Detection of rockfill gradation based on video image recognition

Zhou, Yihong; Zhou, Haiyang; Chen, Tingcai; Hu, Chao; Zhang, Liang; Zhao, Chunju; Wang, Fang

doi:10.1016/j.autcon.2023.104965

Cited by 3 publications

(1 citation statement)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…References Intelligent excavation [29] Assessing the roughness coefficients of rock joints [30] Measurement of local deformation in rock and soil [31] Identification of tunnel lining water leakage [32] Quantification of ceramsite granules in lightweight concrete panels [33] Detection of rockfill gradation [34] As the current factors influencing the UCS of low-density reef limestone remain unclear, this research quantified porous structures of low-density reef limestone by means of an image recognition technique based on testing results. On this basis, by referring to the research methods of other rocks [35][36][37], the influences of basic physical properties and porous structures on the UCS of low-density reef limestone were explored.…”

Section: Application Fieldmentioning

confidence: 99%

Experimental Research into the Uniaxial Compressive Strength of Low-Density Reef Limestone Based on Image Recognition

et al. 2023

View full text Add to dashboard Cite

Low-density reef limestone is widely distributed in tropical oceans; exploring its mechanical properties is of significance to practices in marine foundation engineering. In this research, laboratory experiments on low-density reef limestones with two different types of porous structures were conducted using image recognition methods to study the special mechanical properties of low-reef limestone. S¯ was defined as the parameter quantifying the pore geometry, and the calculation method of S¯ was optimized based on image recognition data. Finally, the influencing factors of uniaxial compressive strength (UCS) of low-density reef limestone were analyzed, and a modified formula considering pore structure was proposed. The results indicate the following: Image recognition methods were used to determine feasibility and convenience of capturing 2D pore geometric information of specimens. The optimization method of S¯ is conducive to improving automatic image recognition accuracy. Low-density reef limestones with different porous structures show small difference in porosity and density, while they exhibit large differences in pore sizes and UCS. The UCS of low-density reef limestone is found to be jointly influenced by pore structure and density (it increases with the decrease in parameter S¯ and increase in density). The results may provide help for those investigating the mechanical properties of reef limestone and practices in marine foundation engineering.

show abstract