scholarly journals Scour depth model for grade-control structures

2017 ◽  
Vol 20 (1) ◽  
pp. 117-133 ◽  
Author(s):  
Ahmed M. A. Sattar ◽  
Karol Plesiński ◽  
Artur Radecki-Pawlik ◽  
Bahram Gharabaghi
Keyword(s):  
Gene Expression ◽  
Parametric Analysis ◽  
Gene Expression Programming ◽  
Fish Habitat ◽  
Mountain Streams ◽  
Scour Depth ◽  
Control Variables ◽  
Control Structures ◽  
River Bed ◽  
Grade Control

Abstract Grade-control structures (GCS) are commonly used to protect fish habitat by preventing excessive river-bed degradation in mountain streams. However, flow over the GCS can cause localized scour immediately downstream of the weir. This paper aims to develop more accurate models for prediction of the maximum scour depth downstream of GCS, using a more extensive dataset and evolutionary gene expression programming (GEP). Three GEP models are developed relating maximum scour depth and various control variables. The developed models had the lowest error compared to available models. A parametric analysis is performed for further verification of the developed GEP model. The results indicate that the proposed relations are simple and can more accurately predict the scour depth downstream GCS.

scholarly journals Modeling scour depth downstream of grade-control structures using data driven and empirical approaches

2016 ◽  
Vol 18 (6) ◽  
pp. 946-960 ◽  
Author(s):  
Kiyoumars Roushangar ◽  
Samira Akhgar ◽  
Ali Erfan ◽  
Jalal Shiri
Keyword(s):  
Gene Expression Programming ◽  
Field Measurements ◽  
Data Driven ◽  
Scour Depth ◽  
Feed Forward Neural Network ◽  
Control Structures ◽  
Grade Control ◽  
Programming Techniques ◽  
Using Data ◽  
Controlled Structure

Local scour occurs in the immediate vicinity of structures as a result of impinging on a bed with a high velocity flow. Prediction of scour depth has an important role in control structure management and water resource engineering issues, so a study of new heuristic expressions governing it is necessary. The present study aims to investigate different methods' capabilities to estimate scour depth downstream of grade-control structures using field measurements from the literature. Accordingly, data driven feed forward neural network and gene expression programming techniques were selected for the investigation. Additionally, the optimum data driven based scour depth models were compared with the corresponding physical–empirical based formulas. Three data categories corresponding to (a) scouring downstream of a ski-jump bucket, (b) a sharp-crested weir, and (c) an inclined slope controlled structure (as grade-control structures) were applied as reference patterns for developing and validating the applied models. A sensitivity analysis was also performed to identify the most influential parameters on scouring. The obtained results indicated that the applied methods have promising performance in estimating the scour depth downstream of spillways and control structures. Nevertheless, the applied data driven approaches show higher accuracy than the corresponding traditional formulas.

scholarly journals Gene-expression programming to predict scour at a bridge abutment

2011 ◽  
Vol 14 (2) ◽  
pp. 324-331 ◽  
Author(s):  
H. Md. Azamathulla
Keyword(s):  
Neural Network ◽  
Gene Expression ◽  
Gene Expression Programming ◽  
Accurate Estimation ◽  
Scour Depth ◽  
Laboratory Measurements ◽  
Ann Modeling ◽  
Bridge Abutment ◽  
Alternative Approach ◽  
Artificial Neural Network Ann

The process involved in the local scour at an abutment is so complex that it makes it difficult to establish a general empirical model to provide accurate estimation for scour. This study presents the use of gene-expression programming (GEP), which is an extension of genetic programming (GP), as an alternative approach to estimate the scour depth. The datasets of laboratory measurements were collected from the published literature and used to train the network or evolve the program. The developed network and evolved programs were validated by using the observations that were not involved in training. The proposed GEP approach gives satisfactory results compared with existing predictors and artificial neural network (ANN) modeling in predicting the scour depth at an abutment.

scholarly journals Gene-expression programming to predict the local scour depth at downstream of stilling basins

2016 ◽  
Vol 23 (1) ◽  
pp. 102-113 ◽  
Author(s):  
M. Mesbahi ◽  
N. Talebbeydokhti ◽  
S.-A. Hosseini ◽  
S.-H. Afzali
Keyword(s):  
Gene Expression ◽  
Gene Expression Programming ◽  
Local Scour ◽  
Scour Depth ◽  
Stilling Basins

scholarly journals Gene-expression programming to predict pier scour depth using laboratory data

2011 ◽  
Vol 14 (3) ◽  
pp. 628-645 ◽  
Author(s):  
Mujahid Khan ◽  
H. Md. Azamathulla ◽  
M. Tufail
Keyword(s):  
Gene Expression ◽  
Gene Expression Programming ◽  
Laboratory Data ◽  
Bridge Pier ◽  
Scour Depth ◽  
Bridge Scour ◽  
Regression Equations ◽  
Pier Scour ◽  
Depth Prediction ◽  
Bridge Pier Scour

Prediction of bridge pier scour depth is essential for safe and economical bridge design. Keeping in mind the complex nature of bridge scour phenomenon, there is a need to properly address the methods and techniques used to predict bridge pier scour. Up to the present, extensive research has been carried out for pier scour depth prediction. Different modeling techniques have been applied to achieve better prediction. This paper presents a new soft computing technique called gene-expression programming (GEP) for pier scour depth prediction using laboratory data. A functional relationship has been established using GEP and its performance is compared with other artificial intelligence (AI)-based techniques such as artificial neural networks (ANNs) and conventional regression-based techniques. Laboratory data containing 529 datasets was divided into calibration and validation sets. The performance of GEP was found to be highly satisfactory and encouraging when compared to regression equations but was slightly inferior to ANN. This slightly inferior performance of GEP compared to ANN is offset by its capability to provide compact and explicit mathematical expression for bridge scour. This advantage of GEP over ANN is the main motivation for this work. The resulting GEP models will add to the existing literature of AI-based inductive models for bridge scour modeling.

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