scholarly journals Estimating the Ultimate Bearing Capacity for Strip Footing Near and within Slopes Using AI (GP, ANN, and EPR) Techniques

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Ahmed M. Ebid ◽  
Kennedy C. Onyelowe ◽  
Emmanuel E. Arinze
Keyword(s):  
Bearing Capacity ◽  
Polynomial Regression ◽  
Soil Bulk Density ◽  
Ultimate Bearing Capacity ◽  
Overburden Pressure ◽  
Evolutionary Polynomial Regression ◽  
Multiple Data ◽  
Unique Nature ◽  
Artificial Neural Network Ann ◽  
Interface Parameters

Numerical and computational analyses surrounding the behavior of the bearing capacity of soils near or adjacent to slopes have been of great importance in earthwork constructions around the globe due to its unique nature. This phenomenon is encountered on pavement vertical curves, drainages, and vertical infrastructure foundations. In this work, multiple data were collected on the soil and footing interface parameters, which included width of footing, depth of foundation, distance of slope from the footing edge, soil bulk density, slope and frictional angles, and bearing capacity factors of cohesion and overburden pressure determined for the case of a foundation on or adjacent to a slope. The genetic programming (GP), evolutionary polynomial regression (EPR), and artificial neural network (ANN) intelligent techniques were employed to predict the ultimate bearing capacity of footing on or adjacent to a slope. The performance of the models was evaluated as well as compared their accuracy and robustness with the findings of Prandtl. The results were observed to show the superiority of GP, EPR, and ANN techniques over the computational works of Prandtl. In addition, the ANN outclassed the other artificial intelligence methods in the exercise.

Estimating bearing capacity of shallow foundations by artificial neural networks

2017 ◽  
Vol 3 (4) ◽  
pp. 151
Author(s):  
Mustafa Aytekin
Keyword(s):  
Bearing Capacity ◽  
Ultimate Bearing Capacity ◽  
Training Data ◽  
Coefficient Of Determination ◽  
Average Error ◽  
Large Set ◽  
Ann Models ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Capacity Data

In this study, the Artificial Neural Network, ANN is applied to data extracted from a large set of random data created by using Terzaghi and Meyerhof formulae. By using MS Excel, 3750 sets of data for Terzaghi's equation, 4000 for Meyerhof's equation were generated. A simulated ANN was trained on a subset of bearing capacity data, and the performance was tested on the remaining data. The performances of the ANN models were compared to Terzaghi and Meyerhof results. ANN models were as accurate as the other techniques in estimating the ultimate bearing capacity. The models estimated the ultimate bearing capacity with an average error of around 1% of the value obtained from Terzaghi and Meyerhof equations, and the coefficient of determination (r2) was almost equal to 1. Their sensitivity and specificity is dependent on the function and the algorithm used in the training process. Validation subset is crucial in preventing the over-fitting of the ANN models to the training data. ANN models are potentially useful technique for estimating the bearing capacity of the soil. Large training data sets are needed to improve the performance of data-derived algorithms, in particular ANN models.

scholarly journals The synergistic interference effect of silica nanoparticles concentration and the wavelength of ELISA on the colorimetric assay of cell toxicity

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Fariba Abbasi ◽  
Hassan Hashemi ◽  
Mohammad Reza Samaei ◽  
Amir SavarDashtaki ◽  
Abooalfazl Azhdarpoor ◽  
...  
Keyword(s):  
Mtt Assay ◽  
Interference Effect ◽  
Polynomial Regression ◽  
Colorimetric Assay ◽  
Synergistic Effects ◽  
Cell Toxicity ◽  
Diphenyltetrazolium Bromide ◽  
Nanoparticles Concentration ◽  
Artificial Neural Network Ann ◽  
First Time

AbstractThe 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay is the most common method for the determination of cell toxicity, but some factors limit the sensitivity of this method, such as pH. Less attention had been paid to the interference effect of optical and plasmonic properties of SiO2 nanoparticles (NPs) in the wavelength range assigned to MTT. This study investigated the synergistic interference effect of SiO2 NPs and wavelength on MTT assay for the first time. The examined variables included the type of SiO2 NPs concentrations (1, 10, and 100 mM) and different wavelengths (470, 490, 520, and 570 nm). The results showed that optical density (OD) increased (p < 0.05) when wavelength and the concentration of crystalline SiO2 NPs increased. So, the maximum OD at 10 and 100 mM were attributed to crystalline SiO2 NPs (p < 0.05) due to the functional group, whereas it was related to amorphous at 1 mM (p > 0.05). According to polynomial regression modeling (PRM), the maximum interference effect was predicted at crystalline SiO2 NPs and wavelength > 550 nm. Besides, the synergistic effects of SiO2 NPs, wavelength, and concentration of NPs had been a good fitting with first-order PRM. Thus, the concentration of SiO2 NPs had a confounder factor in colorimetric for MTT assay. The best artificial neural network (ANN) structure was related to the 3:7:1 network (Rall = 0.936, MSE = 0.0006, MAPE = 0.063). The correlation between the actual and predicted data was 0.88. As SiO2 NPs presence is an interfering factor in MTT assay concerning wavelength, it is suggested wavelength use with minimum confounding effect for MTT assay.

scholarly journals Study on the ultimate bearing capacity of a strip footing influenced by an irregular underlying cavity in karst areas

2021 ◽  
Author(s):  
Lianheng Zhao ◽  
Shan Huang ◽  
Zhonglin Zeng ◽  
Rui Zhang ◽  
Gaopeng Tang ◽  
...  
Keyword(s):  
Bearing Capacity ◽  
Ultimate Bearing Capacity ◽  
Strip Footing ◽  
Karst Areas

Experimental Investigation for Horizontal Ultimate Bearing Capacity and Target Ductility Factor Improvement Results of the RC Column Reinforced by AFL with Damages

2014 ◽  
Vol 488-489 ◽  
pp. 497-500
Author(s):  
You Lin Zou ◽  
Pei Yan Huang
Keyword(s):  
Bearing Capacity ◽  
Ultimate Bearing Capacity ◽  
Test Results ◽  
Rc Columns ◽  
Static Test ◽  
Rc Column ◽  
Ductility Factor ◽  
Secant Stiffness ◽  
Function Relationship ◽  
Reversed Cyclic

Deem test results from the low reversed cyclic loading quasi-static test with 2 RC columns as the basic information of secant stiffness damage of the reference column and take use of the TMS instrument in the test to artificially make the damage percentage of secant stiffness of the RC column as 33%, 50% and 66%, 6 damaged columns in total; reinforce the 6 damaged columns and 2 undamaged ones under the same conditions with AFL, through quasi-static contrast test. Test results show that it is able to effectively boost horizontal ultimate bearing capacity and ductility deformability of the RC columns with AFL for reinforcement; besides, there is a linear function relationship between horizontal ultimate bearing capacity, target ductility factor, and damage percentage of secant stiffness.

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