Gene-expression programming to predict pier scour depth using laboratory data

Khan, Mujahid; Azamathulla, Hazi Mohammad; Tufail, Muhammad

doi:10.2166/hydro.2011.008

Cited by 47 publications

(11 citation statements)

References 3 publications

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“…Various studies have addressed scour prediction using feedforward neural networks (FFNN) [26]; gene expression programming (GEP) [27][28][29]; adaptive neuro-fuzzy inference systems (ANFIS) [30]; support vector machines (SVM) [31]; group method of data handling (GMDH) [32][33][34][35], and model trees [36]. Bateni and Jeng [37] applied an ANFIS-based method to estimate the scour at pile groups.…”

Section: Introductionmentioning

confidence: 99%

A Non-Tuned Machine Learning Technique for Abutment Scour Depth in Clear Water Condition

Bonakdari

Moradi

Ebtehaj

et al. 2020

Water

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Abutment scour is a complex three-dimensional phenomenon, which is one of the leading causes of marine structure damage. Structural integrity is potentially attainable through the precise estimation of local scour depth. Due to the high complexity of scouring hydrodynamics, existing regression-based relations cannot make accurate predictions. Therefore, this study presented a novel expansion of extreme learning machines (ELM) to predict abutment scour depth (ds) in clear water conditions. The model was built using the relative flow depth (h/L), excess abutment Froude number (Fe), abutment shape factor (Ks), and relative sediment size (d50/L). A wide range of experimental samples was collected from the literature, and data was utilized to develop the ELM model. The ELM model reliability was evaluated based on the estimation results and several statistical indices. According to the results, the sigmoid activation function (correlation coefficient, R = 0.97; root mean square error, RMSE = 0.162; mean absolute percentage error, MAPE = 7.69; and scatter index, SI = 0.088) performed the best compared with the hard limit, triangular bias, radial basis, and sine activation functions. Eleven input combinations were considered to investigate the impact of each dimensionless variable on the abutment scour depth. It was found that ds/L = f (Fe, h/L, d50/L, Ks) was the best ELM model, indicating that the dimensional analysis of the original data properly reflected the underlying physics of the problem. Also, the absence of one variable from this input combination resulted in a significant accuracy reduction. The results also demonstrated that the proposed ELM model significantly outperformed the regression-based equations derived from the literature. The ELM model presented a fundamental equation for abutment scours depth prediction. Based on the simulation results, it appeared the ELM model could be used effectively in practical engineering applications of predicting abutment scour depth. The estimated uncertainty of the developed ELM model was calculated and compared with the conventional and artificial intelligence-based models. The lowest uncertainty with a value of ±0.026 was found in the proposed model in comparison with ±0.50 as the best uncertainty of the other models.

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Section: Introductionmentioning

confidence: 99%

A Non-Tuned Machine Learning Technique for Abutment Scour Depth in Clear Water Condition

Bonakdari

Moradi

Ebtehaj

et al. 2020

Water

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“…In the past studies, several investigators have derived numerous dimensionless scour depth relationships [2,14,[22][23][24][25][26][27][28]. Present experimental results show that scour depth is not only a function of flow parameters, but also depends on particle size and geometric standard deviation of parent bed material and armor layer particle size.…”

Section: Implications For Designmentioning

confidence: 87%

Local Scour of Armor Layer Processes around the Circular Pier in Non-Uniform Gravel Bed

Pandey

Chen

Sharma

et al. 2019

Water

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Flume experiments have been carried out under clear water scour conditions to analyze the maximum equilibrium scour depth and scour processes in armored streambeds. A total of 85 experiments have been carried out using different diameters of circular piers and non-uniform gravels. A graphical approach for dimensionless scour depth in equilibrium condition around the circular pier in armored streambeds has been developed. As per this curve, the maximum dimensionless scour depth variation with dimensionless armor particle size depends on the densimetric particle Froude number (Frd50), and the decreasing rate of dimensionless scour depth decreases with the value of Frd50.

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“…In Table 1 some of the most important of these equations have been presented. Recently, several studies used artificial intelligence for modeling complicated hydraulic phenomenon in water resource management and civil-environmental engineering (Chang, Azamathulla, Zakaria, & Ab Ghani, 2012;Dehghani, Saghafian, Nasiri Saleh, Farokhnia, & Noori, 2014;Dehghani et al, 2019;Juahir, Zain, Aris, Yusoff, & Mokhtar, 2010;Khan, Azamathulla, & Tufail, 2012;Najah, El-Shafie, Karim, & El-Shafie, 2013;Najafzadeh, Saberi-Movahed, & Sarkamaryan, 2018;Pandey, Zakwan, Sharma, & Ahmad, 2019;Seifi & Riahi-Madvar, 2019). In this study the MATLAB 2014 environment is used for developing soft computing models and SPSS 8 is used for linear and non-linear model developments.…”

Section: Theoretical and Dimensional Analysismentioning

confidence: 99%

Comparative analysis of soft computing techniques RBF, MLP, and ANFIS with MLR and MNLR for predicting grade-control scour hole geometry

Riahi-Madvar

Dehghani

Seifi

et al. 2019

Engineering Applications of Computational Fluid Mechanics

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The main aims and contributions of the present paper are to use new soft computing methods for the simulation of scour geometry (depth/height and locations) in a comparative framework. Five models were used for the prediction of the dimension and location of the scour pit. The five developed models in this study are multilayer perceptron (MLP) neural network, radial basis functions (RBF) neural network, adaptive neuro fuzzy inference systems (ANFIS), multiple linear regression (MLR), and multiple non-linear regression (MNLR) in comparison with empirical equations. Four non-dimensional geometry parameters of scour hole shape are predicted by these models including the maximum scour depth (S), the distance of S from the weir (XS), the maximum height of downstream deposited sediments (hd), and distance of hd from the weir (XD). The best results over train data derived for XS/Z and hd/Z by the MLP model with R 2 are 0.95 and 0.96 respectively; the best predictions for S/Z and XD/Z are from the ANFIS model with R 2 0.91 and 0.96 respectively. The results indicate that the application of MLP and ANFIS results in the accurate prediction of scour geometry for the designing of stable grade control structures in alluvial irrigation channels.

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Gene-expression programming to predict pier scour depth using laboratory data

Cited by 47 publications

References 3 publications

A Non-Tuned Machine Learning Technique for Abutment Scour Depth in Clear Water Condition

A Non-Tuned Machine Learning Technique for Abutment Scour Depth in Clear Water Condition

Local Scour of Armor Layer Processes around the Circular Pier in Non-Uniform Gravel Bed

Comparative analysis of soft computing techniques RBF, MLP, and ANFIS with MLR and MNLR for predicting grade-control scour hole geometry

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