Sandification degree classification of sandy dolomite base on convolutional neural networks.

Wang, Meiqian; Zhang, Changxing; Liu, Haiming; Xie, Ting; Xu, Wei

Wang, Meiqian; Zhang, Changxing; Liu, Haiming; Xie, Ting; Xu, Wei.

Afiliación

Wang M; Faculty of Civil Engineering and Mechanics, Kunming University of Science and Technology, Kunming, 650500, Yunnan, China.
Zhang C; Intelligent Infrastructure Operation and Maintenance Technology Innovation Team of Yunnan Provincial Department of Education, Kunming University of Science and Technology, Kunming, 650500, Yunnan, China.
Liu H; Faculty of Civil Engineering and Mechanics, Kunming University of Science and Technology, Kunming, 650500, Yunnan, China.
Xie T; Intelligent Infrastructure Operation and Maintenance Technology Innovation Team of Yunnan Provincial Department of Education, Kunming University of Science and Technology, Kunming, 650500, Yunnan, China.
Xu W; Faculty of Civil Engineering and Mechanics, Kunming University of Science and Technology, Kunming, 650500, Yunnan, China.

Sci Rep ; 14(1): 18537, 2024 Aug 09.

Article en En | MEDLINE | ID: mdl-39122797

ABSTRACT

ABSTRACT

Sandification can degrade the strength and quality of dolomite, and to a certain extent, compromise the stability of a tunnel's surrounding rock as an unfavorable geological boundary. Sandification degree classification of sandy dolomite is one of the non-trivial challenges faced by geotechnical engineering projects such as tunneling in complex geographical environments. The traditional methods quantitatively measuring the physical parameters or analyzing some visual features are either time-consuming or inaccurate in practical use. To address these issues, we, for the first time, introduce the convolutional neural network (CNN)-based image classification methods into dolomite sandification degree classification task. In this study, we have made a significant contribution by establishing a large-scale dataset comprising 5729 images, classified into four distinct sandification degrees of sandy dolomite. These images were collected from the vicinity of a tunnel located in the Yuxi section of the CYWD Project in China. We conducted comprehensive classification experiments using this dataset. The results of these experiments demonstrate the groundbreaking achievement of CNN-based models, which achieved an impressive accuracy rate of up to 91.4%. This accomplishment underscores the pioneering role of our work in creating this dataset and its potential for applications in complex geographical analyses.

Palabras clave

Convolutional neural network (CNN); Image classification; Sandification degree classification; Sandy dolomite

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Sci Rep Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Reino Unido

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