Bearing-capacity prediction of spatially random <i>c</i> <i>ϕ</i> soils
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Machine scores (provisional)
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- Teacher spread
- 0.158 · how far apart the two teachers sit on this one work
- Validation status
score_only:v0-immature-baseline· verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it
Abstract
Soils with spatially varying shear strengths are modeled using random field theory and elasto-plastic finite element analysis to evaluate the extent to which spatial variability and cross-correlation in soil properties (c and ϕ) affect bearing capacity. The analysis is two dimensional, corresponding to a strip footing with infinite correlation length in the out-of-plane direction, and the soil is assumed to be weightless with footing placed on the soil surface. Theoretical predictions of the mean and standard deviation of bearing capacity, for the case where c and ϕ are independent, are derived using a geometric averaging model and then verified via Monte Carlo simulation. The standard deviation prediction is found to be quite accurate, while the mean prediction is found to require some additional semi-empirical adjustment to give accurate results for "worst case" correlation lengths. Combined, the theory can be used to estimate the probability of bearing-capacity failure, but also sheds light on the stochastic behaviour of foundation bearing failure.Key words: bearing capacity, probability, random fields, geometric averaging, cϕ soil, Monte Carlo simulation.
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The record
- Venue
- Canadian Geotechnical Journal
- Topic
- Geotechnical Engineering and Analysis
- Field
- Engineering
- Canadian institutions
- —
- Funders
- National Science Foundation
- Keywords
- Monte Carlo methodBearing capacityRandom fieldStandard deviationMathematicsGeotechnical engineeringSpatial variabilitySoil waterSpatial correlationFinite element methodStatistical physicsStructural engineeringGeologyStatisticsEngineeringSoil sciencePhysics
- Has abstract in OpenAlex
- yes