|
Title |
ANN Model for Response Correction of Structural Components Subjected to Near-Field Explosions Based on Single Degree of Freedom Analysis
|
|
Authors |
이상훈(Sang-Hoon Lee) ; 김재민(Jae-Min Kim) ; 김재현(Jae Hyun Kim) ; 김강수(Kang Su Kim) |
|
DOI |
https://doi.org/10.4334/JKCI.2024.36.5.505 |
|
Keywords |
폭발하중; 근접폭발; 단자유도; 수치해석; 인공신경망 blast load; near-field explosion; single-degree of freedom; numerical analysis; artificial neural network |
|
Abstract |
Deriving the response of structures subjected to blast loads is essential for protecting human lives and ensuring structural safety. Structural safety can be assessed through blast-resistant analysis, and the behavior of blast-resistant structures can be derived using a single-degree-of-freedom numerical analysis method based on reasonable assumptions. Generally, blast loads are treated as uniformly distributed in such analyses; however, in the case of close-in explosions, where the blast source is near the structure, the blast pressure does not act uniformly on the structure. In this study, a single-degree-of-freedom numerical analysis response database considering the effects of close-in explosions was established, and a close-in explosion response correction artificial neural network (ANN) model was developed and validated to adjust the responses derived from the single-degree-of- freedom analysis method, assuming uniformly distributed loads, for the specific effects of close-in explosions.
|