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

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.

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Gene expression programming to predict local scour using laboratory and field data

ISH Journal of Hydraulic Engineering ◽

10.1080/09715010.2020.1846144 ◽

2020 ◽

pp. 1-9

Author(s):

Praveen Rathod ◽

V.L. Manekar

Keyword(s):

Gene Expression ◽

Field Data ◽

Gene Expression Programming ◽

Local Scour

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Prediction of maximum scour depth around piers with debris accumulation using EPR, MT, and GEP models

Journal of Hydroinformatics ◽

10.2166/hydro.2016.212 ◽

2016 ◽

Vol 18 (5) ◽

pp. 867-884 ◽

Cited By ~ 52

Author(s):

Mohammad Najafzadeh ◽

Mohammad Rezaie Balf ◽

Esmat Rashedi

Keyword(s):

Polynomial Regression ◽

Gene Expression Programming ◽

Laboratory Data ◽

Experimental Studies ◽

Local Scour ◽

Bridge Piers ◽

Scour Depth ◽

Effective Parameters ◽

Pier Scour ◽

Debris Accumulation

Pier scour phenomena in the presence of debris accumulation have attracted the attention of engineers to present a precise prediction of the local scour depth. Most experimental studies of pier scour depth with debris accumulation have been performed to find an accurate formula to predict the local scour depth. However, an empirical equation with appropriate capacity of validation is not available to evaluate the local scour depth. In this way, gene-expression programming (GEP), evolutionary polynomial regression (EPR), and model tree (MT) based formulations are used to develop to predict the scour depth around bridge piers with debris effects. Laboratory data sets utilized to perform models are collected from different literature. Effective parameters on the local scour depth include geometric characterizations of bridge piers and debris, physical properties of bed sediment, and approaching flow characteristics. The efficiency of the training stages for the GEP, MT, and EPR models are investigated. Performances of the testing results for these models are compared with the traditional approaches based on regression methods. The uncertainty prediction of the MT was quantified and compared with those of existing models. Also, sensitivity analysis was performed to assign effective parameters on the scour depth prediction.

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Scour depth model for grade-control structures

Journal of Hydroinformatics ◽

10.2166/hydro.2017.149 ◽

2017 ◽

Vol 20 (1) ◽

pp. 117-133 ◽

Cited By ~ 11

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.

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

Journal of Hydroinformatics ◽

10.2166/hydro.2011.008 ◽

2011 ◽

Vol 14 (3) ◽

pp. 628-645 ◽

Cited By ~ 25

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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