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Stochastic Geomechanics: From Experimentation to Forward Modeling
This Topic introduces a series of papers that address the impact of uncertainty during the sequence of scientific discovery, which is found across multiple disciplines working with different types, scales, and applications of geomaterials, including i) experimentation, ii) data analysis, and iii) the formulation of corresponding forward models, either based on physical and/or life sciences, statistics, artificial intelligence, or a combination of them, when geomaterial properties and responses are defined in space and time.
This Topic has a special focus on cases wherein a series of a geomaterial experiments were collected in a laboratory setting or in the field and these were repeated under similar conditions to capture the space, time, or spatio-temporal variability of the geomaterial properties and response when subjected to varying control variables such as boundary conditions, initial and loading conditions, sensing technology, and even the operator’s experience. By capturing the effect of the expeimental variability and the associated material properties and response, it is hypothesized that a better understanding of geomaterial performance can be achieved, including its likely failure mechanisms. Consequently, it is anticipated that a more realistic definition of a geomaterial behavior will lead to improved risk assessment and management for physical and/or life processes that depend on it.
The original effort to define the scope of this multi-journal MDPI Topic started with the integration of a series of papers produced by the lead and associate guest editors, including the development of a comprehensive experimental database and the mechanical and stochastic modeling of sand specimens, built and tested under similar experimental conditions, which captured 3D displacement fields from their undeformed to their critical states. This allowed for the geomechanical kinematic analysis of localization effects, as well the computation of spatio-temporal statistics of both the displacement fields and of the kinematic effects. This set the basis for the use of other forward modeling methods, including the use of emerging artificial intelligence methods, in multiple geoscientific and geoengineering applications. We also welcome submissions in this field from Applied Sciences, Geosciences, Materials, Minerals, and Modelling.
Dr. Zenon Medina-Cetina
Dr. Yichuan Zhu
- stochastic geomechanics
- geomaterials experimentaiton
- geomeaterials forward modeling
- uncertainty quantification
- identification and characterization of localization phenomena
- simulation of spatio-temporal geoscientific and geoengineering processes
|Journal Name||Impact Factor||CiteScore||Launched Year||First Decision (median)||APC|
|2.838||3.7||2011||14.9 Days||2300 CHF|
|-||4.8||2011||22.5 Days||1500 CHF|
|3.748||4.7||2008||13.9 Days||2300 CHF|
|2.818||3.7||2011||16.2 Days||2000 CHF|
|-||-||2020||21.5 Days||1000 CHF|
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