Document Type : Original Article

Authors

• Eghbal Khorami ¹
• Mohammad Mehdi Heidari ²
• RASOOL Ghobadian ³

¹ Razi university

² Assistant Professor, Campus of Agriculture and Natural ‎Resources, Razi ‎University, Kermanshah, Iran.

³ Razi UNIVERSITY

Abstract

Introduction

Ski jump as one of the energy dissipater systems at the end of weir has been considered by hydraulic engineers due to its cost-effectiveness. Despite the dissipation of a significant portion of the flow energy, the bed is subject to scouring and can pose hazards to the structure. In recent decades, equations have been proposed to estimate the scour rate of this structure, each of which has a specific problem. For example, in some of these relationships, there are some factors that affect scouring, such as the bucket radius or the lip angle of bucket. The equation of Azmatullah et al. Is one of the most complete relationships mentioned in most sources. In this equation, the depth of scour increases with increasing mean sediment size of bed materials, which contradicts previous research.

In the present study, the neural network was trained to estimate the depth of scour and the transmission functions, number of layers and type of network training were optimally selected. Using neural network and sensitivity analysis, the effect of different variables on the scour depth was determined. A high-precision relationship was presented to predict the scour depth using explicit genetic programming



Methodology

In order to investigate the scour depth in flip-bucket, a laboratory model with a width of 0.5 m, height of 45 cm, length of 59 cm, bucket radius of 15 cm and lip angle of bucket of 45 degrees was constructed. The flow rate through the laboratory model is 7 to 21.2 lit/s, head between upper reservoir water level and tail water level is 0.3 to 0.38 m, the tail water depth is 0.028 to 0.1 m and the D50 bed materials is 4.3 mm. In the present study, in addition to the measured laboratory data, used data related to the research of Azmatullah et al.

In this study, neural network has been used to predict the depth of scour hole. The optimal structure of the neural network is affected by variables such as the number of neurons in the latent layer, the stimulus functions between the neurons, and the number of latent layers.

In the present study, the optimal structure of the neural network was determined by trial and error.

It should be noted that in this study, NeuroSolutions software was used for the architecture of the multilayer neural network of perceptron.

Results

Stimulus functions, number of hidden layers and type of training are factors that affect the accuracy of the model. In this study, the optimal structure of the neural network was determined by considering the variables affecting network accuracy and using various laboratory data. The results show that if the number of hidden layers of a number is a function of hyperbolic tangent transfer and network training type, Levenberg-Marquardt, the accuracy of the neural network in estimating the maximum scour depth is better.

Also in this study, the scour depth sensitivity index to discharge per unit width flow, upstream head, lip angle of bucket, bucket radius, tail water depth and mean sediment size were calculated. The sensitivity index of scour depth to discharge per unit of flow width, fall height and lip angle of bucket is more than zero and this shows that with the increase of these variables, the depth of the scour hole increases. In order to reduce the amount of scour holes, the depth of the tail water can be increased or a riprap can be used. According to the calculated sensitivity indices, if the depth of tail water or particle diameter increases by 10%, the maximum depth of the scour hole decreases by about 3.9 and 1.3, respectively. Depending on the economic considerations and the feasibility of each case, the appropriate option can be selected.

In this study, using the data of Azmatullah et al. And using GEP software, a relationship was presented to estimate the maximum scour rate in the flip bucket, with an average error rate of 9.8% which shows the model's ability to estimate scour.



Conclusions

In this study a neural network model was given to estimate the depth of the scour hole in the flip bucket.

The results show that if the number of hidden layers of a number is a function of hyperbolic tangent transfer and the type of network training is Levenberg-Marquardt, the neural network accuracy in estimating the maximum scour depth is better. Also in this study, the sensitivity index of scour depth to input variables was calculated. The greatest effect on the scour depth is related to the water discharge per unit width rate, if the flow rate increases by 10%, the maximum depth of the scour hole will increase by 8.5%.

In this study, a relation was presented to estimate the depth of the erosion hole, the accuracy of which is evaluated very well according to the criteria.

Keywords

• Estimation of scour depth
• ski jump
• neural network model
• sensitivity indicators

Main Subjects

• River engineering