Effects of reinforcement ratio and arrangement on the structural behavior of a nuclear building under aircraft impact

Thai, Duc‐Kien; Kim, Seung-Eock; Lee, Hyuk-Kee

doi:10.1016/j.nucengdes.2014.04.013

Cited by 18 publications

(1 citation statement)

References 12 publications

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“…If an element lacks the material capacity given by its yield surface, the element should be removed from the simulation of the behavior of concrete. Thai et al, El-tawil et al, and Murray selected 1.4, 1.4, and 1.1 for “erode”, respectively [ 16 , 23 , 24 ]. However, in the current study, 1.0 was selected for “erode” following Kim et al [ 2 ].…”

Section: Introduction Of the Developed Local Impact Modelmentioning

confidence: 99%

Prediction of Concrete Fragments Amount and Travel Distance under Impact Loading Using Deep Neural Network and Gradient Boosting Method

Kim

Seo

et al. 2022

Materials

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In the present study, the amount of fragments generated and their travel distances due to vehicle collision with concrete median barrier (CMB) was analyzed and predicted. In this regard, machine learning was applied to the results of numerical analysis, which were developed by comparing with field test. The numerical model was developed using smoothed particle hydrodynamics (SPH). SPH is a mesh-free method that can be used to predict the amount of fragments and their travel distances from concrete structures under impact loading. In addition, deep neural network (DNN) and gradient boosting machine (GBM) were also employed as machine learning methods. In this study, the results of DNN, GBM, and numerical analysis were then compared with the conducted field test. Such comparisons revealed that numerical analysis generated lower error than both DNN and GBM. When prediction results of both the amount of fragments and their travel distances were considered, the result of DNN showed smaller errors than that of GBM. Therefore, in studies where machine learning is used to predict the amount of fragments and their travel distances, careful selection of an appropriate method from the various available machine learning methods such as DNN, GBM, and random forest is absolutely important.

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Section: Introduction Of the Developed Local Impact Modelmentioning

confidence: 99%

Prediction of Concrete Fragments Amount and Travel Distance under Impact Loading Using Deep Neural Network and Gradient Boosting Method

Kim

Seo

et al. 2022

Materials

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Vibration analysis of third generation nuclear power plant considering soil‐structure‐interaction effect under the impact of large commercial aircraft

Mei

Wang

et al. 2020

Structural Design Tall Build

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Despite the rapid development of nuclear power, the risk of unforeseen beyonddesign-basis events has become a major concern for nuclear power plant (NPP) operators such as malicious aircraft impacts (AIs). This paper aims to perform vibration analysis on a Generation III NPP subjected to a large commercial aircraft crash, considering soil-structure-interaction (SSI) effect. Based on the verified finite element (FE) models of the aircraft Airbus A340 and the NPP, the whole collision process is reproduced numerically by adopting the missile-target-interaction method. The selection of soil type was based on AP1000 six-design soil sites, and five of which were selected for this study. The effect of impact direction, impact height, and embedment is also considered. Results showed that the soil type has a significant effect on the vibration response of points on the base-mat. Generally, the flexibility of soil increases the response of these points. In the process of propagation, the more flexible the soil is, the faster the weakening range will be. Different impact location will have great influence on the AI-induced vibration intensity of some areas of the NPP, such as the base-mat in the shield building. Increasing embedded depth reduces the response of points on the base-mat.

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Evaluation of concrete barriers with novel shock absorbers subjected to impact loading

Kim

Lee²,

Kim

et al. 2019

Archives of Civil and Mechanical Engineering

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Effects of reinforcement ratio and arrangement on the structural behavior of a nuclear building under aircraft impact

Cited by 18 publications

References 12 publications

Prediction of Concrete Fragments Amount and Travel Distance under Impact Loading Using Deep Neural Network and Gradient Boosting Method

Prediction of Concrete Fragments Amount and Travel Distance under Impact Loading Using Deep Neural Network and Gradient Boosting Method

Vibration analysis of third generation nuclear power plant considering soil‐structure‐interaction effect under the impact of large commercial aircraft

Evaluation of concrete barriers with novel shock absorbers subjected to impact loading

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