Earth-rock Dam Research Articles

A Multi-Point Correlation Model to Predict and Impute Earth-Rock Dam Displacement Data for Deformation Monitoring

Deformation is a critical indicator of structural integrity, and monitoring deformation is essential for ensuring the long-term safety of dams. However, characterizing the spatial correlations among dam deformation sequences and the similarity between displacements at various measurement points poses significant challenges when using single-point measurement models. Considering the limitations inherent in conventional models for processing spatiotemporal data, this paper introduces a novel model for predicting and imputing multi-point displacement monitoring data from earth-rock dams. The model integrates a convolutional neural network (CNN) with a bidirectional long short-term memory neural network (BiLSTM) while also incorporating an attention mechanism (AM). The CNN captures the spatial features of the displacement data, while the BiLSTM extracts temporal features. The AM assigns varying weights to input features, thereby enhancing the predictive accuracy of the model. The proposed model was experimentally validated, demonstrating its robust capabilities in data prediction and the imputation of missing data. The model provides a new strategy for forecasting dam deformation and addressing issues related to incomplete data.

An Integrated Model for Dam Break Flood Including Reservoir Area, Breach Evolution, and Downstream Flood Propagation

The reasonable and efficient prediction of dam failure events is of great significance to the emergency rescue operations and the reduction in dam failure losses. This work presents a model that is based on the physical mechanism. It is coupled with a multi-architecture (multi-CPU and GPU) open-source two-dimensional flood model, which is based on high-precision terrain and land use data. The aim is to enhance the accuracy of dam break flood process simulations. The model uses DEM data as a computational grid and updates it at each time step to reflect breach evolution. Simultaneously, the breach evolution model incorporates an analysis of stress on sediment particles, establishing the initial erosion state and lateral expansion model while accounting for seepage. The determination of the overflow of the breach is resolved through the application of a two-dimensional hydrodynamic model. This approach achieves a robust connection between the upstream reservoir, the dam structure, and the downstream inundation area. The coupled model is utilized to calculate the failure of earth-rock dams and landslide dams, and a sensitivity analysis is conducted. Taum Sauk Dam and Tangjiashan landslide dam were selected to represent earth dam break and barrier lake break, respectively, which are the main types of dam breaks. The obtained results demonstrate strong concurrence with the measured data, the relative errors of the four important parameters of the application case, the peak discharge of the breach, the top width of the final breach, the depth of the breach and the arrival time of the maximum peak discharge are all within ±10%. Although the relative error of the completion time of the final breach is greater than 10%, it is about 30% less than the relative error of the physical model.

Deep learning-based inversion of soil and rock compaction density utilizing Falling Weight Deflectometer (FWD) and Surface Waves

Methods relying on a single dynamic parameter to estimate the material density for the compaction quality testing of earth-rock dams and roadbed projects, present certain limitations in terms of applicability, testing accuracy, and work efficiency. The theory of elastic wave propagation reveals that the compaction density of the medium results from the combined interaction of multiple material properties. This study proposes a method that utilizes the transient surface wave method and a vehicle-mounted Falling Weight Deflectometer (FWD) to jointly enhance the accuracy of soil and rock material compaction detection. The transient surface wave method is employed to extract surface wave velocity and compressional wave velocity. The analysis of the FWD signal involves extracting dynamic parameters closely related to material density, including stiffness coefficients, central frequencies, and stiffness impedances, from time-domain, frequency-domain, and mechanical impedance analyses. A fully connected deep neural network is introduced to intelligently estimate the compaction density. The deep learning model is optimized by selecting the optimal activation function and optimization algorithm, as well as additional tuning. Extensive testing shows that deep learning model produce results closely approximating the true compaction density values. The average relative errors for the wet density and the dry density are 2.9% and 2.85%, respectively, meeting the quality control requirements for density testing. The proposed approach enables rapid detection and control of the compaction quality of soil and rock materials, providing a new method for the in-situ rapid detection of compaction quality.

Investigation of the particle crushing characteristics of stacked stone materials under different stress path conditions

Stone fill materials are widely used in engineering projects such as earth-rock dams, railways, and highway embankments. Nevertheless, they are susceptible to particle breakage, which can lead to a decline in engineering performance. A complete set of studies on crushed stone aggregates was carried out to investigate particle crushing characteristics under various stress path conditions. To quantify the amount of particle crushing, the relative particle crushing rate was utilized as an indicator. The plastic work performed during the trials was compared across different stress routes, and the relationships between particle crushing and average stress p and generalized shear stress q were investigated. In triaxial testing, the differences in particle crushing behavior under different stress routes were thoroughly examined. The results show that particle crushing is substantially lower in the modelling of a rockfill dam constructed with a core wall and subjected to reservoir water pressure pathways than in conventional triaxial tests. A clear relationship was discovered. Across several stress routes, a clear link between plastic work and the relative particle crushing rate was detected, which was successfully characterized by a power function. Notably, the contributions of different stress components to the overall plastic work varied significantly. The plastic work exerted by the generalized shear stress q accounted for a large percentage of traditional triaxial testing, although the proportion of plastic work given by p and q changed significantly in complex stress path tests. This research enhances the understanding of the particle breakage characteristics of stone fill materials under complex stress paths.

Modeling of particle migration in piping based on an improved discrete element method

Pore-scale particle migration in piping is the main reason of the suffusion-induced damage, which poses a significant threat to earth-rock dams. In order to investigate the micro-mechanism of piping seepage process, an improved fluid-solid coupling discrete element method is proposed in this paper. In this method, particles in a packed model are divided into coarse- and fine particle groups. Pores can be defined based on the coordinates of the coarse particles and the Delaunay triangulation algorithm. A pore density flow method is introduced to calculate the overall fluid pressure of each pore and the fluid flow via pore throats. Further, the drag force on fine particles inside a pore can be calculated according to the fluid velocities of the neighboring four pore throats. The proposed method was implemented in the discrete element software MatDEM, and was successfully used to simulate fine particle migration of piping, the particle loss process, and the related variation of permeability coefficient. The pore-jamming phenomenon during the fine particle migration is observed. The model provides an effective way for the numerical analysis and mechanism study of piping seepage process at the pore scale.

Study on the seismic wave input method for earth-rock dam on overburden foundation with dynamic nonlinearity based on nonlinear artificial boundary

Accurately obtaining the seismic response of earth-rock dams on overburden foundations is crucial for seismic safety assessment and design, and a reasonable seismic input method is necessary. The seismic wave input method based on artificial boundaries is widely used in elastic foundations. However, the overburden with obvious dynamic nonlinearity is an insurmountable obstacle for the traditional method. In this research, relying on a specific earth-rock dam project, the essence of the seismic wave input method for nonlinear foundations is analyzed in detail from equivalent loads and viscous boundaries, respectively. The mechanism of inapplicability of the traditional seismic wave input method is illustrated, and an effective modified method is validated and presented. The research indicated that equivalent loads for the nonlinear dynamic response of the overburden foundation can be accurately acquired with the shear box model. The dynamic parameters of soil can be captured dynamically by the nonlinear viscous coupling boundary. The states of the lateral boundary of the overburden foundation are consistent with the free field precisely with the modified seismic wave input method by combining the shear box model and the nonlinear viscous coupling boundary. A large error will be caused by neglecting the dynamic nonlinear behavior of the overburden foundation, where the maximum deviation of peak acceleration may be over 55 % or more. A numerical model with a relatively small lateral range of overburden foundation will be obtained when the modified seismic wave input method is engaged. Moreover, the computational accuracy is enhanced remarkably, with a maximum deviation of no more than 5 %. This study provides effective theoretical support for seismic analysis and safety assessment of earth-rock dams on deep overburden foundations.

Study on the Shear Strength and Erosion Resistance of Sand Solidified by Enzyme-Induced Calcium Carbonate Precipitation (EICP).

Sand solidification of earth-rock dams is the key to flood discharge capacity and collapse prevention of earth-rock dams. It is urgent to find an economical, environmentally friendly, and durable sand solidification technology. However, the traditional grouting reinforcement method has some problems, such as high costs, complex operations, and environmental pollution. Enzyme-induced calcium carbonate precipitation (EICP) is an anti-seepage reinforcement technology emerging in recent years with the characteristics of economy, environmental protection, and durability. The erosion resistance and shear strength of earth-rock dams solidified by EICP need further verification. In this paper, EICP-solidified standard sand is taken as the research object, and EICP-cemented standard sand is carried out by a consolidated undrained triaxial test. A two-stage pouring method is adopted to pour samples, and the effects of dry density, cementation times, standing time, and confining pressure on the shear strength of cemented standard sand are emphatically analyzed. The relationship between cohesion, internal friction angle, and CaCO3 formation was analyzed. After the optimal curing conditions are obtained through the triaxial shear strength test, the erosion resistance model test is carried out. The effects of erosion angle, erosion flow rate, and erosion time on the erosion resistance of EICP-solidified sand were analyzed through an erosion model test. The results of triaxial tests show that the standard sand solidified by EICP exhibits strain softening, and the peak strength increases with the increase in initial dry density, cementation times, standing time, and confining pressure. When the content of CaCO3 increases from 2.84 g to 12.61 g, the cohesive force and internal friction angle change to 23.13 times and 1.18 times, and the determination coefficients reach 0.93 and 0.94, respectively. Erosion model test results indicate that the EICP-solidified sand dam has good erosion resistance. As the increase in erosion angle, erosion flow rate, and erosion time, the breach of solidified samples gradually becomes larger. Due to the deep solidification of sand by EICP, the development of breaches is relatively slow. Under different erosion conditions, the solidified samples did not collapse and the dam broke. The research results have important reference value and scientific significance for the practice of sand consolidation engineering in earth-rock dams.

Study on Mechanical Properties of Sandy Soil Solidified by Enzyme-Induced Calcium Carbonate Precipitation (EICP)

Earth–rock dams are widely distributed in China and play an important role in flood control, water storage, water-level regulation, and water quality improvement. As an emerging seepage control and reinforcement technology in the past few years, enzyme (urease)-induced calcium carbonate precipitation (EICP) has the qualities of durability, environmental friendliness, and great economic efficiency. For EICP-solidified standard sand, this study analyzes the effect of dry density, amount of cementation, standing time, perfusion method, and other factors on the permeability and strength characteristics of solidified sandy soil by conducting a permeability test and an unconfined compression test and then working out the optimal solidification conditions of EICP. Furthermore, a quantitative relationship is established between the permeability coefficient (PC), unconfined compressive strength (UCS), and CaCO3 generation (CG). The test findings indicate that the PC of the solidified sandy soil decreases and the UCS rises as the starting dry density, amount of cementation, and standing time rise. With the increase of CG, the PC of the solidified sandy soil decreases while the UCS increases, indicating a good correlation among PC, UCS, and CG. The optimal condition of solidification by EICP is achieved by the two-stage grouting method with an initial dry density of 1.65 g/cm3, cementation time of 6 d, and standing time of 5 d. Under such conditions, the permeability of the solidified sandy soil is 6.25 × 10−4 cm/s, and the UCS is 1646.94 kPa. The findings of this study are of great theoretical value and scientific significance for guiding the reinforcement of earth–rock dams.

Research on Failure Modes and Causes of 100-m-High Core Wall Rockfill Dams

Rockfill dams are the most competitive type of dam in complex geological environments. Identifying the failure modes and causes in high dams over 100 m is critical for better guiding high dam designs and implementing safety prevention and control measures. To this end, this paper investigated numerous cases of earth–rock dam breaches and failure modes in rockfill dams globally, with a particular focus on dams over 100 m in height, encompassing all such dams in China. The study categorized dam failure modes based on whether the dams were built before or after 1980. It also examined the causes of dam failures in terms of dam height, foundation characteristics and thickness, and failure time. Additionally, the paper analyzed a rockfill dam in China, with a height of 136 m and over ten years of operation, as a case study. We analyzed the spatial and temporal characteristics and causes of failures, such as dam crest cracking, high-level seepage, and gallery cracking, using the design situation, monitoring data, and numerical simulation. The paper also addressed issues related to dam design and foundation treatment, providing recommendations for improvement. The study indicated that the overall risk of total failure for dams over 100 m is already low. However, longitudinal cracks on the dam crest, core wall seepage, hydraulic splitting, and seepage damage to the dam foundation are primary issues in the current high core wall rockfill dams. These issues are mainly caused by uneven structural deformation of the dam and its foundation. A reasonable design of rockfill materials and foundations can mitigate these failures.

A comprehensive DEMATEL-ISM model-based safety analysis of the Lianghekou earth-rock dam

Earth-rock dam failures account for the largest proportion of all dam failure accidents. There are many factors inducing accidents in hydroelectric projects, and the relationships between them are intricate and complex. Therefore, it is crucial to explore the relationship between the influencing factors and identify the key factors leading to accidents. Through an analysis of the factors influencing earth rock dam failures, an index system for failure influence factors was constructed in this paper. Considering complexity and integration in influence factors analysis, a DEMATEL-ISM model (Decision Making Trial and Evaluation Laboratory; Interpretive Structural Model) analysis method was employed to examine the internal relationship among various factors based on the influence degree between them, and a MICMAC model (Matrix Impacts Cross-reference Multiplication Applied to a Classification methodology) was introduced to analyze the hierarchical relationship between various factors. The results showed that The results show that the seismic capacity and flood discharge capacity of the dam body are the key influencing factors of dam safety during the operation of the earth-rock dams. The comprehensive method employed in this paper overcame the complexity of evaluation results and was capable of more directly presenting relationships of factors. As suggested by these results, the analysis model employed in this paper has great significance for preparing a flexible-efficient management scheme for earth-rock dams.

Durability Analysis of Concrete Cutoff Wall of Earth-Rock Dams Considering Seepage and Dissolution Coupling Effect

In this paper, a novel numerical model for characterizing the seepage and dissolution coupling effect on the durability of anti-seepage walls of earth-rock dams is proposed. The model considers the influence of hydraulic gradient-driven seepage on the non-equilibrium decomposition of the calcium dissolution in concrete, as well as the effects of seepage dissolution on pore structure, permeability, and diffusivity. The reasonableness of the model is validated by experimental and literature data, which is then applied to analyze the deterioration and failure processes of a concrete cutoff wall of an earth-rock dam in Zhejiang Province, China. On this basis, the seepage dissolution durability control indices of anti-seepage walls are identified. The findings demonstrate that the suggested method accurately explains the calcium leaching process in concrete. Under the seepage and dissolution coupling effect, calcium in the wall continuously decomposes and precipitates, leading to varying degrees of increases in structural performance parameters, which weaken the seepage control performance of the walls and consequently result in an increase in seepage discharge and hydraulic gradient. By proposing the critical hydraulic gradient as a criterion, the service life of the wall is projected to be 42.8 years. Additionally, the upstream hydraulic head, the initial permeability coefficient, and the calcium hydroxide (CH) content are three crucial indices affecting the durability of walls, and these indices should be reasonably controlled during the engineering design, construction, and operational phases.

Multisource information fusion model for deformation safety monitoring of earth and rock dams based on deep graph feature fusion

Constructing a long-term deformation monitoring model for earth–rock dams that integrates multisource monitoring information is highly important for enhancing the safety state evaluation and monitoring effectiveness of such dams. In this paper, we propose a new health monitoring model named the deformation–seepage–water level multimeasurement point health monitoring (DSW-MPHM) model for earth–rock dams based on deep graph feature fusion. This model fuses coupled seepage, deformation, and water level features from different monitoring sites of the dam body, base, and shoulder. To achieve this goal, we first establish a new module to fuse spatial and temporal features using graph convolutional networks and long short-term memory. Seepage features and water level features are then extracted using graph attention mechanisms. Subsequently, we employ the feature fusion technique, which incorporates principal component analysis and gated fusers, to construct the DSW-MPHM model, which effectively fuses information from multiple sources. This novel approach successfully addresses the issues of information redundancy and the limited reliability of monitoring models. To verify the validity of the model, it is applied to an endoscopic deformation monitoring program of a panel rockfill dam with a height of 185.5 m. The results demonstrate the superior stability and effectiveness of the proposed method compared to those of 10 baseline prediction models. Additionally, the characterization of the seepage and water level features extracted from the model is verified for its reasonableness. Thus, our proposed model is well suited for practical engineering applications.

Thermodynamic Analysis of Coupled Seepage and Thermal Conduction Effects in Earth-Rock Dams

Thermodynamic Analysis of Coupled Seepage and Thermal Conduction Effects in Earth-Rock Dams

Numerical simulation of frost heaving damage of earth-rock dam berms in cold regions with thermo-hydro-mechanical coupling

In order to investigate the freezing damage problem of berms of earth-rock dams in cold regions, an earth-rock dam in a cold region was selected as the research object in this study. A finite element model, considering the effect of thermo-hydro-mechanical coupling, has been developed to solve the problem by combining the characteristics of the earth-rock dam. The whole process of freezing damage of berms under the influence of the reservoir level and the water migration of the dam filling was investigated, and the laws in temperature, humidity and displacement of earth-rock dams were analyzed. The calculated displacement field was then compared with the measured frozen deformation data to validate the results of the finite element simulation. The results showed that the freezing influence range of the dam slope was about 2 m, the range of temperature influence on the dam slope mainly depended on the depth of freezing, and the temperature change in the shallow range (0–2 m) of the dam slope was influenced by the outside air temperature. Also, the internal temperature of the dam body was small relative to the shallow dam slope, and there was a certain hysteresis. In addition, the effect of negative temperature was such that the shallow pore water phase of the dam slope turned into ice, manifesting macroscopically as a reduction in unfrozen water content. Water phase change, water migration from the dam filling to the dam slope, and the movement of the ice peak towards the dam body were found to be the main causes of berm freezing and expansion damage. The calculated amount expansion (due to freezing) of the dam slope was found to be in the range of 20–30 cm with a maximum value of 36 cm, which was consistent with the measured results. It was also found that the freezing and expansion damage of the berm is mainly caused by the joint action of freezing and expansion of the soil and rock mixture such as gravel bedding and dam filling, as well as the ice thrust force. It is expected that the results of this research can provide a basis for the design of berms of earth-rock dams in cold regions.

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.

Physical data-driven modeling of deformation mechanism constraints on earth-rock dams based on deep feature knowledge distillation and finite element method

Constructing an interpretable model for the long-term deformation Structural Health Monitoring (SHM) of earth-rock dams is of great significance for improving the safety state evaluation and monitoring effect. In this paper, a physics-data-driven model for the deformation SHM of earth-rock dams is proposed based on deep mechanism knowledge distillation. Firstly, the deterministic model is established based on the Finite Element Model (FEM) and outputs the hydraulic load component curve and aging component curve. Then a regression prediction model (HTSGAN) between influencing factors and deformation measurements at multiple measurement points is established based on the Graph Convolutional Network (GCN) and attention mechanism. Finally, the Teacher-Hydraulic-Time-Seepage Graph Attention Networks (T-HTSGAN) model is established based on the feature-based multi-teacher knowledge distillation using the knowledge of hydraulic loading physics and soil-rock creep physics of the FEM for mechanism constraints. The model effectively solves the problems of poor model interpretability and lack of physics knowledge constraints in previous earth-rock dam SHM models. The research results are applied to a project of a 185.5-meter-high concrete-faced rockfill dam, and the predictive performance of the model is more effective and stable through the comparison of six baseline models. The comparative analysis of the component curves proves the effectiveness of the proposed knowledge distillation method for mechanism constraints and improves the interpretability of the neural network model. Therefore, the model is more suitable for engineering applications.

Seepage prediction model of the earth-rock dam based on TCN considering rainfall lag effect

Renewable energy has the highest conversion efficiency, is the most flexible in regulating peak power in the grid, and has the potential to significantly reduce emissions. Hydropower is one of the main ways to optimize power energy structure by building earth-rock dams that block water and generate electricity. Seepage is a physical quantity that characterizes the safety of earth-rock dams. Studying the intelligent prediction model of earth-rock dams is an effective means of understanding the evolution of seepage behavior, and it is also crucial for the safe operation and energy efficiency of earth-rock dams. To create a rainfall factor expression reflecting the hysteresis effect of rain, actual monitoring data of different piezoelectric tubes on the upstream and downstream sides of the soil core wall of an earth-rock dam is considered. Based on the key influencing factors of the seepage behavior of earth-rock dams, the novel temporal convolutional network (TCN) algorithm in deep learning is introduced into the seepage behavior prediction of earth-rock dams, constructing the intelligent prediction model of seepage of earth-rock dams based on TCN. The engineering example shows that the seepage prediction model of the earth-rock dam based on TCN has better prediction performance than the seepage prediction model of the earth-rock dam based on support vector regression (SVR), extreme learning machine, and long-short term memory. The determination coefficient is more significant than 0.9, and the relative error of prediction is less than 1‰. The model’s prediction accuracy is high, and the stability of the prediction performance is good. The model’s prediction performance also improves after considering the rainfall lag effect.

Study on Impoundment Deformation Characteristics and Crack of High Core Rockfill Dam Based on Inversion Parameters

In the numerical simulation of earth-rock dam, accurate and reliable mechanical parameters of the dam material are the important basis for dam deformation predictions and dam safety evaluations. Based on the deformation monitoring data of Luding core wall rockfill dam, the rheological parameters of rockfill and core wall materials are inverted in this paper. Combined with the actual filling and impoundment process of the dam, the numerical simulation is carried out, and the stress deformation and differential settlement of the dam after completion and impoundment are analyzed. The results showed that the stress deformation results of the dam based on the inversion parameters were in good agreement with the actual deformation. The horizontal displacement, settlement, and principal stress of the dam during the completion period were symmetrically distributed along the core wall. The maximum horizontal displacement occurred at the main dam on both sides of the core wall and the upstream and downstream dam slopes, and the maximum settlement occurred in the middle of the core wall. During the impoundment period, under the action of reservoir water pressure and upstream rockfill wetting deformation, the deformation and stress of the dam body no longer met the symmetrical distribution law, and the maximum horizontal displacement of the dam body during the impoundment period was located at 2/3 of the upstream dam slope. The maximum settlement of the dam body was located at 1/2 of the dam height. The maximum principal stress on the upstream side of the core wall was located on the left side of the bottom of the core wall, and the minimum principal stress was also located on the left side of the bottom of the core wall. The simulation results of the deformation and stress met the general law of earth-rock dam engineering. During the completion period, the deformation inclination of the dam crest was less than 1%. During the impoundment period, the deformation inclination of the dam crest area increased due to the wetting deformation of the upstream rockfill material. At the same time, the deformation inclination of the dam crest axis was larger than that of the upstream and downstream sides, and the deformation inclination of the dam crest at the middle of the valley was the largest, but it did not exceed 3%, that is, there would be no longitudinal cracks, which is consistent with the actual situation. The research results can better predict the stress deformation and crack of the dam body, and provide important support for dam safety evaluations.

A combined optimization prediction model for earth-rock dam seepage pressure using multi-machine learning fusion with decomposition data-driven

Seepage pressure data analysis is considered one of the most direct ways to evaluate seepage safety in dam engineering. Therefore, accurate and consistent prediction of seepage pressure is essential. However, satisfactory predictive performance is rugged due to the irregular, nonlinear, and non-stationary nature of seepage pressure monitoring data. This paper proposes a combined optimization prediction model for seepage pressure prediction to resolve the deficiency. The model is coupled with a data decomposition technology, multi-machine learning fusion technology, adaptive variable weight combined strategy, and an improved optimization algorithm. The interaction between high and low frequency components is eliminated by improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) technique to achieve the smoothing of non-stationary data. Multi-machine learning models are integrated by the combined strategy to compensate for the shortcomings of single models. Combined weights are adaptively changed by the kernel extreme learning machine (KELM) variable weighting strategy to overcome the limitations of traditional fixed weights. For the challenge of determining KELM parameters, the artificial electric field algorithm is improved to optimize KELM parameters to accelerate the determination of adaptive weights. The application of the proposed model to an earth-rock dam demonstrates enhanced predictive accuracy of seepage pressure by implementing strategies. The model is an effective tool for the seepage pressure prediction of dams and is conducive to dam operation and maintenance management.

Monitoring heat transfer for seepage in earth-rock dams with clay cores considering damaged areas: Laboratory experiments and numerical modeling

This study conducted a laboratory test and two-dimensional numerical simulation on seepage heat monitoring of a core rockfill dam. A fully automatic laboratory test system for dam seepage monitoring has been developed, which can achieve automatic control of water flow and temperature, as well as automatic data collection. A flow-heat coupling model based on [Johansen, O., (1975). Thermal conductivity of soils. P. D. thesis. University of Trondheim, Norway, Trondheim] empirical thermal conductivity model was established through secondary development using COMSOL Multiphysics finite element software, and its accuracy was verified. The influence of the location of damaged leakage in the core wall of the dam on flow and heat migration was analyzed. Among the investigated conditions, a damaged channel with a curved cross-section causes the formation of the prominent arc in the saturation line of the dam body downstream, induces the largest increase in the average seepage discharge, and makes the dam body most unstable. The effects of damage on the temperature and seepage fields are positively correlated for different damage types. The presence of a damaged area induces sudden changes in the pressure, velocity, and temperature at each monitoring point. The pressure, velocity and temperature are correlated, and there is a lag in temperature transfer. This study provides a reference for the application of distributed fiber optic temperature measurement technology in core rockfill dam engineering, especially in terms of fiber optic placement position. The established numerical model can provide an accurate and effective reference method for identifying abnormal temperature data and locating dam leakage. Our next step will be to study the flow and heat transfer characteristics throughout the entire process from undamaged to dam failure.