A Comprehensive Method for Similarity Evaluation in Discrete Fracture Network Modeling of Jointed Rock Masses

Chen, Jiayao; Seo, Hyungjoon; Gao, Chengzhan; Fang, Qian; Zhang, Dingli; Huang, Hongwei

doi:10.1007/s00603-023-03572-0

Cited by 2 publications

(1 citation statement)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In heritage buildings, maintenance is performed based on thermal infrared images and point cloud data [ 12 , 13 ]. In the field of tunnels and pillars, research has not only been conducted to predict joints and cracks in tunnel faces and pillars [ 14 ], but research has also been conducted to predict the ground in front of the tunnel through artificial intelligence technology [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

Automatic Damage Detection of Pavement through DarkNet Analysis of Digital, Infrared, and Multi-Spectral Dynamic Imaging Images

Seo,

Shi,

2024

Sensors

Self Cite

View full text Add to dashboard Cite

It is important to maintain the safety of road driving by automatically performing a series of processes to automatically measure and repair damage to the road pavement. However, road pavements include not only damages such as longitudinal cracks, transverse cracks, alligator cracks, and potholes, but also various elements such as manholes, road marks, oil marks, shadows, and joints. Therefore, in order to separate categories that exist in various road pavements, in this paper, 13,500 digital, IR, and MSX images were collected and nine categories were automatically classified by DarkNet. The DarkNet classification accuracies of digital images, IR images, and MSX images are 97.4%, 80.1%, and 91.1%, respectively. The MSX image is a enhanced image of the IR image and showed an average of 6% lower accuracy than the digital image but an average of 11% higher accuracy than the IR image. Therefore, MSX images can play a complementary role if DarkNet classification is performed together with digital images. In this paper, a method for detecting the directionality of each crack through a two-dimensional wavelet transform is presented, and this result can contribute to future research on detecting cracks in pavements.

show abstract