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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Civil Engineering".

Deadline for manuscript submissions: 20 July 2025 | Viewed by 2762

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Special Issue Editors

Prof. Dr. Fengfeng Wu

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Guest Editor

School of Mines, China University of Mining and Technology, Xuzhou 221116, China
Interests: mine pressure and strata control; rock mechanics; fracturing of hard rock strata; large mining height; dynamic load impact effect

Dr. Peiju Yang

E-Mail Website
Guest Editor

School of Mines, China University of Mining and Technology, Xuzhou 221116, China
Interests: safe and efficient mining technology; top coal caving; PFC particle flow; deep learning

Dr. Ningbo Zhang

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Guest Editor

School of Mines, China University of Mining and Technology, Xuzhou 221116, China
Interests: identification of coal and gangue based on γ ray; time sequence regularity; falling law of coal, gangue and rock

Dr. Yiqi Chen

E-Mail Website
Guest Editor Assistant

School of Mines, China University of Mining and Technology, Xuzhou 221116, China
Interests: data driven; spatiotemporal characteristics of overburden rock instability movement; microseismic monitoring and prediction; multimodal generation model; information flow integration

Special Issue Information

Dear Colleagues,

Rock mechanics research serves as the foundation for the safe and efficient implementation of geotechnical engineering in construction, transportation, water conservancy, mining, urban underground space, and other fields. Throughout the longstanding advancement of geotechnical engineering, new research findings have been proposed. Summarizing and promoting these findings are crucial to driving the development of this field. This Special Issue on ‘Novel Research on Rock Mechanics and Geotechnical Engineering’ welcomes submissions of recent work on this important area, including (but not limited to) the following topics:

The optimization of geotechnical engineering processes driven by rock mechanics research.
Disaster mechanisms and preventive measures during geotechnical engineering construction.
Efficient rock-breaking technologies for hard rock formations.
Multiphase and multiscale macro- and micro-mechanical behaviors of rock failure.
Application and practice of deep learning in geotechnical engineering.
Solutions addressing the randomness, fuzziness, and uncertainty of geotechnical engineering parameters.
Numerical analysis and simulation techniques in rock mechanics and geotechnical engineering.
New equipment, experimental methods, and monitoring technologies for geotechnical engineering.

Prof. Dr. Fengfeng Wu
Dr. Peiju Yang
Dr. Ningbo Zhang
Guest Editors

Dr. Yiqi Chen
Guest Editor Assistant

Manuscript Submission Information

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Keywords

rock mechanics
geotechnical engineering
process optimization
disaster mechanisms
efficient rock breaking
multiscale mechanical behavior
deep learning
numerical simulation
multifield coupling

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Published Papers (7 papers)

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Research

15 pages, 9170 KiB

Open AccessArticle

Research and Application of Structural Plane Identification for Roadway Surrounding Based on Deep Learning

by Qiang Xu, Ze Xia, Gang Huang, Xuehua Li, Xu Gao and Yukuan Fan

Appl. Sci. 2025, 15(9), 4756; https://doi.org/10.3390/app15094756 - 25 Apr 2025

Viewed by 88

Abstract

The accurate evaluation of rock mass quality and competent roadway-support decision-making requires the rapid and accurate acquisition of the distribution of structural planes in rocks. To address this need, a program was developed that uses deep learning to automatically recognize the structural plane in-borehole images. First, borehole images from 30 mines in China were collected during field tests, and the structural planes in the images were categorized into five types. Second, a deep Coral architecture based on a convolutional neural network (CNN) was established to automatically extract features from the borehole images and classify the structural planes therein. The experimental results indicate that the CNN model classifies the structural planes in the borehole images with an overall accuracy of 86%. Validation tests in field applications demonstrated recognition accuracies ranging from 0.76 to 1.0 compared to manual markings, meeting engineering requirements. Finally, based on the proposed method, an intelligent system to recognize surrounding rock fracture was developed. Engineering application cases are presented and discussed to demonstrate the method and confirm the accuracy of this approach. Compared with traditional classification methods, the proposed method rapidly recognizes and classifies structural planes in borehole images at low cost, with precision, and in a non-destructive and automated manner. Full article

(This article belongs to the Special Issue Novel Research on Rock Mechanics and Geotechnical Engineering)

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20 pages, 5234 KiB

Open AccessArticle

Research on Pressure Exertion Prediction in Coal Mine Working Faces Based on Data-Driven Approaches

by Yiqi Chen, Changyou Liu, Ningbo Zhang, Huaidong Liu, Xin Yu, Shibao Liu and Jianning Hu

Appl. Sci. 2025, 15(8), 4192; https://doi.org/10.3390/app15084192 - 10 Apr 2025

Viewed by 208

Abstract

Coal is the main energy source in China, but coal mining is a high-risk industry, making the prevention and control of coal mining hazards an important topic. Constrained by the complexity and unpredictability of underground spaces, current research on coal mining disaster prevention and control technologies mainly focuses on the characteristics of overlying strata and the laws of mine pressure, resulting in significant deficiencies in accuracy. Given this, a data-driven pressure prediction method is proposed, which uses deep learning models to learn the patterns in existing data and generate the required predictions. This approach avoids the challenges of accurately extracting rock mass physical and mechanical parameters and geological structure modeling, thereby improving the accuracy of disaster prevention and control. The stage of working face pressure exertion is a period prone to disasters during coal mining. To achieve accurate prediction of working face pressure, the task is divided into three steps: the first step is to predict support resistance data ahead of the working face, the second step is to classify the pressure labels of coordinate units, and the third step is to predict the characteristic parameters of pressure exertion. Deep learning models were designed and trained separately for each of the three steps: For the first step, a deep Spatiotemporal sequence model was selected, and the trained model achieved a mean absolute error of 4.65 kN in prediction. For the second step, an image segmentation-based classification model was chosen, with the trained model reaching a classification accuracy of 97.77%. For the third step, a fusion model consisting of three LSTM (Long Short-Term Memory) networks was designed. The trained model achieved a mean absolute error of 0.17 for the dynamic pressure coefficient, a maximum resistance error of 810.93 kN during the pressure period, an error of 9.96 cycles for the pressure duration, and a classification accuracy of 92.35% for the pressure type. Simulating the actual situation of application scenarios, the input data for the second and third steps were set as the output data from the previous step, and the model was evaluated. The model achieved a mean absolute error of 1035.21 kN for the prediction of support resistance and classification accuracy of 82.90% for the pressure labels of coordinate units. In the simulated scenario, there were 9922 instances of pressure exertion, and the model predicted 10,336 instances, with 9046 of them matching the actual instances. The prediction of characteristic parameters was evaluated for 4946 instances of pressure exertion, which included three complete pressure exertion cycles. The mean absolute error for the dynamic pressure coefficient was 0.21, the maximum resistance error during the pressure period was 1218.31 kN, the error for the duration of the pressure cycle was 11.03 cycles, and the classification accuracy for the pressure exertion type was 91.75%. Full article

(This article belongs to the Special Issue Novel Research on Rock Mechanics and Geotechnical Engineering)

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17 pages, 16119 KiB

Open AccessArticle

Stability Analysis of Isolated Roof in Overlapping Goaf Based on Strength Reduction

by Chang Liu, Kui Zhao, Peng Zeng and Cong Gong

Appl. Sci. 2025, 15(6), 3067; https://doi.org/10.3390/app15063067 - 12 Mar 2025

Viewed by 317

Abstract

An isolated roof is an indispensable component of overlapping goaf. Focusing on the influence of dislocated width and width ratio on the stability of the isolated roof, this study analyzes the change rule of the safety factor of the roof supported by misaligned pillars and reveals the evolution characteristics of it by integrating numerical simulation into the strength reduction method. Firstly, with the increase of the dislocated width, the safety factor experienced three stages of sharp decrease, change from decrease to increase, and rapid increase. Secondly, the width ratio λ = 2 can be determined as the critical value of the safety reserve of the roof. In the interval λ ˂ 2, F decreases sharply with the increase of λ, but when λ ˃ 2, F decreases slowly and tends to 0. Thirdly, the overlap rate of pillars is a determinant of the type of damage but not of the safety factor of the roof. When η = 0, the safety factor is independent of the overlap rate. Furthermore, increasing the dislocated width can make the failure units accumulate continuously and then promote the plastic zone to expand gradually, resulting in roof collapse due to the penetration of the failure units. In this process, the tensile failure zone evolves from a single fold line to a wavy line, and the shear failure zone changes from a diagonal strip to a square strip. The study provides a new method to improve the stability of the roof, which is helpful to significantly reduce the collapse risk of overlapping goaf. Full article

(This article belongs to the Special Issue Novel Research on Rock Mechanics and Geotechnical Engineering)

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26 pages, 10926 KiB

Open AccessArticle

Instability Characteristics of and Control Techniques for Mudstone–Clay Composite Roof Roadways

by Kaiqiang Sun, Huaidong Liu, Jun Wang, Changyou Liu and Jingxuan Yang

Appl. Sci. 2025, 15(6), 3027; https://doi.org/10.3390/app15063027 - 11 Mar 2025

Viewed by 392

Abstract

In China’s northwest mining areas, shallow buried coal seams commonly feature double soft composite roof structures of mudstone and clay, resulting in poor roadway stabilization and proving challenging for effective roadway-surrounding rock (RSR) control. A mudstone–clay composite roof is particularly difficult to maintain due to the complex interactions between the soft rock layers and their sensitivity to moisture changes. Previous studies have investigated the properties of these soft rocks individually, but there is limited research on the behavior and control of double soft composite roofs. This study investigated the hydrophilic mineral composition and microstructure of mudstone and clay through X-ray diffraction (XRD) and scanning electron microscopy (SEM) experiments. Through an orthogonal experimental design, the influence of the clay layer thickness, number of layers, layer position, and relative moisture content on the stability of a mudstone–clay composite roof was studied. The results revealed the following: (1) Kaolinite, the primary hydrophilic component, constitutes a high proportion of clay, while both mudstone and clay exhibit abundant pores and cracks under SEM observation; (2) The relative moisture content emerged as the most significant factor affecting roadway deformation; and (3) A combined support of bolts, a short anchor cable, and a long anchor cable effectively controls RSR deformation in the case of a double soft composite roof. The methodology combining comprehensive material characterization and systematic parametric analysis can be extended to the study of other complex soft rock engineering problems, particularly those involving moisture-sensitive composite roof structures. Full article

(This article belongs to the Special Issue Novel Research on Rock Mechanics and Geotechnical Engineering)

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14 pages, 5298 KiB

Open AccessArticle

Seepage Law of Coal Rock Body in Overburden Zones During Multiple Protection Mining of High-Gas Outburst Coal Seams

by Jiao Zhu and Bo Li

Appl. Sci. 2025, 15(6), 2997; https://doi.org/10.3390/app15062997 - 10 Mar 2025

Viewed by 456

Abstract

Coal and gas outburst accident is a significant risk in high-gas outburst coal seams, and effective pressure relief gas extraction plays a crucial role in mitigating these hazards. The core challenge lies in understanding the seepage behavior of the coal rock body in the three zones of the overburden during multiple protective layer mining. This study employed a damaged coal rock body seepage test system to conduct repeated loading and unloading seepage tests on coal rock samples from these zones. The results show that the permeability of the broken coal rock body in the caving zone decreases with increasing stress, while it increases with (a) larger particle sizes of the broken coal rock body and (b) with a higher proportion of rock in the sample. The permeability distribution in the goaf follows an “O”-shaped circle pattern and gradually increases from the center outward. Additionally, When the protected layer is located within the fracture zone of the protective layer mining, and the first protective layer mining has already resulted in significant stress relief and permeability improvement, the effect of stress release and permeability enhancement from the second protective layer mining becomes less pronounced. In contrast, if the first protective layer mining does not sufficiently relieve stress or enhance permeability, the second protective layer mining has a more substantial effect. These findings are significant for analyzing the effects of pressure relief enhancement in multi-protective layer mining of high-gas outburst coal seams and for optimizing gas extraction. Full article

(This article belongs to the Special Issue Novel Research on Rock Mechanics and Geotechnical Engineering)

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20 pages, 17772 KiB

Open AccessArticle

Failure Law of Sandstone and Identification of Premonitory Deterioration Information Based on Digital Image Correlation–Acoustic Emission Multi-Source Information Fusion

by Zhaohui Chong, Guanzhong Qiu, Xuehua Li and Qiangling Yao

Appl. Sci. 2025, 15(5), 2506; https://doi.org/10.3390/app15052506 - 26 Feb 2025

Viewed by 363

Abstract

Efficiently extracting effective information from the massive experimental data from physical mechanics and accurately identifying the premonitory failure information from coal rock are key and difficult points of intelligent research on rock mechanics. In order to reveal the deterioration characteristics and the forewarning law of fractured coal rock, the digital image correlation method and the acoustic emission technology were adopted in this study to non-destructively detect the strain field, displacement field, and acoustic emission response in time and frequency domains. Additionally, by introducing the derivative functions of the multi-source information function for quantitative analysis, a comprehensive evaluation method was proposed based on the multi-source information fusion monitoring to forewarn red sandstone failure by levels during loading. The results show that obvious premonitory failure information, such as strain concentration areas, appears on red sandstone’s surface before macro-cracks can be observed. With an increase in the inclination angle of the prefabricated crack, the macroscopic failure mode gradually transforms from tensile splitting failure to tensile-shear mixed failure. Moreover, the dominant frequency signals of high frequency–low amplitude (HF–LA), intermediate frequency–low amplitude (IF–LA) and low frequency–low amplitude (LF–LA) are denser near the stress peak. The initial crack expansion time and failure limit time measured by multi-source information fusion are 20.72% and 26.71% earlier, respectively, than those measured by direct observation, suggesting that the forewarning of red sandstone failure by levels is realized with multi-source information fusion. Full article

(This article belongs to the Special Issue Novel Research on Rock Mechanics and Geotechnical Engineering)

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26 pages, 26651 KiB

Open AccessArticle

Deformation and Failure Mechanism and Control of Water-Rich Sandstone Roadways in the Huaibei Mining Area

by Zhisen Zhang, Yukuan Fan, Qiang Xu, Kai Li, Minkang Han and Lixiang Fei

Appl. Sci. 2025, 15(3), 1177; https://doi.org/10.3390/app15031177 - 24 Jan 2025

Cited by 1 | Viewed by 583

Abstract

The sandstone roof rock in the Huaibei mining area contains abundant water at depths of 2–3 m. Water–rock interactions in the rock-surrounding roadway can cause significant deformation, seriously threatening the safety of mine operations. Investigating the deformation and failure mechanisms of water-rich sandstone is therefore of critical importance. In this study, X-ray diffraction and scanning electron microscopy were used to analyze the composition and microstructure of water-rich sandstone. Based on the stress state during the roadway excavation, a true triaxial loading scheme with four different stress paths was designed to study the effects of different moisture contents and loading methods on the mechanical properties of the sandstone. The results show that the deviatoric stress decreased for all stress paths. Acoustic emission (AE) characteristics during the deformation and failure processes were also studied, which indicated that the AE b-value decreased, increased, and then decreased again corresponding to the primary compaction, elastic deformation, and plastic deformation evolutionary processes in the internal microstructure of the rock. The variation in the b-value reflected the development and expansion of internal fractures. These findings provide useful insights for controlling the stability of the surrounding rock in water-rich roadways in coal mines. Full article

(This article belongs to the Special Issue Novel Research on Rock Mechanics and Geotechnical Engineering)

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