Traditional Dam Research Articles

Comprehensive evaluation of dam seepage safety combining deep learning with Dempster-Shafer evidence theory

Seepage state is one of the most important factors affecting the safety and durability of dams, which calls for accurate observation and evaluation. Traditional dam seepage evaluation mainly focusses on single monitoring points, and ignores the uncertainty and subjectivity with multi-sensor fusion. This paper presents an evaluation method of dam seepage based on Dempster-Shafer (D-S) evidence theory and deep neural network (DNN). In view of the nonlinearity of the monitoring data and the difference of seepage influencing factors, attention mechanism (AM) and long short-term memory (LSTM) model are combined to evaluate dam seepage safety of single monitoring points, which provides the basis for the comprehensive evaluation. The case study demonstrates that the average RMSE, MAE and MAPE values of AM-LSTM are of minimal losses and higher fitting accuracy. The forecast results of the proposed comprehensive evaluation approach are consistent with the actual dam seepage state, demonstrating its reasonability and reliability.

MHA-ConvLSTM Dam Deformation Prediction Model Considering Environmental Volume Lag Effect

The construction of a reasonable and reliable deformation prediction model is of great practical significance for dam safety assessment and risk decision-making. Traditional dam deformation prediction models are susceptible to interference from redundant features, weak generalization ability, and a lack of model interpretation. Based on this, a deformation prediction model that considers the lag effect of environmental quantities is proposed. The model first constructs a new deformation lag influence factor based on the plain HST model through the lag quantization algorithm. Secondly, the attention and memory capacity of the model is improved by introducing a multi-head attention mechanism to the features of the long-time domain deformation influence factor, and finally, the extracted dynamic features are transferred to the ConvLSTM model for learning, training, and prediction. The results of the simulation tests based on the measured deformation data of an active dam show that the introduction of the deformation lag factor not only improves the interpretation of the prediction model for deformation but also makes the prediction of deformation more accurate, and it can improve the evaluation indexes such as RMSE by 50%, the nMAPE by 40%, and R2 by 10% compared with the traditional prediction model. The combined prediction model is more capable of mining the hidden features of the data and has a deeper picture of the overall peak and local extremes of the deformation data, which provides a new way of thinking for the dam deformation prediction model.

Determination of Monitoring Control Value for Concrete Gravity Dam Spatial Deformation Based on POT Model

Deformation can directly reflect the working behavior of the dam, so determining the deformation monitoring control value can effectively monitor the safety of dam operation. The traditional dam deformation monitoring control value only considers the single measuring point. In order to overcome the limitation, this paper presents a new method to determine the monitoring control value for concrete gravity dam based on the deformations of multi-measuring points. A dam’s comprehensive deformation displacement is determined by the measured values at different measuring points on the positive inverted vertical line and the corresponding weight of each measuring point. The projection pursuit method (PPM) combined with the grey wolf optimization (GWO) algorithm is used to determine the weight of each measuring point according to the spatial correlation distribution characteristics of dam deformation. The peaks over threshold (POT) model based on the extreme value theory is adopted to determine the monitoring control value with the obtained dam comprehensive deformation displacement. In addition, the POT model is improved with the automatic threshold determination method based on the criterion in probability theory and the GWO algorithm, which can avoid subjectivity and randomness in determining the threshold. The results of the engineering application show the feasibility and applicability of the proposed method.

Dam Crack Image Detection Model on Feature Enhancement and Attention Mechanism

Dam crack detection can effectively avoid safety accidents of dams. To solve the problem that the dam crack image samples are not available and the traditional algorithm detects cracks with low accuracy, we provide a dam crack image detection model based on crack feature enhancement and attention mechanism. Firstly, we expand the dam crack image dataset through a generative adversarial network based on crack feature enhancement (Cracks Enhancements GAN, CE-GAN). It can fully expand the dam crack data samples and improve the quality of the training data. Secondly, we propose a crack image detection model based on the attention mechanism (Attention-based Faster-RCNN, AF-RCNN). The attention mechanism is added in the crack detection module to give different weights to the proposal boxes around the crack target and fuse the candidate boxes with high weights to accurately detect the crack target location. The experimental results show that our algorithm achieves 81.07% mAP on the expanded dam crack dataset, which is 8.39% higher than the original Faster-RCNN algorithm. The detection accuracy is significantly improved compared with other traditional dam crack detection algorithm models.

Research on Dam Deformation Prediction Model Based on Optimized SVM

Although constructing a dam can bring significant economic and social benefits to a region, it can be catastrophic for the population living downstream when it breaks. Given the dynamic and nonlinear characteristics of dam deformation, the traditional dam prediction model has been unable to meet the actual engineering demands. Consequently, this paper advocates for a novel method to solve this issue. The proposed method is based on the optimization of improved chicken swarm (ICSO) and support vector machine (SVM). To begin with, the mean square error is used as the objective function, and then, we apply the improved chicken swarm algorithm to iterate continuously, and finally, the optimal SVM parameters are obtained. Through the modeling and simulation experiments of a nonlinear system, the validity of the improved chicken swarm algorithm to optimize an SVM model has been verified. Based on the horizontal displacement monitoring data of FengMan Dam, this paper analyzed the influencing factors of horizontal displacement. According to the results, three prediction models have been established, respectively: the SVM prediction model optimized by the improved chicken swarm algorithm, the SVM prediction model optimized by the basic chicken swarm algorithm and the BP neural network prediction model optimized by the genetic algorithm. The obtained results from the experiment authenticate the validity and superiority of the proposed method.

A Novel Outlier Detection Method of Long-Term Dam Monitoring Data Based on SSA-NAR

Outlier generally exists in dam monitoring data which may seriously affect the accuracy of dam safety evaluation results. Aiming at the outlier detection of dam monitoring data, a novel dynamic detection method of dam outlier data based on SSA-NAR is proposed. This combined method does not depend on the effect quantity and influence quantity relationship of traditional dam safety theory and only uses the time series of effect quantity to mine the variation, which can avoid the impact of missing or abnormal of the influence quantity. The Nonlinear Autoregression (NAR) is a classical time series neural network widely used in engineering field. However, the prediction accuracy of NAR is greatly affected by the selection of model parameters, the Sparrow Search Algorithm (SSA) which is a novel model parameter solution method and can be combined with NAR to derive the optimal parameters of NAR prediction model. The outlier is identified through the analysis of the residual distribution between the predicted data and the measured data. The case study shows that when the original data does not contain outliers, the prediction accuracy of the model is high. When the outlier is included, the proposed model has good robustness which the outlier has little influence on the prediction effect. It can effectively detect the outlier in the original dam monitoring data and provide a reliable data basis for dam safety evaluation.

An APPSO–SVM approach building the monitoring model of dam safety

Data-based dam monitoring model can predict dam behavior and provide scientific basis for risk assessment and decision-making of dam engineering. Traditional dam safety monitoring models established by multiple regression, stepwise regression, gray theory, and other statistics-based methods have poor robustness and generalization. For this reason, an optimized support-vector machine (SVM) whose novelty lies in simple implementation, self-adaptive hyperparameter selection with aid of adaptive position particle swarm optimization (APPSO), and retention of influence factor combination is presented for constructing the dam safety monitoring model. A real-world concrete gravity dam engineering is used to verify the presented approach, and the results indicate the monitoring model built by APPSO–SVM outperforms benchmark models in terms of fitting and prediction accuracy.

“In Montana, you're only a week away from a drought”: Ranchers’ perspectives on flood irrigation and beaver mimicry as drought mitigation strategies

• The concept of natural water storage has gained traction as an alternative to traditional dams that can potentially mitigate the impacts of changing precipitation patterns by slowing runoff and increasing aquifer recharge. We investigated the barriers and opportunities for two natural water storage practices, flood irrigation and beaver mimicry. • We interviewed 8 amenity and 14 traditional ranchers in the Red Rock Watershed in southwest Montana. We found ranchers predominately rely on reactive, rather than proactive drought strategies. Most amenity ranchers had formal drought plans in place, but none of the traditional ranchers had formal drought plans. • Ranchers perceived the two natural water storage practices differently. While all agreed on the benefits of flood irrigation, they saw the barriers, such as labor issues and loss of efficiency to outweigh the benefits. Many ranchers were skeptical of the benefits beaver mimicry could provide and voiced concerns over the cost, permits, water rights, and operational impacts. • While there are barriers to both strategies, local agencies and actors can work to build trust and practice flexibility when working with ranchers. Ranchers mentioned potential incentives for implementing these strategies, which local agencies can use when working with them.

Sand dams for sustainable water management: Challenges and future opportunities

Sand dams are impermeable water harvesting structures built to collect and store water within the volume of sediments transported by ephemeral rivers. The artificial sandy aquifer created by the sand dam reduces evaporation losses relative to surface water storage in traditional dams. Recent years have seen a renaissance of studies on sand dams as an effective water scarcity adaptation strategy for drylands. However, many aspects of their functioning and effectiveness are still unclear. Literature reviews have pointed to a range of research gaps that need further scientific attention, such as river corridors and network dynamics, watershed-scale impacts, and interaction with social dynamics. However, the scattered and partially incomplete information across the different reviews would benefit from an integrated framework for directing future research efforts. This paper is a collaborative effort of different research groups active on sand dams and stems from the need to channel future research efforts on this topic in a thorough and coherent way. We synthesize the pivotal research gaps of a) unclear definition of “functioning” sand dams, b) lack of methodologies for watershed-scale analysis, c) neglect of social aspects in sand dam research, and d) underreported impacts of sand dams. We then propose framing future research to better target the synthesized gaps, including using the social-ecological systems framework to better capture the interconnected social and biophysical research gaps on sand dams, fully utilizing the potential of remote sensing in large-scale studies and collecting sand dam cases across the world to create an extensive database to advance evidence-based research on sand dams.

Test on Stability of Concrete-Rockfill Combination Dam

ABSTRACT The aim of the present study is to investigate the stability of a new cost-efficient dam structure, which makes use of materials to reduce the size of the traditional dam. The new dam structure is termed as concrete-rockfill combination dam (CRCD), covering a downstream inclined rockfill body as well as an upstream concrete wall. Small-scale models of CRCD are conducted by shaking table. Two different types of seismic records are utilized as typical input peak ground motions in the tests. The presented results demonstrate seismic characteristics and lay a basis for the subsequent aseismic design of CRCD.

Dam Deformation Interpretation and Prediction Based on a Long Short-Term Memory Model Coupled with an Attention Mechanism

An accurate dam deformation prediction model is vital to a dam safety monitoring system, as it helps assess and manage dam risks. Most traditional dam deformation prediction algorithms ignore the interpretation and evaluation of variables and lack qualitative measures. This paper proposes a data processing framework that uses a long short-term memory (LSTM) model coupled with an attention mechanism to predict the deformation response of a dam structure. First, the random forest (RF) model is introduced to assess the relative importance of impact factors and screen input variables. Secondly, the density-based spatial clustering of applications with noise (DBSCAN) method is used to identify and filter the equipment based abnormal values to reduce the random error in the measurements. Finally, the coupled model is used to focus on important factors in the time dimension in order to obtain more accurate nonlinear prediction results. The results of the case study show that, of all tested methods, the proposed coupled method performed best. In addition, it was found that temperature and water level both have significant impacts on dam deformation and can serve as reliable metrics for dam management.

A method of determining monitoring indexes for spatial deformation of super high arch dam based on extreme value theory

In the long service of arch dam, deformation is one of the main performance parameters which can directly reflect the working behavior of the dam. In order to effectively monitor the safety of dam operation, it is necessary to determine the monitoring control value of deformation. The traditional dam deformation monitoring control values only consider the single point, ignoring the spatial correlation between the measurement points. In order to overcome this limitation, this paper focuses on the spatial distribution characteristics of deformation measuring points of the arch dam, then by building dam deformation field and using the Projection Pursuit Analysis (PPA) project high-dimensional data to low dimensional space. On this basis, the POT model proposed to determine the monitoring control values. The PPA-POT model is applied to practical engineering, and the feasibility and applicability of the method in this paper are tested by comparing with the traditional methods, so as to provide a new analysis technique for the long-term service status safety evaluation of arch dams.

Optimization study on super-high arch dam temperature control standard and measure

Currently, for several super-high arch dams under construction which have caused a series of issues after being strict with temperature control standards, this paper has a sensitivity analysis through a variety of programs and a systematic study about the impact which the highest temperature, the cooling height, the same cooling zone height and other key factors have on the temperature stress of the construction period dam, and then proposes that the temperature control standard and the selection of measures of the ultra-high arch dam are relatively stricter than the regulatory requirements of the traditional dam as long as we have a reasonable control about the temperature drop on the time domain and the temperature gradient on the spatial domain. However we need not make all the dams as a constraint zone to have a temperature stress control. After separated from the basis constraints zone, the maximum temperature of the dam can increase 3-4 °C at least. In addition, while the dam’s same cooling zone height is controlled by the 0.2L (L for the bottom width), it can meet the requirements for filling and anti-cracking without using two cooling areas. This finding will help reduce the difficulty of height control of the cantilever when the dam is under construction.

An exact source-term balancing scheme on the finite element solution of shallow water equations

In this paper, a numerical model is presented based on a semi-discrete Streamline Upwind Petrov–Galerkin scheme for solving the two-dimensional shallow water equations with arbitrary bed topography and wetting–drying fronts. The present finite element method is implemented for moving-boundary problems on fixed numerical meshes. In order to satisfy the C-property for both still water and dry regions, a new exact source-term balancing method is presented within the finite element framework. The formulation is stabilized using two different stabilization terms, and a discontinuity-capturing scheme is used to simulate steep wave fronts. The second-order scheme is applied for both temporal and spatial discretization. The proposed mathematical balancing method is verified through well-known test cases including the traditional dam break problem, evolution of a dam break wave over an obstacle, and oscillation of a bead of water in a parabolically-shaped basin.

Mechanical Behavior of Dam Foundation with Vertical Sand Drain, Case Study: Sombar Dam

Installing vertical sand drains is a traditional dam foundation consolidation solution that is economical and provides good drainage efficiency. Vertical sand drains can shorten the path of water flow inside the soil and speeds up soil consolidation. This study investigated the effect of sand drains in the foundation of the Sombar Dam in Iran on its mechanical behavior. The Sombar Dam is a project to control flooding and provide agricultural water to Gholaman city in northeastern Iran. The investigation included the mechanical behavior of the dam with vertical sand drains. The studied parameters were drain diameter, depth and spacing in addition to the vertical-to-horizontal permeability ratio (ky/kx) of the foundation soil during the time of construction of the dam using a Mohr-Coulomb (MC) model in the software application GeoStudio. The results revealed that reducing the drain spacing (increasing the number of drains) and increasing the depth and diameter of the drains led to an increase of the settlement rate (up to 90%) and the stability of the dam over a shorter period of time (24 months) compared to no drain condition. With a decrease in the ratio of vertical-to-horizontal permeability (ky/kx = 0.1) for all drain parameters there was a decrease in the value of the dam settlement rate and the safety factor.

A Combinatorial Procedure to Determine the Full Range of Potential Operating Scenarios for a Dam System

Traditional dam safety assessment tends to place the focus on estimating the probabilities of failure for the system based on a few subjectively-chosen operating scenarios. The techniques used to assess these systems rely on linear chains of events and are incapable of considering component interactions, feedbacks and non-linear behaviour. This paper uses a systems approach to develop a new technique that puts possibilities first and is capable of generating an exhaustive list of potential component operating state scenarios for the system. The scenarios can then be simulated using a Monte-Carlo approach to determine a wide range of system behaviour (outcomes) for each scenario, taking into account component interactions and feedbacks. A component operating states database is presented where the system can be broken down into various levels of detail and the operating states and causal factors for each component can be defined. The Cartesian product of each operating state set is used to derive the full range of potential operating scenarios, with each scenario consisting of a single operating state for every component in the system. The list of scenarios can be used as an input to a simulation model which can then be run many times for each scenario, using Monte-Carlo inputs which vary the timing and severity of events as well as the inflows. The approach for automated scenario generation is demonstrated on a simple and complex representation of a hydropower system and the scenarios are generated and counted. Results show that increasing system complexity results in exponentially increasing numbers of potential operating scenarios, with the simple system having a total of 1.1 × 106 operating states in comparison to the complex system which has 1.83 × 1027. The approach presented in this paper (a) reduces the subjectivity associated with traditional dam safety assessments through automated scenario generation, and (b) improves the ability to understand component interaction and feedbacks by describing a Monte Carlo simulation approach which can be used for scenario simulation. By understanding how the system responds to the full range of potential operating conditions, dam owners and asset managers can make informed decisions relating to the improvement of operating strategies and implementation of system upgrades.

Tsunami simulation using particle method

Tsunami is a natural disaster that have resulted in dreadful damages over time. Extensive researches have been conducted to scrutinize and counteract the natural hazard using three major research components which are: field monitoring, laboratory tests, and numerical methods. However, laboratory tests are high-priced and arduous. Numerical simulation overcomes these drawbacks and can be utilized in collaboration with laboratory tests. Recently, newly introduced meshless Lagrangian particle method called Smoothed Particle Hydrodynamics (SPH) has gained attention. In this paper, SPH method has been employed to simulate tsunami. A SPH code is developed from scratch. To validate the code, a traditional dam break simulation is conducted. Lastly, a tsunami model is simulated using the developed SPH code and compared with past experimental data. The results indicate that the code is in accordance with previous experimental data and numerical simulation. Whereby, there’s been a slight deviation arises in tsunami simulation. The velocity of the code is relatively less to that of the experimental data. Such inconsistencies could emerge due to a number of reasons, i.e. the choice of the SPH parameters and model simplification. Generally, the developed SPH code had a satisfactory performance to model tsunami and dam-break problem.

Multi-block combined diagnosis indexes based on dam block comprehensive displacement of concrete dams

Timely diagnosis of the service condition of concrete dam is required to prevent the potential great loss of life and property in case of dam failure. Scientific and reliable diagnosis indexes are significant for effective dam diagnosis. Traditional dam diagnosis focused on displacement at a single point. Rational combined diagnosis based on multi-point displacement is rare. This paper presents a multi-block combined diagnosis method for concrete dam displacement. This method combines comprehensive block displacement and multidimensional confidence region method. The hybrid model, together with structure analysis, is selected to perform prediction. Comprehensive block displacement, a displacement system made up of single-point displacements with different weights, is introduced to describe block displacement. The weights are determined with improved projection pursuit method, which is adept in analyzing and processing high-dimensional data. Multi-block combined diagnosis indexes are then established in multidimensional space based on the residual sequences, and the obtained ellipse diagram can be a straightforward and visualized tool for dam diagnosis. The multi-block combined diagnosis method is recommended because of its high accuracy and reliability.

Cleanup and valuation of waters of the aquifer of M’zab Valley (Algeria)

Abstract The M’zab valley is a hyper arid region of average rainfall not exceeding 100 mm per year. However, the rare floods that occur in M’zab River drain large volumes of surface water. Thanks to the genius of the local population, traditional dams were made for artificial recharge of groundwater. Grace of traditional wells drilled in the valley, farmers irrigate their palm groves and gardens. However, since more than half a century, the contribution of deep drilling for the exploitation of the aquifer of the Continental Intercalary posed environmental problems. On the basis of investigations and surveys of the local population during the years 2010, 2011, 2012 and 2013, it appears that these modern techniques in water catchment caused harmful consequences to the region like the rising of water consumption, pollution of groundwater and soil salinity. Solutions and recommendations are outlined in this article.

Three‐dimensional numerical analysis of concrete‐faced rockfill dam using dual‐mortar finite element method with mixed tangential contact constraints

SummaryConcrete‐faced rockfill dam (CFRD) is a popular alternative to traditional dam types in the last two decades. The modelling of CFRD involves complex multi‐body contact and strong geometry and material nonlinearities. We present a numerical approach for the modelling of CFRDs in this paper. Based on the dual‐mortar finite element method, the presented approach considers different parts of rockfill and all concrete slabs as independent deformable continuum. The multi‐body contacts are modelled using Lagrange multipliers with a weak form segment‐to‐segment contact strategy. To alleviate instability induced by strong geometry nonlinearity in the slab–slab contact, we propose a mixed type of constraints for the tangential contact. A general transformation scheme is introduced to simplify the implementation of contact constraints. Three‐dimensional analysis of Tianshengqiao‐1 CFRD is performed. The nonlinear and time‐dependent deformation of the rockfill is considered. We study the influence of the rockfill deformation on the reliability of the concrete face. Three major concerns of the face, that is, the axial compression, the slab–slab separation and the face‐rockfill separation, are discussed in detail. The numerical results are compared with data from in‐situ observation. Copyright © 2016 John Wiley & Sons, Ltd.