Abutment scour depth modeling using neuro-fuzzy-embedded techniques

2018 ◽  
Vol 37 (2) ◽  
pp. 190-200 ◽  
Author(s):  
Fatemeh Moradi ◽  
Hossein Bonakdari ◽  
Ozgur Kisi ◽  
Isa Ebtehaj ◽  
Jalal Shiri ◽  
...  
Keyword(s):  
Scour Depth ◽  
Neuro Fuzzy ◽  
Abutment Scour

Abutment scour in clear-water and live-bed conditions by GMDH network

2013 ◽  
Vol 67 (5) ◽  
pp. 1121-1128 ◽  
Author(s):  
Mohammad Najafzadeh ◽  
Gholam-Abbas Barani ◽  
Masoud Reza Hessami Kermani
Keyword(s):  
Back Propagation ◽  
Support Vector ◽  
Group Method ◽  
Scour Depth ◽  
Back Propagation Algorithm ◽  
Clear Water ◽  
Sediment Size ◽  
Svm Model ◽  
Vector Machines ◽  
Abutment Scour

In the present study, the Group Method of Data Handling (GMDH) network has been utilized to predict abutments scour depth for both clear-water and live-bed conditions. The GMDH network was developed using a Back Propagation algorithm (BP). Input parameters that were considered as effective variables on abutment scour depth included properties of sediment size, geometry of bridge abutments, and properties of approaching flow. Training and testing performances of the GMDH network were carried out using dimensionless parameters that were collected from the literature. The testing results were compared with those obtained using the Support Vector Machines (SVM) model and the traditional equations. The GMDH network predicted the abutment scour depth with lower error (RMSE (root mean square error) = 0.29 and MAPE (mean absolute percentage of error) = 0.99) and higher (R = 0.98) accuracy than those performed using the SVM model and the traditional equations.

Estimation of current-induced scour depth around pile groups using neural network and adaptive neuro-fuzzy inference system

2009 ◽  
Vol 9 (2) ◽  
pp. 746-755 ◽  
Author(s):  
Mohammad Zounemat-Kermani ◽  
Ali-Asghar Beheshti ◽  
Behzad Ataie-Ashtiani ◽  
Saeed-Reza Sabbagh-Yazdi
Keyword(s):  
Neural Network ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Scour Depth ◽  
Pile Groups ◽  
Inference System ◽  
Neuro Fuzzy

Pareto design of multiobjective evolutionary neuro-fuzzy system for predicting scour depth around bridge piers

2021 ◽  
pp. 491-517
Author(s):  
Hossein Bonakdari ◽  
Isa Ebtehaj ◽  
Amir Hossein Azimi ◽  
Pijush Samui ◽  
Ahmed A. Sattar ◽  
...  
Keyword(s):  
Fuzzy System ◽  
Bridge Piers ◽  
Scour Depth ◽  
Neuro Fuzzy

scholarly journals A pareto design of evolutionary hybrid optimization of ANFIS model in prediction abutment scour depth

Sadhana ◽  
2019 ◽  
Vol 44 (7) ◽  
Author(s):  
Hamed Azimi ◽  
Hossein Bonakdari ◽  
Isa Ebtehaj ◽  
Saeid Shabanlou ◽  
Seyed Hamed Ashraf Talesh ◽  
...  
Keyword(s):  
Hybrid Optimization ◽  
Scour Depth ◽  
Anfis Model ◽  
Abutment Scour

scholarly journals Estimation of maximum scour depth downstream of an apron under submerged wall jets

2019 ◽  
Vol 21 (4) ◽  
pp. 523-540 ◽  
Author(s):  
Mohammad Aamir ◽  
Zulfequar Ahmad
Keyword(s):  
Scour Depth ◽  
Regression Equations ◽  
Wall Jets ◽  
Statistical Parameters ◽  
Densimetric Froude Number ◽  
Sediment Size ◽  
Fuzzy Interference System ◽  
Interference System ◽  
Neuro Fuzzy ◽  
Artificial Neural Network Ann

Abstract An analysis of laboratory experimental data pertaining to local scour downstream of a rigid apron developed under wall jets is presented. The existing equations for the prediction of the maximum scour depth under wall jets are applied to the available data to evaluate their performance and bring forth their limitations. A comparison of measured scour depth with that computed by the existing equations shows that most of the existing empirical equations perform poorly. Artificial neural network (ANN)- and adaptive neuro-fuzzy interference system (ANFIS)-based models are developed using the available data, which provide simple and accurate tools for the estimation of the maximum scour depth. The key parameters that affect the maximum scour depth are densimetric Froude number, apron length, tailwater level, and median sediment size. Results obtained from ANN and ANFIS models are compared with those of empirical and regression equations by means of statistical parameters. The performance of ANN (RMSE = 0.052) and ANFIS (RMSE = 0.066) models is more satisfactory than that of empirical and regression equations.

scholarly journals Length Parameter for Scaling Abutment Scour Depth

Water ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 3508
Author(s):  
Puer Xu ◽  
Niansheng Cheng ◽  
Maoxing Wei
Keyword(s):  
Large Scale ◽  
Scour Depth ◽  
Length Scale ◽  
Bed Shear Stress ◽  
Combined Effects ◽  
Flow Depth ◽  
Vortex System ◽  
Bridge Abutment ◽  
Abutment Scour ◽  
Scale Turbulence

Flow constriction caused by bridge abutment increases bed shear stress and thus enhances local scour. For scaling the maximum scour depth at the abutment, either abutment length or flow depth has been empirically used in previous studies. By performing a step-by-step analysis, this study proposes a new length scale, which is able to represent combined effects of abutment length, approach flow depth and channel width. Physically, the new length scale describes the maximum possible dimension of the associated vortex system (or large-scale turbulence). Six series of data compiled from the published literature were used in the analysis. The results indicate that the new length scale helps improve the agreement of predictions with the experimental data.

scholarly journals Application of improved neuro-fuzzy GMDH to predict scour depth at sluice gates

2014 ◽  
Vol 8 (1) ◽  
pp. 187-196 ◽  
Author(s):  
Mohammad Najafzadeh ◽  
Siow Yong Lim
Keyword(s):  
Scour Depth ◽  
Neuro Fuzzy

scholarly journals ANFIS-based approach to scour depth prediction at abutments in armored beds

2010 ◽  
Vol 13 (4) ◽  
pp. 699-713 ◽  
Author(s):  
Mohammad Muzzammil ◽  
Javed Alam
Keyword(s):  
Fuzzy Inference ◽  
Accurate Estimation ◽  
Scour Depth ◽  
Bridge Abutments ◽  
Anfis Model ◽  
Inference System ◽  
Depth Prediction ◽  
Neuro Fuzzy ◽  
Artificial Neural Network Ann ◽  
The Comparative Study

An accurate estimation of the maximum possible scour depth at bridge abutments is of paramount importance in decision-making for the safe abutment foundation depth and also for the degree of scour countermeasures to be implemented against excessive scouring. Most of the scour depth prediction formulae available in the literature have been developed based on the analysis of laboratory and field data using statistical methods such as the regression method (RM). The alternative approaches, such as artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS), are generally preferred to provide better solutions in cases where the available data is incomplete or ambiguous in nature. In the present study, an attempt has, therefore, been made to develop the ANFIS model for the prediction of scour depth at the bridge abutments embedded in an armored bed and make the comparative study for the performance of ANFIS over RM and ANN in modeling the scour depth. It has been found that the ANFIS model performed best amongst all of these methods. The causative variables in raw form result in a more accurate prediction of the scour depth than that of their grouped form.

Sign in / Sign up

Export Citation Format

Share Document