Rock mass fracture maps prediction based on spatiotemporal image sequence modeling

Xue, Yadong; Cao, Yupeng; Zhou, Mingliang; Zhang, Feng; Shen, Kai; Jia, Fei

doi:10.1111/mice.12841

Cited by 7 publications

(3 citation statements)

References 52 publications

(68 reference statements)

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“…Additionally, Xue et al. (2023) proposed a spatial‐aware recurrent neural network that takes historical fracture maps as input to predict unexcavated sections.…”

Section: Related Workmentioning

confidence: 99%

“…Liu et al, 2023). Among the various geological features, joints and fissures play a significant role in rock masses and are crucial for geological forecasting and construction safety (Xue et al, 2023). Joints are natural cracks or fractures within rocks that exhibit patterns such as parallel, intersecting, or crosscutting arrangements.…”

Section: Introductionmentioning

confidence: 99%

“…However, the concealed nature of these projects presents numerous challenges, especially in dealing with the complex geological environment (E. Deng et al., 2018; N. Liu et al., 2023). Among the various geological features, joints and fissures play a significant role in rock masses and are crucial for geological forecasting and construction safety (Xue et al., 2023). Joints are natural cracks or fractures within rocks that exhibit patterns such as parallel, intersecting, or cross‐cutting arrangements.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Intelligent recognition of joints and fissures in tunnel faces using an improved mask region‐based convolutional neural network algorithm

Lei,

Zhang,

Deng

et al. 2023

Computer aided Civil Eng

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To address the challenges of low recognition accuracy, low robustness, and low detection efficiency in existing tunnel face joint and fissure recognition methods, we present a deep learning recognition segmentation algorithm called the mask region convolutional neural network (Mask R‐CNN) that is enhanced by an advanced Transformer attention mechanism and deformable convolution network (Mask R‐CNN‐TD). The Transformer attention mechanism improves the backbone network's ability to extract image features by focusing on important areas. A deformable convolutional network enables the network to more precisely conform to the morphological characteristics of joints and fissures on the tunnel face, thereby enhancing the accuracy of detection. Experimental results demonstrate that Mask R‐CNN‐TD achieves superior performance, compared to Mask R‐CNN series algorithms and other instance segmentation methods in terms of detection accuracy, with mean average precision scores of 70.5%, 70.8%, 53.2%, and 63.3% for detection box and mask segmentation at thresholds of 0.5 and 0.75, respectively. Based on the stable and efficient Mask R‐CNN‐TD model, we developed a mobile application called tunnel face detector to automatically detect tunnel faces on the construction site.

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“…Additionally, Xue et al. (2023) proposed a spatial‐aware recurrent neural network that takes historical fracture maps as input to predict unexcavated sections.…”

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%