Application of ANFIS and LR in prediction of scour depth in bridges

Akib, Shatirah; Mohammadhassani, Mohammad; Jahangirzadeh, Afshin

doi:10.1016/j.compfluid.2013.12.004

Cited by 54 publications

(18 citation statements)

References 28 publications

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“…ANFIS, as a hybrid intelligent system that enhances the ability to automatically learn and adapt, was used by researchers in various engineering systems (Akib et al 2014c;Shamshirband et al 2014;Basser et al 2014). So far, there are many studies of the application of ANFIS for estimation and real-time identification of many different systems (Bektas Ekici and Aksoy 2011; Khajeh et al 2009;İnal 2008;Akib et al 2014b). Fuzzy Inference System (FIS) is the main core of ANFIS.…”

Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: Adaptive neuro-fuzzy estimation of the influence of slot on local scour at bridge pier groups

et al. 2014

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Scouring around bridge pier is a major cause of bridge instability. Thus, providing appropriate methods in order to reduce and control the scour depth have received much attention. Using a slot in the bridge piers is one of modern methods of reducing bridge local scouring. In the present study, the effects of a rectangular slot on local scour mitigation around bridge pier groups have been investigated with adaptive neurofuzzy (ANFIS) method. ANFIS shows very good learning and prediction capabilities, which makes it an efficient tool to deal with encountered uncertainties in any system like scouring. The results show that the scour depth increased in the first pier by reinforcing effect and it decreased in the rear piers because of sheltering effect in compare with single pier. In addition, application of the slot in pier groups leads to an increase in the impact of reinforcing effect and reduce the influence of sheltering effect.The use of slot is more influential in front bridge piers than the rear piers in pier groups with 4D distance, however, this effectiveness doesn't have significant difference among the piers with 2D distance and same as single pier. Laboratory experiments were conducted to create experimental training and checking data for ANFIS network. This intelligent estimator is implemented using Matlab/Simulink and the performances are investigated. The simulation results presented in this paper show the effectiveness of the developed method.

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Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: Adaptive neuro-fuzzy estimation of the influence of slot on local scour at bridge pier groups

et al. 2014

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“…Recent studies propose usefulness of neuro-fuzzy, in finding a neural network's optimal architecture to maintain the maximal output power of predicting optimum parameters in simulations [22]. Akib et al [23] used adaptive network-based fuzzy inference system (ANFIS) as a modeling tool to predict scouring depth in bridges. The results from ANFIS were compared with the classical linear regression (LR).…”

Section: Introductionmentioning

confidence: 99%

Hybrid ANFIS–PSO approach for predicting optimum parameters of a protective spur dike

Basser

Karami

Shamshirband

et al. 2015

Applied Soft Computing

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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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Application of ANFIS and LR in prediction of scour depth in bridges

Cited by 54 publications

References 28 publications

RETRACTED ARTICLE: Adaptive neuro-fuzzy estimation of the influence of slot on local scour at bridge pier groups

RETRACTED ARTICLE: Adaptive neuro-fuzzy estimation of the influence of slot on local scour at bridge pier groups

Hybrid ANFIS–PSO approach for predicting optimum parameters of a protective spur dike

Prediction of local scour around bridge piers using the ANFIS method

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