Probabilistic and Statistical Analysis in Engineering Geology

A special issue of Axioms (ISSN 2075-1680). This special issue belongs to the section "Mathematical Analysis".

Deadline for manuscript submissions: closed (31 March 2024) | Viewed by 3132

Special Issue Editor


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Guest Editor
School of Civil and Environmental Engineering, Nanyang Technological University, Singapore, Singapore
Interests: hydraulic; bayes; unsaturated soil; geotechnical engineering; landslides; boreholes; uncertainty

Special Issue Information

Dear Colleagues,

Probabilistic and statistical analysis are essential tools in engineering geology that help to better understand and quantify uncertainty in geological systems. These methods allow engineers to improve the accuracy of predictions and reduce the uncertainty in the design process, leading to more reliable and safe structures. The spectrum of probabilistic and statistical analysis in engineering geology is constantly evolving as new techniques and methods are developed, and as existing methods are refined. However, all these techniques have the common goal of improving our understanding of uncertainty in geological systems and allowing for more informed decision-making in the design and management of geotechnical structures. This Special Issue encompasses a wide range of probabilistic and statistical analyses for the interests of engineering geology.

The methods may include, but are not limited to:

  • Probabilistic hazard analysis used to assess the likelihood of geological hazards such as earthquakes, landslides, and rock falls;
  • Reliability analysis used to evaluate the reliability of geotechnical structures and systems;
  • Monte Carlo simulation used to model and analyze complex geological systems that are influenced by many interrelated variables;
  • Frequency analysis used to determine the frequency of occurrence of geological events;
  • Statistical regression analysis used to develop relationships between geological parameters and their associated uncertainties;
  • Sensitivity analysis used to assess the effect of uncertainty on the performance of geological systems. 

Research areas may include, but are not limited to:

  • Geological engineering;
  • Geotechnical engineering;
  • Geophysical engineering;
  • Geo-environmental engineering;
  • Geospatial engineering;
  • Hydrogeological engineering;
  • Rock engineering;
  • Earthquake engineering;
  • Underground engineering;
  • Mining engineering.

In this Special Issue, original research articles and reviews are welcome. I look forward to receiving your contributions.

Dr. Haoqing Yang
Guest Editor

Manuscript Submission Information

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Keywords

  • Monte Carlo
  • uncertainty
  • Bayesian methods
  • probability
  • reliability
  • sensitivity

Published Papers (1 paper)

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Research

13 pages, 10764 KiB  
Article
Investigating the Number of Monte Carlo Simulations for Statistically Stationary Model Outputs
by Jiahang Zhang and Shengai Cui
Axioms 2023, 12(5), 481; https://doi.org/10.3390/axioms12050481 - 16 May 2023
Cited by 2 | Viewed by 1936
Abstract
The number of random fields required to capture the spatial variability of soil properties and their impact on the performance of geotechnical systems is often varied. However, the number of random fields required to obtain higher-order statistical moments of model outputs has not [...] Read more.
The number of random fields required to capture the spatial variability of soil properties and their impact on the performance of geotechnical systems is often varied. However, the number of random fields required to obtain higher-order statistical moments of model outputs has not yet been studied. This research aims to investigate the number of Monte Carlo simulations needed to achieve stationary higher-order statistics of a pore pressure head in an unsaturated soil slope under steady-state infiltration. The study recommends using at least 500 Monte Carlo samples for the probabilistic analysis of geotechnical engineering models. A more conservative choice for up to second-moment analysis is 1000 samples. The analysis reveals significant variations in skewness, which become stationary for all mesh grids when the number of samples exceeds 15,000. Kurtosis stabilizes only when the number of samples reaches 25,000. The pore pressure head in the unsaturated zone is less uncertain. Additionally, the probability density function of the pore pressure head follows a leptokurtic distribution. Full article
(This article belongs to the Special Issue Probabilistic and Statistical Analysis in Engineering Geology)
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