Prediction of bond strength of reinforced concrete structures based on feature selection and GWO-SVR model

Fan, Congcong; Zheng, Ying‐Ying; Wang, Shaoqiang; Ma, Jing

doi:10.1016/j.conbuildmat.2023.132602

Cited by 9 publications

(1 citation statement)

References 54 publications

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“…A suitable method needs to be chosen to optimize the initial weights of the CNN model to improve the model accuracy. Compared with traditional optimization algorithms such as genetic algorithm (GA), the grey wolf optimizer (GWO) algorithm has a stronger global search capability [25][26][27]. Therefore, in order to obtain reliable landslide susceptibility evaluation results for Mangshan Mountain, the GWO algorithm is introduced to search for the best initial weights of the one-dimensional CNN (1D CNN) model.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Landslide Susceptibility of Mangshan Mountain in Zhengzhou Based on GWO-1D CNN Model

Hu,

Yan

2024

Sustainability

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The Mangshan Mountain is located in the south bank of the Yellow River, which belongs to the typical loess plateau. Landslide disasters occur frequently in this region, so it is urgent to carry out the evaluation of landslide susceptibility. Therefore, this study takes Mangshan Mountain as the research object, selects 13 evaluation factors through multicollinearity diagnostic, Pearson correlation coefficient, and random forest importance analysis, and uses grey wolf optimizer (GWO) algorithm to optimize the initial weights of one-dimensional convolutional neural network model (1D CNN), so as to build a GWO-1D CNN model to carry out the evaluation of landslide susceptibility. The results show that the GWO algorithm can significantly improve the accuracy of 1D CNN model. The final accuracy of the GWO-1D CNN model reaches 0.903, and the accuracy, area under the ROC curve, and kappa coefficients increase by 0.091, 0.098, and 0.187, respectively; The percentage of area of very low, low, medium, high, and very high susceptibility areas in Mangshan Mountain is 40.2%, 23.6%, 14.1%, 12.9%, and 9.2%. The findings of this study provide scientific basis for the prevention and control of landslide disaster in Mangshan Mountain and expand the application of CNN model in the evaluation of landslide susceptibility.

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Section: Introductionmentioning

confidence: 99%