Rock mass fracture maps prediction based on spatiotemporal image sequence modeling. (11th April 2022)

• Record Type: Journal Article
• Authors: Xue, Yadong; Cao, Yupeng; Zhou, Mingliang; Zhang, Feng; Shen, Kai; Jia, Fei
• Is Part Of: Computer-aided civil and infrastructure engineering. Volume 38:Number 4(2023)
• Page Start: 470
• Page End: 488
• Publication Date: 2022-04-11
• Subjects: Civil engineering -- Data processing -- Periodicals; Computer-aided engineering -- Periodicals; 624.0285
• DOI: 10.1111/mice.12841
• Languages: English
• ISSNs: 1093-9687

Abstract

Abstract: Discontinuities in rock mass are the characteristic challenge of rock tunnel engineering projects, which have a vital impact on rock mass exposures' mechanical and hydrological characteristics. There is a growing demand for predicting the fracture maps during tunnel excavation to ensure a smooth tunnel excavation process. The computer vision measurement of fractures in the tunnel surface is a current hot spot, but the traditional statistical analysis methods for fractures are still mainstream. This paper uses a novel perspective of the time‐space sequence to explain the continuously exposed rock mass during tunneling. A spatial‐aware recurrent neural network is proposed, which takes the historical fracture maps as the input to predict the unexcavated part. The experimental results suggest that the proposed model produces reliable performance and is superior to the other two state‐of‐the‐art deep learning models. Moreover, the test on the site rock tunnel data suggested promising results for fracture map predictions.