Probabilistic analysis of reinforced slopes using RFEM and considering spatial variability of frictional soil properties due to compaction

Luo, Ning; Bathurst, Richard J.

doi:10.1080/17499518.2017.1362443

Cited by 35 publications

(5 citation statements)

References 42 publications

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“…The obtained results are consistent with the findings of Sert et al [12], Fenton and Griffiths [30], and Luo et al [13] who suggests that a smaller scale of fluctuation results yields a smaller variation of the response, i.e., the lateral wall deflection and the maximum bending moment for the case herein. It is important to note that the scenario herein with a much higher SOF of 21 meters can be considered as a case where spatial variability is not considered or simply neglected, resembling a spatially constant condition.…”

Section: Resultssupporting

confidence: 92%

“…Sert et al [12] conducted a study on the impact of vertical spatial variability of soil properties for excavations supported by cantilever walls in sand. Luo et al [13] investigated multiple structural and geotechnical failure modes, considering the influence of vertical spatial variability on supported excavations in sand. However, it is important to highlight that these mentioned studies that explore the contribution of spatial variability of cohesionless soils have only utilized hypothetical design examples, with a limited number of research based on actual case studies.…”

Section: Introductionmentioning

confidence: 99%

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The Effect of Incorporating Vertical Spatial Variability on the Probabilistic Analysis of a Deep Excavation: A Case Study

KORKUT,

AKBAŞ

2023

Politeknik Dergisi

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This study explores the impact of including the vertical spatial variability in effective stress friction angle of clay on the probabilistic analysis of deep excavations. The proposed methodology is demonstrated and verified by conducting random finite element modeling (RFEM) of an instrumented deep excavation project situated in Ankara, Turkey. The excavation has a depth of 20 meters and is supported by six levels of pre-stressed ground anchors. To simulate the vertical spatial variability of effective stress friction angle in the clay, Monte Carlo simulation method and the random field theory are employed. The simulated parameters are then inserted into the finite element model via Python programming language to analyze the probabilistic distribution of lateral deflections and bending moments in the drilled shaft wall. The results obtained from the Monte Carlo simulations reveal that the incorporation and selected value of spatial variability significantly impacts the resulting lateral movements, bending moments, and the probability of failure of the system.

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Section: Resultssupporting

confidence: 92%

Section: Introductionmentioning

confidence: 99%

The Effect of Incorporating Vertical Spatial Variability on the Probabilistic Analysis of a Deep Excavation: A Case Study

KORKUT,

AKBAŞ

2023

Politeknik Dergisi

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“…[28] combined random field simulation with the nonlinear finite element method to investigate the active earth pressure and stability of the retaining wall. Luo and Bathurst [29] performed probabilistic stability analyses for reinforced slopes using RFEM. Selmi et al [30] studied the probabilistic failure envelopes of skirted foundations based on RFEM.…”

Section: Introductionmentioning

confidence: 99%

A novel random finite element model for holistically modeling of the frost effects on soils and cold region pavements

Dong

Jiang

2022

J Infrastruct Preserv Resil

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This paper describes the development of a random finite element model (RFEM) that allows holistic simulation of frozen soil behaviors, including the effects of phase transition and the consequent internal stress and volume changes. The performance of the model is firstly validated with laboratory experiments. The model is implemented to simulate the effects of frost action on pavement. The coupled thermal-mechanical actions including the mechanical responses of subgrade soils subjected to freezing temperature and its effects on the pavement structure are analyzed. The results show that the frost action and expansion of ice lenses change the interaction modes between pavement layers. This leads to increase of stress and deformation in the pavement layer. Methods to mitigate the effects of frost heave are analyzed with this model. The simulation results indicate that the detrimental effects of frost heave on the pavement structure can be mitigated by increasing the thickness of base layer, use of thermal insulation layer or improve drainage in the subgrade layer. The RFEM combines the advantages of discrete element model (DEM) in holistically describing the microstructure effects and in the finite element model (FEM) in terms of computational efficiency. This allows to focus research on understanding the behaviors of individual soils phase and their interfacial interactions.

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“…The International Geosynthetics Society (IGS 1996) defines a geosynthetic product as a planar, polymeric (synthetic or natural) material used in contact with soil/rock and/or any other geotechnical material in civil-engineering applications [ 3 ]. During the past three decades, the advantages of using geosynthetic reinforcements have been verified by researchers [ 4 , 5 , 6 , 7 , 8 ]. Geosynthetics improve soil conditions by providing tensile resistance, reducing ground-improvement costs, and simplifying construction procedures.…”

Section: Introductionmentioning

confidence: 99%

Reliability Estimation of Reinforced Slopes to Prioritize Maintenance Actions

BahooToroody

Khalaj

Leoni

et al. 2021

IJERPH

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Geosynthetics are extensively utilized to improve the stability of geotechnical structures and slopes in urban areas. Among all existing geosynthetics, geotextiles are widely used to reinforce unstable slopes due to their capabilities in facilitating reinforcement and drainage. To reduce settlement and increase the bearing capacity and slope stability, the classical use of geotextiles in embankments has been suggested. However, several catastrophic events have been reported, including failures in slopes in the absence of geotextiles. Many researchers have studied the stability of geotextile-reinforced slopes (GRSs) by employing different methods (analytical models, numerical simulation, etc.). The presence of source-to-source uncertainty in the gathered data increases the complexity of evaluating the failure risk in GRSs since the uncertainty varies among them. Consequently, developing a sound methodology is necessary to alleviate the risk complexity. Our study sought to develop an advanced risk-based maintenance (RBM) methodology for prioritizing maintenance operations by addressing fluctuations that accompany event data. For this purpose, a hierarchical Bayesian approach (HBA) was applied to estimate the failure probabilities of GRSs. Using Markov chain Monte Carlo simulations of likelihood function and prior distribution, the HBA can incorporate the aforementioned uncertainties. The proposed method can be exploited by urban designers, asset managers, and policymakers to predict the mean time to failures, thus directly avoiding unnecessary maintenance and safety consequences. To demonstrate the application of the proposed methodology, the performance of nine reinforced slopes was considered. The results indicate that the average failure probability of the system in an hour is 2.8×10−5 during its lifespan, which shows that the proposed evaluation method is more realistic than the traditional methods.

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Probabilistic analysis of reinforced slopes using RFEM and considering spatial variability of frictional soil properties due to compaction

Cited by 35 publications

References 42 publications

The Effect of Incorporating Vertical Spatial Variability on the Probabilistic Analysis of a Deep Excavation: A Case Study

The Effect of Incorporating Vertical Spatial Variability on the Probabilistic Analysis of a Deep Excavation: A Case Study

A novel random finite element model for holistically modeling of the frost effects on soils and cold region pavements

Reliability Estimation of Reinforced Slopes to Prioritize Maintenance Actions

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