Artificial neural network prediction of residual compressive strength of composite stiffened panels with open crack

Liu, Yan; Lei, Zhenkun; Zhu, Rongxin; Shang, Yuxiang; Bai, Ruixiang

doi:10.1016/j.oceaneng.2022.112771

Cited by 2 publications

(2 citation statements)

References 28 publications

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“…This form of power generation not only pollutes the environment, but also coal is a non-renewable resource in the short term. With the development of science and technology, more and more people begin to pay attention to reducing the use of energy consumption and switch to a large number of green and clean energy, such as solar energy, geothermal energy, wind energy, etc [2][3][4] . There is a problem with the use of these green and clean energy, that is, uncertainty and discontinuity.…”

Section: Figure 1 Energy System Composition Of Pressurized Oxygen Sup...mentioning

confidence: 99%

Research on Energy Management of Pressurized Oxygen-Supplemented Buildings

Zhang,

Wang,

Zhou

et al. 2023

J. Phys.: Conf. Ser.

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The energy system of supercharged and oxygenated buildings is a hybrid energy system. The energy used is solar energy, electric energy, pressure energy, etc. In order to make the hybrid energy system more stable and controllable, and to achieve the purpose of energy saving under the condition of stable power consumption of supercharged and oxygenated buildings, a power consumption prediction and management model of supercharged and oxygenated buildings based on neural network is proposed. According to the power consumption system structure of the pressurized oxygenation building, this model establishes a power consumption prediction model based on neural network, and then verifies by simulation that the model can better meet the power consumption prediction conditions of the pressurized oxygenation building. Finally, according to the experiment, this power consumption prediction model can make the power consumption management of the pressurized oxygenation building more stable and controllable. When meeting the daily and stable power consumption requirements of the pressurized oxygenation building, the model can be used to predict the power consumption of the pressurized oxygenation building. Green energy is also reasonably used to save energy in pressurized and oxygen-supplemented buildings.

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Section: Figure 1 Energy System Composition Of Pressurized Oxygen Sup...mentioning

confidence: 99%

Research on Energy Management of Pressurized Oxygen-Supplemented Buildings

Zhang,

Wang,

Zhou

et al. 2023

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

show abstract

“…The results obtained from the ANN model are in good agreement with the finite element analysis, indicating that the model can accurately predict the mechanical properties of composite laminates. Liu et al [ 19 ] examined the impact of crack parameters on the residual compressive strength of composite panels using an ANN. The results show that the ANN model not only has a strong learning ability, it can also quickly and accurately predict the compression strength of a composite laminate.…”

Section: Introductionmentioning

confidence: 99%

A Fast and Efficient Approach to Strength Prediction for Carbon/Epoxy Composites with Resin-Missing Defects

Li,

Zhu

2024

Polymers

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A novel method is proposed to quickly predict the tensile strength of carbon/epoxy composites with resin-missing defects. The univariate Chebyshev prediction model (UCPM) was developed using the dimension reduction method and Chebyshev polynomials. To enhance the computational efficiency and reduce the manual modeling workload, a parameterization script for the finite element model was established using Python during the model construction process. To validate the model, specimens with different defect sizes were prepared using the vacuum assistant resin infusion (VARI) process, the mechanical properties of the specimens were tested, and the model predictions were analyzed in comparison with the experimental results. Additionally, the impact of the order (second–ninth) on the predictive accuracy of the UCPM was examined, and the performance of the model was evaluated using statistical errors. The results demonstrate that the prediction model has a high prediction accuracy, with a maximum prediction error of 5.20% compared to the experimental results. A low order resulted in underfitting, while increasing the order can improve the prediction accuracy of the UCPM. However, if the order is too high, overfitting may occur, leading to a decrease in the prediction accuracy.

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Artificial neural network prediction of residual compressive strength of composite stiffened panels with open crack

Cited by 2 publications

References 28 publications

Research on Energy Management of Pressurized Oxygen-Supplemented Buildings

Research on Energy Management of Pressurized Oxygen-Supplemented Buildings

A Fast and Efficient Approach to Strength Prediction for Carbon/Epoxy Composites with Resin-Missing Defects

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