scholarly journals Application of nature-inspired optimization algorithms to ANFIS model to predict wave-induced scour depth around pipelines

2020 ◽  
Vol 22 (6) ◽  
pp. 1425-1451 ◽  
Author(s):  
Ahmad Sharafati ◽  
Ali Tafarojnoruz ◽  
Davide Motta ◽  
Zaher Mundher Yaseen
Keyword(s):  
Fuzzy Inference ◽  
Prediction Performance ◽  
Scour Depth ◽  
Model Structure ◽  
Clear Water ◽  
Inference System ◽  
Depth Prediction ◽  
Dimensional Parameters ◽  
Variable Combination ◽  
Wave Induced

Abstract Wave-induced scour depth below pipelines is a physically complex phenomenon, whose reliable prediction may be challenging for pipeline designers. This study shows the application of adaptive neuro-fuzzy inference system (ANFIS) incorporated with particle swarm optimization , ant colony (), differential evolution and genetic algorithm () and assesses the scour depth prediction performance and associated uncertainty in different scour conditions including live-bed and clear-water. To this end, the non-dimensional parameters Shields number (), Keulegan–Carpenter number () and embedded depth to diameter of pipe ratio () are considered as prediction variables. Results indicate that the model ( and ) is the most accurate predictive model in both scour conditions when all three mentioned non-dimensional input parameters are included. Besides, the model shows a better prediction performance than recently developed models. Based on the uncertainty analysis results, the prediction of scour depth is characterized by larger uncertainty in the clear-water condition, associated with both model structure and input variable combination, than in live-bed condition. Furthermore, the uncertainty in scour depth prediction for both live-bed and clear-water conditions is due more to the input variable combination than it is due to the model structure .

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.

scholarly journals ANFIS approach to the scour depth prediction at a bridge abutment

2010 ◽  
Vol 12 (4) ◽  
pp. 474-485 ◽  
Author(s):  
Mohammad Muzzammil
Keyword(s):  
Fuzzy Inference ◽  
Accurate Estimation ◽  
Scour Depth ◽  
Bridge Abutments ◽  
Anfis Model ◽  
Inference System ◽  
Model Studies ◽  
Depth Prediction ◽  
Bridge Abutment ◽  
Conventional Regression

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 counter-measure to be implemented against excessive scouring. Despite analysis of innumerable prototype and hydraulic model studies in the past, the scour depth prediction at the bridge abutments has remained inconclusive. This paper presents an alternative to the conventional regression model (RM) in the form of an adaptive network-based fuzzy inference system (ANFIS) modelling. The performance of ANFIS over RM and artificial neural networks (ANNs) is assessed here. It was found that the ANFIS model performed best among 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.

ANFIS Model for Time Series Prediction

2013 ◽  
Vol 385-386 ◽  
pp. 1411-1414 ◽  
Author(s):  
Xue Bo Jin ◽  
Jiang Feng Wang ◽  
Hui Yan Zhang ◽  
Li Hong Cao
Keyword(s):  
Time Series ◽  
Fuzzy Inference System ◽  
Prediction Error ◽  
Fuzzy Inference ◽  
Time Series Prediction ◽  
Prediction Performance ◽  
Membership Functions ◽  
Adaptive Network ◽  
Anfis Model ◽  
Inference System

This paper describes an architecture of ANFIS (adaptive network based fuzzy inference system), to the prediction of chaotic time series, where the goal is to minimize the prediction error. We consider the stock data as the time series. This paper focuses on how the stock data affect the prediction performance. In the experiments we changed the number of data as input of the ANFIS model, the type of membership functions and the desired goal error, thereby increasing the complexity of the training.

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

scholarly journals Development of scour around a circular pier and its modelling using genetic algorithm

2020 ◽  
Vol 20 (8) ◽  
pp. 3358-3367
Author(s):  
Manish Pandey ◽  
Mohammad Zakwan ◽  
Mohammad Amir Khan ◽  
Swati Bhave
Keyword(s):  
Experimental Data ◽  
Genetic Algorithm ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Bridge Pier ◽  
Scour Depth ◽  
Clear Water ◽  
Depth Prediction ◽  
Depth Relationship

Abstract This paper deals with generalized scour estimation to investigate maximum scour depth at equilibrium scour condition using experimental data obtained from experiments conducted by the authors along with data of previous researchers. Three hundred experimental data were used to derive the generalized clear water scour relationship around circular a bridge pier by using genetic algorithm (GA) and multiple linear regression (MLR) techniques. The GA-based maximum scour depth relationship showed more precise results than MLR. In addition, the present GA and MLR relationships were compared with some equations developed by earlier researchers. Graphically and statistically, it was observed that the GA and MLR relationships provide better agreement with experimental data as compared to earlier relationships. The present study highlights that the GA approach could be effectively used for estimation of maximum scour depth prediction around the bridge pier.

scholarly journals ANFIS-based approach for scour depth prediction at piers in non-uniform sediments

2009 ◽  
Vol 12 (3) ◽  
pp. 303-317 ◽  
Author(s):  
M. Muzzammil ◽  
M. Ayyub
Keyword(s):  
Fuzzy Inference ◽  
Bridge Piers ◽  
Scour Depth ◽  
Fuzzy Inference Systems ◽  
Foundation Design ◽  
Pier Scour ◽  
Depth Prediction ◽  
Alternative Approach ◽  
Inference Systems ◽  
Bridge Pier Scour

An estimation of scour depth is a prerequisite for the efficient foundation design of important hydraulic structures such as bridge piers and abutments. Most of the scour depth prediction formulae available in the literature have been developed based on the analysis of the laboratory/field data using statistical methods such as the regression method (RM). Conventional statistical analysis is generally replaced in many fields of engineering by the alternative approach of artificial neural networks (ANN) and adaptive network-based fuzzy inference systems (ANFIS). These recent techniques have been reported to provide better solutions in cases where the available data is incomplete or ambiguous by nature. An attempt has been made to compare the performance of ANFIS over RM and ANN in modeling the depth of bridge pier scour in non-uniform sediments. It has been found that the ANFIS performed best amongst all these methods.

scholarly journals Estimation of current-induced pile groups scour using a rule-based method

2012 ◽  
Vol 15 (2) ◽  
pp. 516-528 ◽  
Author(s):  
N. Ghaemi ◽  
A. Etemad-Shahidi ◽  
B. Ataie-Ashtiani
Keyword(s):  
Soft Computing ◽  
Fuzzy Inference ◽  
Computing Methods ◽  
Accurate Estimation ◽  
Scour Depth ◽  
Pile Groups ◽  
Empirical Formulas ◽  
Inference System ◽  
Acceptable Risks ◽  
Soft Computing Methods

Scour phenomenon around piles could endanger the stability of the structures placed on them. Therefore, an accurate estimation of the scour depth around piles is very important for engineers. Due to the complexity of the interaction between the current, seabed and pile group; prediction of the scour depth is a difficult task and the available empirical formulas have limited accuracy. Recently, soft computing methods such as artificial neural networks (ANN) and adaptive neuro-fuzzy inference system (ANFIS) have been used for the prediction of the scour depth. However, these methods do not give enough insight into the generated models and are not as easy to use as the empirical formulas. In this study, new formulas are given that are compact, accurate and physically sound. In comparison with the other soft computing methods, this approach is more transparent and robust. Comparison between the developed formulas and previous empirical formulas showed the superiority of the developed ones in terms of accuracy. In addition, the given formulas can be easily used by engineers to estimate the scour depth around pile groups. Moreover, in this study, design factors are given for different levels of acceptable risks, which can be useful for design purposes.

scholarly journals Combined particle swarm optimization and fuzzy inference system model for estimation of current-induced scour beneath marine pipelines

2010 ◽  
Vol 13 (3) ◽  
pp. 558-573 ◽  
Author(s):  
M. Zanganeh ◽  
A. Yeganeh-Bakhtiary ◽  
R. Bakhtyar
Keyword(s):  
Particle Swarm Optimization ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Subtractive Clustering ◽  
Scour Depth ◽  
Swarm Optimization ◽  
Inference System ◽  
Input Variables

In this paper the capability of Particle Swarm Optimization (PSO) is employed to deal with an Adaptive Network based Fuzzy Inference System (ANFIS) model's inherent shortcomings to extract optimum fuzzy if–then rules in noisy areas arising from the application of nondimensional variables to estimate scour depth. In the model, a PSO algorithm is employed to optimize the clustering parameters controlling fuzzy if–then rules in subtractive clustering while another PSO algorithm is employed to tune the fuzzy rule parameters associated with the fuzzy if–then rules. The PSO model's objective function is the Root Mean Square (RMSE), by which the model attempts to minimize the error in scour depth estimation with respect to its generalization capability. To evaluate the model's performance, the experimental datasets are used as training, checking and testing datasets. Two-dimensional and nondimensional models are developed such that in the dimensional model the mean current velocity, mean grain size, water depth, pipe diameter and shear boundary velocity are used as input variables while in the nondimensional model the pipe, boundary Reynolds numbers, Froude number and normalized depth of water are set as input variables. The results show that the model provides an alternative approach to the conventional empirical formulae. It is evident that the developed PSO–FIS–PSO is superior to the ANFIS model in the noisy area in which the input and output variables are slightly related to each other.

scholarly journals Meta-heuristic Optimization Algorithms for Predicting the Scouring Depth Around Bridge Piers

2019 ◽  
Author(s):  
Yousef Hassanzadeh ◽  
Amin Jafari-Bavil-Olyaei ◽  
Mohammad Taghi-Aalami ◽  
Nazila Kardan
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Bridge Pier ◽  
Accurate Estimation ◽  
Scour Depth ◽  
Adaptive Network ◽  
Pier Scour ◽  
Inference System ◽  
Colliding Bodies Optimization ◽  
Bridge Pier Scour

An accurate estimation of bridge pier scour has been considered as one of the important parameters in designing of bridges. However, due to the numerous involved parameters and convolution of this phenomenon, many existing approaches cannot predict scour depth with an acceptable accuracy. Obtained results from the empirical relationships show that these relationships have low accuracy in determining the maximum scour depth and they need a high safety factor for many cases, which leads to uneconomic designs of bridges. To cover these disadvantages, three new models are provided to estimate the bridge pier scour using an adaptive network-based fuzzy inference system. The parameters of the system are optimized by using the colliding bodies optimization, enhanced colliding bodies optimization and vibrating particles system methods. To evaluate the efficiency of the proposed methods, their results were compared with those of simple adaptive network-based fuzzy inference system and its improved versions by using the particle swarm optimization and genetic algorithm as well as the empirical equations. Comparison of results showed that the new vibrating particles system based algorithm could find better results than other two ones. In addition, comparison of the results obtained by the proposed methods with those of the empirical relations confirmed the high performance of the new methods.

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