Heave analysis of shallow foundations founded in swelling clayey soil at N’Gaous city in Algeria

Khennouf, Abdelaziz; Bahaj, Mohamed

doi:10.2478/sgem-2019-0051

Cited by 3 publications

(1 citation statement)

References 21 publications

(22 reference statements)

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“…In this study, a three-dimensional numerical model of soil swelling was proposed to calculate the bearing capacity of a rough square shallow foundation founded in swelling soil mass using the finite difference software FLAC3D (2016). The model proposed by Khennouf and Baheddi (2020) was used to simulate soil swelling, which is based on the application of a swelling pressure to the homogeneous soil mass at the initial state. Furthermore, the effect of several parameters on bearing capacity was analyzed, such as the swelling pressure variation, interface type and soil strength.…”

Section: Introductionmentioning

confidence: 99%

Bearing capacity of a square shallow foundation on swelling soil using a numerical approach

Khennouf

Bahaj

2021

WJE

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Purpose The estimation of bearing capacity for shallow foundations in swelling soil is an important and complex context. The complexity is due to the unsaturated swelling soil related to the drying and humidification environment. Hence, a serious study is needed to evaluate the effect of swelling potential soil on the foundation bearing capacity. The purpose of this paper is to analyze the bearing capacity of a rough square foundation founded on a homogeneous swelling soil mass, subjected to vertical loads. Design/methodology/approach A proposed numerical model based on the simulation of the swelling pressure in the initial state, followed by an elastoplastic behavior model may be used to calculate the foundation bearing capacity. The analyses were carried out using the finite-difference software (FLAC 3 D) with an elastic perfectly plastic Mohr–Coulomb constitutive model. Moreover, the numerical results obtained are compared with the analytical solutions proposed in the literature. Findings The numerical results were in good agreement with the analytical solutions proposed in the literature. Also, reasonable capacity and performance of the proposed numerical model. Originality/value The proposed numerical model is capable to predict the bearing capacity of the homogeneous swelling soil mass loaded by a shallow foundation. Also, it will be of great use for geotechnical engineers and researchers in the field.

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Section: Introductionmentioning

confidence: 99%

Bearing capacity of a square shallow foundation on swelling soil using a numerical approach

Khennouf

Bahaj

2021

WJE

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ANN-based swarm intelligence for predicting expansive soil swell pressure and compression strength

Jalal,

Iqbal,

Khan

et al. 2024

Sci Rep

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This research suggests a robust integration of artificial neural networks (ANN) for predicting swell pressure and the unconfined compression strength of expansive soils (PsUCS-ES). Four novel ANN-based models, namely ANN-PSO (i.e., particle swarm optimization), ANN-GWO (i.e., grey wolf optimization), ANN-SMA (i.e., slime mould algorithm) alongside ANN-MPA (i.e., marine predators’ algorithm) were deployed to assess the PsUCS-ES. The models were trained using the nine most influential parameters affecting PsUCS-ES, collected from a broader range of 145 published papers. The observed results were compared with the predictions made by the ANN-based metaheuristics models. The efficacy of all these formulated models was evaluated by utilizing mean absolute error (MAE), Nash–Sutcliffe (NS) efficiency, performance index ρ, regression coefficient (R2), root mean square error (RMSE), ratio of RMSE to standard deviation of actual observations (RSR), variance account for (VAF), Willmott’s index of agreement (WI), and weighted mean absolute percentage error (WMAPE). All the developed models for Ps-ES had an R significantly > 0.8 for the overall dataset. However, ANN-MPA excelled in yielding high R values for training dataset (TrD), testing dataset (TsD), and validation dataset (VdD). This model also exhibited the lowest MAE of 5.63%, 5.68%, and 5.48% for TrD, TsD, and VdD, respectively. The results of the UCS model’s performance revealed that R exceeded 0.9 in the TrD. However, R decreased for TsD and VdD. Also, the ANN-MPA model yielded higher R values (0.89, 0.93, and 0.94) and comparatively low MAE values (5.11%, 5.67, and 3.61%) in the case of PSO, GWO, and SMA, respectively. The UCS models witnessed an overfitting problem because the aforementioned R values of the metaheuristics were 0.62, 0.56, and 0.58 (TsD), respectively. On the contrary, no significant observation was recorded in the VdD of UCS models. All the ANN-base models were also tested using the a-20 index. For all the formulated models, maximum points were recorded to lie within ± 20% error. The results of sensitivity as well as monotonicity analyses depicted trending results that corroborate the existing literature. Therefore, it can be inferred that the recently built swarm-based ANN models, particularly ANN-MPA, can solve the complexities of tuning the hyperparameters of the ANN-predicted PsUCS-ES that can be replicated in practical scenarios of geoenvironmental engineering.

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Mapping of clay soils by remote sensing in the area of Mers El Kébir, Algeria

Rahal

Hamed

2022

Rev.Fac.Ing.Univ.Antioquia

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The identification of clay soils is of great importance for the construction industry. Indeed, this type of soil presents an economic interest related to the exploitation of its clayey materials. Furthermore, the assessment of the potential for differential ground movement through the dryness of the soils and their rehydration is also an important issue to identify in order to reduce the swelling-shrinking hazard. The region of Mers El Kébir located in northwest Algeria, is at risk of this phenomenon because it is known for its marly clay compositions. Mapping this type of soil using satellite imagery offers an alternative to the engineering practice of establishing soil mineralogy, based on X-ray diffraction analysis, which is expensive and time-consuming. The objective of this research is to use ASTER satellite images to map different clay minerals. This analysis is based on remote sensing of spectral signatures corresponding to illite, kaolinite, and montmorillonite.The study of satellite images of the Mers El Kébir region validates the methodology developed, which could be used to help in the development and planning of new infrastructures adapted to the different types of soil.

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Heave analysis of shallow foundations founded in swelling clayey soil at N’Gaous city in Algeria

Cited by 3 publications

References 21 publications

Bearing capacity of a square shallow foundation on swelling soil using a numerical approach

Bearing capacity of a square shallow foundation on swelling soil using a numerical approach

ANN-based swarm intelligence for predicting expansive soil swell pressure and compression strength

Mapping of clay soils by remote sensing in the area of Mers El Kébir, Algeria

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