In this paper, a data augmentation method Conditional Residual Deep Convolutional Generative Adversarial Network (CRDCGAN) based on Deep Convolutional Generative Adversarial Network (DCGAN) is proposed to address the problem that the accuracy of exis...
In tunnel design and construction, proper and accurate classification of rock surrounding tunnels is needed to ensure tunnel construction safety, guarantee construction quality, and reduce project costs. With rapid urbanization, numerous small clear-...
Empirical classification systems do not provide details of the factors that affect the performance of fully grouted rock bolts, as they are based on average values. Fully grouted rock-bolt patterns during tunnel-support design are a part of the compo...
The accurate and reliable classification of rock mass is the basis of a reasonable engineering design. In the Xishan mining region of Sanshandao Gold Mine, three conventional rock mass classification methods of Tunneling Quality Index (Q), Rock Mass...
Currently, standardized classification methods of surrounding rock are relatively insufficient. The classification of surrounding rock mainly relies on the subjective judgment of technicians, leading to diverse evaluation results. This study focuses...
Rock image classification is a fundamental and crucial task in the creation of geological surveys. Traditional rock image classification methods mainly rely on manual operation, resulting in high costs and unstable accuracy. While existing methods ba...
Accurate classification of rock fracture modes is essential for understanding rock mass instability mechanisms. To address the limitation of traditional acoustic emission (AE) classification methods that treat a single AE signal as a single fracture...
The issues of the same material with different spectra and the same spectra for different materials pose challenges in hyperspectral rock classification. This paper proposes a multidimensional feature network based on 2-D convolutional neural network...
The quality evaluation of the surrounding rock is the cornerstone of tunnel design and construction. Previous studies have confirmed the existence of a relationship between drilling parameters and the quality of surrounding rock. The application of d...
The accurate classification of rocks is crucial for applications such as earthquake prediction, resource exploration, and geological analysis. Traditional methods rely on expert examination of thin-section images under a microscope, making the proces...
Experimental studies were carried out to analyze the impact of optimizers and learning rate on the performance of deep learning-based algorithms for rock thin-section image classification. A total of 2634 rock thin-section images including three rock...
The rock classification system was initially applied to drill-and-blast tunnels and subsequently adapted for TBM tunnels; however, the majority of these systems primarily focused on rock stability while neglecting considerations of boreability. Compa...
Rock mass conditions are extremely sensitive to tunnel boring machine (TBM) tunneling. Therefore, establishing a surrounding rock excavatability (SRE) classification system applicable to TBM tunnels. Accurately and intelligently identifying excavatab...
Accurate rock mass quality classification is crucial for the design and construction of underground projects. Traditional methods often rely on expert experience, introducing subjectivity, and struggle with complex geological conditions. Machine lear...
Reality capture technologies, also known as close-range sensing, have been increasingly popular within the field of engineering geology and particularly rock slope management. Such technologies provide accurate and high-resolution n-dimensional spati...
This research aims to classify rock types based on the vibration signal propagated from the experimental rotary drilling process, where the generated vibration signal is a source of information. Its measurement and processing provide important inform...
The slope stability in an open-pit mine is closely related to the production safety and economic benefit of the mine. As a result of the increase in the number and scale of mine slopes, slope instability is frequently encountered in mines. Therefore,...
The accurate prediction of rock mass quality ahead of the tunnel face is crucial for optimizing tunnel construction strategies, enhancing safety, and reducing geological risks. This study developed three hybrid models using random forest (RF) optimiz...
Purpose—Accurate classification of tunnel surrounding rock is essential for safe excavation, timely support adjustment, and risk control during construction. This study proposes an intelligent classification method that links tunnel face image...
To address the challenges of small-sample rock fine classification—such as overfitting caused by limited sample size and the increased complexity resulting from high inter-class similarity—this study proposes a Stacking ensemble method ta...
Rock image classification represents a challenging fine-grained image classification task characterized by subtle differences among closely related rock categories. Current contrastive learning methods prevalently utilized in fine-grained image class...
To address overfitting due to limited sample size, and the challenges posed by “Spectral Homogeneity with Material Heterogeneity (SHMH)” and “Material Consistency with Spectral Divergence (MCSD)”—which arise from subtle...
To mitigate unforeseen incidents, such as key block failure or tunnel collapse during excavation, an appropriate support pattern that correlates with the geological conditions of the rock mass at the tunnel face should be designed. Rock mass evaluati...
The geological condition monitoring and identification based on TBM-equipped sensors is of great significance for efficient and safe tunnel construction. Full-scale rotary cutting experiments are carried out using tunnel-boring machine disc cutters....
Accurate and efficient rock mass quality classification is a prerequisite for assessing slope stability, designing support schemes, and ensuring mining safety in open-pit mines. However, traditional empirical classification methods rely heavily on ex...
The high intra-class variability of rock spectra is an important factor affecting classification accuracy. The discrete wavelet transform (DWT) can capture abrupt changes in the signal and obtain subtle differences between the spectra of different ro...
Tunnel construction requires accurate and timely classification of surrounding rock masses to ensure safety and guide excavation. This research addresses the limitations of conventional methods and unimodal intelligent approaches by proposing a novel...
Rock strength is a key parameter for mine planning and operational optimization, but conventional laboratory testing is costly and provides limited spatial coverage. This study develops a methodology for classifying operational rock-strength classes...
In tunnel engineering, rock–concrete composite structures may be subjected to dynamic loading induced by earthquakes, blasting, and rockbursts. This study investigated the dynamic response and damage evolution of granite- and sandstone-based co...
The degree of rock mass discontinuity is crucial for evaluating surrounding rock quality, yet its accurate and rapid measurement at construction sites remains challenging. This study utilizes fractal dimension to characterize the geometric characteri...
Measurement while drilling (MWD) data reflect the drilling rig–rock mass interaction; they are crucial for accurately classifying the rock mass ahead of the tunnel face. Although machine-learning methods can learn the relationship between MWD d...
Hydropower Classification (HC) is a widely used rock mass classification method in tunneling construction projects. Predicting the HC of the surrounding rock is significant for selecting tunneling parameters and guaranteeing tunneling safety. To pred...
This paper presents new valuable field data and a comprehensive approach to the determination of the rock mass classification and tunnel support systems for tunnels in unconsolidated sedimentary rocks as a special soil condition. The main objective o...
After the original support system in the auxiliary transportation roadway of the northern wing of the Zhaoxian Mine failed, the extent of damage and deformation varied significantly across different sections of the drift. A single support method coul...
The fast and accurate classification of surrounding rock mass is the basis for tunnel design and construction and has significant value in engineering applications. Therefore, this paper proposes a method for classifying and predicting surrounding ro...
Quantitative microstructure evaluation of sedimentary rock thin sections is essential for revealing reservoir flow mechanisms and assessing reservoir quality. However, traditional manual identification is inefficient and prone to subjectivity. Althou...
Q-slope is one of the most recent empirical geomechanical classification systems and the least studied in South America. This study aims to expand the knowledge base regarding the Q-slope geomechanical method and demonstrate its applicability and rel...
Rockbursts represent hazardous dynamic disasters for underground coal mines and other underground rock engineering projects. Some bursting liability indices are put forward and applied to identify the likelihood of rock burst occurrence. The classifi...
In large-scale water conservancy and hydropower projects, complex rock structures are considered to be the main factor controlling the stability of hydraulic structures. The classification of rock mass structure plays an important role in the safety...
Despite the availability of several scour calculation methods, bridge scour analysis for scour susceptible rocks is much less understood than its counterpart for sands, because scour calculation equations were traditionally obtained using flume tests...
Efficient and convenient rock image classification methods are important for geological research. They help in identifying and categorizing rocks based on their physical and chemical properties, which can provide insights into their geological histor...
Hydraulic rock drills are widely used in drilling, mining, construction, and engineering applications. They typically operate in harsh environments with high humidity, large temperature differences, and vibration. Under the influence of environmental...
Rock lithology recognition plays a fundamental role in geological survey research, mineral resource exploration, mining engineering, etc. However, the objectivity of researchers, rock variable natures, and tedious experimental processes make it diffi...
At the initial phases of tunnel design, information on rock properties is often limited. In such instances, the engineering classification of the rock is recommended as a primary assessment of its geotechnical condition. This paper reviews different...
Measurement while drilling (MWD) data are gathered during drilling operations and can provide information about the strength of the rock penetrated by the boreholes. In this paper MWD data from a marble open-pit operation in northern Norway are studi...
Considering the effect of surrounding rock on lining in the design of tunnel lining within fractured rock masses is challenging, particularly in accurately predicting the reserved deformation of the tunnel. This study bases a rock mass classification...
Classifying rocks based on core data is the most common method used by geologists. However, due to factors such as drilling costs, it is impossible to obtain core samples from all wells, which poses challenges for the accurate identification of rocks...
Though multitudes of industries depend on the mining industry for resources, this industry has taken hits in terms of declining mineral ore grades and its current use of traditional, time-consuming and computationally costly rock and mineral identifi...
While machine learning techniques have been increasingly applied to land cover classification problems, these techniques have not focused on separating exposed bare rock from soil covered areas. Therefore, we built a convolutional neural network (CNN...
Rock drillability classification provides an important basis for drilling-parameter optimization, equipment selection, and improved mining efficiency. Existing drillability evaluation methods often rely on fixed empirical weights and rigid grade boun...
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