Explore the vast range of titles available.

\"Hydraulic Modeling and GIS is a best practices guide for developing a sustainable hydraulic model and maintenance strategy that makes optimal use of your GIS. This book addresses GIS-centered concepts and applications that will help you understand and improve hydraulic model structures, advanced spatial analysis, network connectivity and topology, hydraulic model development and maintenance strategies, and GIS database design. Hydraulic Modeling and GIS is a practical handbook for GIS managers, engineers, and decision makers in the water and wastewater utility industry\"-- Provided by publisher.

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.

Hydro-pedotransfer functions (PTFs) relate easy-to-measure and readily available soil information to soil hydraulic properties (SHPs) for applications in a wide range of process-based and empirical models, thereby enabling the assessment of soil hydraulic effects on hydrological, biogeochemical, and ecological processes. At least more than 4 decades of research have been invested to derive such relationships. However, while models, methods, data storage capacity, and computational efficiency have advanced, there are fundamental concerns related to the scope and adequacy of current PTFs, particularly when applied to parameterise models used at the field scale and beyond. Most of the PTF development process has focused on refining and advancing the regression methods, while fundamental aspects have remained largely unconsidered. Most soil systems are not represented in PTFs, which have been built mostly for agricultural soils in temperate climates. Thus, existing PTFs largely ignore how parent material, vegetation, land use, and climate affect processes that shape SHPs. The PTFs used to parameterise the Richards–Richardson equation are mostly limited to predicting parameters of the van Genuchten–Mualem soil hydraulic functions, despite sufficient evidence demonstrating their shortcomings. Another fundamental issue relates to the diverging scales of derivation and application, whereby PTFs are derived based on laboratory measurements while often being applied at the field to regional scales. Scaling, modulation, and constraining strategies exist to alleviate some of these shortcomings in the mismatch between scales. These aspects are addressed here in a joint effort by the members of the International Soil Modelling Consortium (ISMC) Pedotransfer Functions Working Group with the aim of systematising PTF research and providing a roadmap guiding both PTF development and use. We close with a 10-point catalogue for funders and researchers to guide review processes and research.

Soft cyclic hydraulic fracturing has become an effective technology used in subsurface energy extraction which utilises cyclic hydraulic flow pressure to fracture rock. This new technique induces fatigue of rock to reduce the breakdown pressure and potentially the associated risk of seismicity. To control the fracturing process and achieve desirable fracture networks for enhanced permeability, the rock response under cyclic hydraulic stimulation needs to be understood. However, the mechanism for cyclic stimulation-induced fatigue of rock is rather unclear and to date there is no implementation of fatigue degradation in modelling the rock response under hydraulic cyclic loading. This makes accurate prediction of rock fracture under cyclic hydraulic pressure impossible. This paper develops a numerical method to model rock fracture induced by hydraulic pulses with consideration of rock fatigue. The fatigue degradation is based on S–N curves (S for cyclic stress and N for cycles to failure) and implemented into the constitutive relationship for fracture of rock using in-house FORTRAN scripts and ABAQUS solver. The cohesive crack model is used to simulate discrete crack propagation in the rock which is coupled with hydraulic flow and pore pressure capability. The developed numerical model is validated via experimental results of pulsating hydraulic fracturing of the rock. The effects of flow rate and frequency of cyclic injection on borehole pressure development are investigated. A new loading strategy for pulsating hydraulic fracturing is proposed. It has been found that hydraulic pulses can reduce the breakdown pressure of rock by 10–18% upon 10–4000 cycles. Using the new loading strategy, a slow and steady rock fracture process is obtained while the failure pressure is reduced.

Precise assessment of suspended sediment load (SSL) is vital for many applications in hydrological modeling and hydraulic engineering. In this study, a smoothed long short-term memory (SM-LSTM) model was used to predict day-to-day SSL at two stations over two rivers namely Thebes station on the Mississippi River and Omaha station on the Missouri River. The model first removes the interference factors in the SSL time series by Fourier Transformation (FT) de-noising and then feeds into a long short-term memory (LSTM) network to forecast the SSL. Before de-noising, missing data in the time series is computed using the Monte Carlo multiple imputation technique. LSTM networks are a type of recurrent neural network (RNN) that incorporates memory cells, which makes them well-suited for learning temporal associations over the previous time steps. The model was built using daily observed time series of SSL in the Mississippi and Missouri rivers in the United States. The developed model was then assessed and compared to LSTM and RNN. These models were trained using 4 different time lags of the SSL time series as inputs. The SM-LSTM model with 12 lagged inputs outperformed the other models with the lowest root mean square errors (RMSE) = 32254 ton and mean absolute errors (MAE) = 19517 ton, and the highest Nash–Sutcliffe efficiency (NSE) = 0.99 for the Thebes Station while the model with 3 lagged inputs acted as the best with the lowest RMSE = 2244 ton and MAE = 1370 ton, and the highest NSE = 0.989 for the Omaha Station. The comparison of prediction accuracies showed that the SM-LSTM model can more satisfactorily predict daily SSL time series compared to LSTM and RNN.

Large-volume hydraulic concrete structures, such as concrete dams, often suffer from damage due to the influence of alternating loads and material aging during the service process. The occurrence and further expansion of cracks will affect the integrity, impermeability, and durability of the dam concrete. Therefore, monitoring the changing status of cracks in hydraulic concrete structures is very important for the health service of hydraulic engineering. This study combines computer vision and artificial intelligence methods to propose an automatic damage detection and diagnosis method for hydraulic structures. Specifically, to improve the crack feature extraction effect, the Xception backbone network, which has fewer parameters than the ResNet backbone network, is adopted. With the aim of addressing the problem of premature loss of image detail information and small target information of tiny cracks in hydraulic concrete structures, an adaptive attention mechanism image semantic segmentation algorithm based on Deeplab V3+ network architecture is proposed. Crack images collected from concrete structures of different types of hydraulic structures were used to develop crack datasets. The experimental results show that the proposed method can realize high-precision crack identification, and the identification results have been obtained in the test set, achieving 90.537% Intersection over Union (IOU), 91.227% Precision, 91.301% Recall, and 91.264% F1_score. In addition, the proposed method has been verified on different types of cracks in actual hydraulic concrete structures, further illustrating the effectiveness of the method.

Leak detection and location in water distribution systems (WDSs) is of utmost importance for reducing water loss, which is, however, a major challenge for water utility companies. To this end, researchers have proposed a multitude of methods to detect such leaks in WDSs. Model-based and data-driven approaches, in particular, have found widespread uses in this area. In this paper, we reviewed both these approaches and classified the techniques used by them according to their leak detection methods. It is seen that model-based approaches require highly calibrated hydraulic models, and their accuracies are sensitive to modeling and measurement uncertainties. On the contrary, data-driven approaches do not require an in-depth understanding of the WDS. However, they tend to result in high false positive rates. Furthermore, neither of these approaches can handle anomalous variations caused by unexpected water demands.

Accurate evaluation of the influence range of borehole hydraulic fracturing (HF) in coal seam is crucial for optimizing the design scheme of HF. In this study, we adopted the microseismic (MS) monitoring technology to monitor and characterize the spatial shape of cracks caused by borehole HF in coal seam in an underground coal mine. And we also tested and analyzed the stress and moisture content changes of coal mass at different distances from the borehole after HF program. The number of MS waveforms and the energy of MS events show a good positive correlation with the water pressure curve. The response of the energy curve to the extension of the hydraulic cracks is ahead of the water pressure curve. Based on the short-time average/long-time average (STA/LTA), the interference signal recognition method (ISR) and the improved Akaike information criterion (AIC) method, we developed a comprehensive MS event detection and arrival time picking (CMDP) program that are suitable for the weak MS signals with low signal to noise ratio (SNR) induced by HF in coal seam. And then we were able to more accurately locate the MS events of the hydraulic cracks using the simplex source location method. We have conducted a comprehensive analysis of the relationship between the temporal and spatial distribution of MS events and hydraulic cracks propagation. The results show that there is an apparent correlation between the MS activities and HF operation. The expansion of hydraulic cracks generates MS events, and the larger the size of the cracks, the greater the energy of MS events. Based on the MS monitoring results, the HF produces a crack network of flat ellipsoid in the No. 6 coal seam, which indicates that there is obvious stimulated reservoir volume (SRV) fracturing effect during the borehole HF process. The influence radius of HF based on the moisture content is the smallest (about 20 m), followed by the stress monitoring (about 30 m), and the MS monitoring is the largest (about 40 m). The high-precision MS source location (location errors < 2.5 m) results combined with roadway roof watering phenomenon indicate that the influence radius of HF based on the moisture content and stress release may be underestimated. And the effective influence radius of the borehole HF is about 40 m for this HF program.

Hydraulic systems have the characteristics of strong fault concealment, powerful nonlinear time-varying signals, and a complex vibration transmission mechanism; hence, diagnosis of these systems is a challenge. To provide accurate diagnosis results automatically, numerous studies have been carried out. Among them, signal-based methods are commonly used, which employ signal processing techniques based on the state signal used for extracting features, and further input the features into the classifier for fault recognition. However, their main deficiencies include the following: (1) The features are manually designed and thus may have a lack of objectivity. (2) For signal processing, feature extraction and pattern recognition are conducted using independent models, which cannot be jointly optimized globally. (3) The machine learning algorithms adopted by these methods have a shallow architecture, which limits their capacity to deeply mine the essential features of a fault. As a breakthrough in artificial intelligence, deep learning holds the potential to overcome such deficiencies. Based on deep learning, deep neural networks (DNNs) can automatically learn the complex nonlinear relations implied in a signal, can be globally optimized, and can obtain the high-level features of multi-dimensional data. In this paper, the main technology used in an intelligent fault diagnosis and the current research status of hydraulic system fault diagnosis are summarized and analyzed. The significant prospect of applying deep learning in the field of intelligent fault diagnosis is presented, and the main ideas, methods, and principles of several typical DNNs are described and summarized. The commonality between a fault diagnosis and other issues regarding typical pattern recognition are analyzed, and research ideas for applying DNNs for hydraulic fault diagnosis are proposed. Meanwhile, the research advantages and development trend of DNNs (both domestically and overseas) as applied to an intelligent fault diagnosis are reviewed. Furthermore, the fault characteristics of a complex hydraulic system are summarized and discussed, and the key problems and possible research ideas of applying DNNs to an intelligent hydraulic fault diagnosis are presented and comprehensively analyzed.