Noncontact vision-based impact force reconstruction and spatial-temporal deflection tracking of a flexible barrier system under rockfall impact

Tian, Yongding; Luo, Liru; Yu, Zhixiang; Hu, Xu; Ni, Futao

doi:10.1016/j.compgeo.2022.105070

Cited by 5 publications

(1 citation statement)

References 41 publications

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“…Abaker et al [31] have introduced a deep learning-and IoT-based framework for rock-fall early warning, devoted to reducing rock-fall risk with high accuracy. Tian et al [32] present a non-contact vision method using deep learning and computer vision technology to study collapse.…”

Section: Introductionmentioning

confidence: 99%

Collapse Susceptibility Assessment in Taihe Town Based on Convolutional Neural Network and Information Value Method

Li,

Hu,

Lin

et al. 2024

Water

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The cause mechanism of collapse disasters is complex and there are many influencing factors. Convolutional Neural Network (CNN) has a strong feature extraction ability, which can better simulate the formation of collapse disasters and accurately predict them. Taihe town’s collapse threatens roads, buildings, and people. In this paper, road distance, water distance, normalized vegetation index, platform curvature, profile curvature, slope, slope direction, and geological data are used as input variables. This paper generates collapse susceptibility zoning maps based on the information value method (IV) and CNN, respectively. The results show that the accuracy of the susceptibility assessment of the IV method and the CNN method is 85.1% and 87.4%, and the accuracy of the susceptibility assessment based on the CNN method is higher. The research results can provide some reference for the formulation of disaster prevention and control strategies.

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Section: Introductionmentioning

confidence: 99%