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

Zounemat‐Kermani, Mohammad; Beheshti, Aliasghar; Ataie-Ashtiani, Behzad; Sabbagh-Yazdi, Saeed-Reza

doi:10.1016/j.asoc.2008.09.006

Cited by 127 publications

(41 citation statements)

References 21 publications

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“…However, as validated by empirical evaluations (Zounement-Kermani et al, 2009;Hosseini et al, Submitted) for the case of scour depth estimation around pile groups conventional methods have very low performance and therefore researchers tempted to use soft-computing methods like neural networks for scour depth estimation around pile groups.…”

Section: Neural Network Proceduresmentioning

confidence: 99%

Scour depth estimation methods around pile groups

Hosseini

Amini

2015

KSCE J Civ Eng

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Pile groups have become more popular in structural designs due to economical and geotechnical reasons. It is also known that scour as the main cause of bridge failure can make serious damages and considerably threaten the safety of our environment. Thus, predicting scour depth is an essential step in designing bridges. This paper is a comprehensive review of local scour depth estimation methods around pile groups. Few studies investigated the effect of various parameters on the scour depth and some of them derived empirical equation for estimating the scour depth. Therefore, this review is divided into two different parts. In the first part, the experimental studies and results in the literature are reviewed. In the second part, those works that introduced methods for estimating scour depth are described. The methods of the second part are categorized into two sections: (1) empirical equations; (2) neural network procedures. The first section is the summary of those works that introduced empirical equations for estimating scour depth and the second section is the summary of recently introduced neural network procedures.

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Section: Neural Network Proceduresmentioning

confidence: 99%

Scour depth estimation methods around pile groups

Hosseini

Amini

2015

KSCE J Civ Eng

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“…In recent years, artificial intelligence technique such as neuro-fuzzy has become increasingly popular in hydrology and water resources among researchers and practicing engineers. For instance; neuro-fuzzy has been used successfully for prediction of suspended sediment ( [24], [8], [26], [37]), evaporation and evapotranspiration modeling ( [23], [25], [3], [30]), real time reservoir operation ( [6], [7], [36]), ground-water vulnerability [10], modeling stage-discharge relationship ( [9], [28]), water quality problems [29], estimation of scour depth near pile groups [49], short-term flood forecasting [33], rainfall-runoff modeling ( [14], [20]), prediction of water level in reservoir [5], modeling hydrological time series ([32], [13]). …”

Section: Introductionmentioning

confidence: 99%

Prediction Of Flow Through Rockfill Dams Using A Neuro-fuzzy Computing Technique

Heydari¹,

Talaee²

2011

J. Math. Computer Sci.

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Rockfill dams are economical and fast tools for flood detention and control purposes. Artificial intelligence approaches may provide user-friendly alternatives to very complex and time-consuming numerical methods such as finite volume and finite element for predicting flow through rockfill dam. Therefore, this paper examines the potential of coactive neuro-fuzzy inference system (CANFIS) for estimation of flow through trapezoidal and rectangular rockfill dams. The results showed that accurate flow predictions can be achieved with a CANFIS with the Takagi-Sugeno-Kang (TSK) fuzzy model and the Bell membership function for both trapezoidal and rectangular rockfill dams. Furthermore, LevenbergMarquardt and Delta-Bar-Delta were the best algorithms for training the network in order to estimate flow through rectangular and trapezoidal rockfill dams, respectively. Overall, the results of this study suggest the possibility for using CANFIS for prediction of flow through rockfill dam.

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“…The findings indicated that the ANFIS method is the best predictor and various ANN models predict the scour depth better than the regression method. Local scour due to pile groups was also studied by the ANN model or the ANFIS method [13][14][15]. Keshavarzi et al [16] predicted local scour around a bed sill using the ANFIS method.…”

Section: Introductionmentioning

confidence: 99%

Prediction of local scour around bridge piers using the ANFIS method

Choi

Lee

2015

Neural Comput & Applic

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Local scour around bridge piers is a complicated physical process and involves highly three-dimensional flows. Thus, the scour depth, which is directly related to the safety of a bridge, cannot be given in the form of the exact relationship of dependent variables via an analytical method. This paper proposes the use of the adaptive neuro-fuzzy inference system (ANFIS) method for predicting the scour depth around a bridge pier. Five variables including mean velocity, flow depth, size of sediment particles, critical velocity for particles' initiation of motion, and pier width were used for the scour depth. For comparison, predictions by the artificial neural network (ANN) model were also provided. Both the ANN model and ANFIS method were trained and validated. The findings indicate that the modeling with dimensional variables yields better predictions than when normalized variables are used. The ANN model was applied to a field-scale dataset. Prediction results indicated that the errors are much larger compared to the case of a laboratory-scale dataset. The MAPE by the ANN model trained with part of the field data was not seriously different from that by the model trained with the laboratory data. However, the application of the ANFIS method improved the predictions significantly, reducing the MAPE to the half of that by the ANN model. Five selected empirical formulas were also applied to the same dataset, and Sheppard and Melville's formula was found to provide the best prediction. However, the MAPEs for the scour depths predicted by empirical formulas are much larger than MAPEs by either the ANN or the ANFIS method. The ANFIS method predicts much better if the range of the training dataset is sufficiently wide to cover the range of the application dataset.

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

Cited by 127 publications

References 21 publications

Scour depth estimation methods around pile groups

Scour depth estimation methods around pile groups

Prediction Of Flow Through Rockfill Dams Using A Neuro-fuzzy Computing Technique

Prediction of local scour around bridge piers using the ANFIS method

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