Reliability-based robust geotechnical design using Monte Carlo simulation

Peng, Xingyue; Li, Dian Qing; Cao, Zhiyuan; Gong, Wenping; Juang, C. Hsein

doi:10.1007/s10064-016-0905-3

Cited by 36 publications

(13 citation statements)

References 36 publications

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“…Reliability-based design (RBD) approach has been developed and utilized to address the shortcomings of the deterministic approach (Baecher and Christian 2003;Fenton and Griffiths 2008;Peng et al 2017;Aladejare and Wang 2018). Although, the RBD approach can consider uncertainties explicitly, an accurate statistical characterization of uncertainties of geotechnical parameters is necessary.…”

Section: Introductionmentioning

confidence: 99%

Design and Sensitivity Analysis of Rock Slope Using Monte Carlo Simulation

Aladejare

Akeju

2019

Geotech Geol Eng

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A probabilistic approach that is based on Monte Carlo simulation (MCS) was developed in this study to design and perform sensitivity analysis of rock slope. The probabilistic approach uses MCS to perform a series of single objective optimizations for design of rock slope and to perform sensitivity analysis of rock slope stability. The MCS-based approach was used to evaluate the failure probability of a rock slope system and to determine a safe maximum slope height for rock slope design. To achieve this, the performance of different rock properties and rock slope conditions were explicitly considered towards achieving the target reliability index of the rock slope. The approach can achieve multiple rock slope design specifications using different target reliability indexes from a single run of MCS. The proposed probabilistic approach was illustrated through an example of rock slope design to determine feasible designs under different rock slope conditions. Also, sensitivity studies were performed to explore the effects of uncertainties in tension crack depth and water depth in tension crack, and variability in rock unit weight. The results show that the effects of uncertainties and variability on rock slope stability can be significant and should be incorporated during design analysis. Incorporating such uncertainties and variability in rock slope design is achieved with relative ease using the proposed approach.

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Section: Introductionmentioning

confidence: 99%

Design and Sensitivity Analysis of Rock Slope Using Monte Carlo Simulation

Aladejare

Akeju

2019

Geotech Geol Eng

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“…Many MC variance reduction techniques are developed on a high scale, e.g., stratified sampling and Latin hypercube sampling [5]. The MC simulation method is fundamental in reliability-based robust geotechnical design (RGD) [6].…”

Section: Introductionmentioning

confidence: 99%

The Effect of the Selection of Three-Dimensional Random Numerical Soil Models on Strip Foundation Settlements

2021

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This paper delivers a probabilistic attempt to prove that the selection of a random three-dimensional finite element (FE) model of a subsoil affects the computed settlements. Parametric analysis of a random soil block is conducted, assuming a variable subsoil Young’s modulus in particular finite elements. The modulus is represented by a random field or different-sized sets of random variables; in both cases, the same truncated Gaussian model is assumed. Mean values and standard deviations of random soil settlement are estimated by a Monte Carlo simulation procedure. With regard to the adopted FE model, the estimated settlement mean values do not vary significantly, but standard deviations do strongly. Similarities also appear in the diagrams of random field correlation length versus settlement standard deviation and the diagrams displaying a total number of model random variables versus settlement standard deviation. Thus, relevant single random variable models represent the random field approach well with regard to settlement parameter estimation. This remark is verified upon a settlement analysis of a three-dimensional FE model of a hypothetical strip foundation. Following the preliminary model observations, various probabilistic geotechnical analyses may be supported, e.g., continuous footing design, slope stability analysis, and foundation reliability assessment.

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“…Available methods for probability simulation based on the Monte Carlo method could be categorized as directional sampling, Importance sampling, Subset simulation and Line sampling/stepwise simulation [30][31][32][33]. Monte Carlo simulations have been widely used to manage or estimate uncertainties, and there is a large body of literature using it in the geotechnical field providing risk assessments, e.g., Peng et al [34], Cao et al [35], Jin et al [36] and Cao et al [26]. A successful example of its use in relation to tunneling involved investigation of the potential hazards induced by TBM excavations [37].…”

Section: Introductionmentioning

confidence: 99%

Assessing the Economic Risk of Building Damage due to the Tunneling-Induced Settlement Using Monte Carlo Simulations and BIM

2020

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Due to the increasing use of underground space to align with sustainability needs, geohazard risk assessments have become a valuable tool for decision-making. One common issue in relation to urban geohazard assessments relates to ground movements due to tunneling affecting adjacent buildings. A framework for assessing costs related to subsequent building damage, using integrated data, statistics and considering the uncertainties involved, is presented in this paper. The proposed methodology provides an integration of Monte Carlo simulations to support uncertainty estimations with an analysis for building-damage cost risk due to tunneling-induced settlements. The analysis involves analytical models using green-field conditions and a typically used building damage assessment method. BIM is capable of collating, combining and visualizing information with advanced analysis techniques into a risk-based tool. The resulting tool provides a clear way of assessing building-damage costs risk due to tunneling-induced settlements. This uses a BIM-based environment and incorporates 3D visualizations and an integrated analysis via MATLAB to reveal and highlight hazardous areas and the severity of economic risk along the tunneling route. This informs the need for additional ground investigations or secondary analyses to ensure engineering processes reduce or remove the risk of economic damage and advance sustainable decision-making.

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Reliability-based robust geotechnical design using Monte Carlo simulation

Cited by 36 publications

References 36 publications

Design and Sensitivity Analysis of Rock Slope Using Monte Carlo Simulation

Design and Sensitivity Analysis of Rock Slope Using Monte Carlo Simulation

The Effect of the Selection of Three-Dimensional Random Numerical Soil Models on Strip Foundation Settlements

Assessing the Economic Risk of Building Damage due to the Tunneling-Induced Settlement Using Monte Carlo Simulations and BIM

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