Water

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19 pages, 6647 KiB

Open AccessArticle

A Hybrid Prediction Model for Rock Reservoir Bank Slope Deformation Considering Fractured Rock Mass Parameters

by Jiachen Liang, Jian Chen and Chuan Lin

Water 2024, 16(13), 1880; https://doi.org/10.3390/w16131880 - 30 Jun 2024

Viewed by 1001

Deformation monitoring data provide a direct representation of the structural behavior of reservoir bank rock slopes, and accurate deformation prediction is pivotal for slope safety monitoring and disaster warning. Among various deformation prediction models, hybrid models that integrate field monitoring data and numerical [...] Read more.

Deformation monitoring data provide a direct representation of the structural behavior of reservoir bank rock slopes, and accurate deformation prediction is pivotal for slope safety monitoring and disaster warning. Among various deformation prediction models, hybrid models that integrate field monitoring data and numerical simulations stand out due to their well-defined physical and mechanical concepts, and their ability to make effective predictions with limited monitoring data. The predictive accuracy of hybrid models is closely tied to the precise determination of rock mass mechanical parameters in structural numerical simulations. However, rock masses in rock slopes are characterized by intersecting geological structural planes, resulting in reduced strength and the creation of multiple fracture flow channels. These factors contribute to the heterogeneous, anisotropic, and size-dependent properties of the macroscopic deformation parameters of the rock mass, influenced by the coupling of seepage and stress. To improve the predictive accuracy of the hybrid model, this study introduces the theory of equivalent continuous media. It proposes a method for determining the equivalent deformation parameters of fractured rock mass considering the coupling of seepage and stress. This method, based on a discrete fracture network (DFN) model, is integrated into the hybrid prediction model for rock slope deformation. Engineering case studies demonstrate that this approach achieves a high level of prediction accuracy and holds significant practical value. Full article

(This article belongs to the Special Issue Safety Evaluation of Dam and Geotechnical Engineering, Volume II)

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25 pages, 9573 KiB

Open AccessArticle

A Multi-Point Joint Prediction Model for High-Arch Dam Deformation Considering Spatial and Temporal Correlation

by Wenhan Cao, Zhiping Wen, Yanming Feng, Shuai Zhang and Huaizhi Su

Water 2024, 16(10), 1388; https://doi.org/10.3390/w16101388 - 13 May 2024

Cited by 3 | Viewed by 1397

Abstract

Deformation monitoring for mass concrete structures such as high-arch dams is crucial to their safe operation. However, structure deformations are influenced by many complex factors, and deformations at different positions tend to have spatiotemporal correlation and variability, increasing the difficulty of deformation monitoring. [...] Read more.

Deformation monitoring for mass concrete structures such as high-arch dams is crucial to their safe operation. However, structure deformations are influenced by many complex factors, and deformations at different positions tend to have spatiotemporal correlation and variability, increasing the difficulty of deformation monitoring. A novel deep learning-based monitoring model for high-arch dams considering multifactor influences and spatiotemporal data correlations is proposed in this paper. First, the measurement points are clustered to capture the spatial relationship. Successive multivariate mode decomposition is applied to extract the common mode components among the correlated points as spatial influencing factors. Second, the relationship between various factors and deformation components is extracted using factor screening. Finally, a deep learning prediction model is constructed with stacked components to obtain the final prediction. The model is validated based on practical engineering. In nearly one year of high-arch dam deformation prediction, the root mean square error is 0.344 and the R² is 0.998, showing that the modules within the framework positively contribute to enhancing prediction performance. The prediction results of different measurement points as well as the comparison results with benchmark models show its superiority and generality, providing an advancing and practical approach for engineering structural health monitoring, particularly for high-arch dams. Full article

(This article belongs to the Special Issue Safety Evaluation of Dam and Geotechnical Engineering, Volume II)

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19 pages, 7201 KiB

Open AccessArticle

Study on Seismic Source Parameter Characteristics of Baihetan Reservoir Area in the Lower Reaches of the Jinsha River

by Jing Shi, Cuiping Zhao, Zhousheng Yang and Lisheng Xu

Water 2024, 16(10), 1370; https://doi.org/10.3390/w16101370 - 11 May 2024

Cited by 1 | Viewed by 761

Abstract

The source parameters of earthquakes (stress drop, corner frequency, seismic moment, source size, radiant energy, etc.) provide important information about the source features, the state of stress, and the mechanism of earthquake rupture dynamics. Using the digital observation data obtained from a high-density [...] Read more.

The source parameters of earthquakes (stress drop, corner frequency, seismic moment, source size, radiant energy, etc.) provide important information about the source features, the state of stress, and the mechanism of earthquake rupture dynamics. Using the digital observation data obtained from a high-density seismic monitoring network deployed in the Baihetan reservoir area of the lower Jinsha River, we obtained Brune source parameters of the 459 earthquakes ranging in magnitude M_L 1.50~4.70. The results revealed seismic moments M₀ within the range of 2.03 × 10¹²~1.45 × 10¹⁶ N·m, corner frequencies

f c

between 2.00 and 10.00 Hz, and source dimensions varying from 130.00 to 480.00 m, with stress drops spanning from 0.12 to 61.24 MPa. It is noteworthy that the majority of the earthquakes had stress drops less than 10.00 MPa, with as much as 73.30% of these events having stress drops within the range of 0.10 to 2.00 MPa. We found that stress drop, corner frequency, and source size in the study area exhibited positive correlations with earthquake magnitude. Earthquakes occurring at shallower depths for the same magnitude tended to have smaller stress drops and corner frequencies, but larger rupture scales. During the first 2 years of impoundment with significant water level fluctuation, earthquakes beneath or near the reservoir released higher stress drops relative to pre-reservoir conditions, with average stress drops significantly elevated from 5.52 to 13.562 Mpa for events above M_L3 since the impoundment. The radiated energy released by earthquakes with magnitudes below M_L3.0 are significantly more than before impoundment, indicating that earthquakes of similar magnitudes in the reservoir area may produce greater intensity and perceptibility following the impoundment. According to our result, the triggered seismicity will continue to be active under annual regulation changes in the water level of the Baihetan Dam at high elevations in future years. Full article

(This article belongs to the Special Issue Safety Evaluation of Dam and Geotechnical Engineering, Volume II)

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24 pages, 7370 KiB

Open AccessArticle

Greedy Weighted Stacking of Machine Learning Models for Optimizing Dam Deformation Prediction

by Patricia Alocén, Miguel Á. Fernández-Centeno and Miguel Á. Toledo

Water 2024, 16(9), 1235; https://doi.org/10.3390/w16091235 - 25 Apr 2024

Viewed by 1099

Abstract

Dam safety monitoring is critical due to its social, environmental, and economic implications. Although conventional statistical approaches have been used for surveillance, advancements in technology, particularly in Artificial Intelligence (AI) and Machine Learning (ML), offer promising avenues for enhancing predictive capabilities. We investigate [...] Read more.

Dam safety monitoring is critical due to its social, environmental, and economic implications. Although conventional statistical approaches have been used for surveillance, advancements in technology, particularly in Artificial Intelligence (AI) and Machine Learning (ML), offer promising avenues for enhancing predictive capabilities. We investigate the application of ML algorithms, including Boosted Regression Trees (BRT), Random Forest (RF), and Neural Networks (NN), focussing on their combination by Stacking to improve prediction accuracy on concrete dam deformation using radial displacement data from three dams. The methodology involves training first-level models (experts) using those algorithms, and a second-level meta-learner that combines their predictions using BRT, a Linear Model (LM) and the Greedy Weighted Algorithm (GWA). A comparative analysis demonstrates the superiority of Stacking over traditional methods. The GWA emerged as the most suitable meta-learner, enhancing the optimal expert in all cases, with improvement rates reaching up to 16.12% over the optimal expert. Our study addresses critical questions regarding the GWA’s expert weighting and its impact on prediction precision. The results indicate that the combination of accurate experts using the GWA improves model reliability by reducing error dispersion. However, variations in optimal weights over time necessitate robust error estimation using cross-validation by blocks. Furthermore, the assignment of weights to experts closely correlates with their precision: the more accurate a model is, the more weight that is assigned to it. The GWA improves on the optimal expert in most cases, including at extreme values of error, with improvement rates up to 41.74%. Our findings suggest that the proposed methodology significantly advances AI applications in infrastructure monitoring, with implications for dam safety. Full article

(This article belongs to the Special Issue Safety Evaluation of Dam and Geotechnical Engineering, Volume II)

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11 pages, 4830 KiB

Open AccessArticle

Geophysical Characterization and Seepage Detection of the Chimney Rock Dam Embankment Near Salina, Oklahoma

by Peter Adetokunbo, Ahmed Ismail, Farag Mewafy and Oluseun Sanuade

Water 2024, 16(9), 1224; https://doi.org/10.3390/w16091224 - 25 Apr 2024

Cited by 2 | Viewed by 1432

Abstract

The operator of Chimney Rock Dam observed the emergence of increasing seepage at the toe of the dam when the water level in the reservoir exceeded a particular elevation. However, the source and the pathways of the seepage were not identified. To address [...] Read more.

The operator of Chimney Rock Dam observed the emergence of increasing seepage at the toe of the dam when the water level in the reservoir exceeded a particular elevation. However, the source and the pathways of the seepage were not identified. To address this issue, integrated geophysical methods were employed to delineate the different units of the dam embankment and identify potential seepage zones and pathways. The methods utilized in this study included electrical resistivity tomography (ERT), self-potential (SP), and multichannel analysis of surface waves (MASW). The ERT profiles revealed variations in the dam’s fill properties, including areas with anomalously low resistivity, interpreted as zones of relatively high moisture content. The two long SP profiles conducted along the dam embankment displayed similar spatial correlations with these low-resistivity zones, suggesting potential preferential seepage pathways. The SP map generated from a suite of parallel SP profiles conducted over the abutment depicts a pattern of positive background and negative potential anomalies, which may suggest fluid movement or seepage potential. The MASW profile along the top of the dam characterized an upper low shear-wave velocity layer corresponding to the top dry section of the embankment underlain by a higher shear-wave velocity layer, interpreted as saturated zone. The utilized geophysical methods successfully characterized the different materials of the embankment and identified zones of potential seepage. Full article

(This article belongs to the Special Issue Safety Evaluation of Dam and Geotechnical Engineering, Volume II)

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18 pages, 2822 KiB

Open AccessArticle

A Dam Safety State Prediction and Analysis Method Based on EMD-SSA-LSTM

by Xin Yang, Yan Xiang, Yakun Wang and Guangze Shen

Water 2024, 16(3), 395; https://doi.org/10.3390/w16030395 - 24 Jan 2024

Cited by 4 | Viewed by 1860

Abstract

The safety monitoring information of the dam is an indicator reflecting the operational status of the dam. It is a crucial source for analyzing and assessing the safety state of reservoir dams, possessing strong real-time capabilities to detect anomalies in the dam at [...] Read more.

The safety monitoring information of the dam is an indicator reflecting the operational status of the dam. It is a crucial source for analyzing and assessing the safety state of reservoir dams, possessing strong real-time capabilities to detect anomalies in the dam at the earliest possible time. When using neural networks for predicting and warning dam safety monitoring data, there are issues such as redundant model parameters, difficulty in tuning, and long computation times. This study addresses real-time dam safety warning issues by first employing the Empirical Mode Decomposition (EMD) method to decompose the effective time-dependent factors and construct a dam in a service state analysis model; it also establishes a multi-dimensional time series analysis equation for dam seepage monitoring. Simultaneously, by combining the Sparrow Optimization Algorithm to optimize the LSTM neural network computation process, it reduces the complexity of model parameter selection. The method is compared to other approaches such as RNN, GRU, BP neural networks, and multivariate linear regression, demonstrating high practicality. It can serve as a valuable reference for reservoir dam state prediction and engineering operation management. Full article

(This article belongs to the Special Issue Safety Evaluation of Dam and Geotechnical Engineering, Volume II)

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20 pages, 12540 KiB

Open AccessArticle

A Multi-Strategy Improved Sooty Tern Optimization Algorithm for Concrete Dam Parameter Inversion

by Lin Ma, Fuheng Ma, Wenhan Cao, Benxing Lou, Xiang Luo, Qiang Li and Xiaoniao Hao

Water 2024, 16(1), 119; https://doi.org/10.3390/w16010119 - 28 Dec 2023

Cited by 3 | Viewed by 1323

Abstract

A original strategy for optimizing the inversion of concrete dam parameters based on the multi-strategy improved Sooty Tern Optimization algorithm (MSSTOA) is proposed to address the issues of low efficiency, low accuracy, and poor optimizing performance. First, computational strategies to improve the traditional [...] Read more.

A original strategy for optimizing the inversion of concrete dam parameters based on the multi-strategy improved Sooty Tern Optimization algorithm (MSSTOA) is proposed to address the issues of low efficiency, low accuracy, and poor optimizing performance. First, computational strategies to improve the traditional Sooty tern algorithm, such as chaos mapping to improve the initial position of the population, a new nonlinear convergence factor, the LIMIT threshold method, and Gaussian perturbation to update the optimal individual position, are adopted to enhance its algorithmic optimization seeking ability. Then, the measured and finite element data are combined to create the optimization inversion fitness function. Based on the MSSTOA, the intelligent optimization inversion model is constructed, the inversion efficiency is improved by parallel strategy, and the optimal parameter inversion is searched. The inversion strategy is validated through test functions, hypothetical arithmetic examples, and concrete dam engineering examples and compared with the inversion results of the traditional STOA and other optimization algorithms. The results show that the MSSTOA is feasible and practical, the test function optimization results and computational time are better than the STOA and other algorithms, the example inversion of the elastic modulus is more accurate than the traditional STOA calculation, and the results of the MSSSTOA inversion are reasonable in the engineering example. Compared with other algorithms, the local extremes are skipped, and the time consumption is reduced by at least 48%. The finite element hydrostatic components calculated from the inversion results are well-fitted to the statistical model with minor errors. The intelligent inversion strategy has good application in concrete dam inverse analysis. Full article

(This article belongs to the Special Issue Safety Evaluation of Dam and Geotechnical Engineering, Volume II)

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24 pages, 14903 KiB

Open AccessArticle

Fragility Analysis for Water-Retaining Structures Resting on Spatially Random Soil

by Sung-Eun Cho

Water 2023, 15(23), 4165; https://doi.org/10.3390/w15234165 - 1 Dec 2023

Viewed by 1325

Abstract

The maintenance of water-retaining structures involves evaluating their performance against current and future operating water levels. Fragility curves are commonly used for this purpose, as they indicate the conditional probability of failure for various load conditions and accurately characterize a structure’s performance. Monte [...] Read more.

The maintenance of water-retaining structures involves evaluating their performance against current and future operating water levels. Fragility curves are commonly used for this purpose, as they indicate the conditional probability of failure for various load conditions and accurately characterize a structure’s performance. Monte Carlo simulation (MCS) can be used to determine the fragility curve of water-retaining structures by calculating the probability of failure as the water level changes. However, performing repetitive MCS involves extensive calculations, thus making it inefficient for practical applications. Therefore, it is essential to develop efficient methods that require a minimum number of MCS runs to estimate the fragility curve. This study proposed two methods to estimate the fragility curves of water-retaining structures, thereby allowing for the assessment of failure probabilities related to important quantities such as the steady-state seepage rate, exit gradient, and uplift force, which make them suitable for practical applications. The fragility curves obtained using the proposed methods are valuable for risk assessment, design, and decision-making purposes, as they offer information to evaluate the performance of the water-retaining structure under various water level conditions. Full article

(This article belongs to the Special Issue Safety Evaluation of Dam and Geotechnical Engineering, Volume II)

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16 pages, 2966 KiB

Open AccessArticle

Prediction Model of Residual Soil Shear Strength under Dry–Wet Cycles and Its Uncertainty

by Jiefa Ding, Shijun Wang, Haoran Huang, Fengqian Pan, Yunxing Wu, Yanchang Gu and Yan Zhang

Water 2023, 15(22), 3931; https://doi.org/10.3390/w15223931 - 10 Nov 2023

Cited by 1 | Viewed by 1592

Abstract

Granite residual soil is widely distributed in Southeast Fujian. Large-scale engineering construction leads to the exposure of residual soil slopes to the natural environment. Affected by seasonal climate factors, the soil of slopes experiences a dry–wet cycle for a long time. The repeated [...] Read more.

Granite residual soil is widely distributed in Southeast Fujian. Large-scale engineering construction leads to the exposure of residual soil slopes to the natural environment. Affected by seasonal climate factors, the soil of slopes experiences a dry–wet cycle for a long time. The repeated changes in water content seriously affect the shear strength of soil, and then affect the stability of the slope. In order to explore the influence of the dry–wet cycle on the shear strength of granite residual soil in Fujian, an indoor dry–wet cycle simulation test was carried out for shallow granite residual soil on a slope in Fuzhou, and the relationship between water content, dry–wet cycle times, and the shear strength index, including the cohesion and internal friction angle of the granite residual soil, was discussed. The results show that when the number of dry–wet cycles is constant, the cohesion and internal friction angle of the granite residual soil decrease with an increase in water content. The relationship between the cohesion, internal friction angle, and water content can be described using a power function. Meanwhile, the fitting parameters of the power function are also a function of the number of wet and dry cycles. The prediction formulas of the cohesion and internal friction angle considering the number of dry–wet cycles and water content are established, and then the prediction formula of shear strength is obtained. The ratio of the predicted value of shear strength to the test value shall be within ±15%. An error transfer analysis based on the point estimation method shows that the overall uncertainty of the predicted value of shear strength caused by the combined uncertainty of the predicted value of cohesion and the internal friction angle and the single-variable uncertainty of the predicted value of shear strength caused only by the uncertainty of the predicted value of either the cohesion or internal friction angle increases first and then decreases with an increase in the number of dry–wet cycles. All increase with an increasing water content. The maximum standard deviation of the proposed shear strength prediction model of granite residual soil is less than 9%. Full article

(This article belongs to the Special Issue Safety Evaluation of Dam and Geotechnical Engineering, Volume II)

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15 pages, 5236 KiB

Open AccessArticle

Experimental Investigation on Reinforcement Application of Newly Permeable Polymers in Dam Engineering with Fine Sand Layers

by Heng Liu, Zixian Shi, Zhenyu Li and Yuke Wang

Water 2023, 15(21), 3761; https://doi.org/10.3390/w15213761 - 27 Oct 2023

Cited by 4 | Viewed by 1290

Abstract

The grinding reinforcement of fine sand layers is a difficult problem in dam engineering construction. As a new type of grouting material, permeable polymer with excellent impermeability and high strength is widely used in dam engineering. In this paper, a series of compressive [...] Read more.

The grinding reinforcement of fine sand layers is a difficult problem in dam engineering construction. As a new type of grouting material, permeable polymer with excellent impermeability and high strength is widely used in dam engineering. In this paper, a series of compressive tests were designed considering different grouting pressures, curing days, moisture content, and porosity of fine sand. The influence of grouting parameters and sand layer conditions on the strength of fine sand layers reinforced by permeable polymers was analyzed. SEM and XDR tests were conducted to analyze the microscopic characteristics of the grouting stone. The functional calculation model of the strength and the influencing factors was established to explore the main factors influencing grouting stones. The compressive strength of grouted stones increases rapidly from the 7th to the 14th day, reaching about 96% of the maximum strength. The degree of influence of different factors is grouting pressure > moisture content > porosity. The compressive strength of the grouted stones increases with the increase of grouting pressure and the number of curing days. The compressive strength decreases with the sand layer’s increasing moisture content and porosity. Full article

(This article belongs to the Special Issue Safety Evaluation of Dam and Geotechnical Engineering, Volume II)

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11 pages, 6284 KiB

Open AccessArticle

Research on Stability of Dam Substation on Inclined Soft Soil Foundation Reinforced by Pile Foundation

by Jisheng Lin, Shaowen Yu, Yunhua Luo, Teng Xu, Yuequ Lin, Weiwen Zheng, Wanxun Li and Yuke Wang

Water 2023, 15(20), 3527; https://doi.org/10.3390/w15203527 - 10 Oct 2023

Viewed by 1415

Abstract

For the construction of dam substations in coastal or mountainous areas, inclined soft soil foundations are very common. The unique engineering characteristics of inclined soft soil foundations can bring great difficulties to the construction of dam substations. In this paper, a pile foundation [...] Read more.

For the construction of dam substations in coastal or mountainous areas, inclined soft soil foundations are very common. The unique engineering characteristics of inclined soft soil foundations can bring great difficulties to the construction of dam substations. In this paper, a pile foundation reinforcement dam slope model on an inclined soft soil foundation is established; the influence of different pile spacings, the pile length, and the soft soil foundation angle on the slope safety factor is studied; and the failure mechanism and stability of pile-supported dam slope foundation are analyzed. The research results indicate that pile foundation reinforcement can reduce the deformation of the dam slope foundation and improve stability. The pile layout has an important impact on stability, but a change in the pile spacing has little effect on the settlement surface at the bottom of the dam slope. The pile length has a significant impact on the safety of the slope within a certain range. The main stress area of the pile is 0–2 m above the pile, and its main deformation is the lateral deformation of the upper part of the pile. The research results of this article can provide parameter support and theoretical guidance for the construction of dam substations. Full article

(This article belongs to the Special Issue Safety Evaluation of Dam and Geotechnical Engineering, Volume II)

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25 pages, 16841 KiB

Open AccessArticle

Direct Probability Integral Method for Seismic Performance Assessment of Earth Dam Subjected to Stochastic Mainshock–Aftershock Sequences

by Weijie Huang, Yuanmin Yang, Rui Pang and Mingyuan Jing

Water 2023, 15(19), 3485; https://doi.org/10.3390/w15193485 - 5 Oct 2023

Viewed by 1187

Abstract

Studying the impact of mainshock–aftershock sequences on dam reliability is crucial for effective disaster prevention measures. With this purpose in mind, a new method for stochastic dynamic response analyses and reliability assessments of dams during seismic sequences has been proposed. Firstly, a simulation [...] Read more.

Studying the impact of mainshock–aftershock sequences on dam reliability is crucial for effective disaster prevention measures. With this purpose in mind, a new method for stochastic dynamic response analyses and reliability assessments of dams during seismic sequences has been proposed. Firstly, a simulation method of stochastic seismic sequences is described, considering the dependence between mainshock and aftershock based on Copula function. Then, a novel practical framework for stochastic dynamic analysis is established, combined with the improved point selection strategy and the direct probability integration method (DPIM). The DPIM is employed on a nonlinear system with one degree of freedom and compared with Monte Carlo simulation (MCS). The findings reveal that the method boasts exceptional precision and efficiency. Finally, the seismic performance of a practical dam was evaluated based on the above method, which not only accurately estimates the response probability distribution and dynamic reliability of the dam, but also greatly reduces the required calculations. Furthermore, the impact of aftershocks on dam seismic performance is initially evaluated through a probability approach in this research. It is found that seismic sequences will significantly increase the probability of earth dam failure compared with sequences of only mainshocks. In addition, the influence of aftershocks on reliability will further increase when the limit state is more stringent. Specifically, the novel analysis method proposed in this paper provides more abundant and objective evaluation indices, providing a dynamic reliability assessment for dams that is more effective than traditional evaluation methods. Full article

(This article belongs to the Special Issue Safety Evaluation of Dam and Geotechnical Engineering, Volume II)

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