Nonlinear Mechanical Behavior Analysis of Flexible Lateritic Pavements of Senegal (West Africa) by FEM for M.-E. Pavement Design

Samb, Fatou; Berthaud, Yves; Bâ, Makhaly; Fall, M; Benboudjema, Farid

doi:10.1007/s10706-018-0514-y

Cited by 4 publications

(1 citation statement)

References 3 publications

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“…This stage of soil treatment can be accomplished only if the different physical characteristics of this soil are known. The study of the mechanical behavior of materials used in public works in general has been of interest to the scientific community for a very long time [1]- [9], among others. In geotechnical engineering, engineers use several systems to classify geomaterials by assigning them to each category by the similarity of their physical and/or mechanical properties a code.…”

Section: Introductionmentioning

confidence: 99%

Coupling Discriminating Statistical Analysis and Artificial Intelligence for Geotechnical Characterization of the Kampemba’s Municipality Soils (Lubumbashi, DR Congo)

Elysée¹,

Portance²,

Sow³

et al. 2020

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This study focuses on the determination of physical and mechanical characteristics based on in vitro tests, by using field samples for the Kampemba urban area in the city of Lubumbashi. At the end of this study, we identified the soils according to their parameters, and established the geotechnical classification by determining their bearing capacity by the group index method using from the identification tests carried out. By using the AASHTO classification method (American Association for State Highway Transportation Official), the results obtained after our studies revealed five classes of soil: A-2, A-4, A-5, A-6, A-7 in a general way, and particularly eight subgroups of soil: A-2-4, A-2-6, A-2-7, A-4, A-5, A-6, A-7-5 and A-7-6 for the concerned area. The latter has given statistical analysis and deep learning based on multi-layer perceptron, the global values of the physical parameters. It's about: 31.77% ± 1.05% for the limit of liquidity; 18.71% ± 0.76% for the plastic limit; 13.06% ± 0.79% for the plasticity index; 83.00% ± 3.33% for passing of 2 mm sieve; 76.22% ± 3.2% for passing of 400 μm sieve; 89.07% ± 2.99% for passing of 4.75 mm sieve; 70.62% ± 2.39% passing of 80 μm sieve; 1.66 ± 0.61 for the consistency index; −0.67 ± 0.62 for the liquidity index and 8 ± 1 for the group index.

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Section: Introductionmentioning

confidence: 99%

Coupling Discriminating Statistical Analysis and Artificial Intelligence for Geotechnical Characterization of the Kampemba’s Municipality Soils (Lubumbashi, DR Congo)

Elysée¹,

Portance²,

Sow³

et al. 2020

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Improving Pavement Design by Estimating Resilient Modulus Using Experimental Data and Ensemble Methods

Xiao,

Tilahun,

Ashongo

et al. 2024

Geotech Geol Eng

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Numerical Modelling and Predicting Performance of Geogrid-Reinforced Low-Volume Unpaved Roads over Soft Subgrades

Chua,

Nepal

2023

Int. J. Pavement Res. Technol.

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Pavement performance is usually predicted by large-scale laboratory experiments, expensive field tests and/or comparatively cheaper numerical modelling alternative. In this study, a finite element limiting strain model has been developed to investigate the strain response of both unreinforced and geogrid-reinforced pavements on unpaved roads where the geogrid is placed at the bottom of the unbound granular base layer. A two-dimensional (2D) axisymmetric finite element model (FEM) is used to analyse the behaviour of both unreinforced and geogrid-reinforced granular base. The critical pavement responses (vertical surface deformation, compressive strain and compressive stress at the top of subgrade, etc.) are simulated numerically using ABAQUS. These critical responses are used to develop the pavement performance models (strain limiting models) and to predict long-term service life of pavement or the reduction of thickness of granular base for equivalent service life or a combination of both. The parametric results are then used to develop design charts to aid practitioners to use directly in designs. The numerical model is verified using published literature-based information and found to be reasonable. The results show that the traffic benefit ratio (TBR) of 3.1 at a 20 mm rut depth can be achieved for a thin granular base built over soft subgrade using a medium stiff geogrid.

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Nonlinear Mechanical Behavior Analysis of Flexible Lateritic Pavements of Senegal (West Africa) by FEM for M.-E. Pavement Design

Cited by 4 publications

References 3 publications

Coupling Discriminating Statistical Analysis and Artificial Intelligence for Geotechnical Characterization of the Kampemba’s Municipality Soils (Lubumbashi, DR Congo)

Coupling Discriminating Statistical Analysis and Artificial Intelligence for Geotechnical Characterization of the Kampemba’s Municipality Soils (Lubumbashi, DR Congo)

Improving Pavement Design by Estimating Resilient Modulus Using Experimental Data and Ensemble Methods

Numerical Modelling and Predicting Performance of Geogrid-Reinforced Low-Volume Unpaved Roads over Soft Subgrades

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