- Title
- Virtual modelling aided safety assessment for ductile structures against high-velocity impact
- Creator
- Feng, Yuan; Alamdari, Mehrisadat Makki; Wu, Di; Luo, Zhen; Ruan, Dong; Egbelakin, Temitope; Chen, Xiaojun; Gao, Wei
- Relation
- Engineering Structures Vol. 301, Issue 15 February 2024, no. 117373
- Publisher Link
- http://dx.doi.org/10.1016/j.engstruct.2023.117373
- Publisher
- Elsevier
- Resource Type
- journal article
- Date
- 2024
- Description
- This paper introduces a virtual modelling-aided computational analysis framework for assessing the safety of a ductile engineering object against high-velocity impact. By evaluating the data feedback continuously collected from the working site, the presented Virtual Modelling Aided Safety Assessment (VMASA) scheme is competent to effectively report the current safety level of the engineering object. The safety level (alternatively known as the reliability), of the impacted system can be quantified by using the first-passage theory. By considering various mercurial factors that are influencing the high-velocity impact for ductile materials, in this research, a new machine learning-aided virtual modelling technique as the clustering extended support vector regression (CXSVR), is implemented to evaluate the capacity of the engineering product against different conditions. Also, the John-Cook failure model is transformed into a random format to simulate the dynamic response of the engineering product against high-velocity impacts. A new T-spline kernel has also been developed within the CXSVR scheme. By using the VMASA, the inherent limit state function can be quantitatively certified by analysing the relationship between the system inputs and outputs. Both experimental and numerical investigations are implemented to demonstrate the accuracy, practicability, and efficiency of the proposed safety assessment framework.
- Subject
- high-velocity impact; ductile material; virtual modelling; machine learning; safety assessment
- Identifier
- http://hdl.handle.net/1959.13/1499504
- Identifier
- uon:54704
- Identifier
- ISSN:0141-0296
- Rights
- © 2023 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
- Language
- eng
- Full Text
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