Search Results (118)

In concrete dam foundations, failure mechanisms are primarily influenced by natural rock discontinuities, the dam foundation interface, or weaker strata. This paper proposes a large displacement contact model (LDCM) based on spherical particle/surface interactions, which is computationally more robust and simpler than contact models that adopt the real block polyhedral geometry. To reduce computational costs, whenever possible, the contact interaction is defined in small displacements. The proposed LDCM is applied to a masonry arch under static loading and to the stability analysis of both a gravity dam and an arch dam. The results presented validate the proposed LDCM, and the numerical predictions are close to results obtained experimentally and closely match those obtained with a more complex polyhedral-based model. The advantages of the LDCM are highlighted, namely the decoupling of contact refinement from block refinement, which significantly reduces the computational costs for the masonry arch example. The relevance of adopting a LDCM to predict a physically accepted failure mode is emphasized for dam safety. Finaly, it is shown that the LDCM contact model can be readily adopted to assess the stability of complex dam foundation systems, with reasonable computational running times if a hybrid contact approach is used. Full article

Arch–gravity dams feature both arch action and large concrete volume, yet targeted research on temperature control and crack prevention for this type remains insufficient. To address this, a Two-Parameter Decision Chart Method for predicting allowable placing temperature, an Analytical–Numerical Hybrid Estimation Method for estimating cooling durations, and the Comprehensive Cracking Risk Index (CCRI) for assessing lifecycle concrete safety are proposed, forming a complete design methodology. A case study on a proposed project using full-process simulation quantitatively evaluates the contribution of various measures in mitigating thermal stress across dam zones. Results show that without measures, the CCRI values for interior and surface concrete reach 68.9% and 38.1%, respectively. After implementing combined optimization measures targeting the control of maximum temperature, final temperature before grouting, and internal–external temperature difference throughout the entire process, both CCRI values are reduced to zero. Contribution analysis reveals distinct zonal effectiveness: for interior concrete, low-temperature placement with first-stage cooling contributes most (59.9%); for surface concrete, second- and third-stage cooling dominates (72.7%). Therefore, in practical engineering applications for temperature control and crack prevention in arch–gravity dams, a combination of measures centered on controlling the maximum temperature, optimizing the cooling process, and enhancing surface insulation should be adopted based on the characteristics of interior and surface zones, thereby improving cracking safety. Full article

Rapid agricultural expansion threatens water security in one of the world’s largest hydroelectric systems, the Itaipu dam, located on the Brazil–Paraguay border. Yet regional hydrological responses to land cover change and climate variability remain insufficiently characterized at management-relevant scales. The Upper Paraná River Basin (UPRB), which sustains agriculture, hydropower, and municipal water supply across both countries, exemplifies this challenge as accelerating cropland conversion raises concerns about long-term water availability. This study investigates hydrological transitions and their statistical associations with land cover changes in the Itaipu study region from 2002 to 2023. We integrate GRACE/GRACE-FO (Gravity Recovery and Climate Experiment Follow-On), Terrestrial Water Storage Anomalies (TWSAs), MODIS (Moderate Resolution Imaging Spectroradiometer) land cover, CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data) precipitation, and LandScan population density using Pettitt’s breakpoint test and Mann–Kendall trend analysis to detect temporal breakpoints and quantify co-variability between hydrology and land surface dynamics. Together, these methods identify a significant basin-wide shift in TWSAs in mid-2009, with storage increases of 151.6 cm at Itaipu and 103.1 cm at Yguazú Reservoir. Over the study period, cropland expanded from 13.5% to 37.9% of total land cover, while savanna declined from 28.1% to 24.2%. After 2009, correlations between land cover and TWSAs strengthened substantially, particularly for wetlands (r = 0.88), croplands (r = 0.73), and savannas (r = −0.81; all p < 0.001), indicating strong coupling between landscape transformation and basin-scale storage variability. Principal Component Analysis shows land use change explains 39–41% of TWSA variance, exceeding hydroclimatic contributions. Granger causality analysis reveals bidirectional coupling between wetlands and water storage at Itaipu, while cropland and savanna dynamics exert predictive influence on downstream hydrology in the Yguazú basin. Water balance decomposition further indicates a post-2009 regime shift, with residual storage transitioning from −10.6 to +4.7 and 78% greater runoff generation per unit precipitation, consistent with reduced infiltration capacity. Together, these findings underscore intensifying land–water feedback and the need for adaptive watershed management under expanding agriculture and climate variability. Full article

Precise forecasting and physical elucidation of seepage behavior are crucial for maintaining the operational safety of concrete dams. Nonetheless, current monitoring methodologies frequently fail to adequately encompass nonlinear temporal relationships in seepage processes and exhibit a deficiency in straightforward interpretability. This paper provides an explainable monitoring approach that combines an alpha-evolution Bidirectional Gated Recurrent Unit (AE-BiGRU) with Shapley Additive Explanations (SHAP)-based interpretability analysis to solve these shortcomings. An AE-BiGRU prediction model is first developed, in which the BiGRU architecture exploits bidirectional temporal dependencies to enhance prediction accuracy and robustness. The alpha-evolution algorithm is then employed to optimize key hyperparameters of the neural network, thereby further improving model performance. Subsequently, SHAP interpretability analysis is applied to quantify the contribution of individual input variables and to elucidate the physical drivers of seepage variation. Validation utilizing long-term seepage monitoring data from a roller-compacted concrete (RCC) gravity dam indicates that the proposed AE-BiGRU model substantially surpasses benchmark models, including LSTM and traditional GRU variations. Furthermore, SHAP interpretability analysis reveals the predominant influences of reservoir water level fluctuations and cumulative temporal factors on seepage evolution patterns. The suggested approach attains high-precision seepage prediction while ensuring physically meaningful interpretability, thus providing a dependable foundation for safety evaluation and intelligent monitoring of concrete dams. Full article

To address the challenge that traditional dam model materials are difficult to simultaneously meet the requirements of microstructural similarity, dynamic damage simulation, and environmental friendliness, a novel microparticle mortar simulated concrete was developed. This new material consists of cement, sand, gypsum, mineral oil, water, and baryte sand. Through systematic material mechanical tests, the effects of each component on the material’s strength, density, and elastic modulus were revealed, and the optimal mix ratio was determined. This enabled precise control of low elastic modulus and had a high density, while the material is environmentally friendly, non-toxic, and compatible with direct contact with natural water. Its mechanical properties are highly similar to those of the prototype concrete. Based on a 1:70 geometric scale, a shaking table model test of the concrete gravity dam-reservoir system was conducted. The dynamic response and damage evolution under empty and full reservoir conditions were compared and analyzed. The study shows that this material can accurately simulate the stress-strain relationship and failure mode of prototype concrete. Under the full reservoir condition, the dam’s fundamental frequency showed only a 2.72% deviation from the numerical simulation, and as the seismic excitation amplitude increased, the changes in the fundamental frequency effectively reflected the accumulation of damage. Under the design seismic motion, the measured accelerations and stress responses for both empty and full reservoir conditions were in good agreement with numerical calculations. Under overload conditions, the acceleration amplification factor at the dam crest decreased with damage accumulation, and the dam neck was identified as the seismic weak zone. As the peak ground acceleration (PGA) increased from 0.15 g to 0.70 g, the fundamental frequency changes effectively reflected the damage accumulation process in the dam, while the hydrodynamic pressure at the dam heel showed a linear increase (457% increase). The experimentally measured hydrodynamic pressure distribution was between the rigid dam and elastic dam hydrodynamic pressures, reflecting the real fluid-structure interaction effect. This study provides a reliable material solution and data support for dam seismic physical model testing. Full article

An explicit coupled two-dimensional (2D) hydromechanical model (HMM) that can simulate discontinuous features in the foundation, as well as the effects of grout curtains and drainage systems, is employed to evaluate the influence of key parameters such as dam height, foundation behaviour, joint patterns, joint stiffness and strength, hydraulic apertures, and grout curtain permeability. A parametric sensitive study using four gravity dams, and a real case study of an operating dam are presented. The results presented show that dam height influences the relationship between water level in the reservoir and drain discharges, with higher dams showing more pronounced curved nonlinearity. The strength properties of the concrete–rock interface are also shown to have a meaningful influence on the HM response, especially for an elastic foundation and for higher dams, showing the need to properly characterize this interface through in situ testing. The joint aperture at nominal zero stress is shown to be the parameter with the most significant effect on the HM response. The results also show that a progressive degradation scenario of the concrete–rock interface or of the grout curtain permeability is easier to identify through the hydraulic measurements than in the mechanical displacement field. Full article

The Hisayamada Dam (22.5 m high, 75.4 m long), constructed in 1924 as a water supply facility, is a masonry arch–gravity concrete dam with a slender arch shape. Although it was the first theoretically designed arch-type dam in Japan, seismic forces were not considered at the time of construction. This study evaluates its seismic performance using a three-dimensional (3D) dynamic Finite Element Method (FEM) in accordance with current Japanese governmental guidelines. A detailed 3D model incorporating the dam body, surrounding topography, foundation, and reservoir was developed, and expected earthquake motions in three directions were applied simultaneously. The analysis showed that localized tensile stress exceeding the tensile strength occurred near the upstream heel of the dam base. However, these stress concentrations were limited to small regions and did not form continuous damage paths across the dam body. Based on the linear dynamic analysis and engineering judgment, the overall structural integrity and water storage function of the dam are considered to be maintained. Additional analyses were conducted by varying the elastic modulus of the foundation rock and dam concrete to clarify the influence of material stiffness on seismic response and stability. Full article

A series of machine learning models have been proposed in the past decades, but it remains undetermined which is optimal for specific applications. Establishing mathematical prediction models for dam deformation and structural health monitoring based on environmental factors is crucial to dam safety assessment. This paper takes Zhexi Dam, a concrete gravity-type dam in China, as an example to conduct a comparative study on the performance of deformation prediction models. The physical factors that cause dam deformation include the air temperature, reservoir water temperature, reservoir water level, and dam aging. The correlations between environmental factors and dam deformation are evaluated by maximum information coefficient (MIC) and Pearson, Kendall, and Spearman correlation coefficients. The monitoring data reveal that the deformation has a high correlation with environmental factors. A number of the most representative monitoring points from hundreds of monitoring points are selected for modeling. For comparison, seven modeling methods, i.e., multiple linear regression (MLR), gradient boosting decision tree (GBDT), random forest (RF), support vector machine (SVM), and long short-term memory network (LSTM), weighted average model (WAM) of the above five algorithms, and Transformer-based neural network, are introduced to establish dam deformation prediction models. The experimental results indicate that both the weighted average model and the Transformer-based neural network achieve consistently high accuracy, showing strong agreement with the monitoring data generally. However, in scenarios involving small sample sizes, the SVM model demonstrates relatively superior predictive performance compared to the other models. Full article

This paper addresses the challenge of systemic extreme risk in long-service gravity dams under human-controlled operation. It is the first study to construct a Generalized Extreme Value (GEV) distribution model using long-term operational monitoring data. The model, validated by multiple statistical tests and engineering boundary conditions, is then applied within a Response Surface Method-Monte Carlo (RSM-MC) reliability framework. Results indicate that the historical GEV model accurately captures the high-water-level tail characteristics and significantly overcomes the risk underestimation inherent in the uniform distribution model. Compared to the Log-Pearson Type III (Log-P3) design condition model, the GEV model yields a significantly lower probability of failure, e.g., the probability of cracking at the dam heel, the most sensitive failure mode, is reduced by nearly six times. This quantitative difference fully demonstrates GEV’s ability to precisely quantify the effective risk reduction achieved by human control, establishing a more scientific and realistic foundation for risk assessment of long-service gravity dams. Full article

For concrete structures dominated by fracture failure, e.g., containment and gravity dams, sustained load deformations primarily arise from crack extension and concrete viscoelasticity. As cracks progressively grow under sustained loads, accurate prediction of the time-dependent fracture process in concrete accounting for crack-viscoelasticity interactions are crucial for the stability and safe design of concrete structures. This paper presents an initial fracture toughness ($K_{\mathrm{IC}}^{ini}$)-based numerical model to predict the time-dependent crack extension in concrete under sustained loads. The model integrates a time-dependent tension-softening constitutive relation, the generalized Kelvin chain model for viscoelastic behavior and $K_{\mathrm{IC}}^{ini}$-based criterion for crack extension. The accuracy of the model was verified with two sets of experimental data available in the literature. The results indicated that the tension-softening constitutive law that quantifies the relation cohesive stress (s_w), loading time (t), and COD can be successfully implemented in the numerical model. The predicted CMOD versus time and crack length versus time curves show good agreements with the test results regardless of loading level, specimen configuration and material property, demonstrating the predictive capability of the model in describing the crack extension in concrete exposed to sustained loads. Full article

Single-point deformation monitoring models fail to reflect the structural integrity of the concrete gravity dams, and traditional regression methods also have shortcomings in capturing complex nonlinear relationships among variables. To solve these problems, this paper develops a data–physics-driven multi-point hybrid deformation monitoring model based on Bayesian Optimization Algorithm–Light Gradient-Boosting Machine (BOA-LightGBM). Building upon conventional single-point models, spatial coordinates are incorporated as explanatory variables to derive a multi-point deformation monitoring model that accounts for spatial correlations. Subsequently, the finite element method (FEM) is employed to simulate the hydrostatic component at each monitoring point under actual reservoir water levels. Finally, a hybrid model is constructed by integrating the derived mathematical expression, simulated hydrostatic components, and the BOA-LightGBM algorithm. A case study demonstrates that the proposed model effectively incorporates spatial deformation characteristics within dam sections and achieves satisfactory fitting and prediction accuracy compared to traditional single-point monitoring models. With further refinement and extension, the proposed modeling theory and methodology presented in this study can also provide valuable references for safety monitoring of other hydrostatic structures. Full article

As a fundamental metric for assessing carbon sequestration, Net Primary Productivity (NPP) and the mechanisms driving its spatiotemporal dynamics constitute a critical research domain within global change science. This research centered on the Huang–Huai–Hai Plain (HHHP), combining 2001–2023 MODIS-NPP data with natural (landform, temperature, precipitation, soil) and socio-economic (population density, GDP density, land use) drivers. Trend analysis, coefficient of variation, and Hurst index were applied to clarify the spatiotemporal evolution of NPP and its future trends, while geographic detectors and structural equation models were used to quantify the contribution of drivers. Key findings: (1) Across the HHHP, the multi-year average NPP ranged between 30.05 and 1019.76 gC·m⁻²·a⁻¹, with higher values found in Shandong and Henan provinces, and lower values concentrated in the northwestern dam-top plateau and central plain regions; 44.11% of the entire region showed a statistically highly significant increasing trend. (2) The overall fluctuation of NPP was low-amplitude, with a stable center of gravity and the standard deviation ellipse retaining a southwest-to-northeast direction. (3) Future changes in NPP exhibited persistence and anti-persistence, with 44.98% of the region being confronted with vegetation degradation risk. (4) NPP variations originated from the synergistic impacts of multiple elements: among individual elements, precipitation, soil type, and elevation had the highest explanatory capacity, while synergistic interactions between two elements notably enhanced the explanatory capacity. (5) Climate variation exerted the strongest influence on NPP (direct coefficient of 0.743), followed by the basic natural environment (0.734), whereas human-related activities had the weakest direct impact (−0.098). This research offers scientific backing for regional carbon sink evaluation, ecological security early warning, and sustainable development policies. Full article

Evaluation of seismic risk by capturing the influences of strong motion duration and frequency contents of ground motion through probabilistic approaches is the main element of this study. Unlike most existing studies that mainly focus on intensity measures such as peak ground acceleration or spectral acceleration, this work highlights how duration and frequency characteristics critically influence dam response. To achieve this, a total of 45 ground motion records, categorized by strong motion duration (long, medium, and short) and frequency content (low, medium, and high), were selected from the PEER database. Nonlinear numerical dynamic analysis was performed by scaling each ground motion from 0.05 g to 0.5 g, with the drift ratio at the dam crest used as the Engineering Demand Parameter. It is revealed that long-duration and low-frequency ground motions induced significantly higher drift demands. The fragility analysis was conducted using a lognormal distribution considering extensive damage threshold drift ratio. Finally, the probabilistic seismic risk was carried out by integrating the site-specific hazard curve and fragility curves which yield the height risk for long durations and low frequencies. The outcomes emphasize the importance of ground motion strong duration and frequency in seismic performance and these findings can be utilized in the dam safety evaluation. Full article

The failure of large gravity dams during an earthquake could lead to calamitous flooding, severe infrastructural damage, and massive environmental destruction. This paper aims to demonstrate reliable methods for evaluating dam performance after a seismic event. The work included a seismic hazard analysis and nonlinear finite element modelling of concrete cracking for two large dams (D1 and D2, of 35 and 90 m in height, respectively) in Eastern Canada. Dam D1 is located in Montreal, and Dam D2 is located in La Malbaie, Quebec. The modelling approach was validated using the Koyna Dam, which was subjected to the 1967 Mw 6.5 earthquake. This paper reports tensile cracks of D1 and D2 under combined hydrostatic and seismic loading. The latter was generated from ground motion records from 11 sites during the 1988 Mw 5.9 Saguenay earthquake. These records were each scaled to two times the design level. It is shown that D1 remained stable, with minor localised cracking, whereas D2 experienced widespread tensile damage, particularly at the crest and base under high-energy and transverse inputs. These findings highlight the influence of dam geometry and frequency characteristics on seismic performance. The analysis and modelling procedures reported can be adopted for seismic risk classification and safety compliance verification of other dams and for recommendations such as monitoring and upgrading. Full article

Dam displacement serves as a critical visual indicator for assessing structural integrity and stability in dam engineering. Data-driven displacement forecasting has become essential for modern dam safety monitoring systems, though conventional approaches—including statistical models and basic machine learning techniques—often fail to capture comprehensive feature representations from multivariate environmental influences. To address these challenges, a bidirectional gated recurrent unit (BiGRU)-enhanced neural network is developed, incorporating sliding window mechanisms to model time-dependent hysteresis characteristics. The BiGRU’s architecture systematically integrates historical temporal patterns through overlapping window segmentation, enabling dual-directional sequence processing via forward–backward gate structures. Validated with four instrumented measurement points from a major concrete gravity dam, the proposed model exhibits significantly better performance against three widely used recurrent neural network benchmarks in displacement prediction tasks. These results confirm the model’s capability to deliver high-fidelity displacement forecasts with operational stability, establishing a robust framework for infrastructure health monitoring applications. Full article