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Dams and appurtenant hydraulic structures
'Dams and Appurtenant Hydraulic Structures' provides a comprehensive and complete overview of all kinds of dams and appurtenant hydraulic structures throughout the world. The reader is guided through different aspects of dams and appurtenant hydraulic structures in 35 chapters, which are subdivided in five themes: dams and appurtenant hydraulic structures - General; embankment dams; concrete dams; hydromechanical equipment and appurtenant hydraulic structures; and hydraulic schemes.
Book
Hydraulic Performance Modeling of Inclined Double Cutoff Walls Beneath Hydraulic Structures Using Optimized Ensemble Machine Learning
This study investigates the effectiveness of inclined double cutoff walls installed beneath hydraulic structures by employing five machine learning models: Random Forest (RF), Adaptive Boosting (AdaBoost), eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Categorical Boosting (CatBoost). A comprehensive dataset of 630 samples was gathered from previous studies, including key input variables such as the relative distance between the cutoff wall and the structure’s apron width (
L
/
B
), the inclination angle ratio between downstream and upstream cutoffs (
θ
2
/
θ
1
), the depth ratio of downstream to upstream cutoff walls (
d
2
/
d
1
), and the relative downstream cutoff depth to the permeable layer depth (
d
2
/
D
). Outputs considered were the relative uplift force (
U
/
U
o
), the relative exit hydraulic gradient (
i
R
/
i
Ro
), and the relative seepage discharge per unit structure length (
q
/
q
o
). The dataset was split with a 70:30 ratio for training and testing. Hyperparameter optimization was conducted using Bayesian Optimization (BO) coupled with five-fold cross-validation to enhance model performance. Results showed that the CatBoost model demonstrated superior performance over other models, consistently yielding high R
2
values, specifically surpassing 0.95, 0.93, and 0.97 for
U
/
U
o
,
i
R
/
i
Ro
, and
q
/
q
o
, respectively, along with low RMSE scores below 0.022, 0.089, and 0.019 for the same variables. A feature importance analysis is conducted using SHapley Additive exPlanations (SHAP) and Partial Dependence Plot (PDP). The analysis revealed that
L
/
B
was the most influential predictor for
U
/
U
o
and
i
R
/
i
Ro
, while
d
2
/
D
played a crucial role in determining
q
/
q
o
. Moreover, PDPs illustrated a positive linear relationship between
L
/
B
and
U
/
U
o
, a V-shaped impact of
d
2
/
d
1
on
i
R
/
i
Ro
and
q
/
q
o
, and complex nonlinear interactions for
θ
2
/
θ
1
across all target variables. Furthermore, an interactive Graphical User Interface (GUI) was developed, enabling engineers to efficiently predict output variables and apply model insights in practical scenarios.
Journal Article
Hydraulic Modelling – an Introduction
Modelling forms a vital part of all engineering design, yet many hydraulic engineers are not fully aware of the assumptions they make. These assumptions can have important consequences when choosing the best model to inform design decisions.
Considering the advantages and limitations of both physical and mathematical methods, this book will help you identify the most appropriate form of analysis for the hydraulic engineering application in question. All models require the knowledge of their background, good data and careful interpretation and so this book also provides guidance on the range of accuracy to be expected of the model simulations and how they should be related to the prototype.
Applications for models include:
Open channel systems;
Closed conduit flows;
Storm drainage systems;
Estuaries;
Coastal and nearshore structures;
Hydraulic structures.
An invaluable guide for students and professionals.
eBook
Prediction of hydraulic blockage at culverts from a single image using deep learning
Cross-drainage hydraulic structures such as culverts and bridges in urban landscapes are prone to get blocked by the transported debris (e.g., urban, vegetated), which often reduces their hydraulic capacity and triggers flash floods. Unavailability of relevant data from blockage-originated flooding events and complex nature of debris accumulation are highlighted factors hindering the research within the blockage management domain. Wollongong City Council (WCC) blockage conduit policy is the leading formal guidelines to incorporate blockage into design guidelines; however, are criticized by the hydraulic engineers for its dependence on the post-flood visual inspections (i.e., visual blockage) instead of peak floods hydraulic investigations (i.e., hydraulic blockage). Apparently, no quantifiable relationship is reported between the visual blockage and hydraulic blockage; therefore, many consider WCC blockage guidelines invalid. This paper exploits the power of Artificial Intelligence (AI), motivated by its recent success, and attempts to relate visual blockage with hydraulic blockage by proposing a deep learning pipeline to predict hydraulic blockage from an image of the culvert. Two experiments are performed where the conventional pipeline and end-to-end learning approaches are implemented and compared in the context of predicting hydraulic blockage from a single image. In experiment one, the conventional deep learning pipeline approach (i.e., feature extraction using CNN and regression using ANN) is adopted. In contrast, in experiment two, end-to-end deep learning models (i.e., E2E_ MobileNet, E2E_ BlockageNet) are trained and compared with the conventional pipeline approach. Dataset (i.e., Hydraulics-Lab Blockage Dataset (HBD), Visual Hydraulics-Lab Dataset (VHD)) used in this research were collected from laboratory experiments performed using scaled physical models of culverts. E2E_ BlockageNet model was reported best in predicting hydraulic blockage with
R
2
score of 0.91 and indicated that hydraulic blockage could be interrelated with the visual features at the culvert.
Journal Article
Ensemble neural network models for stability prediction and optimization of hydraulic structures considering uplift pressure and exit gradient
This study aims to develop a novel ensemble modeling approach that integrates artificial neural networks with finite element analysis to optimize the stability of hydraulic structures, particularly through the design of cutoff wall configurations. The research investigates the effects of varying cutoff wall positions and inclination angles on key parameters such as uplift pressure, seepage discharge, and exit gradient. Numerical simulations were performed using Geostudio SEEP/W to analyze seepage patterns across multiple configurations. The proposed methodology combines a Feed-Forward Neural Network (FFNN), XGBoost Regressor, and Support Vector Machine (SVM) with a Genetic Algorithm (GA) to create a predictive optimization framework. The findings reveal that the optimal cutoff wall inclination angle for minimizing both uplift pressure and exit gradient is 165° across all positions, while for seepage discharge, the optimal angle varies by position, ranging from 60° to 120° and increasing incrementally by 15° from upstream to downstream. The ensemble model demonstrated robust predictive performance across 5-fold cross-validation trials, achieving mean R-squared values of 0.99 ± 0.01 for uplift pressure, 0.94 ± 0.02 for seepage discharge, and 0.97 ± 0.01 for exit gradient. The small standard deviations indicate consistent performance across different data partitions, validating model stability and generalizability. The Genetic Algorithm results closely aligned with the numerical model outputs, validating the robustness of the proposed framework. This study introduces a significant improvement over traditional analytical methods by providing an integrated approach that enhances the safety and efficiency of hydraulic infrastructure design, particularly under complex conditions where conventional techniques may fall short.
Journal Article
Numerical investigation of velocity profiles across a crump weir using smoothed particle hydrodynamics (SPH)
This study investigates the velocity profiles across a crump weir using Smoothed Particle Hydrodynamics (SPH) through the DualSPHysics software. The research focuses on flow behaviour upstream, over, and downstream of the weir by simulating steady-state flow conditions. Results show significant velocity variations, particularly at the crest and downstream, where acceleration and turbulence are observed, highlighting the energy dissipation capabilities of the weir. These findings contribute to the optimization of hydraulic structures, providing crucial insights for mitigating erosion and improving hydraulic design. The use of SPH offers a detailed representation of flow dynamics, making it a valuable tool for future hydraulic engineering applications.
Journal Article
An efficient method for identifying surface damage in hydraulic concrete buildings
Traditional hydraulic structures rely on manual visual inspection for apparent integrity, which is not only time-consuming and labour-intensive but also inefficient. The efficacy of deep learning models is frequently constrained by the size of available data, resulting in limited scalability and flexibility. Furthermore, the paucity of data diversity leads to a singular function of the model that cannot provide comprehensive decision support for improving maintenance measures. This paper proposes an efficacious methodology for identifying diverse apparent damages in hydraulic structures to address the limitations of existing technologies. The advanced features of apparent damage in hydraulic structures were elucidated by fine-tuning the top-level parameters of the lightweight pre-trained model, thereby mitigating the data dependency issue inherent in the model. Ensemble learning algorithms are employed to classify high-dimensional samples to enhance the accuracy and stability of the classification. However, ensemble learning algorithms are subject to time consuming issues when applied to high-dimensional datasets. To this end, we propose a robust discriminative feature selection model to identify the most salient features, thereby enhancing the performance of apparent damage recognition in hydraulic structures while concurrently reducing the inference time. The results demonstrated that the accuracies of this method in identifying crack, fracture, hole and normal structures were 87.65%, 87.82%, 96.99%, and 95.25%, respectively. This methodology exhibits significant applicability and practical value for the intelligent inspection of hydraulic structures.
Journal Article
Why structural solutions for flood control should be adapted to climate change?
Anthropogenic activities in intensively managed landscapes have altered mechanisms of runoff generation and flood regimes. Despite utilizing hydraulic structures for decades to control floods, their expected performance under the varied frequency or intensity of extreme precipitations due to climate change may be less certain. This study aimed to examine this hypothesis by employing both remote sensing and field data in the Imamzadeh Davood watershed in Central Northern Iran, which experienced a devastating flash flood in July 2022. To this end, we collected data on river morphology, the structural characteristics of 18 check dams, and sedimentation patterns by surveying the main river path. We also processed satellite imagery to track long-term land-use and land-cover change. Finally, we explored the distinct role of extreme precipitation in intensifying the occurred flood incident using recorded data from synoptic stations. Our findings revealed an unprecedented > 100-year return period precipitation event in the catchment, with devastating consequences that underscore the escalating impact of heavy rainfall due to climate change in many regions. In-situ observations revealed that all 18 check dams were destroyed between 17 and 100% during the flood event, while upstream check dams showed a higher degree of destruction. The external stability analysis demonstrated that under static forces, 100% and 62% of the check dams were potentially resistant against sliding and overturning, respectively. However, given the observed destruction of all check dams and high deposition depth of sediment in the river corridor, our further analysis by considering dynamic forces and rock impact indicated that the shock imposed by the unprecedented debris flow was responsible for the cascade failure of check dams from upstream to downstream. These findings highlight the need to revisit the design principles of hydraulic structures under the impact of climate change to increase the resiliency of flood control systems.
Journal Article
An Experimental Study of Flow and Turbulence Properties near the Rising Sector Gate Mouth Considering the Gate Opening with a PIV Measuring System
Hydraulic structures, such as movable weir gates, are widely installed in rivers and streams for various purposes. Among these is the rising sector gate, which is the focus of this study. This research investigated how different gate openings affect flow velocity and turbulence distributions at the gate mouth. A hydraulic analysis of flow and turbulence characteristics near the mouth of a rising sector gate model was conducted through laboratory experiments with various flow conditions and gate openings utilizing a Particle Image Velocimetry (PIV) system. Experimental tests were carried out with two gate-opening angles (30 and 45 degrees). The PIV measurements revealed significant variations in flow velocity and turbulence properties in response to the gate openings and flow conditions. Notably, in the vicinity of the gate mouth, where the flow regime changes rapidly between the upstream and downstream regions, the turbulence properties in the upstream part of the gate mouth were more than twice those in the downstream part. Additionally, the streamwise distribution of depth-averaged relative turbulence intensity was analyzed. The results showed that the depth-averaged relative turbulence intensity decreased by nearly half as the gate opening increased from 30 to 45 degrees, with the lowest values observed at the gate mouth, followed by an increase downstream. A functional relationship between the maximum flow velocity at the gate mouth during underflow operation and the Froude number was established to guide practical gate operation in the field.
Journal Article
Experimental Studies of Low-Reinforced Concrete Structures Containing Inter-Bay Construction Joints Strengthened with Prestressed Basalt Composite Reinforcements and External Transverse Reinforcements
During the long-term operation of main run-of-river head powerhouses for hydroelectric power plants, technical changes that deteriorate the operational properties of their reinforced concrete structures can occur. Therefore, in order to substantiate the application of prestressed basalt composite reinforcements to strengthen reinforced concrete hydraulic structures in operation, a set of computational and experimental studies was carried out, taking into account their characteristic features. After 4 years of ageing, the serviceability and reliability of the beams with prestressed basalt composite reinforcements were demonstrated through stabilisation of the prestress losses and the values obtained for bearing capacity, deflection, and the width of the opening of the inter-bay construction joints and the deformations of the metal reinforcements and the basalt composite reinforcements. The bearing capacity of the investigated reinforced concrete beams reinforced with external transverse reinforcements was increased 1.4–2.5 times over that of the variants reinforced with longitudinal prestressed basal composite reinforcements. Furthermore, in this study, the impacts of static loads and seismic effects with a magnitude greater than 8 on the run-of-river hydroelectric power plant powerhouse were calculated based on dynamic design theory. Regarding applications to hydroelectric power plant structures and constructions, for which it is not always possible to determine the location of compressed or tensile zones during their operation nor under seismic action, our research results are suggestive of a reasonably positive effect.
Journal Article