The use of neural networks for the prediction of the critical factor of safety of an artificial slope subjected to earthquake forces

AbstractThis study deals with the development of Artificial Neural Network (ANN) and Multiple Regression (MR) models for estimating the critical factor of safety (Fs) value of a typical artificial slope subjected to earthquake forces. To achieve this, while the geometry of the slope and the properties of the man-made soil are kept constant, the natural subsoil properties, namely, cohesion, internal angle of friction, the bulk unit weight of the layer beneath the ground surface and the seismic coefficient, varied during slope stability analyses. Then, the Fs values of this slope were calculated using the simplified Bishop method, and the minimum (critical) Fs value for each case was determined and used in the development of the ANN and MR models. The results obtained from the models were compared with those obtained from the calculations. Moreover, several performance indices, such as determination coefficient, variance account for, mean absolute error and root mean square error, were calculated to check the prediction capacity of the models developed. The obtained indices make it clear that the ANN model has shown a higher prediction performance than the MR model

Helicobacter Genotyping and Detection in Peroperative Lavage Fluid in Patients with Perforated Peptic Ulcer

Introduction and Objectives Certain Helicobacter pylori genotypes are associated with peptic ulcer disease; however, little is known about associations between the H. pylori genotype and perforated peptic ulcer (PPU). The primary aim of this study was to evaluate which genotypes are present in patients with PPU and which genotype is dominant in this population. The secondary aim was to study the possibility of determining the H. pylori status in a way other than by biopsy. Materials and Methods Serum samples, gastric tissue biopsies, lavage fluid, and fluid from the nasogastric tube were collec

Cost-effectiveness of six strategies for Helicobacter pylori diagnosis and management in uninvestigated dyspepsia assuming a high resource intensity practice pattern

<p>Abstract</p> <p>Background</p> <p>Initial assessment of dyspepsia often includes noninvasive testing for <it>Helicobacter pylori </it>infection. Commercially available tests vary widely in cost and accuracy. Although there is extensive literature on the cost-effectiveness of <it>H. pylori </it>treatment, there is little information comparing the cost-effectiveness of various currently used, noninvasive testing strategies.</p> <p>Methods</p> <p>A Markov simulation was used to calculate cost per symptom-free year and cost per correct diagnosis. Uncertainty in outcomes was estimated using probabilistic sensitivity analysis.</p> <p>Results</p> <p>Under the baseline assumptions, cost per symptom-free year was $122 for empiric proton pump inhibitor (PPI) trial, and costs for the noninvasive test strategies ranged from$123 (stool antigen) to $129 (IgG/IgA combined serology). Confidence intervals had significant overlap.</p> <p>Conclusions</p> <p>Under our assumptions for how testing for <it>H. pylori </it>infection is employed in United States medical practice, the available noninvasive tests all have similar cost-effectiveness between one another as well as with empiric PPI trial.</p

MATHEMATICAL GEOSCIENCES

The sign and the magnitude of the zeta potential must be known for many engineering applications. For clay soils, it is usually negative, but it is strongly dependent on the pore fluid chemistry. However, measurement of zeta potential time is time-consuming and requires special and expensive equipment. In this study, the prediction of zeta potential of kaolinite has been investigated by artificial neural networks (ANNs) and multiple regression analyses (MRAs). To achieve this, ANN and MRA models based on zeta potential measurements of kaolinite in the presence of salt and heavy metal cations at different pH values have been developed. The results of the models were compared with the experimental results. The performance indices, including coefficient of determination, root mean square error, mean absolute error, and variance, were used to assess the performance of the prediction capacity of the models developed in this study. The obtained indices make it clear that the constructed ANN models were able to predict zeta potential of kaolinite quite efficiently and outperformed the MRA models. Results showed that ANN models can be used satisfactorily to predict zeta potential of kaolinite as a rapid inexpensive substitute for laboratory techniques

NEURAL NETWORK WORLD

In this study, the performance of three different self organization feature map (SOFM) network models denoted as SOFM1, SOFM2, and SOFM3 having neighborhood shapes, namely, SquareKohonenful, LineKohonenful, and Diamond-Kohenenful, respectively, to predict the critical factor of safety (F-s) of a widely-used artificial slope subjected to earthquake forces was investigated and compared. For this purpose, the reported data sets by Erzin and Cetin (2012) [7], including the minimum (critical) F-s values of the artificial slope calculated by using the simplified Bishop method, were utilized in the development of the SOFM models. The results obtained from the SOFM models were compared with those obtained from the calculations. It is found that the SOFM1 model exhibits more reliable predictions than SOFM2 and SOFM3 models. Moreover, the performance indices such as the determination coefficient, variance account for, mean absolute error, root mean square error, and the scaled percent error were computed to evaluate the prediction capacity of the SOFM models developed. The study demonstrates that the SOFM1 model is able to predict the F-s value of the artificial slope, quite efficiently, and is superior to the SOFM2 and SOFM