Volumetric Sediment Concentration Research Articles

Forecasting particle Froude number in non-deposition scenarios within sewer pipes through hybrid machine learning approaches

Sediment deposition has a substantial effect on the hydraulic capacity of channels in urban drainage and sewer systems. In this sense, the self-cleaning concept has been extensively used in the construction of urban drainage and sewer systems. In this regard, the design of the sewer system heavily depends on the accurate forecasting of the particle Froude number (Fr). This study is conducted on experimental data sets collected from existing studies, including a wide range of dimensionless grain size of particles (Dgr), sediment median size (d), hydraulic radius (R), volumetric sediment concentration (Cv), and pipe friction factor (λ) for the condition of clear bed. We forecasted the particle Froude number using four different input combinations. We employed the Random Tree (RT) and Reduced Error Pruning Tree (REPT) methods as standalone methods, as well as Random Committee (RC) and Bagging (BA) methods as hybrid machine learning (ML) methods in particle Froude number forecasting. Hybrid machine learning methods demonstrate enhanced performance compared to both standalone machine learning methods and empirical equations. In the context of sewer system design under non-deposited bed conditions, there is a need to accurately forecast the particle Froude number, RC-RT (IA = 0.96, R = 0.928, MSE = 0.718, RRMSE = 0.197, NSE = 0.86, and PBias = −0.284) performed the best, followed by BA-RT, RC-REPT, BA-REPT, RT, and REPT. In our research, it was found that, in forecasting the particle Froude number under non-deposited bed conditions, Cv emerges as the most responsive input parameter among others.

Effects and interactions of scouring abrasion and external sulfate attack on deterioration of lining concrete: Experiments and preliminary modeling

The conventional tests of dynamic water scouring abrasion (DWSA) on lining concrete for hydraulic discharge tunnel do not consider concomitant sulfate ions that may exist in aggressive aqueous environment and alter the mechanisms and deterioration kinetics of DWSA. In this study, an abrasion-erosion testing system based on natural conditions of the environment has been used to evaluate the influence of volumetric concentration of sediment and the velocity of the scouring flow on abrasion-erosion synergism of lining concrete. To this end, lining concretes were subjected ambient conditions similar to that in drainage tunnel, spillway and stilling basin geographic locations, while implementing abrasion and accelerated external sulfate attack (ESA) test alternately prior to various physical and microstructural analyses. A dynamically decrease of properties on a macroscopic scale was observed, as well as an increase of precipitated ettringite, leached calcium, harmful pore (>200 nm) and sheeted radial micro-cracks, in lining concretes exposed to sediment-laden flow simulated for discharge, which were involved in the co-occurrence of complex degradation mechanisms in specimens exposed to DWSA-ESA. Results indicate that a water body with a high-velocity flow and a high sediment concentration significantly accelerates the sulfate attack on concrete. In contrast, a water body with a low-velocity flow and a low concentration of sediment alleviates the traverse of sulfate ions in concrete due to the inside-out pressure potential. The implications of these synoptic findings may informatively improve the design of abrasion and erosion resistance for concrete when dual exposure to ESA and abrasion is occurred in hydraulic tunnel engineering.

Flow dynamics and sedimentation at a turbidity channel confluence in the Yinggehai basin, northwestern South China sea

Submarine channel systems transport vast amounts of terrestrial sediment into the deep sea via turbidity currents, however, there is a paucity of research detailing their dynamics, especially regarding the issue of how flows transport sediment and deposit at channel bends and channel junctions. From the analysis of core data, a high-resolution 3D seismic dataset, and local topography, a 3D numerical model is used to simulate the evolution of turbidity currents containing multiple size classes of sediments in the Late Miocene submarine channels in the Yinggehai Basin, northwestern South China Sea. The model solves the Reynolds-averaged Navier–Stokes (RANS) equations, the mass conservation equation, and the Exner equation for bed evolution caused by sediment entrainment and deposition. The results for the tendency of the total volumetric concentration of sediment in the turbidity current and bed thickness distribution reasonably match the infilled thickness of the channel interpreted from seismic data. The turbidity current in submarine channels is limited by the sloping topography and the channel geometry. Geochemical results show that, during the Late Miocene, sediments were derived from mixed sources. The simulation result accurately reveals the scope and magnitude of sediment contributions from each provenance. The confluence zone of the two channels (W and E) receives coarse-grained thick-bedded sediments via E channel flow transport showing a homogeneous distribution, a good hydrocarbon reservoir. This study improves our understanding of the processes of turbidity currents and their depositional characteristics within submarine channels, the reconstruction of their depositional environment, flow structures, spatial partitioning of sediment, and reservoir distributions within sinuous channels.

A novel approach to measuring pore fluid sediment concentrations of debris flows in a volcanic torrent

Pore fluid in a debris flow is not fully understood, despite its contribution to the fluidity of the debris flow. To observe sediment concentrations of the pore fluids in debris flows, we established a novel monitoring system in a volcanic catchment, in southern Japan. An observation culvert, 1.0 m in width, 1.5 m in height and 2.0 m in length, was placed along the stream bank. An intake covered by a grating was equipped on the side of the culvert so that only pore fluid of debris flows was led inside. Measurements of dielectric permittivities were conducted within the culvert and used for the calculation of sediment concentrations of the fluid. The sediment concentrations of the pore fluid were successfully measured for natural debris flows. The preliminary observation revealed that the volumetric sediment concentration of pore fluid varied from approximately 5 to 69 %, which were slightly smaller or similar to those of the entire debris flows. Successive occurrences of debris flow caused decreases in the sediment concentration of the pore fluids with each surge.

A multi-dimensional two-phase mixture model for intense sediment transport in sheet flow and around pipeline

A two-phase mixture model is developed to simulate intense sediment transport covering the bed-load layer and suspended load layer. The proposed model maintains high accuracy as an Eulerian two-phase model but requires low computational cost. The proposed model applies an analytical formula for relative velocity between phases. The dense granular flow rheology is employed to close particle stress economically. The closure of Reynolds stress considers turbulence damping and small-scale fluctuation of fluid–particle interaction and particle collision. A damping function is adopted in eddy viscosity for extra turbulence damping from inter-particle interaction. The optimal exponent of the damping function refers to sediment shape and size. The sediment diffusion includes turbulence diffusion and shear-induced self-diffusion originating from dense sediment. The proposed model is validated by several sets of sheet flow cases (Shields number Θ = 0.44–2.20 and particle Reynolds number Res = 1.6–603.0) and shows a wide applicable range and good accuracy. The small-scale fluctuation and shear-induced self-diffusion improve the computation in the lower sheet flow layer where volumetric sediment concentration is larger than 0.2. Furthermore, the proposed model shows reasonable applicability on the multi-dimensional pipeline scour development. The scour profiles are well predicted and the Brier Skill Score = 0.809. However, the proposed model does not perform the wake characteristic around the pipeline sufficiently, and slight scour difference exists between the simulation and experiment.

Sediment transport modeling in non-deposition with clean bed condition using different tree-based algorithms.

To reduce the problem of sedimentation in open channels, calculating flow velocity is critical. Undesirable operating costs arise due to sedimentation problems. To overcome these problems, the development of machine learning based models may provide reliable results. Recently, numerous studies have been conducted to model sediment transport in non-deposition condition however, the main deficiency of the existing studies is utilization of a limited range of data in model development. To tackle this drawback, six data sets with wide ranges of pipe size, volumetric sediment concentration, channel bed slope, sediment size and flow depth are used for the model development in this study. Moreover, two tree-based algorithms, namely M5 rule tree (M5RT) and M5 regression tree (M5RGT) are implemented, and results are compared to the traditional regression equations available in the literature. The results show that machine learning approaches outperform traditional regression models. The tree-based algorithms, M5RT and M5RGT, provided satisfactory results in contrast to their regression-based alternatives with RMSE = 1.184 and RMSE = 1.071, respectively. In order to recommend a practical solution, the tree structure algorithms are supplied to compute sediment transport in an open channel flow.

Evaluation of Debris Flows for Flood Plain Estimation in a Small Ungauged Tropical Watershed for Hurricane Otto

The variability of climate, increase in population, and lack of territorial plans in Costa Rica have caused intense disasters with human and economic losses. In 2016, Hurricane Otto hit the country’s northern area, leaving substantial damages, including landslides, debris flows, and flooding. The present study evaluated different scenarios to estimate flooded areas for Newtonian (clean water), and non-Newtonian flows with volumetric sediment concentrations (Cv) of 0.3, 0.45, 0.55, and 0.65 using Hydro-Estimator (HE), rain gauge station, and the 100-year return period event. HEC–HMS modeled the rainfall products, and FLO-2D modeled the hydrographs and Cv combinations. The simulation results were evaluated with continuous statistics, contingency table, Nash Sutcliffe Efficiency, measure of fit (F), and mean absolute differences (E) in the floodplains. Flow depths, velocities, and hazard intensities were obtained in the floodplain. The debris flood was validated with field data and classified with a Cv of 0.45, presenting lower MAE and RMSE. Results indicated no significant differences in flood depths between hydrological scenarios with clean-water simulations with a difference of 8.38% in the peak flow. The flood plain generated with HE rainfall and clear-water condition presented similar results compared to the rain gauge input source. Additionally, hydraulic results with HE and Cv of 0.45 presented E and F values similar to the simulation of Cv of 0.3, demonstrating that the HE bias did not influence the determination of the floodplain depth and extent. A mean bias factor can be applied to a sub-daily temporal resolution to enhance HE rain rate quantifications and floodplain determination.

Laboratory Analysis of Debris Flow Characteristics and Berm Performance

In this study, laboratory tests were used to determine the deposition characteristics (runout distance, lateral width, and deposition area) of debris flow and their relationships with the flow characteristics (flow velocity and flow depth) according to the presence of a berm. An experimental flume 1.3 to 1.9 m long, 0.15 m wide, and 0.3 m high was employed to investigate the effects of channel slope and volumetric concentration of sediment with and without the berm. The runout distance (0.201–1.423 m), lateral width (0.045–0.519 m), and deposition area (0.008–0.519 m2) increased as the channel slope increased and as the volumetric concentration of sediment decreased. These quantities also increased with the flow velocity and flow depth. In addition, the maximum reductions in the runout distance, lateral width, and deposition area were 69.1%, 65.9%, and 93%, respectively, upon berm installation. The results of this study illustrate general debris flow characteristics according to berm installation; the reported relationship magnitudes are specific to the experimental conditions described herein. However, the results of this study contribute to the design of site-specific berms in the future by providing data describing the utility and function of berms in mitigating debris flow.

Debris Flow Characteristics in Flume Experiments Considering Berm Installation

This study was conducted to identify the characteristics and mobility of debris flows and analyze the performance of a berm as a debris flow mitigation measure. The debris flow velocity, flow depth, Froude number, flow resistance coefficients, and mobility ratio were accordingly determined using the results of flume tests. To analyze the influence of the berm, the results for a straight channel test without a berm were compared with those for a single-berm channel test. The debris flow velocity was observed to increase with increasing channel slope and decreasing volumetric concentration of sediment, whereas the mobility ratio was observed to increase with increasing channel slope and volumetric concentration of sediment. In addition, it was confirmed that the installation of a berm significantly decreased the debris flow velocity and mobility ratio. This indicates that a berm is an effective method for reducing damage to areas downstream of a debris flow by decreasing its potential mobility. By identifying the effects of berms on debris flow characteristics according to the channel slope and volumetric concentration of sediment, this study supports the development of berms to serve as debris flow damage mitigation measures.

Predicting non-deposition sediment transport in sewer pipes using Random forest

Sediment transport in sewers has been extensively studied in the past. This paper aims to propose a new method for predicting the self-cleansing velocity required to avoid permanent deposition of material in sewer pipes. The new Random Forest (RF) based model was implemented using experimental data collected from the literature. The accuracy of the developed model was evaluated and compared with ten promising literature models using multiple observed datasets. The results obtained demonstrate that the RF model is able to make predictions with high accuracy for the whole dataset used. These predictions clearly outperform predictions made by other models, especially for the case of non-deposition with deposited bed criterion that is used for designing large sewer pipes. The volumetric sediment concentration was identified as the most important parameter for predicting self-cleansing velocity.

Application of time domain reflectometry to high suspended sediment concentration measurements: Laboratory validation and preliminary field observations in a steep mountain stream

The dielectric constant around a sensor was measured with a time domain reflectometry (TDR) system and used to calculate the volumetric sediment concentration (SC) of stream water and deposition on a streambed. The measurements of various SCs in laboratory experiments demonstrated that the TDR system proposed in this study had an accuracy of 0.01 m3 m−3 for practical uses, and that the measured concentrations were not sensitive to particle size. The vertical SC distributions were measured at heights of 17–37 cm in a steep mountain stream. The resulting SCs at the various heights increased by 0.01–0.07 m3 m−3 at the time of peak stream discharge during an extreme storm with a return period of approximately 12 years. After extreme precipitation, the SC of the lowest probe increased rapidly from approximately 0 to 0.4 m3 m−3. This was followed by rapid increases in the SCs at the other heights, indicating deposition around each probe. The TDR measurement system, with its straightforward calibration procedure, effectively measured deposition and a high-concentration layer above the deposited layer even during the storm event.

Combination of sensitivity and uncertainty analyses for sediment transport modeling in sewer pipes

Mitigation of sediment deposition in lined open channels is an essential issue in hydraulic engineering practice. Hence, the limiting velocity should be determined to keep the channel bottom clean from sediment deposits. Recently, sediment transport modeling using various artificial intelligence (AI) techniques has attracted the interest of many researchers. The current integrated study highlights unique insight for modeling of sediment transport in sewer and urban drainage systems. A novel methodology based on the combination of sensitivity and uncertainty analyses with a machine learning technique is proposed as a tool for selection of the best input combination for modeling process at non-deposition conditions of sediment transport. Utilizing one to seven dimensionless parameters, 127 models are developed in the current study. In order to evaluate the different parameter combinations and select the training and testing data, four strategies are considered. Considering the densimetric Froude number (Fr) as the dependent parameter, a model with independent parameters of volumetric sediment concentration (CV) and relative particle size (d/R) gave the best results with a mean absolute relative error (MARE) of 0.1 and a root means square error (RMSE) of 0.67. Uncertainty analysis is applied with a machine learning technique to investigate the credibility of the proposed methods. The percentage of the observed sample data bracketed by 95% predicted uncertainty bound (95PPU) is computed to assess the uncertainty of the best models.

On Bedload and Suspended Load Measurement Performances in Sheet Flows Using Acoustic and Conductivity Profilers

AbstractIntense sediment transport experiments were performed in a gravity‐driven open‐channel flow with two sizes of uniformly distributed nonspherical acrylic particles having median diameters of 1.0 and 3.0 mm and a maximum packing volumetric concentration (ϕ) of 0.55. The flow conditions were adapted to each particle size to ensure similar sediment transport flow regimes as sheet flow corresponding to Shields numbers slightly above unity and a suspension number, ratio of settling velocity to friction velocity, near unity for the two experiments. An acoustic scattering‐based system (Acoustic Concentration and Velocity Profiler) and conductivity probes (Conductivity Concentration Profiler [CCP]) with two different vertical resolutions, 1 mm (CCP1mm) and 2 mm (CCP2mm) were used to measure instantaneous concentration profiles across the bedload and suspension layers. Measured concentration profiles, bed interface position, and sheet flow layer thicknesses are compared between the two techniques. The capabilities and limitations of both technologies are outlined. Average volumetric sediment concentration profiles were overestimated by 10% with the Acoustic Concentration and Velocity Profiler in the dense sheet layer when ⟨ϕ(z)⟩≳ 0.35, and by 100% with the CCP in the more diluted region when ⟨ϕ(z)⟩≲ 0.015 and ⟨ϕ(z)⟩≲ 0.20 for CCP1mm and CCP2mm, respectively. Good agreement is found between the three systems in terms of average and time‐resolved bed level position and sheet layer thickness, validating the different bed interface detection methods on data from the two sensors.

Application of incomplete similarity theory to the estimation of the mean velocity of debris flows

The mean velocity of debris flow is one of the most important parameters in the design of mitigation structures and in quantitative risk analysis. This study develops a model to predict the mean debris flow velocity observed in the field by applying the incomplete similarity argument. An equation for estimating the Darcy-Weisbach resistance coefficient for debris flows with a volumetric sediment concentration larger than 0.19 is accordingly derived using 128 sets of observation data from nine Chinese gullies, in which both the effect of the volumetric sediment concentration and channel slope on resistance are considered. The derived equation is then verified and compared against five previously published equations by using 61 sets of published observation data from six gullies located in four countries. The applications of the proposed equation are discussed, and the improvements made using the proposed equation are clearly very significant when compared with the previously published equations.

Experiments on critical judgment parameter of heavily silt-laden fluid mud

In the heavily silt-laden river, density current belongs to an excellent method for flushing the input sediment without water level drawn down. Heavily silt-laden fluid mud in reservoir comes from heavily silt-laden density current. Density current comes into being in the reservoir and may reach the front of the dam with certain flood duration in addition. And the flood also needs to meet a certain sediment concentration and discharge. That makes water and sediment input need to provide density current with energy to overcome the energy loss during the forming process. For example, in a density current creeping process, the current energy shortage often can unsuccessfully reach the front of the Xiaolangdi dam, the stop makes a muddy pond before the dam. Another situation occurs during the density current flushing process. When the input flood ends without subsequent enough energy retention, the foremost density current creeps slowly and suspends before the dam. In these two cases, the muddy layer along the vertical section in front of the reservoir dam will be called fluid mud in this paper. On the basis of understanding the mixing characteristics of hyper-concentrated flow settlement characteristics, viscoelastic and Bingham fluid in the Yellow River, under the flume experiments combined with theoretical analysis method, the formula which concludes high sediment rheological properties of mud and sediment particle size, the volume ratio of sediment concentration, is established and fluid mud layer theoretical analysis had been carried out and flume experiments had been combined to carry out about the effect between density currents and fluid mud. It is found that the difference between the fluid mud with the Bingham shear stress and the flow shear stress due to different sediment concentration, which can divide the movement regime into 2 types, named invasive and interface. The heterogeneous density flow of different sediment does not only affect the muddy water thickness and blending extent, but also determine the movement regime of the head. The flow discharge mainly affects the muddy water thickness and the velocity of the density current. The density of fluid mud increased from fast to slow with the time lasting, which is formed due to settlement. Through the theoretical deduction, the critical judgment parameter of the fluid mud layer incipient motion is obtained. The calculation formula which concluded volumetric sediment concentration, slope and density current thickness is proposed based on the experiments. Obviously, timely opening the desilting tunnel or with the help of the subsequent flood, this will enable the fluid mud incipient motion and flushing out of the reservoir to achieve the purpose of efficient sediment transport. With the adhesion of the fine sediment mud layer in group settlement mechanism and process, conditions on follow-up density current occurrence, and fluid mud layer motion possibility, and its interaction are the basis and key technology of efficient way flushing sediment out of the reservoir in the heavily silt-laden river. The research results can enrich the contents of river sediment dynamics and provide technical reference for the operation and planning of sediment-laden river reservoir

A new hybrid decision tree method based on two artificial neural networks for predicting sediment transport in clean pipes

A new hybrid decision tree (DT) technique based on two artificial neural networks (ANN), namely multilayer perceptron (MLP) and radial basis function (RBF), is proposed to predict sediment transport in clean pipes (i.e. without deposition). The parameters affecting densimetric Froude number (Fr) prediction were extracted from the literature in order to build the model proposed in this study. The effect of each parameter is first examined using MLP and RBF and a sensitivity analysis. According to the sensitivity analysis, the optimal model indicates that using the volumetric sediment concentration (CV), median diameter of particle size distribution to pipe diameter (d/D) and ratio of median diameter of particle size distribution to hydraulic radius (d/R) parameters yield the best Fr prediction results. Subsequently, the hybrid DT-MLP and DT-RBF model results are compared with MLP and RBF. According to the results, MLP with all models predicted Fr more accurately than RBF, and DT-MLP exhibited the best performance (R2=0.975, MARE=0.063, RMSE=0.328, SI=00.081, BIAS=−0.01). Moreover, the comparison between DT-MLP and existing regression-based equations indicates that the models presented in the current study are superior.

Improved criterion for plunge of reservoir turbidity currents

It is important to investigate the condition of turbidity current formation in reservoir sediment management. Previous criteria for turbidity current plunge are not applicable to hyperconcentrated turbidity currents. The governing equations for the motion of a turbidity current are refined, and an improved criterion for predicting the plunge of turbidity currents is derived based on the refined equations, with the effect of sediment concentration being included explicitly. The proposed criterion is calibrated using laboratory experimental data, which indicates that this improved criterion could be applicable to hyperconcentrated turbidity currents with sediment concentrations less than 400 kg/m3 as a kind of Newtonian fluid, and the plunging depth would be in direct proportion to the unit discharge to the power of 2/3 and would be in inverse proportion to the volumetric sediment concentration to the power of 1/4. The improved criterion is then verified using another three datasets, including measurements obtained from the prototype and model of the Xiaolangdi reservoir, and these verified results indicate that the predicted plunging depths agree well with the observed values, with a comprehensive correlation coefficient of 0·94. Therefore, the improved criterion can be used to predict the plunge of turbidity currents in the Xiaolangdi reservoir.

Design of a fuzzy differential evolution algorithm to predict non-deposition sediment transport

Since the flow entering a sewer contains solid matter, deposition at the bottom of the channel is inevitable. It is difficult to understand the complex, three-dimensional mechanism of sediment transport in sewer pipelines. Therefore, a method to estimate the limiting velocity is necessary for optimal designs. Due to the inability of gradient-based algorithms to train Adaptive Neuro-Fuzzy Inference Systems (ANFIS) for non-deposition sediment transport prediction, a new hybrid ANFIS method based on a differential evolutionary algorithm (ANFIS-DE) is developed. The training and testing performance of ANFIS-DE is evaluated using a wide range of dimensionless parameters gathered from the literature. The input combination used to estimate the densimetric Froude number (Fr) parameters includes the volumetric sediment concentration (CV), ratio of median particle diameter to hydraulic radius (d/R), ratio of median particle diameter to pipe diameter (d/D) and overall friction factor of sediment (λs). The testing results are compared with the ANFIS model and regression-based equation results. The ANFIS-DE technique predicted sediment transport at limit of deposition with lower root mean square error (RMSE = 0.323) and mean absolute percentage of error (MAPE = 0.065) and higher accuracy (R2 = 0.965) than the ANFIS model and regression-based equations.

Rheological Properties of Weathered Soil of Debris Flow Disaster Area with Volumetric Concentration of Sediment

Rheological Properties of Weathered Soil of Debris Flow Disaster Area with Volumetric Concentration of Sediment

Simulation of interactions among multiple debris flows

Adjacent debris flows may interact in many ways: two or more concurrent debris flows may merge; one debris flow can run out over an existing debris flow fan. Such interactions may cause debris flow properties to change in the mixing process as well as more severe adverse effects than those caused by a single debris flow. This paper aims to investigate the interactions among channelized debris flows originated from adjacent catchments. Both concurrent and successive debris flows are considered. If several debris flows originate from different locations concurrently and merge, the volumetric sediment concentration (i.e., the ratio of the volume of solid material to the total volume of debris flow), C v, is a good index to capture the mixing process of these debris flows. The change in C v reflects where mixing occurs and the mixing degree. The debris flow properties (e.g., yield stress and dynamic viscosity) evolve in the mixing process and can be captured by the change in C v. The debris flow with a larger volume dominates the mixing process, and the properties of the mixed debris flow are more similar to those of the larger debris flow. The inundated areas and runout distances of successive debris flows are smaller than those of concurrent debris flows of the same total volume due to the smaller scales of the individual events and blockage by the earlier debris flows. However, the deposit depth in the interacting part of the debris flow fans of successive debris flows can be much larger than that of concurrent debris flows, leading to more destructive cascading hazards (e.g., the formation of debris barrier lakes). The sequence of successive debris flows not only significantly influences the runout characteristics of the debris flows but also substantially affects the cascading hazards.