Study on State Recognition of ASCE Benchmark FEM Based on Lyapunov Exponent Spectrum Entropy

Yang, Jian Wei; Zhou, Jian; Han, Feng Qing

doi:10.4028/www.scientific.net/amm.117-119.1526

Cited by 2 publications

(2 citation statements)

References 5 publications

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“…The Lyapunov index is an important indicator to measure the brittleness behavior [43,51]. The maximum Lyapunov index is introduced to study the brittleness behavior of the supply chain in this research.…”

Section: Evolution Index Model Of the Brittleness Behavior Of The Sup...mentioning

confidence: 99%

Brittleness Evolution Model of the Supply Chain Network Based on Adaptive Agent Graph Theory under the COVID-19 Pandemic

Cao

Wang

2022

Sustainability

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The triggering of supply chain brittleness has a significant impact on enterprise benefits under attack from the COVID-19 pandemic. The complexity of the supply chain system, the uncertainty of the COVID-19 pandemic, and demand uncertainty have made the triggering and propagation of supply chain brittleness complicated. In this study, a brittleness evolution model based on adaptive agent graph theory has been constructed. The parameters of brittleness evolution, including brittleness entropy and the vertex state value, have been quantitatively designed, and the brittleness evolution model in which the adaptability of nodes is considered and is not considered is constructed. A simulation algorithm based on the integrated scheduling model of the supply chain has been established. Finally, the practicability of the proposed model and algorithm is demonstrated via a case study of an electronic supply chain network. The results indicate that the proposed model and algorithm can effectively analyze the brittleness evolution law of the supply chain under the impact of the COVID-19 pandemic, including the evolution law of the vertex state, the brittleness entropy of the vertex, the global entropy of brittleness, the seasonal evolution law of the supply chain brittleness, and the evolution law of the brittleness behavior.

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Section: Evolution Index Model Of the Brittleness Behavior Of The Sup...mentioning

confidence: 99%

Brittleness Evolution Model of the Supply Chain Network Based on Adaptive Agent Graph Theory under the COVID-19 Pandemic

Cao

Wang

2022

Sustainability

View full text Add to dashboard Cite

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“…For the bridge structure system, due to the structure itself and the non-linear materials, coupled with the effects of random factors in the external environment, bridges and other structural systems exhibit complex non-linear dynamic behaviour. Numerous structural response data from the health monitoring system are bound to exhibit non-linear characteristics [12]. Therefore, methods based on ESN can be applied for feature extraction of bridge monitoring signal, thus revealing the mechanism of signal non-linear evolution.…”

Section: Introductionmentioning

confidence: 99%

Statistical Pattern Recognition for Structural Health Monitoring Using Esn Feature Extraction Method

Yang¹,

Sha²,

Zhou³

et al. 2018

International Journal of Robotics and Automation

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For structural dynamic response signal, as the traditional feature extraction methods based on statistical pattern recognition are linear and insensitive, this paper proposes a new signal feature extraction method based on the echo state network (ESN). The proposed method is suitable for analysis of signal with non-linear characteristics. It is more sensitive on the damage identification than the existing methods based on statistical pattern recognition. The proposed method collects structural dynamic response signals under different conditions and uses the ESN network for system identification. The output weights are served as the damage feature values of the bridge structure. A number of experiments are performed by using the non-linear vibration finite element model and a real bridge scale model under environmental motivation. The experimental results show that the proposed non-linear feature extraction method based on ESN is more sensitive than the traditional auto-regressive model. There are obvious differences in the damage sensitivity index value between the healthy case and the damage case. The damage sensitivity index increases linearly with the structural degradation. Moreover, this paper constructs the damage sensitive index by using the Euclidean distance which is consistent with the evolution trend of the structural state. The proposed method offers a more appropriate theoretical and technical support for the existing bridge monitoring data processing and the structural damage evolution trend assessment.

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Study on State Recognition of ASCE Benchmark FEM Based on Lyapunov Exponent Spectrum Entropy

Cited by 2 publications

References 5 publications

Brittleness Evolution Model of the Supply Chain Network Based on Adaptive Agent Graph Theory under the COVID-19 Pandemic

Brittleness Evolution Model of the Supply Chain Network Based on Adaptive Agent Graph Theory under the COVID-19 Pandemic

Statistical Pattern Recognition for Structural Health Monitoring Using Esn Feature Extraction Method

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