Probability analysis of slope stability

Peterson, Jennifer Lynn

doi:10.33915/etd.990

Cited by 2 publications

(5 citation statements)

References 14 publications

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“…The variability of the laboratory test is assessed as a form of uncertainty that will affect the recommendations for determining slope stability [2]. Thus, the factor of safety that becomes the standard for assessing a slope is considered insufficient to describe the slope uncertainty [3].…”

Section: Introductionmentioning

confidence: 99%

Sensitivity analysis of nickel haul road embankment slopes using the coefficient of variation approach

Saptono¹,

Rezky²

2022

Min. miner. depos.

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Purpose. The behavior of slope instability is influenced by many factors, both internal, such as the physical-mechanical properties of materials, and external, such as rain and seismic activity. Sensitivity analysis is used to determine the parameters that have the greatest impact on the level of slope stability. Methods. Sensitivity analysis of embankment slopes uses the coefficient of variation (CV) approach with input parameters namely cohesion and internal friction angle. Findings. The results of the study confirm that the internal friction angle is the most influential parameter on the embankment slope stability. The evidence is that at the highest percentage of CV, there is the highest probability of avalanches, based on these parameters. Originality. In this research, the coefficient of variation method is used to determine which parameters have heterogeneous data distribution and the greatest probability of failure, as well as to test mechanical sensitivity with the concept of changing the percentage of parameters to a safety factor value to validate calculations using the coefficient of variation approach. Practical implications. The sensitivity analysis results are not only limited to values, since the more important is finding out the cause of the influence of these parameters according to field conditions. The reason why the internal friction angle is the most sensitive parameter is the grain size approach, when the limestone mixture in the field has coarse and large grain sizes. Therefore, the surface tends to form rough waves and causes the relatively large grained rocks to have large internal friction angles.

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Section: Introductionmentioning

confidence: 99%

Sensitivity analysis of nickel haul road embankment slopes using the coefficient of variation approach

Saptono¹,

Rezky²

2022

Min. miner. depos.

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“…A These numbers represent, respectively, the (1sS) and (d2s) limits as described in ASTM C 670 B The precision estimates are based on aggregates with nominal maximum size of 19.0 mm. (Peterson, 1999). Monte Carlo simulation is now used routinely in many diverse fields, from the simulation of complex physical phenomena such as radiation transport in the earth's atmosphere to the simulation of a Bingo game (USDOE, 2003).…”

Section: Quality Assurance For Superpavementioning

confidence: 99%

“…The appropriate number of passes for an analysis is a function of the number of input parameters, the complexity of the modeled situation, and the desired precision of the output. The final result of a Monte Carlo simulation is a probability distribution of the output parameter (Peterson, 1999). Excel (Palisade Corporation, 1997).…”

Section: Quality Assurance For Superpavementioning

confidence: 99%

“…@Risk allows the user to select from a variety of distributions, perform the simulation, and then display the results of the simulation. @Risk has been successfully used at WVU for the simulation of slope stability and evaluation of preventive maintenance strategies (Peterson, 1999 andReigle, 2000) One of the advantages of using the @Risk software is the ability to readily define and maximum (%G mm,max ) number of gyrations, and the dust to binder ratio. In order to calculate the volumetric parameters, the following properties were required:…”

Section: Quality Assurance For Superpavementioning

confidence: 99%

“…In many cases a bias cannot be determined for lack of a target value. None of the test methods used for QC/QA have a stated bias.The Monte Carlo simulation method was developed in 1949 by John vonNeumann and Stanislav Ulam(Peterson, 1999) The Neumann and Ulam idea specifically designated the use of random sampling procedures for analyzing deterministic mathematical situations. The Monte Carlo method gained significance with the development of computers to automate the tedious calculations and repetitions…”

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confidence: 99%

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Probability analysis of slope stability

Cited by 2 publications

References 14 publications

Sensitivity analysis of nickel haul road embankment slopes using the coefficient of variation approach

Sensitivity analysis of nickel haul road embankment slopes using the coefficient of variation approach

Evaluation of quality control parameters for Superpave hot mix asphalt

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