Temat: stability embankment - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: Excavations in the vicinity of the antiflood embankments – calculating issues
Autorzy:: Grodecki, Michał
Powiązania:: https://bibliotekanauki.pl/articles/2086776.pdf
Data publikacji:: 2022
Wydawca:: Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
Tematy:: excavation
stability
embankment
flood
filtration
Opis:: According to Polish law, it is prohibited to perform excavations or locate buildings closer than 50 m from the embankment. In order to obtain exemption from this ban, filtration and stability analysis of the embankment and excavation in the flood conditions have to be performed. This paper presents results of the numerical investigations on interactions between excavations and embankment. Complex nature of the problem is presented. Methodology of numerical simulations and real case examples are described.
Źródło:: Studia Geotechnica et Mechanica; 2022, 44, 2; 138--147
0137-6365
2083-831X
Pojawia się w:: Studia Geotechnica et Mechanica
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

Tytuł:: Reviews on Finite Element Modeling Practices of Stone Columns for Soft Soil Stabilization Beneath an Embankment Dam
Autorzy:: Teshager, Daniel Kefelegn
Belayneh, Henok Lemma
Powiązania:: https://bibliotekanauki.pl/articles/2172888.pdf
Data publikacji:: 2022
Wydawca:: Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
Tematy:: embankment
soft soil
settlement
stone column
slope stability
consolidation
Opis:: This article reviews the numerical approach in stone column practices and presents the benefits of stone columns as a ground improvement of soft soil to support an embankment dam. In this article, the methodological approaches to numerically modeling stone columns in both 2D and 3D studies, as well as the selection of an appropriate constitutive model are discussed. The numerical practices for the installation of the stone column and the validation procedures used to ensure the accuracy of the numerical analysis are also explained. In addition to that, the study also presents the benefits of stone columns in improving settlement behavior, slope stability, and decreasing the end time of consolidation. Parameters that influence the performance of the stone column with their respective results are also assessed.
Źródło:: Studia Geotechnica et Mechanica; 2022, 44, 4; 343--353
0137-6365
2083-831X
Pojawia się w:: Studia Geotechnica et Mechanica
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 3.

Tytuł:: Deep neural network and ANN ensemble for slope stability prediction
Autorzy:: Gupta, A.
Aggarwal, Y.
Aggarwal, P.
Powiązania:: https://bibliotekanauki.pl/articles/24200566.pdf
Data publikacji:: 2022
Wydawca:: Stowarzyszenie Komputerowej Nauki o Materiałach i Inżynierii Powierzchni w Gliwicach
Tematy:: upper clay
lower clay
peat
angle of internal friction
embankment
factor of safety
slope stability
deep neural network
ensemble
glina górna
glina dolna
torf
kąt tarcia wewnętrznego
nasyp
współczynnik bezpieczeństwa
stabilność zbocza
głęboka sieć neuronowa
zespół
Opis:: Purpose: Application of deep neural networks (DNN) and ensemble of ANN with bagging for estimating of factor of safety (FOS) of soil stability with a comparative performance analysis done for all techniques. Design/methodology/approach: 1000 cases with different geotechnical and similar Geometrical properties were collected and analysed using the Limit Equilibrium based Morgenstern-Price Method with input variables as the strength parameters of the soil layers, i.e., Su (Upper Clay), Su (Lower Clay), Su (Peat), angle of internal friction (φ), Su (Embankment) with the factor of safety (FOS) as output. The evaluation and comparison of the performance of predicted models with cross-validation having ten folds were made based on correlation-coefficient (CC), Nash-Sutcliffe-model efficiency-coefficient (NSE), root-mean-square-error (RMSE), mean-absolute-error (MAE) and scattering-index (S.I.). Sensitivity analysis was conducted for the effects of input variables on FOS of soil stability based on their importance. Findings: The results showed that these techniques have great capability and reflect that the proposed model by DNN can enhance performance of the model, surpassing ensemble in prediction. The Sensitivity analysis outcome demonstrated that Su (Lower Clay) significantly affected the factor of safety (FOS), trailed by Su (Peat). Research limitations/implications: This paper sets sight on use of deep neural network (DNN) and ensemble of ANN with bagging for estimating of factor of safety (FOS) of soil stability. The current approach helps to understand the tangled relationship of various inputs to estimate the factor of safety of soil stability using DNN and ensemble of ANN with bagging. Practical implications: A dependable prediction tool is provided, which suggests that model can help scientists and engineers optimise FOS of soil stability. Originality/value: Recently, DNN and ensemble of ANN with bagging have been used in various civil engineering problems as reported by several studies and has also been observed to be outperforming the current prevalent modelling techniques. DNN can signify extremely changing and intricate high-dimensional functions in correlation to conventional neural networks. But on a detailed literature review, the application of these techniques to estimate factor of safety of soil stability has not been observed.
Źródło:: Archives of Materials Science and Engineering; 2022, 116, 1; 14--27
1897-2764
Pojawia się w:: Archives of Materials Science and Engineering
Dostawca treści:: Biblioteka Nauki

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