High-Dimensional Uncertainty Quantification in a Hybrid Data + Model-Based Submodeling Method for Refined Response Estimation at Critical Locations

2021 ◽  
Vol 7 (3) ◽  
pp. 04021022
Author(s):  
Bhavana Valeti ◽  
Shamim N. Pakzad
Keyword(s):  
Uncertainty Quantification ◽  
Data Model ◽  
High Dimensional ◽  
Model Based ◽  
Hybrid Data ◽  
Critical Locations

scholarly journals Hybrid Data + Model-Based Submodeling Method for a Refined Response Estimation at Critical Locations

2020 ◽  
Author(s):  
Bhavana Valeti ◽  
Shamim Pakzad
Keyword(s):  
Stress Distribution ◽  
Data Model ◽  
Complex Geometry ◽  
Remaining Useful Life ◽  
Average Error ◽  
Accurate Estimation ◽  
Stress Concentrations ◽  
Model Based ◽  
Hybrid Data ◽  
Critical Locations

The complex geometry of structural components results in uneven stress distribution in structures under loading. The regions with stress concentrations often act as critical locations from where damages can initiate and propagate under different types of loading. Accurate estimation of stress distribution, especially at such critical locations, is vital for a more reliable prediction of possible damage prognosis or remaining useful life (RUL) estimation of the structure or a component. Traditional sensing methods often provide response measurements at a few localized points and demand sensor deployment in large numbers to get a distributed response. This may yet be sparse for the steep changes in stress at critical locations. In this study, we propose a hybrid data + model-based submodeling (HDMS) method to achieve a refined estimate of distributed structural response in and around the critical locations. The HDMS method uses just the measured response on the pre-selected boundaries around the locations of interest, as input to drive the corresponding submodel of a structural component or a connection, given its geometry and material properties are known. The performance of the HDMS method in response estimation is demonstrated through two vertically loaded plates, one with two holes connected by a slit and the other with two wide slits, respectively. The refined response estimated by HDMS could determine asymmetric response, nonlinear behavior, and permanent set at the critical locations with an average error less than 50 μstrain at higher load stages, making HDMS a versatile method for refined response estimation.

scholarly journals Supplementary material to "Hybrid data-model-based mapping of soil thickness in a mountainous watershed"

2021 ◽  
Author(s):  
Qina Yan ◽  
Haruko Wainwright ◽  
Baptiste Dafflon ◽  
Sebastian Uhlemann ◽  
Carl I. Steefel ◽  
...  
Keyword(s):  
Data Model ◽  
Soil Thickness ◽  
Model Based ◽  
Mountainous Watershed ◽  
Hybrid Data ◽  
Supplementary Material

A Qualified Assertion Database for the History of Places

2019 ◽  
Vol 13 (1-2) ◽  
pp. 95-115
Author(s):  
Brandon Plewe
Keyword(s):  
Data Model ◽  
Temporal Change ◽  
Religious Congregations ◽  
Historical Gis ◽  
The Past ◽  
Model Based ◽  
Complex Information ◽  
Invaluable Tool ◽  
History Of ◽  
The Rich

Historical place databases can be an invaluable tool for capturing the rich meaning of past places. However, this richness presents obstacles to success: the daunting need to simultaneously represent complex information such as temporal change, uncertainty, relationships, and thorough sourcing has been an obstacle to historical GIS in the past. The Qualified Assertion Model developed in this paper can represent a variety of historical complexities using a single, simple, flexible data model based on a) documenting assertions of the past world rather than claiming to know the exact truth, and b) qualifying the scope, provenance, quality, and syntactics of those assertions. This model was successfully implemented in a production-strength historical gazetteer of religious congregations, demonstrating its effectiveness and some challenges.

scholarly journals A Design of a Data Model Based on Abstraction of Symbols

1985 ◽  
Vol 28 (3) ◽  
pp. 298-308 ◽  
Author(s):  
T. D. Kimura
Keyword(s):  
Data Model ◽  
Model Based
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