Empty region identification method and experimental verification for the two-dimensional complex structure

Dong, Longjun; Tong, Xiaojie; Hu, Qingchun; Tao, Qing Lin

doi:10.1016/j.ijrmms.2021.104885

Cited by 44 publications

(17 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Take Huangling Mining as an example. On 15 September 2020, the "Intelligent Mining Technology Based on Dynamic Geological Model Big Data Fusion (4) Dynamic update technology The data update of the model is that the coal seam exposed during the mining process is re-realistic and combined with the newly generated geological data during the production process to update the model [36][37][38]. With the continuous increase of data, the accuracy of the model is also continuously improved.…”

Section: Transparent Geological Prediction Modelmentioning

confidence: 99%

A Review of Intelligent Unmanned Mining Current Situation and Development Trend

et al. 2022

View full text Add to dashboard Cite

Intelligent unmanned mining is a key process in coal mine production, which has direct impact on the production safety, coal output, economic benefits and social benefits of coal mine enterprises. With the rapid development and popularization of 5G+ intelligent mines and coal mine intelligent equipment in China, the intelligentization of intelligent unmanned mining has become an important research topic. Especially with the promulgation of some Chinese policies and regulations, intelligent unmanned mining technology has become one of the key technologies of coal mine production. To understand the connotation, status quo and development trends of intelligent unmanned mining, this paper takes the relationship between key technologies and engineering application of intelligent unmanned mining in China as the perspective. It is proposed that the intelligent unmanned mining technology is in the whole process of working face mining. A research structure of unmanned follow-up operation and safe patrol is changing to the mode of intelligent adaptive mining, followed by the basic concepts and characteristics of intelligent unmanned mining. Relevant researches that maybe beneficial to the proposed research content are reviewed in four layers, which include basic theory, key technology, mining mode, and overall design system theory and technology. Finally, the current intelligent unmanned mining mode and future trends in this field in China are summarized.

show abstract

Section: Transparent Geological Prediction Modelmentioning

confidence: 99%

A Review of Intelligent Unmanned Mining Current Situation and Development Trend

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Jiang [ 23 ] proposed an AE tomography method, based on simultaneous algebraic reconstruction, to visualize the internal damages in a steel plate. Dong [ 24 ] proposed a new method, combining the improved A* search algorithm and the match method for empty region identification in complex two-dimensional structures. Nicolas Brantut [ 25 ] proposed active–passive acoustic emission tomography to monitor laboratory rock deformation experiments.…”

Section: Introductionmentioning

confidence: 99%

Non-Destructive Testing for Cavity Damages in Automated Machines Based on Acoustic Emission Tomography

Dong

Pei

2022

Sensors

Self Cite

View full text Add to dashboard Cite

Damage detection is important for the maintenance of automated machines. General non-destructive testing techniques require static equipment and complex analysis processes, which restricts the maintenance of automated machines. Therefore, this paper proposes an acoustic emission (AE) tomography method for detecting cavity damage in automated machines, combining the fast sweeping method (FSM) and the limited-memory Broyden–Fletcher–Goldfarb–Shanno (L-BFGS) method. This approach overcomes the limitations of real-time AE detection for cavity damage in continuous and homogeneous materials. The proposed method has been applied in numerical and laboratory experiments to validate its feasibility. The results show that the inversed low-velocity regions correspond to the actual cavity regions, and the sources of cavity damage can be effectively detected. This paper provides a new perspective for AE testing technologies, and also lays the foundation for other non-destructive testing techniques, in terms of cavity damage detection.

show abstract

“…Therefore, AE data is widely used for the real-time monitoring of the crack evolution process of rocks [ 17 , 18 , 19 , 20 ]. Related studies have shown that the AE signal carries a considerable amount of original fracture information, making it suitable to be a useful information carrier that reflects pore changes inside the rock [ 21 , 22 , 23 ]. The occurrence of rock mass instability event is often accompanied by great changes in AE parameters such as the increase of AE counts and energy [ 24 ].…”

Section: Introductionmentioning

confidence: 99%

Application of Two Novel Acoustic Emission Parameters on Identifying the Instability of Granite

Jiang

et al. 2022

Entropy

View full text Add to dashboard Cite

The issue of monitoring and early warning of rock instability has received increasing critical attention in the study of rock engineering. To investigate the damage evolution process of granite under triaxial compression tests, acoustic emission (AE) tests were performed simultaneously. This study firstly introduced two novel parameters, i.e., the coefficient of variation (CoV) of the information entropy and correlation dimension of the amplitude data from the AE tests, to identify the precursor of the failure of granite. Then the relationship between the changes in these parameters and the stress-time curve was compared and analyzed. The results of this study show that: (1) There is a strong correlation between the CoV of the information entropy and the failure process of granite. The granite failed when the CoV curve raised to a plateau, which could be used as an indicator of rock instability. (2) The fluctuation of the correlation dimension indicates the different stages during the loading process, i.e., the initial compaction stage, the linear elastic stage, the yield stage, and the failure stage. Each stage contains a descending and a rising process in the correlation dimension curve, and the exhibited starting point or the bottom point at the correlation dimension curve could be selected as the indicator point for the rock instability. (3) The combined analysis of the Information entropy and Correlation dimension can improve the accuracy of rock instability prediction. This study provides new insights into the prediction of rock instability, which has theoretical implications for the stability of subsurface engineering rock masses.

show abstract

Empty region identification method and experimental verification for the two-dimensional complex structure

Cited by 44 publications

References 23 publications

A Review of Intelligent Unmanned Mining Current Situation and Development Trend

A Review of Intelligent Unmanned Mining Current Situation and Development Trend

Non-Destructive Testing for Cavity Damages in Automated Machines Based on Acoustic Emission Tomography

Application of Two Novel Acoustic Emission Parameters on Identifying the Instability of Granite

Contact Info

Product

Resources

About