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A deep feed-forward neural network for damage detection in functionally graded carbon nanotube-reinforced composite plates using modal kinetic energy

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Frontiers of Structural and Civil Engineering 2021年第6期15卷 1453-1479页

作者： Huy Q.LE Tam T.TRUONG D.DINH-CONG T.NGUYEN-THOI Division of Computational Mathematics and Engineering Institute for Computational ScienceTon Duc Thang UniversityHo Chi Minh City 700000Vietnam Faculty of Civil Engineering Ho Chi Minh City University of TransportHo Chi Minh City 700000Vietnam Division of Construction Computation Institute for Computational ScienceTon Duc Thang UniversityHo Chi Minh City 700000Vietnam Faculty of Civil Engineering Ton Duc Thang UniversityHo Chi Minh City 700000Vietnam

This paper proposes a new Deep Feed-forward Neural Network(DFNN)approach for damage detection in functionally graded carbon nanotube-reinforced composite(FG-CNTRC)*** the proposed approach,the DFNN model is developed based on a data set containing 20000 samples of damage scenarios,obtained via finite element(FE)simulation,of the FG-CNTRC *** elemental modal kinetic energy(MKE)values,calculated from natural frequencies and translational nodal displacements of the structures,are utilized as input of the DFNN model while the damage locations and corresponding severities are considered as *** state-of-the art Exponential Linear Units(ELU)activation function and the Adamax algorithm are employed to train the DFNN ***,in order to enhance the performance of the DFNN model,the mini-batch and early-stopping techniques are applied to the training process.A trial-and-error procedure is implemented to determine suitable parameters of the network such as the number of hidden layers and the number of neurons in each *** accuracy and capability of the proposed DFNN model are illustrated through two distinct configurations of the CNT-fibers constituting the FG-CNTRC plates including uniform distribution(UD)and functionally graded-V distribution(FG-VD).Furthermore,the performance and stability of the DFNN model with the consideration of noise effects on the input data are also *** results indicate that the proposed DFNN model is able to give sufficiently accurate damage detection outcomes for the FG-CNTRC plates for both cases of noise-free and noise-influenced data.

关键词： damage detection deep feed-forward neural networks functionally graded carbon nanotube-reinforced composite plates modal kinetic energy

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The effect of Typhoon Kalmaegi on the modal energy and period of internal waves near the Dongsha Islands(South China Sea)

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Acta Oceanologica Sinica 2023年第12期42卷 22-31页

作者： Rongwei Zhai Guiying Chen Chenjing Shang Xiaodong Shang Youren Zheng South China Sea Information Center of State Oceanic Administration Guangzhou 510310China State Key Laboratory of Tropical Oceanography South China Sea Institute of OceanologyChinese Academy of SciencesGuangzhou 510315China Shenzhen Key Laboratory of Marine Bioresource and Eco-environmental Science College of Life Sciences and OceanographyShenzhen UniversityShenzhen 518060China

The influence of Typhoon Kalmaegi on internal waves near the Dongsha Islands in the northeastern South China Sea was investigated using mooring observation *** observed,for the first time,that the phenomenon of regular variation characteristics of the 14-d spring-neap cycle of diurnal internal tides(ITs)can be regulated by *** diurnal ITs lost the regular variation characteristics of the 14-d spring-neap cycle during the typhoon period owing to the weakening of diurnal coherent ITs,represented by O_(1)and K_(1),and the strengthening of diurnal incoherent *** of quantitative analysis showed that during the pre-typhoon period,timeaveraged modal kinetic energy(sum of Modes 1–5)of near-inertial internal waves(NIWs)and diurnal and semidiurnal ITs were 0.62 kJ/m^(2),5.66 kJ/m^(2),and 1.48 kJ/m^(2),***,during the typhoon period,the modal kinetic energy of NIWs increased 5.11 times,mainly due to the increase in high-mode kinetic *** the same time,the modal kinetic energy of diurnal and semidiurnal ITs was reduced by 68.9%and 20%,respectively,mainly due to the decrease in low-mode kinetic *** significantly reduced diurnal ITs during the typhoon period could be due to:(1)strong nonlinear interaction between diurnal ITs and NIWs,and(2)a higher proportion of high-mode diurnal ITs during the typhoon period,leading to more energy dissipation.

关键词： internal waves spring-neap cycle modal kinetic energy South China Sea

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