Expected-Outcome: A General Model of Static Evaluation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Generating random numbers on a simplex
Computers & Geosciences
The “Loughborough Loess” Monte Carlo model of soil structure
Computers & Geosciences - Special issue on computers, geoscience and geocomputation
Probabilistic modeling of uncertainties in earthquake-induced landslide hazard assessment
Computers & Geosciences
Deterministic seismic hazard map for Taiwan developed using an in-house Excel-based program
Computers & Geosciences
| Hi-index | 0.00 |
Conventional deterministic analysis for evaluating slope stability, although computationally efficient, is gradually shadowed by probabilistic analysis in two aspects. First deterministic analysis does not account for inevitable soil variability, and secondly its assessment would be deficient especially under a critical condition. However, probabilistic analysis that is computationally expensive sometime deters itself from being used when efficient algorithms are absent. For optimizing the calculation during probabilistic slope analysis, this study introduces a new algorithm in searching for the center of a circular slip surface, referred to as the pole. Not only can this algorithm effectively locate the pole, but its accuracy is improved compared to that of the conventional method. This study further demonstrates the algorithm by performing a probabilistic analysis for a benchmark slope through Monte Carlo Simulation. Such a probabilistic analysis saves 10^6h from the use of the conventional pole-searching algorithm in an equivalent analysis. The results show that circular slope stability is indeed affected by soil variability, which should not be overlooked since it is inevitable owing to natural randomness.