failures. Laboratory tests on seepage damage were conducted to investigate the damage characteristics and distribution laws of red mud tailings dams, including soil pressure, infiltration line, pore water pressure, dam displacement, and crack evolution. The findings revealed the seepage damage mechanisms of red mud slopes, offering insights for the safe operation and seepage damage prevention of red mud tailings dams. The results showed that the higher the water level is in the red mud tailings dam, the higher position the infiltration line is when it reaches the slope face. At the highest infiltration line point of the slope surface, the increase of pore water pressure is the highest and the change of horizontal soil pressure is the highest. Consequently, increased pore water pressure leads to decreased effective stress and shear strength, increasing the susceptibility to damage. Cracks resulting from seepage damage predominantly form below the infiltration line; the higher the infiltration lines is on the slope surface, the higher the position of the main crack formations is. The displacement of the dam body primarily occurs due to the continuous expansion of major cracks; the higher the infiltration lines are on the slope surface, the larger the displacement of the dam body is.

Characteristics and causes of water level variations in the Chenglingji–Jiujiang reach of the Yangtze River following the operation of the Three Gorges Dam

ABSTRACT Water level adjustment downstream of dams significantly impacts river regimes and flood control. However, due to constant strong scouring, our quantitative understanding of the characteristics of water level variations and their causes in the Chenglingji–Jiujiang Reach of the Yangtze River remains limited. Here, we analyzed the water level change trend via the Mann–Kendall method and analyzed geomorphic change and river resistance using 406 cross-sectional profiles as well as data on discharge and water levels from 1991 to 2022. Results showed that the critical conversion discharges (CCD) in the Chenglingji-Hankou Reach and the Hankou-Jiujiang Reach were approximately 35,000 and 30,000 m3/s, respectively, after the operation of the Three Gorges Dam. The water level exhibited an overall decline mainly due to river erosion when the discharge was lower than the CCD. The water level exhibited a nonsignificant upward trend mainly due to increased river resistance (7–20%) when the discharge was higher than the CCD. The obvious increase in the floodwater level in individual years was caused by the effect of downstream water level increase. Our findings further the understanding of downstream geomorphic response to dam operation and their impacts on water levels and have important implications for flood management in such rivers worldwide.

An extended Bayesian network model for calculating dam failure probability based on fuzzy sets and dynamic evidential reasoning

Effective risk analysis is crucial for ensuring the safe operation of hydropower dams. Existing methods typically rely solely on subjective or objective data. The subjectivity of expert opinions and insufficient objective data affect the reliability of results. To reduce subjectivity in the risk analysis process and enhance the utilization of subjective and objective data, this study proposes an extended Bayesian network (BN) model for calculating the dam failure probability based on fuzzy sets (FSs) and dynamic evidential reasoning. To address the fuzziness and uncertainty of expert opinions, a Gaussian FS was introduced to transform expert opinions into fuzzy numbers. Additionally, a dynamic evidential reasoning approach was proposed to combine subjective and objective data, considering their differences, subjectivity, and potential conflicts. To maintain consistency in the fuzzification and defuzzification processes, an improved Onisawa formula was proposed. Finally, the BN was employed for probability prediction and sensitivity analyses. The model was applied to the Z hydropower dam in China. The results showed that the failure probability was 5.2 × 10−4, and the main controllable risk factors were identified. A comparative analysis was conducted and the results showed that the proposed method had a better ability to handle uncertainty, reduce subjectivity, and mitigate conflicts.

A multisource data‐driven monitoring model for assessing concrete dam behavior

AbstractThe pivotal role of dam infrastructure necessitates continuous health monitoring, which results in extensive sets of data. Most monitoring data‐based models in dam engineering concentrate on predicting dam behavior. However, little attention has been systematically paid to the processing of extensive monitoring data, modeling of comprehensive dam behavior, and assessment of overall dam operation status. Here, we propose a novel monitoring model comprising three main aspects: a multidimensional data mining method, a multipoint response prediction method, and a multilayer data fusion‐based assessment method. Utilizing monitoring data from a mega concrete arch dam, we evaluate and discuss the effects of data mining, modeling accuracy for dam behavior, robustness against data pollution, and sensitivity to anomalies. Comparisons with classical benchmarks demonstrate the performance of the proposed model for the dam.

Research and Development of an Embedded Multi-Channel Sensor Data Acquisition Device for Reservoir Dams

The embedded system of intelligent reservoir dam achieves the integration and efficient utilization of water conservancy dam system data through multi-channel data collection and analysis calculated by computer technology, CNC system, and neural network. Compared with traditional data collection and processing methods, both timeliness and accuracy have been greatly improved. This study aims to develop a multi-channel sensor data acquisition device for reservoir dams based on embedded system technology. This device can collect real-time and efficient data from sensors in various parts of the dam, ensuring the safe operation of the reservoir dam. By using advanced embedded system technology, this device has advantages such as low power consumption, high stability, and real-time data transmission. The Analytic Hierarchy Process (AHP) was used to study the embedded multi-channel sensor data acquisition device for reservoir dams in multiple directions and factors. The AHP method provides an effective means for problem decision-making in complex situations. Referring to the AHP method, the factors that affect reservoir dams can be divided into different levels. Compare the importance of two random factors in each level to obtain a specific quantitative expression of the relative important factors on a scale. Then repeat this step to obtain the weight ranking for different levels. At the same time, the device monitors key parameters such as temperature, humidity, displacement, and pressure in various parts of the dam through multiple sensors, providing strong support for early warning and decision-making of reservoir dams. The results of this study have important practical significance and application value for improving the safety and stability of reservoir dams.

Inversion of the Permeability Coefficient of a High Core Wall Dam Based on a BP Neural Network and the Marine Predator Algorithm

The parameters’ inversion of saturated–unsaturated is important in ensuring the safety of earth dams; many scholars have conducted some research regarding the inversion of hydraulic conductivity based on seepage pressure monitoring data. The van Genuchten model is widely used in saturated–unsaturated seepage analysis, which considers the permeability connected to the water content of the soil and the soil’s shape parameters. A BP neural artificial network is a mature prediction technique based on enough data, and the marine predator algorithm is a new nature-inspired metaheuristic inspired by the movement of animals in the ocean. The BP neural artificial network and marine predator algorithm are applied in the permeability coefficient inversion of a core-rock dam in China; the results show that in the normal operation status, the BP network shows better accuracy, and the average of the absolute error and variance of the absolute error are both minimum values, which are 2.21 m and 1.43 m, respectively. While the water storage speed changes, the marine predator algorithm shows better accuracy; the objective function is calculated to be 0.253. So, the marine predator algorithm is able to accurately reverse the desired results in some situations. According to the actual condition, employing suitable methods for the inverse permeability coefficient of a dam can effectively ensure the safe operation of dams.

Proactive dam operation based on inflow prediction by modified long short-term memory for improving resilience

The operation of dams can have a significant downstream impact, causing major flood damage. For efficient dam operation, it is essential to accurately predict the inflow using deep learning such as a long short-term memory (LSTM). The modified LSTM (MLSTM) with new optimizers combined with a metaheuristic optimization algorithm was suggested. The MLSTM were trained using rainfall, water level, and discharge of the Yongdam Dam from 2010 to 2019 as input data and the inflow of the Daecheong Dam as output data. Normalization and time lagged cross correlation (TLCC) were applied as data preprocessing. The inflow of the Daecheong Dam in 2020 was predicted using rainfall, water level, and discharge of the Yongdam Dam. Compared to LSTM, MLSTM showed an accuracy improvement of about 63.85–83.34% based on the mean square error (MSE), which is a loss function. The results of proactive dam operation were calculated based on the predicted inflow considering TLCC. Compared to the existing operation, the peak water level of the reservoir was reduced from 77.54 m to 77.17 m, and the resilience was improved from 0.9225 to 0.9623. New dam operation can maintain low water levels of reservoir and improve resilience to prevent flood damage.

Dam‐induced difference of fish habitat distribution in the fluctuating backwater zone of the Three Gorges Dam

AbstractChanges in fish habitat induced by dam construction in the downstream river have been well documented in an increasing body of literature. Fish habitats in the backwater zone of dams have undergone dramatic changes, which have still remained poorly understood. This study aims to provide a more complete understanding of fish habitat distribution and clarify fish habitat utilization patterns in the upstream dam in order to adopt effective remediation activities. This study was conducted in the Three Gorges Dam (TGD) as it is the world's largest installed capacity hydropower station, with a backwater zone length of about 660 km. Four major Chinese carp (FMCC) were selected as target fish species, and fish habitat distribution upstream of TGD was assessed by integrating the swimming ability and environmental preference of different fish species into their critical life stages. Assessment results at different life stages of the target fish species showed that June was a critical period for spawning activities FMCC in the fluctuating backwater zone of TGD. The riverbank was an important feeding habitat for FMCC, and the high‐velocity flow in the centre of the channel exceeded the limits of the swimming ability of target fish. The fish habitat distribution in the fluctuating backwater zone of TGD was heavily affected by runoff from April to September and by dam operation from October to March of the next year. This work provide valuable information about river conservation and management in the upstream of TGD.

Two decades of clear-cutting threats in the Brazilian Amazonian protected areas around the Jirau, Santo Antônio, and Belo Monte large dams

Deforestation rates in the Amazon have markedly increased in the last few years, affecting non-protected and protected areas (PAs). Brazil is a hotspot of Protected Area Downgrading, Downsizing, and Degazettement (PADDD) events, with most events associated with infrastructure projects. Despite the threats dams impose on PAs, there is a knowledge gap in assessing deforestation in PAs around large dams in the Amazon. This study investigates how deforestation affects Biodiversity Protection Areas (BioPAs) and Indigenous Lands around the Jirau and Santo Antônio (JSA) dams (Madeira River, Rondônia) and Belo Monte dam (Xingu River, Pará) in the Brazilian Amazon. We compared clear-cutting between PAs and control areas and the annual rates of forest change between pre-dam and post-dam periods. We discussed deforestation-related factors (e.g., PADDD events and the presence of management plans or councils). Our results show an increase in deforestation after the operation of the dams when environmental control from licensing agencies decreases and other political and economic factors are in practice. Indigenous Lands experienced a significant increase in deforestation around the Belo Monte dam, which is associated with the demarcation process and land conflicts. Surrounding the JSA dams, sustainable use BioPAs showed high deforestation rates, and 27 PADDD events were reported, four directly related to dams. In addition to dams, deforestation was associated with the crisis of Brazilian democracy and the weakening of environmental policies. In conclusion, the weak environmental control from environmental licensing agencies during dam operation and PADDD events have contributed to increased deforestation rates and additional stresses in the Amazon.

Wonorejo dam operation and storage analysis against climate change

Climate change affecting rain intensity within catchment and increase the potential hydrometeorology disasters. Wonorejo Reservoir as a multipurpose dam requires an operational plan and water allocation to fulfill downstream needs, such as irrigation, hydropower, microhydropower, domestic industry uses and flood control. Wonorejo Dam is a backfill type dam with High Water Level (HWL) elevation is +183.00 mSHVP and Low Water Level (LWL) elevation is +141.00 mSHVP, Total storage capacity Wonorejo Reservoir is 109.118 million m3 (2021) or 89.4% from initial total storage capacity (2000), in 2050 predicted sediment volume increase to 17.73 million m3 or 17.6% reservoir capacity and 2050 capacity volume is 100,585 million m3 or 82.4% from initial total storage capacity. Annual Reservoir Operation Plan and Annual Water Allocation Plan for Wonorejo Reservoir in 2022/2023 as water services December 2022 to November 2023 periode, Wonorejo Reservoir elevation at the end of May 2023 reaches HWL and lowest water elevation is +162.64 mSHVP in the first week of November 2023.

Underwater dam crack image generation based on unsupervised image-to-image translation

Underwater crack detection is necessary for the safe operation of concrete dams. Current deep-learning-based underwater crack detection methods rely heavily on a large number of crack images; these images are difficult to collect because of complex and dangerous underwater environments. This study proposes a method that can generate underwater dam crack images using above-water dam crack images through an image-to-image translation method. CycleGAN, which is more powerful than other models, was optimized to generate underwater dam crack images by translating existing above-water dam crack images into underwater styles. The generated underwater images were validated using the following major computer vision tasks: classification, object detection, and semantic segmentation. The results demonstrate the potential of these generated images for training and optimizing deep-learning-based crack-detection methods. This method is expected to solve the problem of insufficient data and enhance deep-learning-based detection networks.

Evaluation of water shortage and instream flows of shared rivers in South Korea according to the dam operations in North Korea

Abstract The Korean Peninsula's mountainous terrain poses challenges to effective water resource management. Notably, two significant river basins, North Han River and Imjin River basins, are essentially shared rivers originating in North Korea. After the construction of various dams in North Korea, billions of tons per year of water annually decreased from the upper reaches of these rivers of North Korea to South Korea. This study conducted an impact analysis on two major river basins affected by dam operations in North Korea. Both before and after the Imnam Dam operation, significant reduction (27.7%) in the average monthly inflow was observed, and the total release of the Hwacheon Dam in South Korea decreased by 40.2%. The analysis of instream flow indicated that the operation of dams in North Korea had a substantial impact on fulfilling instream flow requirements for dams located in North Han River and Imjin River basins. To ensure instream flow, this study proposed two plans. The first plan involved the utilization of existing dams in the North Han River basin, while the second plan suggested connecting the dams in North Korea, taking into account the shared rivers.

Age and crack length effects on fracture parameters of dam gallery concrete: Experimental investigation and modeling

Cracks of varying lengths may occur in galleries throughout dam construction, operation, and management processes. This study investigates the influence of age and crack length on the fracture parameters of gallery concrete, which is crucial for understanding the mechanisms and risks associated with gallery cracking and for developing appropriate treatment measures. Wedge-splitting tests were performed on gallery concrete specimens with varying crack lengths and at different ages. It was found that the fracture parameters measured at different ages all decrease with the increase of crack length. Based on the test results, size-independent material constants such as fracture toughness and the effective length of the fracture process zone were derived and modeled using an exponential function to reflect their time-varying characteristics. A fracture model was proposed and validated, which can accurately predict the fracture behavior of gallery concrete across varying ages and crack lengths, and is applicable in practical dam gallery crack analysis for assessing crack stability and evaluating the effectiveness of treatment measures.

Flow regime alteration in Arctic rivers due to dam operations and climate change

Arctic rivers and water resources currently experience significant hydrological changes due to climate change and global warming. The flow regime alteration in Arctic rivers strongly influences the conservation and sustainability of the native biodiversity of the riverine ecosystem. The change in major characteristics of the daily and monthly flow regime of seven arctic rivers has been assessed in this study. The daily flow (40–120 years) at the outlet of Lena River, Yenisey River, Kolyma River, and Ob’ River in Russia; Yukon River in the USA; Mackenzie River in Canada; and Tana River, Norway was used. Except for the Tana River, the rest of these rivers have been regulated. In addition, monthly flow alteration in the headwater of these rivers and below sixteen dams was assessed. In this research, we applied ‘Indicator of Hydrologic Alteration’ (IHA) and ‘River Impact’ (RI) methods to estimate daily monthly flow change in the river. Based on the daily analysis, the most significant change was observed for Yenisey and Ob rivers. The Kolyma hydropower shows the lowest impact, while the Shushenskaya Dam on Yenisey shows the highest impact on the flow regime.

Determination of future creep and seismic behaviors of dams using 3D analyses validated by long-term levelling measurements

This study aims to assess the future structural performance of the Kozlu-Ulutan clay core rockfill (CCR) dam, one of the most significant water structures in the Black Sea region of Turkey, by utilizing 35 years of levelling measurements and 3D finite-difference analyses. Settlement measurements were obtained from five different points on the dam surface every 6 months. Subsequently, a three-dimensional (3D) model of the dam was created using the finite-difference method. Time-dependent creep analyses and seismic analyses were conducted sequentially, employing the Burger-Creep and Mohr–Coulomb material models, respectively. Non-reflecting boundary conditions were defined for the boundaries of the dam model. The 3D numerical analysis results were found to be highly compatible with the 35 years of levelling measurements. Additionally, the future seepage and settlement behaviors of the dam over a 100-year period (2023–2123) were analyzed, considering special time functions. Current and future seismic analyses were performed, taking into account the settlement results of the dam in 2023 and 2123. For seismic analyses, data from ten various earthquakes that occurred in Kahramanmaraş, Hatay, Malatya, and Gaziantep in Turkey in 2023 were utilized. The seismic analysis results provided significant information about the future seismic behavior of the Kozlu-Ulutan Dam, revealing notable differences between the current and future earthquake behaviors of the dam. Moreover, it was concluded that the clay core is the most crucial section concerning the current and future seismic behaviors of CCR dams. The study results emphasized the importance of continuous monitoring and periodic seismic evaluations for the safe operation of CCR dams.

Losing flow in free‐flowing Mediterranean‐climate streams

Stream drying is happening globally, with important ecological and social consequences. Most examples of stream drying come from systems influenced by dam operations or those with highly exploited aquifers. Stream drying is also thought to be driven by anthropogenic climate change; however, examples are surprisingly limited. We explored flow trends from the five recognized Mediterranean‐climate regions of the world with a focus on unregulated (non‐dammed or non‐diverted) streams with long‐term gauge records. We found consistent evidence of decreasing discharge trends, increasing zero‐flow days, and steeper downward discharge trends in smaller basins. Beyond directional trends, many systems have recently undergone shifts in flow state, including some streams that have transitioned from perennial to intermittent flow states. Our analyses provide evidence of stream drying consistent with climate change but also highlight knowledge gaps and challenges in empirically and statistically documenting flow regime shifts. We discuss the myriad consequences of losing flow and propose strategies for improving detection of and adapting to flow change.

Damage characteristics and dynamic responses of high arch dams under rockfall impact loads

Rockfall is a typical natural disaster that is sudden, high in speed, unpredictable, and can easily adversely affect the structures. High arch dams are usually constructed in deep valleys, where rockfall is a typical destructive load. However, there is currently a lack of research on the effects of rockfall on the safe operation of high arch dams. Therefore, the Wudongde arch dam in China is selected in this paper for exploring damage characteristics and dynamic responses of high arch dams under rockfall impact loads. To improve the authenticity of numerical simulation, a coupled model is established based on the actual dam shape and terrain characteristics of the dam. The concrete damaged plasticity model is used for dam concrete and the rockfall is described by the JH-2 model to simulate mechanical properties of materials and reflect strain rate effects. The trajectories of falling rocks at the dam site are first investigated to analyze the locations where they may impact the dam. Based on the obtained results, two impact positions are considered possible in the analysis, namely, the dam crest and the gate slot, and then the nonlinear dynamic responses of the dam impacted by rockfall at different positions are compared. Finally, a sensitivity analysis is carried out to evaluate the influence of the rockfall properties on the damage characteristics of the dam. The results indicate that the trajectory and impact velocity of falling rocks are related to the falling height, rock size, and initial velocity. The rockfall impact on the dam crest has little influence on the safety of the dam for the robust mass dam concrete, while the track beam near the gate slot is vulnerable to rockfall impact. The damage degree to the structure is related to rockfall properties. Compared to larger-sized rockfalls, rockfalls with a diameter of 1 m cause minimal damage to the structure and have little impact on its safety. Higher impact velocity and stiffness of the rock lead to more severe damage, while the effect is relatively smaller compared to the rockfall size. The high stiffness also reduces the damage to the rock itself.

Monitoring model group of seepage behavior of earth-rock dam based on the mutual information and support vector machine algorithms

The establishment of a high-precision piezometric water level monitoring model ensures the safe operation of earth-rock dams. The hysteresis effect of the upstream water level and rainfall should be considered during modeling. In the traditional method, the average factors are used to express this effect, and linear regression modeling is adopted. These factors reduce the accuracy of the model. In this paper, the mutual information (MI) and support vector machine (SVM) algorithms are proposed. MI has a powerful correlation analysis capability, and it is innovatively used to address hysteresis effects. SVM has a strong nonlinear modeling ability, and it is used as a modeling algorithm. During this study, it was found that the lag time of rainfall varied. In view of this characteristic, the concept of an innovative model group, which is an important extension of the traditional single model, is proposed. In the example, the mean square error (MSE) is used as the precision index. Compared with the traditional single model established by linear regression, the MSE of the MI–SVM model group can be reduced by approximately 60.98%–68.75%. Compared with the model group established by linear regression, the MSE of the MI–SVM model group can be reduced by approximately 41.28%–45.45%. The new method effectively improves the accuracy of the model and can precisely monitor the seepage state of the dam. Moreover, it is beneficial for improving the level of dam safety management and can be extended to other fields involving hysteresis effects and nonlinear modeling.

Priority Strategy For Improving Dam Operation and Maintenance Performance (Cacaban Dam Case Study)

Dam operation and maintenance activities are critical, so the dam usually works and provides benefits according to plan. Over time, the performance of the Cacaban Dam has decreased significantly. The physical condition of the main building, supporting buildings, and instruments have experienced damage, which could pose a risk to the dam's safety. The main objective of this research is to determine priority strategies for improving operations and maintenance of the Cacaban Dam. Determining priority strategies for improving operations and maintenance in this research uses the DSS (Decision Support System) method with SWOT (Strength, Weakness, Opportunity, and Threat) and QSPM (Quantitative techniques Strategic Planning Matrix). Based on the SWOT analysis results, the resulting strategy is the Diversity Strategy, meaning that the Cacaban Dam is in good internal condition. In contrast, the external condition is not good. The proposed strategy is to minimize external threats/threats with strategic priorities referring to the scoring results using the QSPM method. Meanwhile, from the results of the dam performance assessment, Cacaban Dam obtained an average performance score of 3.45, considered adequate performance. However, if you look at several items of poor performance, there are two items, namely reservoir condition and border and greenbelt conditions, which have poor performance. There are seven components, namely intake buildings, spillway buildings, support hills, dam services, emergency action plans, instrumentation, and surrounding communities, whose performance is quite close to the deficient limit and has the potential for performance to decline. Extra handling is needed to restore the performance of dams and reservoirs.

ResORR: A globally scalable and satellite data-driven algorithm for river flow regulation due to reservoir operations

We propose a globally scalable algorithm, ResORR (Reservoir Operations driven River Regulation), to predict regulated river flow and tested it over the heavily regulated basin of the Cumberland River in the US. ResORR was found able to model regulated river flow due to upstream reservoir operations of the Cumberland River. Over a mountainous basin dominated by high rainfall, ResORR was effective in capturing extreme flooding modified by upstream hydropower dam operations. On average, ResORR improved regulated river flow simulation by more than 50% across all performance metrics when compared to a hydrologic model without a regulation module. ResORR is a timely software algorithm for understanding human regulation of surface water as satellite-estimated reservoir state is expected to improve globally with the recently launched Surface Water and Ocean Topography (SWOT) mission.