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1.
The objective of this experimental study was to examine the effect of an upstream subsidiary triangular pillar on the development of local scour at a single circular bridge pier. The triangular pillar was placed such that its apex faced the approach flow and its base length was equal to the pier diameter (D) in all experiments. The experiments were conducted to investigate the effect of the spacing between the base of the subsidiary pillar and the bridge pier (S) and the apex angle (α) of the triangular pillar on the dimensions of the scour hole and its temporal development at different Froude numbers. The experiments were conducted in a rectangular flume under clear-water scour conditions at Froude number ranging between 0.1 and 0.3. Relative spacing (S/D) of 0, 0.5, 1, 1.5, 2, and 3, as well as apex angle (α) of 60°, 90° and 120° were tested. The results show that the maximum scour depth at the pier could be minimized using three different combinations of spacing and apex angle. The highest reduction in the maximum scour depth (d sm) of approximately 28 % was possible using S/D of 3 and α of 90° compared to the pier-alone case. Overall the performance of the 90° apex angle pillar surpassed the other two angles. 相似文献
2.
The average risk of a bridge over water in the USA collapsing from scour during its 75 years design life is estimated at 3.7×10?3. This risk makes scour of foundations the number one cause of bridge collapse and 3 times larger than the next cause of bridge collapse, which is collisions. The current paper presents a site specific method to estimate the probability that a certain scour depth will be exceeded during the life of a bridge. The methodology is limited to some uncertainties associated with the randomness of hydrologic conditions. It does not include uncertainties associated with other input parameters, such as geometry and soil erodibility or uncertainties associated with the scour prediction model. The SRICOS–EFA method is used as the reference method to predict the scour depth. This method requires three inputs: the hydraulic parameters (e.g. velocity hydrograph), the geometry parameters (e.g. pier size) and the soil erodibility parameters (e.g. erosion function). The input is used together with the program to generate the scour depth versus time over the period of interest. The final scour depth is that reached at the end of the specified period. This paper proposes a probabilistic framework to present the final scour depth as a cumulative density function. The cumulative density function of the flow is sampled randomly to give a future hydrograph, which has the same mean and standard deviation as the original hydrograph. For this synthetic hydrograph a final scour depth is obtained by using SRICOS–EFA. Thousands of equally likely hydrographs are generated and the corresponding final scour depths are organized in a distribution. That final scour depth distribution gives the probability that a chosen scour depth will be exceeded. 相似文献
3.
Farzin Salmasi Gürol Yıldırım Azam Masoodi Parastoo Parsamehr 《Arabian Journal of Geosciences》2013,6(7):2709-2717
Compound broad-crested weir is a typical hydraulic structure that provides flow control and measurements at different flow depths. Compound broad-crested weir mainly consists of two sections; first, relatively small inner rectangular section for measuring low flows, and a wide rectangular section at higher flow depths. In this paper, series of laboratory experiments was performed to investigate the potential effects of length of crest in flow direction, and step height of broad-crested weir of rectangular compound cross-section on the discharge coefficient. For this purpose, 15 different physical models of broad-crested weirs with rectangular compound cross-sections were tested for a wide range of discharge values. The results of examination for computing discharge coefficient were yielded by using multiple regression equations based on the dimensional analysis. Then, the results obtained were also compared with genetic programming (GP) and artificial neural network (ANN) techniques to investigate the applicability, ability, and accuracy of these procedures. Comparison of results from the GP and ANN procedures clearly indicates that the ANN technique is less efficient in comparison with the GP algorithm, for the determination of discharge coefficient. To examine the accuracy of the results yielded from the GP and ANN procedures, two performance indicators (determination coefficient (R 2) and root mean square error (RMSE)) were used. The comparison test of results clearly shows that the implementation of GP technique sound satisfactory regarding the performance indicators (R 2?=?0.952 and RMSE?=?0.065) with less deviation from the numerical values. 相似文献
4.
Suspended sediments present in the flow are known to affect the flow resistance, velocity distribution and turbulent characteristics. Experiments were conducted in the laboratory flume to see the effect of suspended sediment concentration (SSC) on local scour around a cylindrical pier for a wide range of clay–sand mixed sediment beds for SSC up to 2700 mg/L. It has been observed that the effect of SSC on equilibrium scour hole parameters such as maximum equilibrium scour depth, and longitudinal and transverse extent of scour hole can be significant. Present data showed that the presence of SSC in the range 993–1332 mg/L can increase maximum equilibrium scour depth as much as 1.54 times compared to the clear water case. However, tests made for SSC in the range 2456–2700 mg/L showed that the maximum equilibrium scour depth reduced compared to that for SSC in the range 993–1332 mg/L, but these maximum equilibrium scour depths were still larger than that obtained for clear water. The effect of SSC on time variation of scour and equilibrium scour hole geometry was further investigated. 相似文献
5.
Reza Mohammadpour 《Arabian Journal of Geosciences》2017,10(18):416
Local scour around piers is one of the main causes of bridge failures. In this study, three robust techniques, artificial neural networks (ANNs), M5-Tree, and Gene Expression Programming (GEP), were employed for prediction of scour depth around complex piers. The clear water condition was chosen for all experimental tests. The results indicated that pier diameter (b c) and foundation level (Y) are the main parameters for local scour. Furthermore, the minimum scour depth occurs in range of Y/b c = 1.1~1.3. In next step, to evaluate the mentioned techniques, a wide range of dataset was collected from the present study and literature. The radial base function (RBF) with R 2 = 0.945 and RMSE = 0.031 provides better prediction in comparison with conventional equations, M5-Tree (R 2 = 0.883, RMSE = 0.292) and the GEP techniques (R 2 = 0.811 and RMSE = 0.263). The equations developed by M5-Tree and GEP are more useful for practical purposes and can be easily employed to predict the depth of scour at complex piers. 相似文献
6.
In this study,the fractal dimensions of velocity fluctuations and the Reynolds shear stresses propagation for flow around a circular bridge pier are presented.In the study reported herein,the fractal dimension of velocity fluctuations(u′,v′,w′) and the Reynolds shear stresses(u′v′ and u′w′) of flow around a bridge pier were computed using a Fractal Interpolation Function(FIF) algorithm.The velocity fluctuations of flow along a horizontal plane above the bed were measured using Acoustic Doppler Velocity meter(ADV)and Particle Image Velocimetry(P1V).The PIV is a powerful technique which enables us to attain high resolution spatial and temporal information of turbulent flow using instantaneous time snapshots.In this study,PIV was used for detection of high resolution fractal scaling around a bridge pier.The results showed that the fractal dimension of flow fluctuated significantly in the longitudinal and transverse directions in the vicinity of the pier.It was also found that the fractal dimension of velocity fluctuations and shear stresses increased rapidly at vicinity of pier at downstream whereas it remained approximately unchanged far downstream of the pier.The higher value of fractal dimension was found at a distance equal to one times of the pier diameter in the back of the pier.Furthermore,the average fractal dimension for the streamwise and transverse velocity fluctuations decreased from the centreline to the side wall of the flume.Finally,the results from ADV measurement were consistent with the result from PIV,therefore,the ADV enables to detect turbulent characteristics of flow around a circular bridge pier. 相似文献
7.
Several hydraulic techniques were used to estimate the flow depth (0.3 m) associated with the deposition of a tabular set (micro-delta) of cross-stratified sand in the Brampton esker. The competency of the flow, deduced from both the grain size and structural characteristics of the set, gave a value of approximately 0.65 m/sec for the palaeo-velocity of the flow. Estimates of palaeo-depth and velocity facilitated calculation of the Froude and Reynolds numbers, about 0.38 and 1.24 · 105, respectively. Extrapolation of other parameters included bed shear stress τ0 (4.50 N/m2), shear velocity U* (0.067 m/sec), dimensionless Chezy coefficient C/√g (9.7), slope of the energy gradient S(0.00153), Darcy-Weisbach friction factor f(0.085), Manning roughness coefficient n(0.027) and discharge of bed-material load (19 metric tons/day/m). The figures cited are reasonable estimates only. The occurrence of regressive ripples in the bottomset of the micro-delta aided in the hydraulic interpretation. These flow characteristics are only representative of the final stages of deposition at one location on the flank of the esker. The core of the esker was probably deposited under different hydraulic conditions. 相似文献
8.
In this paper, analytical methods, artificial neural network (ANN) and multivariate adaptive regression splines (MARS) techniques were utilised to estimate the discharge capacity of compound open channels (COC). To this end, related datasets were collected from literature. The results showed that the divided channel method with a coefficient of determination (R 2) value of 0.76 and root mean square error (RMSE) value of 0.162 has the best performance, among the various analytical methods tested. The performance of applied soft computing models with R 2=0.97 and RMSE = 0.03 was found to be more accurate than analytical approaches. Comparison of MARS with the ANN model, in terms of developed discrepancy ratio (DDR) index, showed that the accuracy of MARS model was better than that of MLP model. Reviewing the structure of the derived MARS model showed that the longitudinal slope of the channel (S), relative flow depth (H r ) and relative area (A r ) have a high impact on modelling and forecasting the discharge capacity of COCs. 相似文献
9.
In this study, daily rainfall–runoff relationships for Sohu Stream were modelled using an artificial neural network (ANN)
method by including the feed-forward back-propagation method. The ANN part was divided into two stages. During the first stage,
current flows were estimated by using previously measured flow data. The best network architecture was found to utilise two
neurons in the input layer (the delayed flows from the first and second days), two hidden layers, and one output layer (the
current flow). The coefficient of determination (R
2) in this architecture was 81.4%. During the second stage, the current flows were estimated by using a combination of previously
measured values for precipitation, temperature, and flows. The best architecture consisted of an input layer of 2 days of
delayed precipitation, 3 days of delayed flows, and temperature of the current. The R
2 in this architecture was calculated to be 85.5%. The results of the second stage best reflected the real-world situation
because they accounted for more input variables. In all models, the variables with the highest R
2 ranked as the previous flow (81.4%), previous precipitation (21.7%), and temperature. 相似文献
10.
Mohammad Rezaei Masoud Monjezi Saeed Ghorbani Moghaddam Farhad Farzaneh 《Arabian Journal of Geosciences》2011,5(5):1031-1037
Burden prediction is a vital task in the production blasting. Both the excessive and insufficient burden can significantly affect the result of blasting operation. The burden which is determined by empirical models is often inaccurate and needs to be adjusted experimentally. In this paper, an attempt was made to develop an artificial neural network (ANN) in order to predict burden in the blasting operation of the Mouteh gold mine, using considering geomechanical properties of rocks as input parameters. As such here, network inputs consist of blastability index (BI), rock quality designation (RQD), unconfined compressive strength (UCS), density, and cohesive strength. To make a database (including 95 datasets), rock samples are used from Iran’s Mouteh goldmine. Trying various types of the networks, a neural network, with architecture 5-15-10-1, was found to be optimum. Superiority of ANN over regression model is proved by calculating. To compare the performance of the ANN modeling with that of multivariable regression analysis (MVRA), mean absolute error (E a), mean relative error (E r), and determination coefficient (R 2) between predicted and real values were calculated for both the models. It was observed that the ANN prediction capability is better than that of MVRA. The absolute and relative errors for the ANN model were calculated 0.05 m and 3.85%, respectively, whereas for the regression analysis, these errors were computed 0.11 m and 5.63%, respectively. Moreover, determination coefficient of the ANN model and MVRA were determined 0.987 and 0.924, respectively. Further, a sensitivity analysis shows that while BI and RQD were recognized as the most sensitive and effective parameters, cohesive strength is considered as the least sensitive input parameters on the ANN model output effective on the proposed (burden). 相似文献
11.
Prediction of unconfined compressive strength of carbonate rocks using artificial neural networks 总被引：3，自引：2，他引：1
The unconfined compressive strength (UCS) of intact rocks is an important geotechnical parameter for engineering applications. Determining UCS using standard laboratory tests is a difficult, expensive and time consuming task. This is particularly true for thinly bedded, highly fractured, foliated, highly porous and weak rocks. Consequently, prediction models become an attractive alternative for engineering geologists. The objective of study is to select the explanatory variables (predictors) from a subset of mineralogical and index properties of the samples, based on all possible regression technique, and to prepare a prediction model of UCS using artificial neural networks (ANN). As a result of all possible regression, the total porosity and P-wave velocity in the solid part of the sample were determined as the inputs for the Levenberg–Marquardt algorithm based ANN (LM-ANN). The performance of the LM-ANN model was compared with the multiple linear regression (REG) model. When training and testing results of the outputs of the LM-ANN and REG models were examined in terms of the favorite statistical criteria, which are the determination coefficient, adjusted determination coefficient, root mean square error and variance account factor, the results of LM-ANN model were more accurate. In addition to these statistical criteria, the non-parametric Mann–Whitney U test, as an alternative to the Student’s t test, was used for comparing the homogeneities of predicted values. When all the statistics had been investigated, it was seen that the LM-ANN that has been developed, was a successful tool which was capable of UCS prediction. 相似文献
12.
The evolution of the shallow water wave‐dominated Fougueux wreck site in the Gulf of Cadiz was investigated through repeat bathymetric surveys, wave‐ and current‐velocity field measurements, and numerical modeling. This multidisciplinary approach was used to understand the relationships between scouring, morphodynamic change, and hydrodynamic forcing. Field experiments and numerical models indicate that wave processes dominate site evolution. Numerical model outputs indicate current velocity, bed shear stress, orbital velocity, and specially wave fraction breaking (with an increase of 45% and 135% for weak and significant storm conditions, respectively) are all amplified at the site. Scour pits 0.8 m depth inshore and 0.4 m depth offshore of the wreck are developed in response to hydrodynamic forcing. Time‐lapse bathymetric surveys quantify seasonal geomorphological change at the Fougueux. Up to 1.2 m of sediment is deposited and 0.7 m of sediment eroded in response to seasonal wave climate variation (an increase of 0.5 m for mean significant wave height, 0.9 m for significant wave height corresponding to 99% of nonexceedance probability, and 0.4 m·s−1 for mean near‐bed orbital velocity during winter conditions). A two‐dimensional scour model reproduces observed seasonal scour changes. Results have direct applications at all stages of a wreck site investigation. 相似文献
13.
Accurate and reliable prediction of shallow groundwater level is a critical component in water resources management. Two nonlinear models, WA–ANN method based on discrete wavelet transform (WA) and artificial neural network (ANN) and integrated time series (ITS) model, were developed to predict groundwater level fluctuations of a shallow coastal aquifer (Fujian Province, China). The two models were testified with the monitored groundwater level from 2000 to 2011. Two representative wells are selected with different locations within the study area. The error criteria were estimated using the coefficient of determination (R 2), Nash–Sutcliffe model efficiency coefficient (E), and root-mean-square error (RMSE). The best model was determined based on the RMSE of prediction using independent test data set. The WA–ANN models were found to provide more accurate monthly average groundwater level forecasts compared to the ITS models. The results of the study indicate the potential of WA–ANN models in forecasting groundwater levels. It is recommended that additional studies explore this proposed method, which can be used in turn to facilitate the development and implementation of more effective and sustainable groundwater management strategies. 相似文献
14.
Mohammad Isazadeh Seyed Mostafa Biazar Afshin Ashrafzadeh 《Environmental Earth Sciences》2017,76(17):610
The present study attempts to model the spatial variability of three groundwater qualitative parameters in Guilan Province, northern Iran, using artificial neural networks (ANNs) and support vector machines (SVMs). Data collected from 140 observation wells for the years 2002–2014 were used. Five variables, X and Y coordinates of the observation well, distance of the observation well from the shoreline, areal average 6-month rainfall depth, and groundwater level at the day of water quality sampling, were considered as primary input variables. In addition, nine qualitative variables were also considered as auxiliary input variables. Electrical conductivity (EC), sodium concentration (Na+), and sulfate concentration (SO4 2?) of the groundwater in the region were estimated using ANNs and SVMs with different input combinations. The results showed that both ANNs and SVMs work well when the only primary input variable is the well location. The ANN yielded an RMSE of 1.03 mEq/l for SO4 2?, 1.05 mEq/l for Na+, and 203.17 μS/cm for EC, using the X and Y coordinates of the observation wells in the study area. In the case of SVM, these values were, respectively, 0.87, 0.87, and 176.68. Considering the auxiliary input variables (pH, EC, and the concentrations of Na+, K+, Ca2+, Mg2+, Cl?, SO4 2?, and HCO3 ?) resulted in a significant decrease in the RMSE of both ANNs (0.22, 0.30, and 33.04) and SVMs (0.26, 0.34, and 36.23). Comparing these RMSE values with those of cokriging interpolation technique (0.59, 0.98, and 177.59) indicated that ANNs and SVMs produced more accurate estimates of the three qualitative parameters. The relative importance of auxiliary input variables was also determined using Gamma test. The output uncertainty of ANNs and SVMs were determined using p-factor and d-factor. The results showed that SVMs have less uncertainty than ANNs. 相似文献
15.
Application of an Expert System to Predict Maximum Explosive Charge Used Per Delay in Surface Mining
The present paper mainly deals with the prediction of maximum explosive charge used per delay (Q MAX) using an artificial neural network (ANN) incorporating peak particle velocity (PPV) and distance between blast face to monitoring point (D). One hundred and fifty blast vibration data sets were monitored at different vulnerable and strategic locations in and around major coal producing opencast coal mines in India. One hundred and twenty-four blast vibrations records were used for the training of the ANN model vis-à-vis to determine site constants of various conventional vibration predictors. The other 26 new randomly selected data sets were used to test, evaluate and compare the ANN prediction results with widely used conventional predictors. Results were compared based on coefficient of correlation (R), mean absolute error and mean squared between measured and predicted values of Q MAX. It was found that coefficient of correlation between measured and predicted Q MAX by ANN was 0.985, whereas it ranged from 0.316 to 0.762 by different conventional predictor equations. Mean absolute error and mean squared error was also very small by ANN, whereas it was very high for different conventional predictor equations. 相似文献
16.
An artificial neural network model for flood simulation using GIS: Johor River Basin, Malaysia 总被引：7，自引：2，他引：5
Masoud Bakhtyari Kia Saied Pirasteh Biswajeet Pradhan Ahmad Rodzi Mahmud Wan Nor Azmin Sulaiman Abbas Moradi 《Environmental Earth Sciences》2012,67(1):251-264
Flooding is one of the most destructive natural hazards that cause damage to both life and property every year, and therefore the development of flood model to determine inundation area in watersheds is important for decision makers. In recent years, data mining approaches such as artificial neural network (ANN) techniques are being increasingly used for flood modeling. Previously, this ANN method was frequently used for hydrological and flood modeling by taking rainfall as input and runoff data as output, usually without taking into consideration of other flood causative factors. The specific objective of this study is to develop a flood model using various flood causative factors using ANN techniques and geographic information system (GIS) to modeling and simulate flood-prone areas in the southern part of Peninsular Malaysia. The ANN model for this study was developed in MATLAB using seven flood causative factors. Relevant thematic layers (including rainfall, slope, elevation, flow accumulation, soil, land use, and geology) are generated using GIS, remote sensing data, and field surveys. In the context of objective weight assignments, the ANN is used to directly produce water levels and then the flood map is constructed in GIS. To measure the performance of the model, four criteria performances, including a coefficient of determination (R 2), the sum squared error, the mean square error, and the root mean square error are used. The verification results showed satisfactory agreement between the predicted and the real hydrological records. The results of this study could be used to help local and national government plan for the future and develop appropriate (to the local environmental conditions) new infrastructure to protect the lives and property of the people of Johor. 相似文献
17.
A. R. Sayadi S. M. M. Tavassoli M. Monjezi M. Rezaei 《Arabian Journal of Geosciences》2014,7(3):1067-1072
Net present value (NPV) is the most popular economic indicator in evaluation of the investment projects. For the mining projects, this criterion is calculated under uncertainty associated with the relevant parameters of say commodity price, discount rate, etc. Accurate prediction of the NPV is a quite difficult process. This paper mainly deals with the development of a new model to predict NPV using artificial neural network (ANN) in the Zarshuran gold mine, Iran. Gold price (as the main product), silver price (as the byproduct), and discount rate were considered as input parameters for the ANN model. To reach an optimum architecture, different types of networks were examined on the basis of a trial and error mechanism. A neural network with architecture 3-15-10-1 and root mean square error of 0.092 is found to be optimum. Prediction capability of the proposed model was examined through computing determination coefficient (R 2?=?0.987) between predicted and real NPVs. Absolute error of US$0.1 million and relative error of 1.4 % also confirmed powerfulness of the developed ANN model. According to sensitivity analysis, it was observed that the gold price is the most effective and discount rate is the least effective parameter on the NPV. 相似文献
18.
泥石流冲毁桥墩是桥梁在遭受泥石流冲击时的常见破坏形式。为了研究泥石流对桥墩的冲击力大小,通过调整黏土、沙、石子、水的不同含量,配置不同流变特性、不同密度的泥石流,使用所配置的原料在泥石流槽内对两种形状（圆形、方形）的桥墩缩尺模型进行冲击,综合考察了流变特性、流速、桥墩形状以及冲击力的关系。试验表明：试验配置的泥石流原料流变特性差异明显,且可以用简单的选择流变仪测得,用牛顿流体或宾汉体描述。泥石流的流速可用曼宁公式求得,而公式中的糙率系数与泥石流黏度满足幂函数关系。相同工况下,不同形状桥墩所受的冲击力差异明显,方形桥墩阻力系数普遍大于圆形桥墩。使用非牛顿流体雷诺数（Re）可以综合反映流变特性和流速,因此,圆墩的阻力系数可表达为Re的函数,而方墩则没有明显关系。为方便工程应用,可根据黏性泥石流、稀性泥石流对圆墩的阻力系数分别为2.3、0.9,对方墩分别为2.6、1.9进行选用。 相似文献
19.
《Geodinamica Acta》2013,26(1-2):23-34
The event-based bedload yields of a small gravel-bed river (the Esconavette Torrent) have been concomitantly determined by surveying coarse sediment deposition in a trap and by monitoring the active layer of the bed and the displacement of painted tracers. The geometry of the active layer was obtained by means of scour chains and topographic resurveys. The cumulative bedload yield of 4 flow events measured in the trap and by the chain and tracer approach was respectively 174 and 153 m3. The consistency between those two field-based estimates confirms that the deployment of scour chains and tracers in gravel-bed rivers have the potential to provide a robust assessment of bedload transport. This potential theoretically depends on the spatial density of scour chains and the ability of the tracing technique to fit the grain size distribution of the active layer. The results demonstrate that a distance between scour chains that represents 10-15% of the active channel width is sufficient for a rather accurate determination of event bedload transport rate by reconstruction of scour and fill throughout a cross-section. 相似文献
20.
There is a need for research that advances understanding of flow alterations in contemporary watersheds where natural and anthropogenic interactions can confound mitigation efforts. Event-based flow frequency, timing, magnitude, and rate of change were quantified at five-site nested gauging sites in a representative mixed-land-use watershed of the central USA. Statistically independent storms were paired by site (n = 111 × 5 sites) to test for significant differences in event-based rainfall and flow response variables (n = 17) between gauging sites. Increased frequency of small peak flow events (i.e., 64 more events less than 4.0 m3 s?1) was observed at the rural–urban interface of the watershed. Differences in flow response were apparent during drier periods when small rainfall events resulted in increased flow response at urban sites in the lower reaches. Relationships between rainfall and peak flow were stronger with decreased pasture/crop land use and increased urban land use by approximately 20%. Event-based total rainfall explained 40–68% of the variance in peak flow (p < 0.001). Coefficients of determination (r2) were negatively correlated with pasture/crop land use (r2 = 0.92; p = 0.007; n = 5) and positively correlated with urban land use (r2 = 0.90; p = 0.008; n = 5). Significant differences in flow metrics were observed between rural and urban sites (p < 0.05; n = 111) that were not explained by differences in rainfall variables and drainage area. An urban influence on flow timing was observed using median time lag to peak centroid and time of maximum precipitation to peak flow. Results highlight the need to establish manageable flow targets in rapidly urbanizing mixed-land-use watersheds. 相似文献