Estimating the shear stress distribution in circular channels based on the randomized neural network technique

2017 ◽  
Vol 58 ◽  
pp. 441-448 ◽  
Author(s):  
Zohreh Sheikh Khozani ◽  
Hossein Bonakdari ◽  
Amir Hossein Zaji
Keyword(s):  
Neural Network ◽  
Shear Stress ◽  
Stress Distribution ◽  
Shear Stress Distribution

Prediction of Plantar Shear Stress Distribution by Artificial Intelligence Methods

2009 ◽  
Vol 131 (9) ◽  
Author(s):  
Metin Yavuz ◽  
Hasan Ocak ◽  
Vincent J. Hetherington ◽  
Brian L. Davis
Keyword(s):  
Neural Network ◽  
Shear Stress ◽  
Stress Distribution ◽  
Peak Pressure ◽  
Shear Stress Distribution ◽  
Future Research ◽  
Shear Forces ◽  
Inference System ◽  
Vertical Loading ◽  
Stress Dependent

Shear forces under the human foot are thought to be responsible for various foot pathologies such as diabetic plantar ulcers and athletic blisters. Frictional shear forces might also play a role in the metatarsalgia observed among hallux valgus (HaV) and rheumatoid arthritis (RA) patients. Due to the absence of commercial devices capable of measuring shear stress distribution, a number of linear models were developed. All of these have met with limited success. This study used nonlinear methods, specifically neural network and fuzzy logic schemes, to predict the distribution of plantar shear forces based on vertical loading parameters. In total, 73 subjects were recruited; 17 had diabetic neuropathy, 14 had HaV, 9 had RA, 11 had frequent foot blisters, and 22 were healthy. A feed-forward neural network (NN) and adaptive neurofuzzy inference system (NFIS) were built. These systems were then applied to a custom-built platform, which collected plantar pressure and shear stress data as subjects walked over the device. The inputs to both models were peak pressure, peak pressure-time integral, and time to peak pressure, and the output was peak resultant shear. Root-mean-square error (RMSE) values were calculated to test the models’ accuracy. RMSE/actual shear ratio varied between 0.27 and 0.40 for NN predictions. Similarly, NFIS estimations resulted in a 0.28–0.37 ratio for local peak values in all subject groups. On the other hand, error percentages for global peak shear values were found to be in the range 11.4–44.1. These results indicate that there is no direct relationship between pressure and shear magnitudes. Future research should aim to decrease error levels by introducing shear stress dependent variables into the models.

scholarly journals Assessing Machine Learning versus a Mathematical Model to Estimate the Transverse Shear Stress Distribution in a Rectangular Channel

Mathematics ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 596
Author(s):  
Babak Lashkar-Ara ◽  
Niloofar Kalantari ◽  
Zohreh Sheikh Khozani ◽  
Amir Mosavi
Keyword(s):  
Shear Stress ◽  
Stress Distribution ◽  
Fuzzy Inference ◽  
Rectangular Channel ◽  
Tsallis Entropy ◽  
Shear Stress Distribution ◽  
Data Series ◽  
Shear Stresses ◽  
Inference System ◽  
Aspect Ratios

One of the most important subjects of hydraulic engineering is the reliable estimation of the transverse distribution in the rectangular channel of bed and wall shear stresses. This study makes use of the Tsallis entropy, genetic programming (GP) and adaptive neuro-fuzzy inference system (ANFIS) methods to assess the shear stress distribution (SSD) in the rectangular channel. To evaluate the results of the Tsallis entropy, GP and ANFIS models, laboratory observations were used in which shear stress was measured using an optimized Preston tube. This is then used to measure the SSD in various aspect ratios in the rectangular channel. To investigate the shear stress percentage, 10 data series with a total of 112 different data for were used. The results of the sensitivity analysis show that the most influential parameter for the SSD in smooth rectangular channel is the dimensionless parameter B/H, Where the transverse coordinate is B, and the flow depth is H. With the parameters (b/B), (B/H) for the bed and (z/H), (B/H) for the wall as inputs, the modeling of the GP was better than the other one. Based on the analysis, it can be concluded that the use of GP and ANFIS algorithms is more effective in estimating shear stress in smooth rectangular channels than the Tsallis entropy-based equations.

scholarly journals Wall Shear Stress Distribution of Small Aneurysms Prone to Rupture

Stroke ◽  
2014 ◽  
Vol 45 (1) ◽  
pp. 261-264 ◽  
Author(s):  
Vitor Mendes Pereira ◽  
Olivier Brina ◽  
Philippe Bijlenga ◽  
Pierre Bouillot ◽  
Ana Paula Narata ◽  
...  
Keyword(s):  
Shear Stress ◽  
Stress Distribution ◽  
Wall Shear Stress ◽  
Shear Stress Distribution ◽  
Wall Shear Stress Distribution ◽  
Wall Shear ◽  
Small Aneurysms
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