Advanced Materials and Technologies in Deep Rock Engineering

Topic Information

Dear Colleagues,

Underground mineral resources are a necessity for human development. However, with resource mining processes continuously developing deep into Earth, the high ground pressure, large deformation of surrounding rocks, and complex tectonic geologies tend to become increasingly serious. Moreover, mining wastes such as gangue and tailings erode arable land and forests while polluting water and air. These issues deteriorate sustainability and ecological security. Researchers have been working hard to develop, explore, and optimize materials, methods, and technologies for solving these issues. However, due to differences in stress conditions, complexities of material components, and opacities of geological structures, conventional mechanical theories, modification methods, and construction plans find difficulty in achieving achievements and breakthroughs. Developing advanced materials and technologies for safe, green, and efficient mining in deep underground settings is now critical. In addition to new discoveries, insights, and detections in the mechanical behavior of deep rocks, we encourage publications that investigate advanced materials and technologies for solving deep-rock engineering problems. Materials that can both benefit mining safety and sustainability are particularly welcome, such as cemented waste-rock backfill materials for solving the control problems of deep strata, grouting materials for preventing groundwater disasters, functional materials for energy storage, self-healing materials and carbon capture, etc. Moreover, observations of the interactions of these materials with rocks are also welcomed. Overall, this Special Issue will focus on advanced materials and technologies in deep-rock engineering while promoting the sustainable development of deep mineral resources from the multi-disciplinary intersection of geotechnic, materials, and environmental areas. Underground cases, laboratory experiments, and numerical and physical simulation studies related to this topic are encouraged. We welcome both original research and review papers. Potential topics include but are not limited to the following:

• Mechanical behavior of deep rock;
• Cemented waste rock backfill;
• Waste-rock leaching and solidification evaluation;
• Cement-based grouting material;
• Material functionalization applications in deep underground settings;
• Long-term stability of surrounding deep rock;
• Multi-source information monitoring of deep rock;
• Disaster prevention of deep mining;
• Filling mines for controlling strata movements.

Dr. Jiangyu Wu
Prof. Dr. Hong Wong
Prof. Dr. Lang Liu
Dr. Wen Zhong
Prof. Dr. Dan Ma
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Energies energies	3.0	6.2	2008	17.5 Days	CHF 2600
Materials materials	3.1	5.8	2008	15.5 Days	CHF 2600
Minerals minerals	2.2	4.1	2011	18 Days	CHF 2400
Processes processes	2.8	5.1	2013	14.4 Days	CHF 2400
Sustainability sustainability	3.3	6.8	2009	20 Days	CHF 2400

MDPI Topics is cooperating with Preprints.org and has built a direct connection between MDPI journals and Preprints.org. Authors are encouraged to enjoy the benefits by posting a preprint at Preprints.org prior to publication:

1. Immediately share your ideas ahead of publication and establish your research priority;
2. Protect your idea from being stolen with this time-stamped preprint article;
3. Enhance the exposure and impact of your research;
4. Receive feedback from your peers in advance;
5. Have it indexed in Web of Science (Preprint Citation Index), Google Scholar, Crossref, SHARE, PrePubMed, Scilit and Europe PMC.

Published Papers (15 papers)

Order results

Content type Publication Date

Result details

Normal Extended Compact

Journals

All Energies Materials Minerals Processes Sustainability

Show export options expand_more Show export options expand_less

Select all

Export citation of selected articles as:

Plain Text BibTeX BibTeX (without abstracts) Endnote Endnote (without abstracts) Tab-delimited RIS

Error

Oops... you haven't selected anything for export.

Ok

clear

21 pages, 11536 KiB  

Open AccessArticle

A New Hydro-Mechanical Coupling Numerical Model for Predicting Water Inflow in Karst Tunnels Considering Deformable Fracture

by Guodong Li, Changlong Li, Jianxing Liao and Hong Wang

Sustainability 2023, 15(20), 14703; https://doi.org/10.3390/su152014703 - 10 Oct 2023

Cited by 1 | Viewed by 915

Abstract

The accurate prediction of groundwater inflow in tunnels in karst regions has been a difficult problem to overcome for a long time. This study proposes an equivalent fracture model that takes into account unsaturated seepage and fracture deformation to predict tunnel water inflow, [...] Read more.

The accurate prediction of groundwater inflow in tunnels in karst regions has been a difficult problem to overcome for a long time. This study proposes an equivalent fracture model that takes into account unsaturated seepage and fracture deformation to predict tunnel water inflow, which is constructed based on the TOUGH-FLAC3D framework. The proposed model with complete failure mechanisms of fracture, including shear failure and tensile failure, was applied to predict the water inflow of the Jianxing Tunnel in Guizhou Province to verify its effectiveness. The results indicate that the proposed numerical model was found to be comparable to on-site observations in predicting inflow rate. The inflow rate in a fractured network reaches a steady state faster than that in a non-fractured network. There is a significant difference of 100 times between the highest transient rate and the stable rate between the fracture network and the non-fractured model. The excavation-induced stress redistribution resulted in slip fracture occurring within a distance of approximately 8.2 m from the tunnel wall, which can increase the fracture width and in turn increases the amount of water flowing into the tunnel by about 50%. In addition, this paper also analyzes the impact of the factors of fracture density, incline angle, stress anisotropy, and initial fracture width on the inflow rate during tunnel construction. The study emphasizes the significance of considering deformable fractures and provides valuable insights for improving numerical tools for inflow prediction during tunnel construction. Full article

(This article belongs to the Topic Advanced Materials and Technologies in Deep Rock Engineering)

►▼ Show Figures

Figure 1

Figure 1
<p>Presentation of fractured rock.</p> Full article ">Figure 2
<p>Demonstration of shear damage in tunnel construction.</p> Full article ">Figure 3
<p>Permeability of fractured rock consists of fractured and intact rock.</p> Full article ">Figure 4
<p>Schematic diagram of the coupling pattern.</p> Full article ">Figure 5
<p>x-displacement comparing embedded fractures with actual fractures.</p> Full article ">Figure 6
<p>Resistivity tomography of tunnel cross-sections.</p> Full article ">Figure 7
<p>Hole SLH-5 for sampling, the purple circles represent fresh fissures, the other are natural fissures.</p> Full article ">Figure 8
<p>Geological information.</p> Full article ">Figure 9
<p>(<b>a</b>) Demonstration of model dimension and (<b>b</b>) fracture sets.</p> Full article ">Figure 10
<p>Mesh influence on numerical results.</p> Full article ">Figure 11
<p>Pressure contours at different times.</p> Full article ">Figure 12
<p>Pressure contours after 50 h, (<b>a</b>) with fractures and (<b>b</b>) without fractures.</p> Full article ">Figure 13
<p>Flow vectors, (<b>a</b>) with fractures and (<b>b</b>) without fractures.</p> Full article ">Figure 14
<p>Fracture width variation in fracture networks around tunnel section, (<b>a</b>) total fracture width, (<b>b</b>) dilatation fracture width.</p> Full article ">Figure 15
<p>Liquid saturation after 50 h, (<b>a</b>) with fractures and (<b>b</b>) without fractures.</p> Full article ">Figure 16
<p>(<b>a</b>) Comparison of inflow rates for the fractured and unfractured models; (<b>b</b>) comparison of inflow rates considering variable fracture (HM) and constant fracture (H).</p> Full article ">Figure 17
<p>Maximum instantaneous and fixed rates for different numbers of fractures.</p> Full article ">Figure 18
<p>Comparison of pressure contours after 50 h for different fracture number.</p> Full article ">Figure 19
<p>Maximum transient and steady rates for different inclination angles.</p> Full article ">Figure 20
<p>Comparison of pressure contours after 50 h for different incline angle.</p> Full article ">Figure 21
<p>Maximum instantaneous and steady rates at different horizontal stress ratios.</p> Full article ">Figure 22
<p>Comparison of pressure contours after 50 h for different horizontal stress ratios.</p> Full article ">Figure 23
<p>Maximum instantaneous and fixed rates for different initial fracture aperture sizes.</p> Full article ">Figure 24
<p>Comparison of pressure contours after 50 h for different initial fracture aperture models.</p> Full article ">

22 pages, 8492 KiB  

Open AccessArticle

Effects of Confining Stress on Blast-Induced Damage Distribution of Rock with Discontinuity

by Rongjian Pan, Peiyu Wang, Zilong Zhou, Riyan Lan, Lu Chen, Hongquan Yang, Cuigang Chen, Jinkun Zhang and Yang Liu

Sustainability 2023, 15(17), 13278; https://doi.org/10.3390/su151713278 - 4 Sep 2023

Viewed by 1192

Abstract

Discontinuous rock mass, such as joints and fractures, have a great influence on the blasting quality and sometimes induce additional damage at the discontinuity. In deep rock engineering, high in situ stress makes the damage mechanism of rock with discontinuity under blasting loading [...] Read more.

Discontinuous rock mass, such as joints and fractures, have a great influence on the blasting quality and sometimes induce additional damage at the discontinuity. In deep rock engineering, high in situ stress makes the damage mechanism of rock with discontinuity under blasting loading more complicated. Quantitative analysis of blast-induced damage in discontinuous rock under high in situ stress is of great importance in guiding the fine blast design. In this paper, a series of numerical models have been established to quantitatively investigate the effect of confining stress and inclination angle on blast-induced damage of rock with discontinuity. The numerical results show that the discontinuity obviously changes the distribution mode of blast-induced damage, and there is more damage near the discontinuity. The blast-induced damage crack length of discontinuous rock decreases as hydrostatic stress rises. Under non-hydrostatic stress, the damage crack propagation appears to have a higher tendency in the higher confining stress direction. In addition, the inclination angle of discontinuity will affect the damage distribution of rock with discontinuity. The fragmentation degree is greatest when the discontinuity is perpendicular to the direction of higher confining stress. Due to the presence of discontinuity, the guiding effect of higher confining stress on damaged cracks is weakened. The results provide a reference for the tunnel fine-blasting design of rock with discontinuity. Full article

(This article belongs to the Topic Advanced Materials and Technologies in Deep Rock Engineering)

►▼ Show Figures

Figure 1

Figure 1
<p>Diagram of charge and discontinuity in model.</p> Full article ">Figure 2
<p>Parameter verification: (<b>a</b>) size diagram in test; (<b>b</b>) crack pattern after blasting; (<b>c</b>) damage pattern by simulation.</p> Full article ">Figure 3
<p>Parameter verification: (<b>a</b>) crack pattern after blasting; (<b>b</b>) damage pattern by simulation.</p> Full article ">Figure 4
<p>Blasting pressure propagation process in rock with discontinuity and continuity under 30 MPa hydrostatic stress in the model.</p> Full article ">Figure 5
<p>Arrangement of observation points around the borehole.</p> Full article ">Figure 6
<p>Pressure time curve of (<b>a</b>) rock with discontinuity, (<b>b</b>) rock with continuity under 30 MPa hydrostatic stress, and (<b>c</b>) comparative result of maximum pressure between rock with discontinuity and continuity.</p> Full article ">Figure 7
<p>Velocity time curve of (<b>a</b>) rock with discontinuity, (<b>b</b>) rock with continuity under 30 MPa hydrostatic stress, and (<b>c</b>) comparative result of PPV between rock with discontinuity and continuity.</p> Full article ">Figure 8
<p>Damage evolution of rock with discontinuity and continuity under 30 MPa hydrostatic stress.</p> Full article ">Figure 9
<p>Damage distribution of rock with discontinuity under hydrostatic stress of (<b>a</b>) 0 MPa, (<b>b</b>) 10 MPa, (<b>c</b>) 20 MPa, (<b>d</b>) 30 MPa, (<b>e</b>) 40 MPa and (<b>f</b>) 50 MPa.</p> Full article ">Figure 10
<p>Maximum damage length of rock with discontinuity in different areas under different hydrostatic stresses.</p> Full article ">Figure 11
<p>Fragmentation degree in different areas under different hydrostatic stresses.</p> Full article ">Figure 12
<p>Damage distribution of rock with discontinuity under vertical confining stress of (<b>a</b>) 10 MPa, (<b>b</b>) 20 MPa, (<b>c</b>) 30 MPa, (<b>d</b>) 40 MPa, and (<b>e</b>) 50 MPa (maintain the horizontal confining stress at 30 MPa).</p> Full article ">Figure 13
<p>Maximum damage length of rock with discontinuity in different areas under different vertical confining stresses (maintain the horizontal confining stress at 30 MPa).</p> Full article ">Figure 14
<p>Fragmentation degree in different areas under different vertical confining stresses (maintain the horizontal confining stress at 30 MPa).</p> Full article ">Figure 15
<p>Damage distribution of discontinuous rock with inclination angles of (<b>a</b>) 0°, (<b>b</b>) 30°, (<b>c</b>) 45°, (<b>d</b>) 60°, and (<b>e</b>) 90° under 10 MPa vertical stress and 30 MPa horizontal stress.</p> Full article ">Figure 16
<p>Maximum damage length of discontinuous rock with different inclination angles under 10 MPa vertical stress and 30 MPa horizontal stress.</p> Full article ">Figure 17
<p>Fragmentation degree in different areas of discontinuous rock with different inclination angles under 10 MPa vertical stress and 30 MPa horizontal stress.</p> Full article ">Figure 18
<p>Damage distribution of discontinuous rock with inclination angles of (<b>a</b>) 0°, (<b>b</b>) 30°, (<b>c</b>) 45°, (<b>d</b>) 60°, and (<b>e</b>) 90° under 50 MPa vertical stress and 30 MPa horizontal stress.</p> Full article ">Figure 19
<p>Maximum damage length of discontinuous rock with different inclination angles under 50 MPa vertical stress and 30 MPa horizontal stress.</p> Full article ">Figure 20
<p>Fragmentation degree in different areas of discontinuous rock with different inclination angles under 50 MPa vertical stress and 30 MPa horizontal stress.</p> Full article ">Figure 21
<p>(<b>a</b>) Bailongtan tunnel and (<b>b</b>) layered rock formation at the tunnel working face.</p> Full article ">Figure 22
<p>(<b>a</b>) Borehole layout and (<b>b</b>) test result of the original scheme.</p> Full article ">Figure 23
<p>(<b>a</b>) Borehole layout and (<b>b</b>) test result of the optimization scheme.</p> Full article ">

17 pages, 7794 KiB  

Open AccessArticle

Study on the Corrosion Behavior of Cemented Organic Soil in Dianchi Lake, China

by Wenlian Liu, Jing Cao, Yunfei Song, Sugang Sui, Hanhua Xu, Yongfa Guo, Wenyun Ding and Siyang Huang

Materials 2023, 16(17), 5951; https://doi.org/10.3390/ma16175951 - 30 Aug 2023

Viewed by 910

Abstract

To study the corrosion behavior of cement soil in peat soil, the experiment involves the preparation of peat soil by incorporating humic acid into cohesive soil with a lower organic matter content. Cement soil samples are then prepared by adding cement to the [...] Read more.

To study the corrosion behavior of cement soil in peat soil, the experiment involves the preparation of peat soil by incorporating humic acid into cohesive soil with a lower organic matter content. Cement soil samples are then prepared by adding cement to the mixture. These samples are subjected to immersion in fulvic acid solution and deionized water to simulate different working environments of cement soil. The experiment considers immersion time as the variable factor. It conducts observations of apparent phenomena, ion leaching tests, and unconfined compression strength tests on the cement soil. The experiment results are as follows: (1) With increasing immersion time, the surface of the cement soil in the peat soil environment experiences the disappearance of Ca(OH)₂ and calcium aluminate hydrate. Additionally, large amounts of bird dropping crystals precipitate on the surface and within the specimen. The cement soil undergoes localized disintegration due to extensive erosion caused by swelling forces. (2) In the peat soil environment, fulvic acid reacts with the hydration products of cement, resulting in partial leaching of ions such as Ca²⁺, Mg²⁺, Al³⁺, and Fe³⁺ into the immersion solution. The lower the pH of the fulvic acid immersion (indicating higher concentration), the more significant the ion leaching. Increasing the ratio of humic acid to cement can slow down the leaching of ions. The cement soil undergoes dissolutive erosion in the peat soil environment. (3) The peat soil environment exerts both strengthening and corrosive effects on the cement soil. Cement soil without humic acid exhibits noticeable corrosion in the peat soil environment, gradually decreasing strength as immersion time increases. The strength decreases by 83% from 28 to 365 days. In contrast, cement soil with humic acid experiences an initial period of strengthening, leading to a significant increase in strength in the short term (34% increase from 28 to 90 days). However, the corrosive effects gradually dominate, resulting in a decrease in strength over time. The strength decreases by 80% from 90 to 365 days. This study also explores the strengthening effects of peat soil on cement soil. It identifies phenomena such as extensive erosion and new substance precipitation in cement soil. Full article

(This article belongs to the Topic Advanced Materials and Technologies in Deep Rock Engineering)

►▼ Show Figures

Figure 1

Figure 1
<p>Test materials [<a href="#B19-materials-16-05951" class="html-bibr">19</a>].</p> Full article ">Figure 2
<p>Sample production and testing process.</p> Full article ">Figure 3
<p>Changes in apparent phenomena of various samples in fulvic acid solution (pH = 5.0) and deionized water with soaking time.</p> Full article ">Figure 3 Cont.
<p>Changes in apparent phenomena of various samples in fulvic acid solution (pH = 5.0) and deionized water with soaking time.</p> Full article ">Figure 4
<p>XRD diffraction pattern of white matter on the surface of the sample immersed in deionized water.</p> Full article ">Figure 5
<p>Photos of cement soil sample attached with yellow crystals.</p> Full article ">Figure 6
<p>SEM image of yellow crystals on the surface of the sample soaked in fulvic acid solution.</p> Full article ">Figure 7
<p>EDS diagram of yellow crystals on the surface of the sample soaked in fulvic acid solution.</p> Full article ">Figure 8
<p>Distribution of main elements in the yellow crystal measurement area of the sample surface soaked in fulvic acid solution.</p> Full article ">Figure 9
<p>XRD diffraction pattern of yellow crystals on the surface of the sample soaked in fulvic acid solution.</p> Full article ">Figure 10
<p>Relationship curve between strength and soaking time of cement–soil samples with a cement content of 20%.</p> Full article ">Figure 10 Cont.
<p>Relationship curve between strength and soaking time of cement–soil samples with a cement content of 20%.</p> Full article ">

9 pages, 9933 KiB  

Open AccessArticle

Application of AFM on Identifying Mechanical Properties of Individual Minerals and Surface Properties of Crack with High Resolution in Shale

by Shizhong Cheng, Mao Sheng and Peng Xu

Processes 2023, 11(8), 2498; https://doi.org/10.3390/pr11082498 - 19 Aug 2023

Viewed by 1457

Abstract

Improving the resolution and accuracy of the mechanical properties of organic-rich shale is very important. The results can reveal the mechanical properties of shale from micro scale and serve as a guide for the design of hydraulic fracture optimization parameters. This study introduced [...] Read more.

Improving the resolution and accuracy of the mechanical properties of organic-rich shale is very important. The results can reveal the mechanical properties of shale from micro scale and serve as a guide for the design of hydraulic fracture optimization parameters. This study introduced an advanced technique to obtain the mechanical properties of shale with high resolution (58.6 nm/pixel) by combining SEM, EDS, and Atomic Force Microscopy (AFM). To locate the target area in SEM and AFM accurately, a positioning technique that uses special distributions of pyrite was established. AFM PeakForce QNM mode was selected due to its advantages at capturing topography and mechanical properties in material. Results illustrated the ability of AFM to obtain the mechanical properties (modulus) of individual mineral components in shale, the detailed topography of crack, and mechanical properties of minerals in a specific area. In particular, the mechanical properties of minerals around crack explained the layered distribution of minerals around the fractures, and the cracks developed in the clay mineral layer was detected. This article demonstrates the great potential application of AFM in shale. Full article

(This article belongs to the Topic Advanced Materials and Technologies in Deep Rock Engineering)

►▼ Show Figures

Figure 1

Figure 1
<p>Positioning method combining SEM and AFM: (<b>a</b>) SEM image with pyrite (enclosed by red line) and target mineral (enclosed by yellow line) distributing clearly; (<b>b</b>) Corresponding area under the optical microscope in the AFM. A positioning technique that integrates SEM and AFM was employed in this study. (<b>a</b>) The SEM image clearly shows the distribution of pyrite (marked by a red line), the target mineral (marked by a yellow line) and surrounding fracture. (<b>b</b>) The corresponding area under the optical microscope through AFM.</p> Full article ">Figure 2
<p>Schematic representation of an Atomic Force Microscope.</p> Full article ">Figure 3
<p>Identification of mechanical properties of apatite in shale by AFM. (<b>a</b>) SEM topography image of target mineral; (<b>b</b>) EDX element distribution for target area; (<b>c₁</b>–<b>c₃</b>) AFM results including height distribution, DMT modulus, log10(DMT modulus) and deformation; (<b>d₁</b>–<b>d₃</b>) Target mineral area in AFM results; (<b>e</b>) Mean modulus and standard deviation for target mineral areas (<b>d₁</b>–<b>d₃</b>) and contact tests. (<b>f</b>) Six force separation curves tested randomly in the target area through contact test.</p> Full article ">Figure 4
<p>Identification of mechanical characteristics of organic matter in shale by AFM. (<b>a</b>) SEM topography image of target mineral; (<b>b</b>) EDX element distribution for target area; (<b>c</b>) AFM results including height distribution, DMT modulus, log10(DMT modulus) and deformation; (<b>d</b>) Target mineral area in AFM results; (<b>e</b>) Mean modulus and standard deviation for target mineral areas d and contact tests. (<b>f</b>) Six force separation curves tested randomly in the target area through contact mode.</p> Full article ">Figure 5
<p>The topography and modulus around fracture from AFM test. (<b>a</b>) Topography graphs of fracture; (<b>b</b>) Modulus distribution graphs. (<b>c</b>) Crack depth information on sections along the crack extension direction.</p> Full article ">Figure 6
<p>SEM and EDS result of shale sample: (<b>a</b>) SEM morphology of shale sample where stiff and soft minerals distributed by layers and crack extended in soft mineral layer (<b>b</b>) Corresponding EDS images.</p> Full article ">Figure 7
<p>AFM test results. The image on the left is the test area under the optical microscope. The histogram shows the ratio of the average modulus in each test area to the average modulus value of area (<b>a</b>). (<b>a</b>–<b>f</b>) are the modulus results corresponding to each area.</p> Full article ">

19 pages, 13087 KiB  

Open AccessArticle

Energy Dissipation of Hydraulic Support Columns under Rockfall Impact Load in Steeply Dipping Coal Seams

by Ming Liu, Bohao Luan and Yang Xiao

Processes 2023, 11(8), 2497; https://doi.org/10.3390/pr11082497 - 19 Aug 2023

Cited by 2 | Viewed by 828

Abstract

Rockfall disasters have long restricted the further improvement of the safety level of steeply dipping coal seams (SDCSs). When a rockfall disaster occurs, it causes damage to the hydraulic support and other equipment at the working face. An effective way to carry out [...] Read more.

Rockfall disasters have long restricted the further improvement of the safety level of steeply dipping coal seams (SDCSs). When a rockfall disaster occurs, it causes damage to the hydraulic support and other equipment at the working face. An effective way to carry out protection design is using the law of rockfall migration and energy evolution. Therefore, this study used the polyhedral rockfall migration and its impact process on the hydraulic support equipment of the working face as the research object and analyzed the influence of relevant parameters on the maximum contact deformation, maximum impact force, and energy absorption of the column during the collision and contact between the rockfall and the hydraulic support column. Firstly, with hexahedral rockfall as an example, the migration process of rockfall was simulated using PFC3D software. Secondly, according to the Hertz contact theory, the contact model of the shock process between the rockfall and the hydraulic support column was constructed, and the maximum deformation and maximum impact force of the collision contact between the rockfall and the column were obtained. Finally, the Hamilton principle and the Galerkin discrete method were used to construct the dynamic model of the collision between the rockfall and the column, and the energy evolution law of the shock process between the rockfall and the column was studied. The conclusions of this paper can provide a certain theoretical basis for the prediction of rockfall disasters and the design of rockfall protection devices. Full article

(This article belongs to the Topic Advanced Materials and Technologies in Deep Rock Engineering)

►▼ Show Figures

Figure 1

Figure 1
<p>Schematic diagram of trajectory of rockfall. (<b>a</b>) Trajectory of rockfall along the inclination of working face; (<b>b</b>) Boundaries of the rockfall in underground coal mines.</p> Full article ">Figure 2
<p>Rockfall impact damage to hydraulic support column.</p> Full article ">Figure 3
<p>Relationship between coal wall spalling and the working resistance of hydraulic support.</p> Full article ">Figure 4
<p>Contact theoretical model (<b>a</b>) Two particles contact; (<b>b</b>) Particles contact with the wall.</p> Full article ">Figure 5
<p>The schematic diagram of PFC iterative process.</p> Full article ">Figure 6
<p>Hertz contact between cylinder and plane: (<b>a</b>) simulated diagram; (<b>b</b>) theoretical diagram.</p> Full article ">Figure 7
<p>Mechanical model of rockfall impact on the midpoint of a hydraulic support column.</p> Full article ">Figure 8
<p>The contour lines of the working face floor.</p> Full article ">Figure 9
<p>3D model of the working face floor.</p> Full article ">Figure 10
<p>Rockfall migration trajectory image contrast diagram.</p> Full article ">Figure 11
<p>The wave diagram of the midpoint.</p> Full article ">Figure 12
<p>Influence of parameters on maximum deformation for elastic modulus vs. density: (<b>a</b>) two-dimensional graph; (<b>b</b>) three-dimensional graph.</p> Full article ">Figure 13
<p>Influence of parameters on maximum deformation for elastic modulus vs. side length: (<b>a</b>) two-dimensional graph; (<b>b</b>) three-dimensional graph.</p> Full article ">Figure 14
<p>Influence of parameters on maximum deformation for elastic modulus vs. velocity: (<b>a</b>) two-dimensional graph; (<b>b</b>) three-dimensional graph.</p> Full article ">Figure 15
<p>Influence of parameters on maximum impact force for elastic modulus vs. density: (<b>a</b>) two-dimensional graph; (<b>b</b>) three-dimensional graph.</p> Full article ">Figure 16
<p>Influence of parameters on maximum impact force for elastic modulus vs. side length: (<b>a</b>) two-dimensional graph; (<b>b</b>) three-dimensional graph.</p> Full article ">Figure 17
<p>Influence of parameters on maximum impact force for elastic modulus vs. velocity: (<b>a</b>) two-dimensional graph; (<b>b</b>) three-dimensional graph.</p> Full article ">Figure 18
<p>Influence of parameters on energy absorbed by column for elastic modulus vs. density: (<b>a</b>) two-dimensional graph; (<b>b</b>) three-dimensional graph.</p> Full article ">Figure 19
<p>Influence of parameters on energy absorbed by column for elastic modulus vs. side length: (<b>a</b>) two-dimensional graph; (<b>b</b>) three-dimensional graph.</p> Full article ">Figure 20
<p>Influence of parameters on energy absorbed by column for elastic modulus vs. velocity: (<b>a</b>) two-dimensional graph; (<b>b</b>) three-dimensional graph.</p> Full article ">Figure 21
<p>Effect of the parameters on the proportion of energy absorbed by the column from the hexahedron rockfall.</p> Full article ">

14 pages, 3302 KiB  

Open AccessArticle

Triaxial Compression Strength Prediction of Fissured Rocks in Deep-Buried Coal Mines Based on an Improved Back Propagation Neural Network Model

by Yiyang Wang, Bin Tang, Wenbin Tao, Anying Yuan, Tianguo Li, Zhenyu Liu, Fenglin Zhang and An Mao

Processes 2023, 11(8), 2414; https://doi.org/10.3390/pr11082414 - 10 Aug 2023

Cited by 1 | Viewed by 1065

Abstract

In deep coal mine strata, characterized by high ground stress and extensive fracturing, predicting the strength of fractured rock masses is crucial for stability analysis of the surrounding rock in coal mine strata. In this study, rock samples were obtained from construction sites [...] Read more.

In deep coal mine strata, characterized by high ground stress and extensive fracturing, predicting the strength of fractured rock masses is crucial for stability analysis of the surrounding rock in coal mine strata. In this study, rock samples were obtained from construction sites in deep coal mine strata and intact, as well as fissured, rock specimens were prepared and subjected to triaxial compression tests. A numerical model based on the discrete element method was then established and the micro-parameters were calibrated. A total of 288 triaxial compression tests on the rock specimens under different conditions of confining pressure, loading rate, fissure dip angle, and fissure length, were conducted to obtain the triaxial compressive strength of the fractured rock specimens under different conditions. To address the limitations of traditional back propagation (BP) neural networks in solving stochastic problems, a modified BP neural network model was developed using a random factor and an interlayer mean square error corrected network model evaluation function. The traditional and modified BP neural network models were then employed to predict the triaxial compressive strength of the fractured rock specimens. Through comparative analysis, it was found that the modified BP neural network prediction model exhibited smaller errors and significantly reduced overfitting, making it an effective tool for predicting the strength of fractured rocks in deep coal mine strata. Full article

(This article belongs to the Topic Advanced Materials and Technologies in Deep Rock Engineering)

►▼ Show Figures

Figure 1

Figure 1
<p>Fissured rock specimens.</p> Full article ">Figure 2
<p>ROCK 600-50HT rock mechanics testing system.</p> Full article ">Figure 3
<p>Stress–strain curves of the fissured specimens.</p> Full article ">Figure 4
<p>The PFC numerical model of the fissured rock specimens.</p> Full article ">Figure 5
<p>Partial numerical test results for the fissured rock specimens (20 MPa confining pressure and 20 mm fissure length): (<b>a</b>) stress–strain curves for the numerical tests; (<b>b</b>) failure characteristics of the fissured specimens.</p> Full article ">Figure 6
<p>The BP neural network training graph.</p> Full article ">Figure 7
<p>Improved BP network algorithm flow chart.</p> Full article ">Figure 8
<p>Comparison between the predicted values and measured values under the conventional and improved BP neural network model: (<b>a</b>) conventional BP neural network model; (<b>b</b>) improved BP neural network model.</p> Full article ">

21 pages, 7696 KiB  

Open AccessArticle

The Retention and Control Technology for Rock Beams in the Roof of the Roadway: A Case Study

by Xin Wei, Niaz Muhammad Shahani, Xigui Zheng, Jiyu Wang, Yonghui Wang, Chun Chen and Zehui Ren

Processes 2023, 11(6), 1593; https://doi.org/10.3390/pr11061593 - 23 May 2023

Cited by 4 | Viewed by 1577

Abstract

Background: Coal mining requires safe and effective roadway support to ensure production and worker safety. Anchor support is a common method used for controlling the roof of coal seams. This study aims to analyze the effectiveness of different anchor support schemes and provide [...] Read more.

Background: Coal mining requires safe and effective roadway support to ensure production and worker safety. Anchor support is a common method used for controlling the roof of coal seams. This study aims to analyze the effectiveness of different anchor support schemes and provide a theoretical basis for designing safe and effective roadway support. Methods: The authors used a computer simulation tool called FLAC3D to simulate and analyze the spacing between anchor bolts, anchor bolt length, anchor cable length, and effective roadway roof control, and support the schemes at the western wing roadway in the no. 15 coal seam of no. 1 mine of Ping’an Coal Mine. Results: The study found that using different combinations of anchor bolts and cables with varying lengths could effectively control the deformation of the roadway surrounding rock, depending on the spacing between layers of the coal seam. The most effective support schemes were recommended depending on the specific conditions. Conclusion: The study provides a theoretical basis for the design of anchor support in coal mines, which can ensure the safety of production and improve roadway stability. The results could be useful for other mining operations facing similar challenges in roadway support and stability. Full article

(This article belongs to the Topic Advanced Materials and Technologies in Deep Rock Engineering)

►▼ Show Figures

Figure 1

Figure 1
<p>Bolt support suspension effect.</p> Full article ">Figure 2
<p>Model diagram of the loosening and anchoring composite bearing body of the surrounding rock.</p> Full article ">Figure 3
<p>Reinforcement arch effect of bolt support.</p> Full article ">Figure 4
<p>Location map and 150,110 longwall panel at the Ping’an Coal Mine.</p> Full article ">Figure 5
<p>Lithological column of the Ping’an Coal Mine.</p> Full article ">Figure 6
<p>150,110 panel Ping’an Coal Mine support system.</p> Full article ">Figure 7
<p>Numerical block model for simulation.</p> Full article ">Figure 8
<p>Deformation of surrounding rock in the roadway under different anchor spacing.</p> Full article ">Figure 9
<p>Stress contour map of the surrounding rock in the <span class="html-italic">X</span>-axis direction at different bolt lengths.</p> Full article ">Figure 10
<p>Stress contour map of the surrounding rock in the <span class="html-italic">Z</span>-axis direction at different bolt lengths.</p> Full article ">Figure 11
<p>Distribution characteristics of the surrounding rock displacement at different bolt lengths.</p> Full article ">Figure 12
<p>Stress contour map of the surrounding rock in the <span class="html-italic">X</span>-axis at different bolt lengths.</p> Full article ">Figure 13
<p>Stress contour map of the surrounding rock in the <span class="html-italic">Z</span>-axis at different bolt lengths.</p> Full article ">Figure 14
<p>Distribution characteristics of the surrounding rock displacement at different bolt lengths.</p> Full article ">Figure 15
<p>Stress contour map of the surrounding rock in the <span class="html-italic">X</span>-axis at different anchor cable lengths.</p> Full article ">Figure 16
<p>Stress contour map of the surrounding rock in the <span class="html-italic">Z</span>-axis at different anchor cable lengths.</p> Full article ">Figure 17
<p>Support form for layer spacing greater than 6.5 m: (<b>a</b>–<b>d</b>).</p> Full article ">Figure 18
<p>Support form for layer spacing of 4.7 to 6.5 m: (<b>a</b>–<b>d</b>).</p> Full article ">Figure 19
<p>Support form for layer spacing of 2.5 m to 4.7 m: (<b>a</b>–<b>d</b>).</p> Full article ">Figure 19 Cont.
<p>Support form for layer spacing of 2.5 m to 4.7 m: (<b>a</b>–<b>d</b>).</p> Full article ">Figure 20
<p>Support form for layer spacing less than 2.5 m: (<b>a</b>–<b>d</b>).</p> Full article ">Figure 20 Cont.
<p>Support form for layer spacing less than 2.5 m: (<b>a</b>–<b>d</b>).</p> Full article ">

17 pages, 61371 KiB  

Open AccessArticle

Adaptive Modification of TBM Tunneling in Coal Mine Roadway and Disaster Control Technology for Complex Geological Conditions

by Hongtao Wang, Changkuan Zhou, Qingquan Bi, Hao Zhu, Ziwei Ding and Chenchen Zhang

Processes 2023, 11(5), 1389; https://doi.org/10.3390/pr11051389 - 4 May 2023

Cited by 1 | Viewed by 2491

Abstract

Many mines have introduced the tunnel boring machine (TBM) to improve the efficiency of rock tunneling because of its high propulsion capacity, safe working space, and intelligent equipment. In contrast, the operating environment of coal mines is often under complex geological conditions such [...] Read more.

Many mines have introduced the tunnel boring machine (TBM) to improve the efficiency of rock tunneling because of its high propulsion capacity, safe working space, and intelligent equipment. In contrast, the operating environment of coal mines is often under complex geological conditions such as high ground stress, large depth of burial, high temperature, water damage, and large construction angles, making it difficult to apply traditional TBMs in coal mines. Taking the TBM of Gaojiapu Coal Mine of Zhengtong Coal Industry as an example, this paper introduces the coal mine adaptability transformation and construction technology optimization of the equipment, optimizes the design of the roadheader department of the equipment, increases the support operation space and reduces the empty roof distance, shortens the length of the whole machine and transforms the walking structure to enhance its maneuverability and convenience, and applies the monorail crane to the auxiliary transportation system of TBM. This paper proposes the theory of TBM tunneling disaster control in complex geology, research and discussion on TBM jamming, impact pressure, cooling prevention and control, and water damage in complex geological conditions. The results obtained were applied at the Zhengtong Coal Industry in engineering practice, resulting in an average monthly progress of more than 200 m, which is more than three times more efficient than full rock heaving, and also reduces the work intensity of tunneling personnel and promotes the development of coal mining. The final part of the article looks at the future application of TBMs in coal mining. Full article

(This article belongs to the Topic Advanced Materials and Technologies in Deep Rock Engineering)

►▼ Show Figures

Figure 1

Figure 1
<p>Number of TBMs in use.</p> Full article ">Figure 2
<p>Background map of Zhengtong Coal Industry project.</p> Full article ">Figure 3
<p>TBM composition and system.</p> Full article ">Figure 4
<p>Segmented zoning large support work platform.</p> Full article ">Figure 5
<p>Modified walking structure.</p> Full article ">Figure 6
<p>Monorail crane arrangement.</p> Full article ">Figure 7
<p>TBM intelligent tunneling process.</p> Full article ">Figure 8
<p>Deep buried roadway TBM roadway excavation space profile.</p> Full article ">Figure 9
<p>Schematic diagram of joint protection measures for impact ground pressure.</p> Full article ">Figure 10
<p>Comparison of the effect of ZTT6530-TBM boring footage.</p> Full article ">Figure 11
<p>Site plan of underground TBM in Zhengtong Coal Industry.</p> Full article ">Figure 12
<p>TBM intelligent overall research system.</p> Full article ">

17 pages, 7773 KiB  

Open AccessArticle

S-Wave Velocity Forecasting Using Drill Cuttings and Deep Hybrid Neural Networks: A Case Study on a Tight Glutenite Reservoir in Mahu Sag, Junggar Basin

by Fengchao Xiao, Xuechen Li and Shicheng Zhang

Processes 2023, 11(3), 835; https://doi.org/10.3390/pr11030835 - 10 Mar 2023

Viewed by 1431

Abstract

S-wave velocity (Vs) is a critical petrophysical parameter for reservoir characterization. It is desirable to predict Vs based on conventional logging data, but the logging cost is high. Therefore, a deep hybrid neural network coupling the convolutional neural network (CNN), Stacked gated recurrent [...] Read more.

S-wave velocity (Vs) is a critical petrophysical parameter for reservoir characterization. It is desirable to predict Vs based on conventional logging data, but the logging cost is high. Therefore, a deep hybrid neural network coupling the convolutional neural network (CNN), Stacked gated recurrent unit (SGRU) is proposed to predict the Vs, where the inputs to the model are drill cutting features. In the proposed CNN-SGRU hybrid model, CNN is adopted to capture the spatial features from the input data, and SGRU is used to extract the temporal patterns of variation from both the forward and backward directions. To illustrate the prediction effect, the glutenite reservoir in the Baikouquan Formation of Mahu Sag, Junggar Basin is taken as an example. Mineral and pore information of drill cuttings, including siliciclastic content, clay content, quartz content, and void area ratio is chosen as the input data of the CNN-SGRU hybrid model. Three indices are used to quantitatively evaluate the prediction performance, including Mean absolute percentage error (MAPE), Root mean square error (RMSE), and Mean absolute error (MAE). The results show that the prediction accuracy of the proposed model is higher than that of the Xu-White model, CNN, and GRU. Furthermore, the results indicate that drill cuttings can replace logging data to predict Vs. Full article

(This article belongs to the Topic Advanced Materials and Technologies in Deep Rock Engineering)

►▼ Show Figures

Figure 1

Figure 1
<p>Workflow of drill cutting scanning.</p> Full article ">Figure 2
<p>Mineral distribution under EDS.</p> Full article ">Figure 3
<p>Pore morphology under SEM.</p> Full article ">Figure 4
<p>Sketch of the structure of CNN.</p> Full article ">Figure 5
<p>Sketch of the SGRU neural network: (<b>a</b>) structure of SGRU, (<b>b</b>) inner cell structure of GRU.</p> Full article ">Figure 6
<p>Structure of the proposed CNN-SGRU model.</p> Full article ">Figure 7
<p>Overall workflow of the Vs prediction.</p> Full article ">Figure 8
<p>Area map of mineral composition of drill cuttings at different well depths.</p> Full article ">Figure 9
<p>Stacked area map of the distribution of silicate mineral types at different well depths.</p> Full article ">Figure 10
<p>Scatter plot of drill cutting pore information at different well depths.</p> Full article ">Figure 11
<p>Heatmap of Pearson correlation analysis.</p> Full article ">Figure 12
<p>The variation of the loss function with training epoch: (<b>a</b>) CNN, (<b>b</b>) GRU, (<b>c</b>) CNN-SGRU.</p> Full article ">Figure 13
<p>Schematic diagram of the rock physics model.</p> Full article ">Figure 14
<p>Comparison between actual and prediction Vs of the four models.</p> Full article ">Figure 14 Cont.
<p>Comparison between actual and prediction Vs of the four models.</p> Full article ">Figure 15
<p>Comparison of performance of four models in terms of (<b>a</b>) MAPE, (<b>b</b>) RMSE, and (<b>c</b>) MAE.</p> Full article ">Figure 16
<p>Boxplots of relative error of the four models.</p> Full article ">

17 pages, 22867 KiB  

Open AccessArticle

Model Test Study on Natural Thawing Temperature Field of Artificial Ground Frozen Wall

by Haibing Cai, Longfei Yang, Changqiang Pang, Mengkai Li, Chanrui Lu and Rongbao Hong

Sustainability 2023, 15(4), 3186; https://doi.org/10.3390/su15043186 - 9 Feb 2023

Cited by 3 | Viewed by 1402

Abstract

In order to visualize the evolution and distribution law of the ground temperature field during artificial freezing construction, an indoor model test study was carried out based on the independently constructed hygrothermal coupling artificial ground freezing test platform. The test results show that [...] Read more.

In order to visualize the evolution and distribution law of the ground temperature field during artificial freezing construction, an indoor model test study was carried out based on the independently constructed hygrothermal coupling artificial ground freezing test platform. The test results show that the soil temperature in the freezing process went through the three stages of a steep drop, a slow drop, and stabilization, the earliest closure position of the frozen wall was the intermediate point between two freezing pipes, and the thickness of the frozen wall on different sections showed Section 1 > Section 2 > Section 3 after 61 min of positive freezing. The soil temperature in the natural thawing process went through the four stages of a rapid rise, short hysteresis, a second rapid rise, and a linear slow rise. By fitting the test data, the distribution function of the pipe wall temperature along the pipe length under natural thawing conditions was obtained. The research results can provide a valid basis for the numerical calculation model of a three-dimensional non-uniform natural thawing temperature field and can also provide a reference for the design of settlement grouting under natural thawing conditions. Full article

(This article belongs to the Topic Advanced Materials and Technologies in Deep Rock Engineering)

►▼ Show Figures

Figure 1

Figure 1
<p>Layout of freezing pipes (Unit: mm).</p> Full article ">Figure 2
<p>Model test system.</p> Full article ">Figure 3
<p>Model test box: (<b>a</b>) External surface of box; (<b>b</b>) internal condition of box.</p> Full article ">Figure 4
<p>Freezing system: (<b>a</b>) Low-temperature thermostat; (<b>b</b>) insulation pipe; (<b>c</b>) freezing pipe.</p> Full article ">Figure 5
<p>Layout of temperature-measuring points.</p> Full article ">Figure 6
<p>Temperature duration curve of measuring points on each section during freezing: (<b>a</b>) Section 1; (<b>b</b>) Section 2; (<b>c</b>) Section 3; (<b>d</b>) freezing pipe wall.</p> Full article ">Figure 7
<p>Temperature field distribution at each section after positive freezing for 61 min: (<b>a</b>) Section 1; (<b>b</b>) Section 2; (<b>c</b>) Section 3.</p> Full article ">Figure 8
<p>Temperature change curve of measuring points at each section during natural thawing: (<b>a</b>) Section 1; (<b>b</b>) Section 2; (<b>c</b>) Section 3; (<b>d</b>) freezing pipe wall.</p> Full article ">Figure 8 Cont.
<p>Temperature change curve of measuring points at each section during natural thawing: (<b>a</b>) Section 1; (<b>b</b>) Section 2; (<b>c</b>) Section 3; (<b>d</b>) freezing pipe wall.</p> Full article ">Figure 9
<p>Temperature field distribution at three sections after thawing for different times: (<b>a</b>) 5 min; (<b>b</b>) 15 min; (<b>c</b>) 48 min; (<b>d</b>) 85 min.</p> Full article ">Figure 9 Cont.
<p>Temperature field distribution at three sections after thawing for different times: (<b>a</b>) 5 min; (<b>b</b>) 15 min; (<b>c</b>) 48 min; (<b>d</b>) 85 min.</p> Full article ">Figure 9 Cont.
<p>Temperature field distribution at three sections after thawing for different times: (<b>a</b>) 5 min; (<b>b</b>) 15 min; (<b>c</b>) 48 min; (<b>d</b>) 85 min.</p> Full article ">Figure 10
<p>Vertical temperature field after thawing for different times: (<b>a</b>) 0 min; (<b>b</b>) 5 min; (<b>c</b>) 15 min; (<b>d</b>) 85 min.</p> Full article ">Figure 10 Cont.
<p>Vertical temperature field after thawing for different times: (<b>a</b>) 0 min; (<b>b</b>) 5 min; (<b>c</b>) 15 min; (<b>d</b>) 85 min.</p> Full article ">Figure 11
<p>Fitting curves of pipe wall temperature along pipe length after thawing at different times.</p> Full article ">

21 pages, 5943 KiB  

Open AccessEditor’s ChoiceArticle

HPHT-Treated Impact Diamonds from the Popigai Crater (Siberian Craton): XRD and Raman Spectroscopy Evidence

by Anatoly Chepurov, Sergey Goryainov, Sergey Gromilov, Egor Zhimulev, Valeriy Sonin, Aleksey Chepurov, Zakhar Karpovich, Valentin Afanasiev and Nikolay Pokhilenko

Minerals 2023, 13(2), 154; https://doi.org/10.3390/min13020154 - 20 Jan 2023

Cited by 1 | Viewed by 1770

Abstract

Phase change and graphitization of diamonds from the Popigai impact crater (Krasnoyarsk Territory, Siberian platform, Russia) exposed to high-pressure high-temperature (HPHT) conditions of 5.5 GPa and 2000–2200 °C are studied by Raman spectroscopy and X-ray diffractometry (XRD). Light-color diamonds of type 1, free [...] Read more.

Phase change and graphitization of diamonds from the Popigai impact crater (Krasnoyarsk Territory, Siberian platform, Russia) exposed to high-pressure high-temperature (HPHT) conditions of 5.5 GPa and 2000–2200 °C are studied by Raman spectroscopy and X-ray diffractometry (XRD). Light-color diamonds of type 1, free from inclusions, with 0 to 10 % lonsdaleite, are more resistant to HPHT effects than dark diamonds of type 2 rich in lonsdaleite and graphite. The lonsdaleite/diamond ratios in lonsdaleite-bearing impact diamonds become smaller upon annealing, possibly because lonsdaleite transforms to cubic diamond simultaneously with graphitization. Therefore, lonsdaleite is more likely a structure defect in diamond than a separate hexagonal phase. Full article

(This article belongs to the Topic Advanced Materials and Technologies in Deep Rock Engineering)

►▼ Show Figures

Figure 1

Figure 1
<p>Reaction capsule with MgO powder (1), ZrO₂ and CsCl pellets (2), and diamond sample (3) in graphite heater (4).</p> Full article ">Figure 2
<p>Representative Popigai impact diamond samples of types 1 (<b>a</b>), 3/2 (<b>b</b>), and 2 (<b>c</b>), and an enlarged fragment of type 2 diamond (<b>d</b>).</p> Full article ">Figure 3
<p>XRD data (Bruker D8 Venture diffractometer, CuKα-radiation) for colorless transparent diamonds of types 1 and 3/2 (<b>1</b>); yellowish transparent diamonds of type 3/2 (<b>2</b>), and milky-white diamonds of type 1, which is free from lonsdaleite (<b>3</b>).</p> Full article ">Figure 4
<p>Raman spectra of representative impact diamonds of types 1 and 3/2: sample 3 (type 3/2) contains more lonsdaleite than sample 2 (type 1). Sample 1 is almost pure cubic diamond free from lonsdaleite (limiting variety of type 1 sample).</p> Full article ">Figure 5
<p>Raman spectrum of representative type 2 impact diamond (dark) sample with 42.7% lonsdaleite before HPHT runs. Inset shows first-order Raman spectrum deconvolved into Voigt contours.</p> Full article ">Figure 6
<p>Raman spectra of type-1 impact diamond sample 1 and its fragments (<a href="#minerals-13-00154-f002" class="html-fig">Figure 2</a>) before (Ch22-7b-init2, curve 1) and after (Ch22-7a-tr, curves 2–5) HPHT run 4-12-22(7) at 5.5 GPa and 2000 °C, 600 s.</p> Full article ">Figure 7
<p>Raman spectra of sample 2 before (Ch2022-5init, curve 1) and after (Ch2022-5tr, curve 2) HPHT run 4-12-22(5) at 5.5 GPa, 2050 °C, 600 s.</p> Full article ">Figure 8
<p>Raman spectra of sample 3 before (Ch35-init-Pop-1Black1, curve 6) and after (Ch35-Tr, curves 1–5) HPHT run 4-35-21 at 5.5 GPa, 2100 °C, 180 s.</p> Full article ">Figure 9
<p>Raman spectra of sample 4 before (Ch46-init-Pop-2Black1, curve 5) and after (Ch46-Tr, curves 1–4) HPHT run 4-46-21 at 5.5 GPa, 2100 °C, 600 s.</p> Full article ">Figure 10
<p>XRD analysis (Bruker D8 Venture diffractometer, MoKα-radiation) of impact diamonds. (<b>a</b>,<b>b</b>): samples of types 1 (<b>a</b>) and 2 (<b>b</b>) before HPHT runs; (<b>c</b>,<b>d</b>): samples of types 1 (<b>c</b>) and 2 (<b>d</b>) after HPHT runs at 5.5 GPa, 2000 °C, 180 s. D and G are the strongest reflections of diamond and graphite, respectively.</p> Full article ">Figure 11
<p>Raman spectra (150 to 3800 cm⁻¹) of dark sample 5 before (Ch22-1, curve 1) and after (Ch22-1-Tr, curves 2–5) HPHT run 4-3-22-1 at 5.5 GPa, 2050 °C, 1200 s. The curve labels refer to particle 1 at two points (2 and 3); particle 2 at two points (4 and 5); particle 3 at two points (6 and 7); and particle 4 at one point (8). In addition to the diamond-lonsdaleite and graphite bands, the spectra contain narrow resonant Raman peaks of (CrO₄)²⁻ impurity at <span class="html-italic">ν</span>₀ = 845 cm⁻¹, 2<span class="html-italic">ν</span>₀ = 1693 cm⁻¹ and 3<span class="html-italic">ν</span>₀ = 2534 cm⁻¹.</p> Full article ">Figure 12
<p>Raman spectra of sample 6 before (InitialPopigaCh22-2-init-1, curve 1) and after (Ch22-2-treat-1gr, curves 2–5) HPHT run 4-3-22-2 at 5.5 GPa, 2050 °C, for 1200 s. The curve labels refer to particle 1 at two points (2 and 3); particle 2 at one point (4); and particle 5 at one point (5). In addition to the diamond-lonsdaleite and graphite bands, the spectra contain a narrow resonant Raman peak of (CrO₄)²⁻ impurity at <span class="html-italic">ν</span>₀ = 845 cm⁻¹.</p> Full article ">Figure 13
<p>XRD analysis (Bruker D8 Venture diffractometer, CuKα-radiation) of type 3/2 impact diamond, before (1) and after (2) HPHT runs at 5.5 GPa, 1900 °C, for 60 s (run 4-18-21).</p> Full article ">Figure 14
<p>Small flakes of well-crystallized graphite on the surface of a diamond plate exposed to HPHT effects.</p> Full article ">Figure 15
<p>Relative percentages of lonsdaleite (<span class="html-italic">L</span>) and cubic diamond (100 − <span class="html-italic">L</span>) in impact diamonds of types 1 (3.2%–8.0%), 2 (45%–52%), and 3/2 (25.9%–29.7%) before (blue line and rings) and after (purple line and red squares) HPHT runs, according to Raman spectroscopy (<a href="#minerals-13-00154-t001" class="html-table">Table 1</a>). Points (rings and squares) correspond to average content of lonsdaleite in three samples types.</p> Full article ">

19 pages, 29714 KiB  

Open AccessArticle

Experimental Study on the Acoustic Emission Characteristics of Fractured Granite after Repeated High Temperature-Water Cooling

by Dong Zhu, Yuqing Fan, Yang Bai, Xiangling Tao, Leigang Miao and Huiwu Jin

Processes 2023, 11(1), 139; https://doi.org/10.3390/pr11010139 - 3 Jan 2023

Viewed by 1417

Abstract

Using the MTS816 rock mechanics servo tester, an acoustic emission monitoring system and high-speed digital photographic equipment, uniaxial compression tests were conducted on granite specimens containing single fracture slabs after repeated treatment (treatment times 1, 5, 10, 15 and 20) with three types [...] Read more.

Using the MTS816 rock mechanics servo tester, an acoustic emission monitoring system and high-speed digital photographic equipment, uniaxial compression tests were conducted on granite specimens containing single fracture slabs after repeated treatment (treatment times 1, 5, 10, 15 and 20) with three types of high temperature (250, 350 and 450 °C) water cooling, respectively, to analyze the basic mechanical parameters, acoustic emission change characteristics and fracture evolution of the specimens during the uniaxial compression process. It is shown that the heating temperature and the number of treatments not only have a deteriorating effect on the basic mechanical parameters of the specimens but also have an important effect on the changes in the basic parameters of acoustic emission at different compression stages. At 250 °C, the acoustic emission characteristics of the specimens at the initial tightening stage tended to decrease (N = 1 and 5 times) then, increase (N = 10 and 15 times) and then decrease (N = 20 times) as the number of treatments increased. At the same set temperature, the percentage of the bottom amplitude value of the acoustic emission of the specimen gradually decreases, and the percentage of the high amplitude value gradually increases as the number of treatments increases. After the specimen undergoes one and five treatments at 250 °C, the maximum acoustic emission energy value changes less, the maximum acoustic emission energy value decreases with the increase of treatment times in an approximately exponential function, the specimen is transformed from the brittle damage mode to the plastic damage mode and the effect of the prefabricated fracture on the damage of the specimen gradually disappears. Full article

(This article belongs to the Topic Advanced Materials and Technologies in Deep Rock Engineering)

►▼ Show Figures

Figure 1

Figure 1
<p>EGS mining model.</p> Full article ">Figure 2
<p>Granite slab specimen with fissure.</p> Full article ">Figure 3
<p>MXQ1700 heating device.</p> Full article ">Figure 4
<p>Loading and data acquisition system.</p> Full article ">Figure 5
<p>Stress-strain curve of intact granite at 25 °C.</p> Full article ">Figure 6
<p>Changes in acoustic emission characteristic parameters during uniaxial compression of specimens after repeated treatment at 250 °C. (<b>a</b>) 25 °C. (<b>b</b>) 250 °C-1. (<b>c</b>) 250 °C-5. (<b>d</b>) 250 °C-10. (<b>e</b>) 250 °C-15. (<b>f</b>) 250 °C-20.</p> Full article ">Figure 6 Cont.
<p>Changes in acoustic emission characteristic parameters during uniaxial compression of specimens after repeated treatment at 250 °C. (<b>a</b>) 25 °C. (<b>b</b>) 250 °C-1. (<b>c</b>) 250 °C-5. (<b>d</b>) 250 °C-10. (<b>e</b>) 250 °C-15. (<b>f</b>) 250 °C-20.</p> Full article ">Figure 7
<p>Variation curve of accumulated acoustic emission events of specimens. (<b>a</b>) T = 20 °C. (<b>b</b>) T = 250 °C. (<b>c</b>) T = 350 °C. (<b>d</b>) T = 450 °C.</p> Full article ">Figure 8
<p>Frequency distribution of maximum amplitude values of acoustic emission of specimens during uniaxial compression. (<b>a</b>) T = 20 °C. (<b>b</b>) T = 250 °C. (<b>c</b>) T = 350 °C. (<b>d</b>) T = 450 °C.</p> Full article ">Figure 9
<p>Trends in the maximum energy value of acoustic emission of specimens during uniaxial compression. (<b>a</b>) T = 250 °C. (<b>b</b>) T = 350 °C. (<b>c</b>) T = 450 °C.</p> Full article ">Figure 10
<p>Relationship between the basic mechanical parameters of the specimen and the maximum energy of acoustic emission. (<b>a</b>) T = 250 °C. (<b>b</b>) T = 350 °C. (<b>c</b>) T = 450 °C.</p> Full article ">Figure 11
<p>Specimen rupture evolution characteristics. (<b>a</b>) 250 °C-1. (<b>b</b>) 250 °C-5. (<b>c</b>) 250 °C-10. (<b>d</b>) 250 °C-15. (<b>e</b>) 250 °C-20.</p> Full article ">Figure 11 Cont.
<p>Specimen rupture evolution characteristics. (<b>a</b>) 250 °C-1. (<b>b</b>) 250 °C-5. (<b>c</b>) 250 °C-10. (<b>d</b>) 250 °C-15. (<b>e</b>) 250 °C-20.</p> Full article ">Figure 11 Cont.
<p>Specimen rupture evolution characteristics. (<b>a</b>) 250 °C-1. (<b>b</b>) 250 °C-5. (<b>c</b>) 250 °C-10. (<b>d</b>) 250 °C-15. (<b>e</b>) 250 °C-20.</p> Full article ">Figure 11 Cont.
<p>Specimen rupture evolution characteristics. (<b>a</b>) 250 °C-1. (<b>b</b>) 250 °C-5. (<b>c</b>) 250 °C-10. (<b>d</b>) 250 °C-15. (<b>e</b>) 250 °C-20.</p> Full article ">

16 pages, 4044 KiB  

Open AccessArticle

Prediction of Surface Subsidence of Deep Foundation Pit Based on Wavelet Analysis

by Jindong Zhang and Zhangjianing Cheng

Processes 2023, 11(1), 107; https://doi.org/10.3390/pr11010107 - 30 Dec 2022

Cited by 3 | Viewed by 1775

Abstract

Predicting surface settlement in deep foundation pit engineering plays a central role in the safety of foundation pit construction. Recently, static or dynamic methods are usually applied to predict ground settlement in deep foundation pit projects. In this work, we propose a model [...] Read more.

Predicting surface settlement in deep foundation pit engineering plays a central role in the safety of foundation pit construction. Recently, static or dynamic methods are usually applied to predict ground settlement in deep foundation pit projects. In this work, we propose a model combining wavelet noise reduction and radial basis neural network (XW-RBF) to reduce noise interference in monitoring data. The results show that the XW-RBF model predicts an average relative error of 0.77 and a root average square error of 0.13. The prediction performance is better than the original data prediction results with noise structure and has higher prediction accuracy. The noise data caused by the interference of construction and the surrounding environment in the original data can be removed via the wavelet noise reduction method, with the discreteness of the original data reducing by 30%. More importantly, our results show that the XW-RBF model can reflect the law of data change to predict the future data trend with high credibility. The findings of this study indicate that the XW-RBF model could optimize the deep foundation pit settlement prediction model for high accuracy during the prediction, which inspires the potential application in deep foundation pit engineering. Full article

(This article belongs to the Topic Advanced Materials and Technologies in Deep Rock Engineering)

►▼ Show Figures

Figure 1

Figure 1
<p>Principles of neural networks.</p> Full article ">Figure 2
<p>W-RBF forecasting flowchart.</p> Full article ">Figure 3
<p>The layout of monitoring sections of ground settlements.</p> Full article ">Figure 4
<p>Measured curves of surface settlement at points 2−2 and 2−4.</p> Full article ">Figure 5
<p>Ground settlements reconstructed from db12 wavelet denoise at different levels (<b>a</b>) point 2−2 and (<b>b</b>) point 2−4.</p> Full article ">Figure 6
<p>Error curve after denoising by db12 wavelet of measurement points (<b>a</b>) point 2−2 and (<b>b</b>) point 2−4.</p> Full article ">Figure 6 Cont.
<p>Error curve after denoising by db12 wavelet of measurement points (<b>a</b>) point 2−2 and (<b>b</b>) point 2−4.</p> Full article ">Figure 7
<p>Comparison of the measured value of surface settlement at measurement points 2−3 and 2−5 with the predicted value.</p> Full article ">Figure 8
<p>Comparison of error curves at points 2−3 and points 2−5.</p> Full article ">Figure 9
<p>Comparison between the measured and new predicted values of surface subsidence at measuring points 2−3 and points 2−5.</p> Full article ">Figure 10
<p>Error Analysis of Comparison between New Predicted Value and Measured Value.</p> Full article ">

19 pages, 8541 KiB  

Open AccessArticle

Influence of Pipeline Leakage on the Ground Settlement around the Tunnel during Shield Tunneling

by Xin Shi, Yi Cao, Chuanxin Rong, Gangjian An, Houliang Wang and Linzhao Cui

Sustainability 2022, 14(24), 16802; https://doi.org/10.3390/su142416802 - 14 Dec 2022

Cited by 7 | Viewed by 1815

Abstract

Shield tunneling is widely used in urban subway tunnel construction. Old urban underground pipelines generally have small leakages that are difficult to find. The water leakage significantly reduces the stability of the stratum, posing a threat to the safety of tunnel shield construction. [...] Read more.

Shield tunneling is widely used in urban subway tunnel construction. Old urban underground pipelines generally have small leakages that are difficult to find. The water leakage significantly reduces the stability of the stratum, posing a threat to the safety of tunnel shield construction. Therefore, this study established 2D and 3D calculation models for analyzing the law of the leakage diffusion in the ground under water pressure, and the influences of the pipeline leakage range and leakage length on the changes in ground settlement during shield tunneling. The 2D model calculation results show that seepage water mainly diffuses vertically under gravity. As the pipeline leakage gradually reaches a predetermined depth, the simulation results tend to be consistent with the test results. The 3D model is more accurate than the theoretical solution in predicting the ground settlement because it can consider the influences of repeated disturbances in twin tunnel shield construction. The maximum ground surface settlement increases with the extent of the leakage length and leakage range, and the range is the main factor determining the settlement. At the interior of the ground, the seepage water has a greater impact on areas with strong disturbances and large soil losses. Full article

(This article belongs to the Topic Advanced Materials and Technologies in Deep Rock Engineering)

►▼ Show Figures

Figure 1

Figure 1
<p>Layout of measuring points and relative position relationship between tunnel and pipeline: (<b>a</b>) Plan view; (<b>b</b>) section and layout of measuring points.</p> Full article ">Figure 2
<p>Model meshing diagram.</p> Full article ">Figure 3
<p>Laboratory test results of soil suction: (<b>a</b>) permeability suction; (<b>b</b>) Matrix suction.</p> Full article ">Figure 4
<p>Calculation results of pipeline leakage water diffusion: (<b>a</b>) initial diffusion state; (<b>b</b>) final diffusion state.</p> Full article ">Figure 5
<p>Comparison between simulated and measured results of pipeline leakage diffusion (unit: mm): (<b>a</b>) initial diffusion state; (<b>b</b>) final diffusion state.</p> Full article ">Figure 6
<p>3D finite element model of pipeline leakage and tunnel excavation: (<b>a</b>) mesh generation of 3D model; (<b>b</b>) simulation of shield construction.</p> Full article ">Figure 7
<p>Numerical simulation conditions of different leakage ranges and leakage lengths: (<b>a</b>) small range; (<b>b</b>) medium range; (<b>c</b>) large range; (<b>d</b>) 9 m; (<b>e</b>) 27 m; (<b>f</b>) 45 m.</p> Full article ">Figure 7 Cont.
<p>Numerical simulation conditions of different leakage ranges and leakage lengths: (<b>a</b>) small range; (<b>b</b>) medium range; (<b>c</b>) large range; (<b>d</b>) 9 m; (<b>e</b>) 27 m; (<b>f</b>) 45 m.</p> Full article ">Figure 8
<p>Layout of monitoring points.</p> Full article ">Figure 9
<p>Calculation results of ground vertical displacement after the excavation of the left line tunnel.</p> Full article ">Figure 10
<p>Comparison of theoretical analysis, test, and simulation results of ground vertical displacement after excavation of left tunnel without leakage.</p> Full article ">Figure 11
<p>Calculation results of ground vertical displacement after excavation of twin tunnel.</p> Full article ">Figure 12
<p>Comparison of theoretical analysis, test, and simulation results of ground vertical displacement after excavation of twin tunnel without leakage.</p> Full article ">Figure 13
<p>Calculation results of surface settlement caused by pipeline leakage under different conditions: (<b>a</b>) No. 1; (<b>b</b>) No. 2; (<b>c</b>) No. 3; (<b>d</b>) No. 4; (<b>e</b>) No. 5; (<b>f</b>) No. 6; (<b>g</b>) No. 7; (<b>h</b>) No. 8; (<b>i</b>) No. 9.</p> Full article ">Figure 14
<p>Variation of <span class="html-italic">S</span> and <span class="html-italic">V</span> with leakage range and leakage length: (<b>a</b>) the change rule of S and V with leakage length under different leakage ranges; (<b>b</b>) the change rule of S and V with leakage range under different leakage length.</p> Full article ">Figure 15
<p>Surface settlement curve after twin tunnel construction under the influence of pipeline leakage: (<b>a</b>) single tunnel construction completed; (<b>b</b>) twin tunnel construction completed.</p> Full article ">Figure 16
<p>Surface settlement trough caused by the excavation of a twin tunnel.</p> Full article ">

17 pages, 3455 KiB  

Open AccessArticle

Study on Blasting Effect Optimization to Promote Sustainable Mining under Frozen Conditions

by Ping Cheng, Yanbo Li, Caiwu Lu, Song Jiang and Hanhua Xu

Sustainability 2022, 14(24), 16479; https://doi.org/10.3390/su142416479 - 9 Dec 2022

Cited by 3 | Viewed by 1967

Abstract

In order to respond to the theme of national green and healthy sustainable development and in response to the problems of large block rates and pollution of the environment after the blast mining of underground rocks in alpine areas, we conducted research on [...] Read more.

In order to respond to the theme of national green and healthy sustainable development and in response to the problems of large block rates and pollution of the environment after the blast mining of underground rocks in alpine areas, we conducted research on the joints of underground rocks and the blastability of frozen rocks. According to the actual geological conditions of an underground mine blasting in Heilongjiang Province, three kinds of joint blasting geometric models were established. The rock mass blasting process of different types of joints was simulated by LS-DYNA software and the influence law of joints on rock mass blasting was summarized. The blasting crater test and the triaxial compression test of frozen rock were carried out. Combined with the blasting fragmentation characterization function (R-R and G-G-S), the blasting fragmentation, strength and stiffness of frozen rock at different temperatures were obtained. Based on the above, the blasting parameters of a multi-joint underground rock mass in an alpine region were optimized: hole spacing 4.0 m, row spacing 2.5 m, hole depth 11.5 m, V-type initiation network. The optimized blasting parameters significantly improved the mining efficiency and reduced the large lump rate to 3.1%. In order to promote the sustainable exploitation of resources in alpine regions, this study optimized the blasting technology of underground rock mass. Full article

(This article belongs to the Topic Advanced Materials and Technologies in Deep Rock Engineering)

►▼ Show Figures

Figure 1

Figure 1
<p>Blasting model diagram of nodular rock body: (<b>a</b>) infinite joint, (<b>b</b>) semi-infinite joint, (<b>c</b>) local short joint. Note: (<b>a</b>,<b>b</b>) the model size is 60 d × 60 d; the gun hole diameter d = 5 mm; R is the distance between the center of the gun hole and the joint; the joint width 0.1 d; σ₀ is the static stress at the vertical joint surface; α is the angle of incidence of the stress wave.</p> Full article ">Figure 2
<p>Influence of semi-infinite joints on the effect of explosive rupture: (<b>a</b>) R/d = 5, no filling; (<b>b</b>) R/d = 5, filling; (<b>c</b>) R/d = 20, filling.</p> Full article ">Figure 3
<p>Blasting process of rock body containing long joints: (<b>a</b>) t = 6 μs; (<b>b</b>) t = 20 μs; (<b>c</b>) t = 100 μs; 1, frozen rock; 2, blast hole; 3, joint; 4, attenuating medium; 5, blasting stress wave; 6, blasting fissure.</p> Full article ">Figure 4
<p>Effect of nodal location on the effect of explosive rupture.</p> Full article ">Figure 5
<p>Effect of static stress on the effect of tensile damage by reflection of joints (R/d = 15).</p> Full article ">Figure 6
<p>Blasting process at the nodal angle α = 30°:(<b>a</b>) t = 6 μs; (<b>b</b>) t = 30 μs; (<b>c</b>) t = 100 μs.</p> Full article ">Figure 7
<p>Blasting results for different static stresses and different incidence angles. In the figure: (<b>a1–a5</b>) incident angle 0°, (<b>b1–b5</b>) incident angle 15°, (<b>f1–f5</b>) incident angle 75°, (<b>g1–g5</b>) incident angle 90°; the stresses from 1 to 5 are 0, 10, 20, 30 and 40 MPa, respectively. Note: from (<b>a</b>) to (<b>f</b>) α are 0°, 15°, 75° and 90°, respectively, and from 1 to 5 σ0 are 0 Mpa, 10 Mpa, 20 Mpa, 30 Mpa and 40 Mpa, respectively.</p> Full article ">Figure 8
<p>Stress–strain curve of rock specimen.</p> Full article ">Figure 9
<p>Diagram of the detonation network: (<b>a</b>) 1 free-face ‘V’ detonating network; (<b>b</b>) 2 free-face diagonal initiation networks.</p> Full article ">

Show export options expand_more Show export options expand_less

Select all

Export citation of selected articles as:

Plain Text BibTeX BibTeX (without abstracts) Endnote Endnote (without abstracts) Tab-delimited RIS

 

Error

Oops... you haven't selected anything for export.

Ok

clear

Displaying articles 1-15