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Hossein Bonakdari - One of the best experts on this subject based on the ideXlab platform.

  • The uncertainty of the Shannon entropy model for Shear Stress Distribution in circular channels
    International Journal of Sediment Research, 2020
    Co-Authors: Amin Kazemian-kale-kale, Hossein Bonakdari, Azadeh Gholami, Bahram Gharabaghi
    Abstract:

    Abstract The Shear Stress Distribution at alluvial stream beds and banks is one of the essential parameters in channel stability analysis. In the current paper, a novel uncertainty analysis method based on the framework of a Bayesian Forecasting System (BFS) is presented to evaluate the Shannon entropy model for prediction of the Shear Stress Distribution in both circular rigid-bed and alluvial-bed channels. The Johnson and Box-Cox transformation functions were applied to select the optimum sample size (SS) and corresponding transformation factor for determining a 95% confidence bound (CB) for the Shannon entropy model. The Shapiro-Wilk (SW) test is applied according to the SS used to evaluate the power of transformation functions in the data normalization. The results show that the error Distribution between predicted and experimental Shear Stress values generated using the Box-Cox transformation is closer to a Gaussian Distribution than the generated using the Johnson transformation. The indexes of the percentage of the experimental values within the CB (Nin) and Forecast Range Error Estimate (FREE) are applied for the uncertainty analyses. The lower values of FREE equal to 1.724 in the circular rigid-bed channel represent the low uncertainty of Shannon entropy in the prediction of Shear Stress values compared to the uncertainty for the circular alluvial-bed channel with a FREE value equal to 7.647.

  • Formulating the Shear Stress Distribution in circular open channels based on the Renyi entropy
    Physica A: Statistical Mechanics and its Applications, 2018
    Co-Authors: Zohreh Sheikh Khozani, Hossein Bonakdari
    Abstract:

    The principle of maximum entropy is employed to derive the Shear Stress Distribution by maximizing the Renyi entropy subject to some constraints and by assuming that dimensionless Shear Stress is a random variable. A Renyi entropy-based equation can be used to model the Shear Stress Distribution along the entire wetted perimeter of circular channels and circular channels with flat beds and deposited sediments. A wide range of experimental results for 12 hydraulic conditions with different Froude numbers (0.375 to 1.71) and flow depths (20.3 to 201.5 mm) were used to validate the derived Shear Stress Distribution. For circular channels, model performance enhanced with increasing flow depth (mean relative error (RE) of 0.0414) and only deteriorated slightly at the greatest flow depth (RE of 0.0573). For circular channels with flat beds, the Renyi entropy model predicted the Shear Stress Distribution well at lower sediment depth. The Renyi entropy model results were also compared with Shannon entropy model results. Both models performed well for circular channels, but for circular channels with flat beds the Renyi entropy model displayed superior performance in estimating the Shear Stress Distribution. The Renyi entropy model was highly precise and predicted the Shear Stress Distribution in a circular channel with RE of 0.0480 and in a circular channel with a flat bed with RE of 0.0488.

  • An expert system for predicting Shear Stress Distribution in circular open channels using gene expression programming
    Water Science and Engineering, 2018
    Co-Authors: Zohreh Sheikh Khozani, Hossein Bonakdari, Isa Ebtehaj
    Abstract:

    Abstract The Shear Stress Distribution in circular channels was modeled in this study using gene expression programming (GEP). 173 sets of reliable data were collected under four flow conditions for use in the training and testing stages. The effect of input variables on GEP modeling was studied and 15 different GEP models with individual, binary, ternary, and quaternary input combinations were investigated. The sensitivity analysis results demonstrate that dimensionless parameter y/P, where y is the transverse coordinate, and P is the wetted perimeter, is the most influential parameter with regard to the Shear Stress Distribution in circular channels. GEP model 10, with the parameter y/P and Reynolds number (Re) as inputs, outperformed the other GEP models, with a coefficient of determination of 0.7814 for the testing data set. An equation was derived from the best GEP model and its results were compared with an artificial neural network (ANN) model and an equation based on the Shannon entropy proposed by other researchers. The GEP model, with an average RMSE of 0.0301, exhibits superior performance over the Shannon entropy-based equation, with an average RMSE of 0.1049, and the ANN model, with an average RMSE of 0.2815 for all flow depths.

  • Efficient Shear Stress Distribution detection in circular channels using Extreme Learning Machines and the M5 model tree algorithm
    Urban Water Journal, 2017
    Co-Authors: Zohreh Sheikh Khozani, Hossein Bonakdari, Amir Hossein Zaji
    Abstract:

    AbstractWith the aid of 174 laboratory data sets, Extreme Learning Machine (ELM) and decision tree (M5) models were investigated in predicting the Shear Stress Distribution in circular channels. To evaluate the sensitivity of the input variables, 15 different input combinations were applied to each model. The calculation results show that the Re and y/P parameter values greatly affect ELM method performance, while y/P and h/D are sensitive to Shear Stress Distribution modeling with M5. The best models among ELM and M5 were compared with an equation based on the Shannon entropy. According to the comparison results, the two proposed models outperform the Shannon entropy equation. Moreover, the ELM method’s function is superior in estimating the Shear Stress Distribution and more adapted to experimental data with average Root Mean Square Error (RMSE) of 0.0236 compared to the M5 method with RMSE of 0.0364.

  • Estimating the Shear Stress Distribution in circular channels based on the randomized neural network technique
    Applied Soft Computing, 2017
    Co-Authors: Zohreh Sheikh Khozani, Hossein Bonakdari, Amir Hossein Zaji
    Abstract:

    Abstract Predicting the Shear Stress Distribution in channels is crucial in hydraulic engineering problems. Since the equations presented by other researchers for estimating the Shear Stress Distribution in circular channels are complicated, this study focuses on applying Randomized Neural Networks (RNN) to present easier equations for computing the Shear Stress Distribution. The specific aim of this work is to obtain accurate Shear Stress Distribution estimation, something proven difficult through experimental and analytical methods 176 data for four circular channel flow depths serve as the entire dataset, and half are used as the testing dataset. Sensitivity analysis is applied and 15 RNN models with different input combinations are investigated. The model with Re and y/P as input variables produces the most appropriate results in predicting Shear Stress Distribution. The best RNN model (model 10) is also compared with an equation based on the Shannon entropy. The study provides evidence that RNN Model 10 (with average RMSE of 0.0544 and MAE of 0.0463) is capable of modelling the Shear Stress Distribution in circular channels and is more accurate than the Shannon entropy-based equation (with average RMSE of 0.1050 and MAE of 0.0840).

Rolf Deigaard - One of the best experts on this subject based on the ideXlab platform.

  • A note on the three-dimensional Shear Stress Distribution in a surf zone
    Coastal Engineering, 1993
    Co-Authors: Rolf Deigaard
    Abstract:

    The three-dimensional time-mean Shear Stress Distribution in a surf zone is analyzed, thereby determining the vertical Distribution of the driving forces due to wave-breaking. It is found that on a uniform coast the longshore current can be described as driven by a surface Shear Stress equal to the cross-shore gradient in the Shear component of the radiation Stress. The near surface Shear Stress is found to be in the direction of wave propagation, having a magnitude determined by the dissipation of wave energy. It can be determined as the Shear force acting on the waves from the surface rollers of spilling breakers or broken waves.

  • Shear Stress Distribution in the Surf Zone
    Breaking Waves, 1992
    Co-Authors: Rolf Deigaard
    Abstract:

    The vertical Distribution of Shear Stress in the surf zone is described. The significance of the vertical convection of horizontal momentum is discussed. It is explained how this mechanism contributes to the Shear Stress in case of energy dissipation near the surface, near the bed and in case of no energy dissipation.

Zohreh Sheikh Khozani - One of the best experts on this subject based on the ideXlab platform.

  • Shear Stress Distribution prediction in symmetric compound channels using data mining and machine learning models
    Frontiers of Structural and Civil Engineering, 2020
    Co-Authors: Zohreh Sheikh Khozani, Khabat Khosravi, Mohammadamin Torabi, Amir Mosavi, Bahram Rezaei, Timon Rabczuk
    Abstract:

    Shear Stress Distribution prediction in open channels is of utmost importance in hydraulic structural engineering as it directly affects the design of stable channels. In this study, at first, a series of experimental tests were conducted to assess the Shear Stress Distribution in prismatic compound channels. The Shear Stress values around the whole wetted perimeter were measured in the compound channel with different floodplain widths also in different flow depths in subcritical and supercritical conditions. A set of, data mining and machine learning algorithms including Random Forest (RF), M5P, Random Committee, KStar and Additive Regression implemented on attained data to predict the Shear Stress Distribution in the compound channel. Results indicated among these five models; RF method indicated the most precise results with the highest R2 value of 0.9. Finally, the most powerful data mining method which studied in this research compared with two well-known analytical models of Shiono and Knight method (SKM) and Shannon method to acquire the proposed model functioning in predicting the Shear Stress Distribution. The results showed that the RF model has the best prediction performance compared to SKM and Shannon models.

  • Shear Stress Distribution Prediction in Symmetric Compound Channels Using Data Mining and Machine Learning Models.
    arXiv: Fluid Dynamics, 2019
    Co-Authors: Zohreh Sheikh Khozani, Khabat Khosravi, Mohammadamin Torabi, Amir Mosavi, Bahram Rezaei, Timon Rabczuk
    Abstract:

    Shear Stress Distribution prediction in open channels is of utmost importance in hydraulic structural engineering as it directly affects the design of stable channels. In this study, at first, a series of experimental tests were conducted to assess the Shear Stress Distribution in prismatic compound channels. The Shear Stress values around the whole wetted perimeter were measured in the compound channel with different floodplain widths also in different flow depths in subcritical and supercritical conditions. A set of, data mining and machine learning models including Random Forest (RF), M5P, Random Committee (RC), KStar and Additive Regression Model (AR) implemented on attained data to predict the Shear Stress Distribution in the compound channel. Results indicated among these five models, RF method indicated the most precise results with the highest R2 value of 0.9. Finally, the most powerful data mining method which studied in this research (RF) compared with two well-known analytical models of Shiono and Knight Method (SKM) and Shannon method to acquire the proposed model functioning in predicting the Shear Stress Distribution. The results showed that the RF model has the best prediction performance compared to SKM and Shannon models.

  • Formulating the Shear Stress Distribution in circular open channels based on the Renyi entropy
    Physica A: Statistical Mechanics and its Applications, 2018
    Co-Authors: Zohreh Sheikh Khozani, Hossein Bonakdari
    Abstract:

    The principle of maximum entropy is employed to derive the Shear Stress Distribution by maximizing the Renyi entropy subject to some constraints and by assuming that dimensionless Shear Stress is a random variable. A Renyi entropy-based equation can be used to model the Shear Stress Distribution along the entire wetted perimeter of circular channels and circular channels with flat beds and deposited sediments. A wide range of experimental results for 12 hydraulic conditions with different Froude numbers (0.375 to 1.71) and flow depths (20.3 to 201.5 mm) were used to validate the derived Shear Stress Distribution. For circular channels, model performance enhanced with increasing flow depth (mean relative error (RE) of 0.0414) and only deteriorated slightly at the greatest flow depth (RE of 0.0573). For circular channels with flat beds, the Renyi entropy model predicted the Shear Stress Distribution well at lower sediment depth. The Renyi entropy model results were also compared with Shannon entropy model results. Both models performed well for circular channels, but for circular channels with flat beds the Renyi entropy model displayed superior performance in estimating the Shear Stress Distribution. The Renyi entropy model was highly precise and predicted the Shear Stress Distribution in a circular channel with RE of 0.0480 and in a circular channel with a flat bed with RE of 0.0488.

  • An expert system for predicting Shear Stress Distribution in circular open channels using gene expression programming
    Water Science and Engineering, 2018
    Co-Authors: Zohreh Sheikh Khozani, Hossein Bonakdari, Isa Ebtehaj
    Abstract:

    Abstract The Shear Stress Distribution in circular channels was modeled in this study using gene expression programming (GEP). 173 sets of reliable data were collected under four flow conditions for use in the training and testing stages. The effect of input variables on GEP modeling was studied and 15 different GEP models with individual, binary, ternary, and quaternary input combinations were investigated. The sensitivity analysis results demonstrate that dimensionless parameter y/P, where y is the transverse coordinate, and P is the wetted perimeter, is the most influential parameter with regard to the Shear Stress Distribution in circular channels. GEP model 10, with the parameter y/P and Reynolds number (Re) as inputs, outperformed the other GEP models, with a coefficient of determination of 0.7814 for the testing data set. An equation was derived from the best GEP model and its results were compared with an artificial neural network (ANN) model and an equation based on the Shannon entropy proposed by other researchers. The GEP model, with an average RMSE of 0.0301, exhibits superior performance over the Shannon entropy-based equation, with an average RMSE of 0.1049, and the ANN model, with an average RMSE of 0.2815 for all flow depths.

  • Efficient Shear Stress Distribution detection in circular channels using Extreme Learning Machines and the M5 model tree algorithm
    Urban Water Journal, 2017
    Co-Authors: Zohreh Sheikh Khozani, Hossein Bonakdari, Amir Hossein Zaji
    Abstract:

    AbstractWith the aid of 174 laboratory data sets, Extreme Learning Machine (ELM) and decision tree (M5) models were investigated in predicting the Shear Stress Distribution in circular channels. To evaluate the sensitivity of the input variables, 15 different input combinations were applied to each model. The calculation results show that the Re and y/P parameter values greatly affect ELM method performance, while y/P and h/D are sensitive to Shear Stress Distribution modeling with M5. The best models among ELM and M5 were compared with an equation based on the Shannon entropy. According to the comparison results, the two proposed models outperform the Shannon entropy equation. Moreover, the ELM method’s function is superior in estimating the Shear Stress Distribution and more adapted to experimental data with average Root Mean Square Error (RMSE) of 0.0236 compared to the M5 method with RMSE of 0.0364.

Olivier F. Bertrand - One of the best experts on this subject based on the ideXlab platform.

  • Evaluation of the effect of stent strut profile on Shear Stress Distribution using statistical moments
    Biomedical engineering online, 2009
    Co-Authors: Juan Mejia, Bilal Ruzzeh, Rosaire Mongrain, Richard L. Leask, Olivier F. Bertrand
    Abstract:

    In-stent restenosis rates have been closely linked to the wall Shear Stress Distribution within a stented arterial segment, which in turn is a function of stent design. Unfortunately, evaluation of hemodynamic performance can only be evaluated with long term clinical trials. In this work we introduce a set of metrics, based on statistical moments, that can be used to evaluate the hemodynamic performance of a stent in a standardized way. They are presented in the context of a 2D flow study, which analyzes the impact of different strut profiles on the wall Shear Stress Distribution for stented coronary arteries. It was shown that the proposed metrics have the ability to evaluate hemodynamic performance quantitatively and compare it to a common standard. In the context of the simulations presented here, they show that stent's strut profile significantly affect the Shear Stress Distribution along the arterial wall. They also demonstrates that more streamlined profiles exhibit better hemodynamic performance than the standard square and circular profiles. The proposed metrics can be used to compare results from different research groups, and provide an improved method of quantifying hemodynamic performance in comparison to traditional techniques. The strut shape found in the latest generations of stents are commonly dictated by manufacturing limitations. This research shows, however, that strut design can play a fundamental role in the improvement of the hemodynamic performance of stents. Present results show that up to 96% of the area between struts is exposed to wall Shear Stress levels above the critical value for the onset of restenosis when a tear-drop strut profile is used, while the analogous value for a square profile is 19.4%. The conclusions drawn from the non-dimensional metrics introduced in this work show good agreement with an ordinary analysis of the wall Shear Stress Distribution based on the overall area exposed to critically low wall Shear Stress levels. The proposed metrics are able to predict, as expected, that more streamlined profiles perform better hemodynamically. These metrics integrate the entire morphology of the Shear Stress Distribution and as a result are more robust than the traditional approach, which only compares the relative value of the local wall Shear Stress with a critical value of 0.5 Pa. In the future, these metrics could be employed to compare, in a standardized way, the hemodynamic performance of different stent designs.

  • Evaluation of the effect of stent strut profile on Shear Stress Distribution using statistical moments
    BioMedical Engineering OnLine, 2009
    Co-Authors: Juan Mejia, Bilal Ruzzeh, Rosaire Mongrain, Richard L. Leask, Olivier F. Bertrand
    Abstract:

    Background In-stent restenosis rates have been closely linked to the wall Shear Stress Distribution within a stented arterial segment, which in turn is a function of stent design. Unfortunately, evaluation of hemodynamic performance can only be evaluated with long term clinical trials. In this work we introduce a set of metrics, based on statistical moments, that can be used to evaluate the hemodynamic performance of a stent in a standardized way. They are presented in the context of a 2D flow study, which analyzes the impact of different strut profiles on the wall Shear Stress Distribution for stented coronary arteries. Results It was shown that the proposed metrics have the ability to evaluate hemodynamic performance quantitatively and compare it to a common standard. In the context of the simulations presented here, they show that stent's strut profile significantly affect the Shear Stress Distribution along the arterial wall. They also demonstrates that more streamlined profiles exhibit better hemodynamic performance than the standard square and circular profiles. The proposed metrics can be used to compare results from different research groups, and provide an improved method of quantifying hemodynamic performance in comparison to traditional techniques. Conclusion The strut shape found in the latest generations of stents are commonly dictated by manufacturing limitations. This research shows, however, that strut design can play a fundamental role in the improvement of the hemodynamic performance of stents. Present results show that up to 96% of the area between struts is exposed to wall Shear Stress levels above the critical value for the onset of restenosis when a tear-drop strut profile is used, while the analogous value for a square profile is 19.4%. The conclusions drawn from the non-dimensional metrics introduced in this work show good agreement with an ordinary analysis of the wall Shear Stress Distribution based on the overall area exposed to critically low wall Shear Stress levels. The proposed metrics are able to predict, as expected, that more streamlined profiles perform better hemodynamically. These metrics integrate the entire morphology of the Shear Stress Distribution and as a result are more robust than the traditional approach, which only compares the relative value of the local wall Shear Stress with a critical value of 0.5 Pa. In the future, these metrics could be employed to compare, in a standardized way, the hemodynamic performance of different stent designs.

  • Evaluation of the Effect of Stent Strut Profile on Shear Stress Distribution
    ASME 2007 Summer Bioengineering Conference, 2007
    Co-Authors: Bilal Ruzzeh, Rosaire Mongrain, Richard L. Leask, Olivier F. Bertrand
    Abstract:

    In this work, we present a 2D numerical flow study to analyze the impact of different stent strut profiles on the Shear Stress Distribution for stented coronary arteries. In that context, five cross-sectional profiles were considered: square, circular, elliptical, and a two tear-drop shapes. Specific performance metrics based on the statistical moments were developed to assess the variation of Shear Stress Distributions along the wall with respected to a normal reference condition (non-stented segment). The results show that stent strut profile significantly affects the Shear Stress Distribution along the wall. It also demonstrate that more streamline profiles such as the tear-drop and elliptical profiles exhibit better performance than the standard square and circular profiles for Shear Stress.Copyright © 2007 by ASME

Timon Rabczuk - One of the best experts on this subject based on the ideXlab platform.

  • Shear Stress Distribution prediction in symmetric compound channels using data mining and machine learning models
    Frontiers of Structural and Civil Engineering, 2020
    Co-Authors: Zohreh Sheikh Khozani, Khabat Khosravi, Mohammadamin Torabi, Amir Mosavi, Bahram Rezaei, Timon Rabczuk
    Abstract:

    Shear Stress Distribution prediction in open channels is of utmost importance in hydraulic structural engineering as it directly affects the design of stable channels. In this study, at first, a series of experimental tests were conducted to assess the Shear Stress Distribution in prismatic compound channels. The Shear Stress values around the whole wetted perimeter were measured in the compound channel with different floodplain widths also in different flow depths in subcritical and supercritical conditions. A set of, data mining and machine learning algorithms including Random Forest (RF), M5P, Random Committee, KStar and Additive Regression implemented on attained data to predict the Shear Stress Distribution in the compound channel. Results indicated among these five models; RF method indicated the most precise results with the highest R2 value of 0.9. Finally, the most powerful data mining method which studied in this research compared with two well-known analytical models of Shiono and Knight method (SKM) and Shannon method to acquire the proposed model functioning in predicting the Shear Stress Distribution. The results showed that the RF model has the best prediction performance compared to SKM and Shannon models.

  • Shear Stress Distribution Prediction in Symmetric Compound Channels Using Data Mining and Machine Learning Models.
    arXiv: Fluid Dynamics, 2019
    Co-Authors: Zohreh Sheikh Khozani, Khabat Khosravi, Mohammadamin Torabi, Amir Mosavi, Bahram Rezaei, Timon Rabczuk
    Abstract:

    Shear Stress Distribution prediction in open channels is of utmost importance in hydraulic structural engineering as it directly affects the design of stable channels. In this study, at first, a series of experimental tests were conducted to assess the Shear Stress Distribution in prismatic compound channels. The Shear Stress values around the whole wetted perimeter were measured in the compound channel with different floodplain widths also in different flow depths in subcritical and supercritical conditions. A set of, data mining and machine learning models including Random Forest (RF), M5P, Random Committee (RC), KStar and Additive Regression Model (AR) implemented on attained data to predict the Shear Stress Distribution in the compound channel. Results indicated among these five models, RF method indicated the most precise results with the highest R2 value of 0.9. Finally, the most powerful data mining method which studied in this research (RF) compared with two well-known analytical models of Shiono and Knight Method (SKM) and Shannon method to acquire the proposed model functioning in predicting the Shear Stress Distribution. The results showed that the RF model has the best prediction performance compared to SKM and Shannon models.