Application of an evolutionary technique (PSO–SVM) and ANFIS in clear-water scour depth prediction around bridge piers

Sreedhara, B. M.; Rao, M. V. Nageswara; Mandal, S.

doi:10.1007/s00521-018-3570-6

Cited by 46 publications

(12 citation statements)

References 25 publications

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“…The Taylor diagrams (Figure 4) plotted to comparatively evaluate the individual model performances based on multiple statistical indicators (RMSD, R, and standard deviation) portray the superiority and predictive power of the GTB model against the GMDH model. Since the models developed in the current study used the same data and model (input-output) structure of the earlier research by Sreedhara et al (2019), the performance of GTB and GMDH models were further compared against the SVM, (ANFIS), and hybrid particle swarm optimization-based SVM (PSO-SVM) models. Table 5 presents the performance of GTB and GMDH models and other AI models (SVM, ANFIS, PSO-SVM), evaluated in terms of coefficient of determination (R 2 ) statistic.…”

Section: Prediction Of Scour Depth Under Clear-water Scour Conditionmentioning

confidence: 99%

Application of gradient tree boosting regressor for the prediction of scour depth around bridge piers

Sreedhara

Patil²,

Pushparaj

et al. 2021

Journal of Hydroinformatics

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Scour around bridge piers is a complex phenomenon and it is essential to assess or predict the scour hazard around bridge piers in tandem with completely understanding its mechanism. To date, there is no exact method for the estimation of scour depth. Nowadays, machine learning techniques are being recognized as effective tools for the prediction of scour depth using experimental data. In the present study, gradient tree boosting (GTB) technique was used for the prediction of scour depth around various pier shapes under different streambed conditions. Sediment size, sediment quantity, velocity, and flow time were used as input parameters to predict the scour depth under clear-water and live-bed scour conditions. The scour depth was predicted for different pier shapes such as, circular, rectangular, round-nosed and sharp-nosed shaped. The GTB model predicted scour depth values were compared with that of the group method of data handling (GMDH) technique. The performance of GTB and GMDH models were then evaluated based on statistical indices such as RRMSE, NNSE, WI, MNE, SI, and KGE. The study concludes that the GTB model performance was relatively superior to that of GMDH in the prediction of scour depth around different pier shapes.

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Section: Prediction Of Scour Depth Under Clear-water Scour Conditionmentioning

confidence: 99%

Application of gradient tree boosting regressor for the prediction of scour depth around bridge piers

Sreedhara

Patil²,

Pushparaj

et al. 2021

Journal of Hydroinformatics

Self Cite

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show abstract

“…Aside from experimental studies and empirical models, in late decades soft computing techniques, artificial neural network (ANN), adaptive Neuro inference system (ANFIS) [2][3][4], support vector regression (SVR) [5,6], genetic algorithm (GA) [7], group method-data handling (GMDH) [8] etc., are being used widely to predict the scour depth using experimental values. The individual models have been applied by various researchers to estimate scour depth around piers [9,10], risk assessment for structure maintenance [11] and scour below spillways [12].…”

Section: Related Workmentioning

confidence: 99%

“…The individual models have been applied by various researchers to estimate scour depth around piers [9,10], risk assessment for structure maintenance [11] and scour below spillways [12]. In recent years, the combined effect of the evolutionary algorithm is developed using optimization technique, particle swarm optimization (PSO) [2] with ANN as an emerging tool in various fields. The (PSO-ANN) technique is used to identify the surface settlement due to tunneling [13], to check the floating type breakwater efficiency [14], to predict wave transmission of tandem breakwater [15] and fault prediction of objectoriented systems [16].…”

Section: Related Workmentioning

confidence: 99%

Application of Particle Swarm based Neural Network to Predict Scour Depth around the Bridge Pier

M¹,

Kuntoji²,

Manu³

et al. 2019

IJISA

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Scour around the bridge pier is one of the major factors which affect the safety and stability of the bridge structure. Due to the presence of complexity in the scour mechanism, there is no common and simple method to estimate the scour depth. The present paper gives an idea of hybridizing two techniques such as an artificial neural network with swarm intelligence technique particle swarm optimization to estimate the scour depth around the bridge pier and abbreviated as PSO-ANN. The present discussion covers the estimation of scour depth for clear water and live bed scour condition around circular and rectangular pier shapes. The independent variables, Sediment size (d 50), sediment quantity (Sq), velocity (u) and time (t) are used as input to develop the models to estimate or quantify a dependent variable scour depth (ds). The efficiency and accuracy of the model are measured using model performances indicators such as Correlation Coefficient (CC), Normalized Root Mean Square Error (NRMSE), Nash Sutcliffe Error (NSE), and Normalized Mean Bias (NMB). The predicted results of both the models are compared with each other and also compared with measured scour depth. The study concludes that the proposed PSO-ANN model is suitable to estimate the scour depth in both the cases for circular and rectangular pier shapes.

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“…To overcome these difficulties, the focus of researchers has turned to use Artificial Intelligence (AI) method for prediction of bridge pier scour depth. Recently, different artificial intelligence approaches such as artificial neural networks (ANN), adaptive Neuro-Fuzzy inference systems (ANFIS), genetic programming (GP), gene-expression programming (GEP), support vector machines (SVM), model trees (MT), evolutionary polynomial regressions (EPR), POS-SVM, multivariate adaptive regression splines (MARS), and self-adaptive extreme learning machines (SAELM) have been applied to predict the local scour depth around hydraulic structures [7][8][9][10][11][12][13][14][15][16]. Among these soft computing techniques, group method of data handling (GMDH) methods were widely applied to predict the local scour depth around bridge piers and abutments, downstream of ski-jump bucket spillways, downstream of grade-control structures, and below pipelines induced currents and waves [17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Application of Improved GMDH Models to Predict Local Scour Depth at Complex Bridge Piers

Ghodsi

Khanjani

2020

Civ Eng J

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Scour depth prediction is a vital issue in bridge pier design. Recently, good progress has been made in the development of artificial intelligence (AI) to predict scour depth around hydraulic structures base such as bridge piers. In this study, two hybrid intelligence models based on combination of group method of data handling (GMDH) with harmony search algorithm (HS) and shuffled complex evolution (SCE) have been developed to predict local scour depth around complex bridge piers using 82 laboratory data measured by authors and 615 data points from published literature. The results were compared to conventional GMDH models with two kinds of transfer functions called GMDH1 and GMDH2. Based upon the pile cap location, data points were divided into three categories. The performance of all utilized models was evaluated by the statistical criteria of R, RMSE, MAPE, BIAS, and SI. Performances of developed models were evaluated by experimental data points collected in laboratory experiments, together with commonly empirical equations. The results showed that GMDH2SCE was the superior model in terms of all statistical criteria in training when the pile cap was above the initial bed level and completely buried pile cap. For a partially-buried pile cap, GMDH1SCE offered the best performance. Among empirical equations, HEC-18 produced relatively good performances for different types of complex piers. This study recommends hybrid GMDH models, as powerful tools in complex bridge pier scour depth prediction.

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Application of an evolutionary technique (PSO–SVM) and ANFIS in clear-water scour depth prediction around bridge piers

Cited by 46 publications

References 25 publications

Application of gradient tree boosting regressor for the prediction of scour depth around bridge piers

Application of gradient tree boosting regressor for the prediction of scour depth around bridge piers

Application of Particle Swarm based Neural Network to Predict Scour Depth around the Bridge Pier

Application of Improved GMDH Models to Predict Local Scour Depth at Complex Bridge Piers

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