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Wyszukujesz frazę "Boumezbeur, Abderrahmane" wg kryterium: Autor


Wyświetlanie 1-2 z 2
Tytuł:
Landslide stability analysis with the use of the design of experiments method – case study of souk ahras, Algeria
Autorzy:
Charef, Nouar
Mezhoudi, Issam
Boumezbeur, Abderrahmane
Harrat, Nabil
Powiązania:
https://bibliotekanauki.pl/articles/2201668.pdf
Data publikacji:
2022
Wydawca:
Uniwersytet Rolniczy im. Hugona Kołłątaja w Krakowie
Tematy:
landslide
Souk-Ahras region
numerical modeling
safety factor
geotechnical parameters
design of experiments DoE
response surface methodology
RSM
osuwisko
region Souk-Ahras
modelowanie numeryczne
współczynnik bezpieczeństwa
parametry geotechniczne
Opis:
In the northeast of Algeria, Souk Ahras area is known for the severity and spread of landslides, especially in Mechroha and Zaarouria municipalities. Stability analysis of landslides in these areas depends on the calculations of safety factor according to several parameters (physical, mechanical, geological…). The aim of this study is to investigate the parameters affecting the safety factor using the design of experiments (DOE) method, central composite design (CCD) and response surfaces methodology (RSM). These methods use parameter modeling and optimization to discuss a solution of landslide hazard by developing models of safety factor (Fs) considered as response. The other parameters adopted as input independent factors are geotechnical physical and mechanical parameters such as: the dry and wet unit weight (γd, γh), the water content (w), the plasticity and liquidity limits and the plasticity index (WL, WP, IP), the percentage of fine elements Ff (%) < 0.08 mm), the cohesion C and the internal friction angle (Phi). Obtained results show high correlations with a regression coefficient R2 of 0.88 and 0.93 in the two cases study and the predicted factor of safety model fit best to those obtained in the analytical and numerical modeling procedure. The final model is applicable to give reliable results on the safety factor of landslides.
Źródło:
Geomatics, Landmanagement and Landscape; 2022, 4; 137--150
2300-1496
Pojawia się w:
Geomatics, Landmanagement and Landscape
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Land clayey deposits compressibility investigation using principal component analysis and multiple regression tools
Autorzy:
Berrah, Yacine
Chegrouche, Aymen
Brahmi, Serhane
Boumezbeur, Abderrahmane
Powiązania:
https://bibliotekanauki.pl/articles/2201674.pdf
Data publikacji:
2022
Wydawca:
Uniwersytet Rolniczy im. Hugona Kołłątaja w Krakowie
Tematy:
compressibility index
geotechnical parameters
principal component analysis
PCA
multiple regression models
indeks ściśliwości
parametry geotechniczne
analiza głównych składowych
regresja wielokrotna
Opis:
The settlement and compressibility magnitude of the major clayey and marly sediments in Tebessa area (N-E of Algeria) depends on several geotechnical parameters such as compression Cc and recompression Cs indices. The aim of this study was to investigate the parameters related to soil compressibility through tools of statistical analysis, which save time in comparison to multiply repeated laboratory tests. The study also adopted the principal component analysis (PCA) method to eliminate a number of uncorrelated variables that have no influence on the compressibility magnitude, or their impact is insignificant. The highest mean correlation coefficients were obtained for different contributing parameters. Multiple regression analysis has been performed to obtain the best fit model of the output Cc parameter taking into account the best correlation by adding parameters as regressors to reach the highest coefficient of regression R2 . The final obtained model of the present case study gives the best fit model with R2 of 0.92 which is a better value compared to different published models in the literature (R2 of 0.7 as maximum). The chosen input parameters using PCA combined with multiple regression analysis allow identifying the most important input parameters that noticeably affect the soil compression index, and provide with the best model for estimating the Cc index.
Źródło:
Geomatics, Landmanagement and Landscape; 2022, 4; 95--107
2300-1496
Pojawia się w:
Geomatics, Landmanagement and Landscape
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-2 z 2

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