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利用人工神经网络对混凝土碎片进行定量测量，以考虑冲击载荷作用下的不确定性。

Quantitative measure of concrete fragment using ANN to consider uncertainties under impact loading.

作者信息

Kim Kyeongjin, Kim WooSeok, Seo Junwon, Jeong Yoseok, Lee Jaeha

机构信息

Major of Civil Engineering, National Korea Maritime & Ocean University, Busan, 49112, Republic of Korea.

Interdisciplinary Major of Ocean Renewable Energy Engineering, National Korea Maritime & Ocean University, Busan, 49112, Republic of Korea.

出版信息

Sci Rep. 2022 Jul 4;12(1):11248. doi: 10.1038/s41598-022-15253-z.

DOI:10.1038/s41598-022-15253-z
PMID:35787663
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9253129/
Abstract

In this study, numerical analysis was performed to predict amount of fragments and travel distance after collision of a concrete median barrier with a truck under impact loading using Smooth Particle Hydrodynamics (SPH). The obtained results of the SPH analysis showed that amount of fragments and the travel distance can be changed depending on different velocity-to-mass ratios under same local impact energy. Using the results of the SPH analysis, artificial neural network (ANN) was constructed to consider the uncertainties for the prediction of amount of fragments and travel distance of concrete after collision. In addition, the results of the ANN were compared with the results of multiple linear regression analysis (MRA). The ANN results showed better coefficient of determination (R) than the MRA results. Therefore, the ANN showed improvement than the MRA results in terms of the uncertainties of the prediction of amount of fragments and travel distance. Using the constructed ANN, data augmentation was conducted from a limited number of analysis data using a statistical distribution method. Finally, the fragility curves of the concrete median barrier were suggested to estimate the probability of exceed specific amount of fragments and travel distance under same impact energy.

摘要

在本研究中，使用光滑粒子流体动力学（SPH）对混凝土中央分隔带护栏与卡车在冲击载荷下碰撞后的碎片数量和行进距离进行了数值分析，以预测其情况。SPH分析的结果表明，在相同的局部冲击能量下，碎片数量和行进距离会因不同的速度质量比而发生变化。利用SPH分析的结果，构建了人工神经网络（ANN），以考虑在碰撞后预测混凝土碎片数量和行进距离时的不确定性。此外，还将人工神经网络的结果与多元线性回归分析（MRA）的结果进行了比较。人工神经网络的结果显示出比多元线性回归分析结果更好的决定系数（R）。因此，在预测碎片数量和行进距离的不确定性方面，人工神经网络比多元线性回归分析结果有改进。利用构建的人工神经网络，采用统计分布方法从有限数量的分析数据中进行了数据增强。最后，提出了混凝土中央分隔带护栏的易损性曲线，以估计在相同冲击能量下超过特定碎片数量和行进距离的概率。

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