Comparative studies of Computational Modeling and Simulation of Structure Failure Under Extreme Loads

Knowledge on how engineering structures respond to extreme loads is significant in increasing their safety and durability. This dissertation is concerned with numeric and simulations of structural collapse which can be as a result of earthquakes, blasts, or high-speed impacts. Using specifically FEA and the Kriging-based surrogate models, this work will try to model the failure mechanisms and factors affecting structural life. The models used here are therefore first tested for their reliability and performance against experimental data and real-life examples. The results indicate how better modelling would lead to enhanced emergence of safer structural capacity and reliability. The contribution of this research to the structural engineering discipline is in the area of failure prediction by making suggestion for future research and recommending practical applications that may be useful in engineering practices and formulation of policies. Thus, the present research underlines the need for further development of computational approaches to deal with increasing difficulties due to extreme conditions.

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2024-09-08
DOI
https://doi.org/10.5281/zenodo.13731788
Primary Topic
Imbalanced Data Classification Techniques
Type
article
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article

Comparative studies of Computational Modeling and Simulation of Structure Failure Under Extreme Loads

Zenodo (CERN European Organization for Nuclear Research)
Imbalanced Data Classification Techniques
article

Comparative studies of Computational Modeling and Simulation of Structure Failure Under Extreme Loads

article en

Abstract

Knowledge on how engineering structures respond to extreme loads is significant in increasing their safety and durability. This dissertation is concerned with numeric and simulations of structural collapse which can be as a result of earthquakes, blasts, or high-speed impacts. Using specifically FEA and the Kriging-based surrogate models, this work will try to model the failure mechanisms and factors affecting structural life. The models used here are therefore first tested for their reliability and performance against experimental data and real-life examples. The results indicate how better modelling would lead to enhanced emergence of safer structural capacity and reliability. The contribution of this research to the structural engineering discipline is in the area of failure prediction by making suggestion for future research and recommending practical applications that may be useful in engineering practices and formulation of policies. Thus, the present research underlines the need for further development of computational approaches to deal with increasing difficulties due to extreme conditions.

Zenodo (CERN European Organization for Nuclear Research)
Sustainable cities and communities
Openalex Percentile: Top 61%
Imbalanced Data Classification Techniques
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