scholarly journals Prediction of time variation of scour depth around spur dikes using neural networks

2011 ◽  
Vol 14 (1) ◽  
pp. 180-191 ◽  
Author(s):  
Hojat Karami ◽  
Abdollah Ardeshir ◽  
Mojtaba Saneie ◽  
S. Amin Salamatian
Keyword(s):  
Neural Networks ◽  
Regression Model ◽  
Time Variation ◽  
Back Propagation ◽  
Maximum Depth ◽  
Scour Depth ◽  
Effective Parameters ◽  
Spur Dike ◽  
Feed Forward Back Propagation ◽  
Spur Dikes

The maximum depth of scouring around spur dikes plays an important role in the hydraulic design process. There have been many studies on the maximum depth of scouring, but there is little information available on the time variation of scour depth. In this paper, the time variation of scouring around the first spur dike in a series was investigated experimentally. Experiments were carried out in four different bed materials under different flow intensities (U/Ucr). To achieve a time development of scouring around the first spur dike, more than 750 sets of experimental data were collected. The results showed that 70–90% of the equilibrium scour depths were occurring during the initial 20% of the overall time of scouring. Based on the data analysis, a regression model and artificial neural networks (ANNs) were developed. The models were compared with other empirical equations in the literature. However, the results showed that the developed regression model is quite accurate and more practical, but the ANN models by feed forward back propagation and radial basis function provide a better prediction of observation. Finally, by sensitivity analysis, the most and the least effective parameters, which affected time variation of scouring, were determined.

scholarly journals Estimation of maximum scour depth near a spur dike

2016 ◽  
Vol 43 (3) ◽  
pp. 270-278 ◽  
Author(s):  
Manish Pandey ◽  
Z. Ahmad ◽  
P.K. Sharma
Keyword(s):  
Natural Phenomenon ◽  
Scour Depth ◽  
Flowing Water ◽  
Performance Indices ◽  
Spur Dike ◽  
Bed Erosion ◽  
River Bed ◽  
Spur Dikes ◽  
Dike Failure ◽  
Better Than

Scour is a natural phenomenon in rivers caused by the erosive action of the flowing water on the bed and banks. Spur dikes are constructed across the flow to protect the bank from erosion by shifting of the river away from the bank. The spur dike undermines due to river-bed erosion and scouring, which is generally recognized as the main cause of spur dike failure. In this study, accuracy of existing equations for the computation of maximum scour depth has been checked with available data in the literature and data collected in the present study using graphical and statistical performance indices. Three new relationships are also proposed to estimate the maximum scour depth and maximum scour length upstream and downstream of spur dike. This new relationship for maximum scour depth is shown to perform better than other existing equations.

scholarly journals Artificial neural networks predictive models. A case study: carbon and bromine concentrations prediction based on chlorination time

2013 ◽  
Vol 14 (1) ◽  
pp. 10-17
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Potable Water ◽  
Back Propagation ◽  
Ann Model ◽  
Data Set ◽  
Test Set ◽  
Feed Forward Back Propagation ◽  
Artificial Neural ◽  
Comparison Of The Results

Artificial neural networks (ANNs) are being used increasingly to predict water variables. This study offers an alternative approach to quantify the relationship between time of chlorination in potable water (due to convectional treatment procedure) and chlorination by-products concentration (expressed as carbon and bromine) with an ANN model, i.e., capturing non-linear relationships among the water quality variables. Thus, carbon and bromine concentrations in potable water (the second chosen due to the toxicity of brominated trihalomethanes, THMs) were predicted using artificial neural networks (ANNs) based mainly on multi-layer perceptrons (MLPs) architecture. The chlorination (detention) time as much as 58 hours in Athens distributed network, comprised the input variables to the ANNs models. Moreover, to develop an ANN model for estimating carbon and bromine, the available data set was partitioned into training, validation and test set. In order to reach an optimum amount of hidden layers or nodes, different architectures were tested. The quality of the ANN simulations was evaluated in terms of the error in the validation sample set for the proper interpretation of the results. The calculated sum-squared errors for training, validation and test set were 0.056, 0.039 and 0.060 respectively for the best model selected. Comparison of the results showed that a two-layer feed-forward back propagation ANN model could be used as an acceptable model for predicting carbon and bromine contained in potable water THMs.

CONDITION MONITORING OF OFF-HIGHWAY TRUCK TIRES AT SUNGUN COPPER MINE USING NEURAL NETWORKS / MONITOROWANIE STANU TECHNICZNEGO OPON W CIĘŻKICH POJAZDACH TERENOWYCH EKSPLOATOWANYCH W KOPALNI MIEDZI SUNGUN, PRZY UŻYCIU SIECI NEURONOWYCH

2013 ◽  
Vol 58 (4) ◽  
pp. 1133-1144 ◽  
Author(s):  
Amin Moniri Morad ◽  
Javad Sattarvand
Keyword(s):  
Neural Networks ◽  
Learning Algorithm ◽  
Unit Cost ◽  
Back Propagation ◽  
Maintenance Cost ◽  
Copper Mine ◽  
Sungun Copper Mine ◽  
Feed Forward Back Propagation ◽  
Truck Tires ◽  
Traditional Approaches

Abstract Maintenance cost of the equipment is one of the most important portions of the operating expenditures in mines; therefore, any change in the equipment productivity can lead to major changes in the unit cost of the production. This clearly shows the importance and necessity of using novel maintenance methods instead of traditional approaches, in order to reach the minimum sudden occurrence of the equipment failure. For instance, the tires are costly components in maintenance which should be regularly inspected and replaced among different axles. The paper investigates the current condition of equipment tires at Sungun Copper Mine and uses neural networks to estimate the wear of the tires. The Input parameters of the network composed of initial tread depth, time of inspection and consumed tread depth by the time of inspection. The output of the network is considered as the residual service time ratio of the tires. The network trained by the feed-forward back propagation learning algorithm. Results revealed a good coincidence between the real and estimated values as 96.6% of correlation coefficient. Hence, better decisions could be made about the tires to reduce the sudden failures and equipment breakdowns.

A Study on the Application of Artificial Neural Networks on Green Self Consolidating Concrete (SCC) under Hot Weather

2016 ◽  
Vol 677 ◽  
pp. 254-259 ◽  
Author(s):  
Mohamed Al Khatib ◽  
Samer Al Martini
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Fly Ash ◽  
Ambient Temperature ◽  
Back Propagation ◽  
Self Consolidating Concrete ◽  
Feed Forward Back Propagation ◽  
Slump Flow ◽  
Ann Models ◽  
Hot Weather

Self-consolidating concrete (SCC) has recently drawn attention to the construction industry in hot weather countries, due to its high fresh and mechanical properties. The slump flow is routinely used for quality control of SCC. Experiments were conducted by the current authors to investigate the effects of hot weather conditions on the slump flow of SCC. Self-consolidating concrete mixtures were prepared with different dosages of fly ash and superplasticizer and under different ambient temperatures. The results showed that the slump flow of SCC is sensitive to changes in ambient temperature, fly ash dosage, and superplasticizer dosage. In this paper, several artificial neural networks (ANNs) were employed to predict the slump flow of self-consolidating concrete under hot weather. Some of the data used to construct the ANNs models in this paper were collected from the experimental study conducted by the current authors, and other data were gathered from literature. Various parameters including ambient temperature and mixing time were used as inputs during the construction of ANN models. The developed ANN models employed two neural networks: the Feed-Forward Back Propagation (FFBP) and the Cascade Forward Back Propagation (CFBP). Both FFBP and CFBP showed good predictability to the slump flow of SCC mixtures. However, the FFBP network showed a slight better performance than CFBP, where it better predicted the slump flow of SCC than the CFBP network under hot weather. The results in this paper indicate that the ANNs can be employed to help the concrete industry in hot weather to predict the quality of fresh self-consolidating concrete mixes without the need to go through long trial and error testing program.Keywords: Self-consolidating concrete; Neural networks; Hot weather, Feed-forward back-propagation, Cascade-forward back propagation.

scholarly journals Numerical Simulation of the Influence of Spur Dikes Spacing on Local Scour and Flow

2019 ◽  
Vol 9 (11) ◽  
pp. 2306
Author(s):  
Jian Ning ◽  
Guodong Li ◽  
Shanshan Li
Keyword(s):  
Shear Stress ◽  
Pearson Correlation ◽  
Fixed Bed ◽  
Maximum Velocity ◽  
Turbulent Energy ◽  
Local Scour ◽  
Shielding Effect ◽  
Scour Depth ◽  
Spur Dike ◽  
Spur Dikes

The spacing of spur dikes is an important consideration for the layout of spur dike channels. This study focuses on the local scour morphology and flow field characteristics of spur dikes with different spacings. The results show that the maximum scour depth is generally found in the vicinity of the first spur dike head. With the increase of the spacing of spur dikes, the shielding effect of the first spur dike is weakened. The maximum velocity in the main flow zone is twice that of the approach flow velocity in the fixed bed. But it is approximately the same as the incoming velocity in equilibrium scouring. The maximum turbulent energy appears to be mainly located in the backflow area of the fourth spur dike in the fixed bed, while the maximum value appears at the second spur dike head in the movable bed. Further, the shear stress decreases as scouring develops. Pearson correlation analysis was carried out between scour depth and shear stress. The analysis results are significantly correlated, indicating that the bed shear stress plays a prominent role in the scouring process. These discoveries can serve as a guide to determine the most reasonable spacing of spur dikes.

River flow forecasting using different artificial neural network algorithms and wavelet transform

2009 ◽  
Vol 36 (1) ◽  
pp. 26-38 ◽  
Author(s):  
Turgay Partal
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Wavelet Transform ◽  
Back Propagation ◽  
Propagation Model ◽  
River Flows ◽  
Feed Forward ◽  
Feed Forward Back Propagation ◽  
Artificial Neural

In this study, the wavelet–neural network structure that combines wavelet transform and artificial neural networks has been employed to forecast the river flows of Turkey. Discrete wavelet transforms, which are useful to obtain to the periodic components of the measured data, have significantly positive effects on artificial neural network modeling performance. Generally, the feed-forward back-propagation method was studied with respect to artificial neural network applications to water resources data. In this study, the performance of generalized neural networks and radial basis neural networks were compared with feed-forward back-propagation methods. Six different models were studied for forecasting of monthly river flows. It was seen that the wavelet and feed-forward back-propagation model was superior to the other models in terms of selected performance criteria.

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