Order of intermittent rock fractured surfaces

According to chaos theory, some underlying patterns can disclose the order of disordered systems. Here, it has been discussed that intermittency of rough rock fractured surfaces is an orderable disorder at intermediate length scales. However, this kind of disorder is more complicated than simple fractal or even multi-scaling behaviours. It is planned to deal with some multifractal spectra that systematically change as a function of the analysed domain. Accordingly, some parameters are introduced that can perfectly take into account such systematic behaviour and quantify the intermittency of the studied surfaces. This framework can be used to quantify and model the roughness of fractured surfaces as a prerequisite factor for the analysis of fluid flow in rock media as well as the shear strength of rock joints. Ultimately, the presented framework can be used for analysing the intermittency of time series and developing new models for predicting, for instance, seismic or flood events in a short time with higher accuracy. © 2022 by the authors

Computing elastic moduli of igneous rocks using modal composition and effective medium theory

Elastic constants of rock materials are the basic parameters required for modeling the response of rock materials under mechanical loads. Experimental tests for determining these properties are expensive, time-consuming and suffer from a high uncertainty due to both experimental limitations and the heterogeneous nature of rock materials. To avoid such experimental difficulties, in this paper a method is suggested for determining elastic constants of rock materials by determining their porosity and modal composition and employing effective medium theory. The Voigt–Reuss–Hill average is used to determine effective elastic constants of the studied igneous rocks according to the elastic moduli of their mineral constituents. Then, the effect of porosity has been taken into account by considering rock as a two-phase material, and the Kuster–Toksoz formulation is used for providing a close estimation of different moduli. The solutions are provided for different isotropic igneous rocks. This sustainable method avoids destructive tests and the usage of energy for performing time-consuming and expensive tests and requires simple equipment. © 2022 by the authors

Intermittency of rock fractured surfaces : a power law

Roughness of rock fractured surfaces is one of the most important factors controlling fluid flow in rock masses. Roughness quantification is of prime importance for modelling the flow of ground waters as well as reservoir fluid mechanics. In this study, with the aid of high-resolution 3D X-ray CT scanning and image processing techniques, the roughness of four different rock types is reconstructed with a resolution of 16.5 microns. Moreover, the correlation and structure functions are used to analyse height fluctuations as well as statistical intermittency of the studied rock fractured surfaces. It is observed that at length scales smaller than a critical length scale, fractures surfaces are correlated and show multifractality. Monofractals are neither intermittent nor correlated; hence, a meaningful link between statistical intermittency and the correlation function of multifractals is expected. However, a model that considers this relationship and predicts multifractal spectra of disordered systems is still missing. A simple power law that can exactly forecast the multiscaling spectrum of rock fracture process zone is being introduced. It is explained how the exponent of this power functio

In vivo effects of nonionic and ionic contrast media on beta-thromboglobulin and fibrinopeptide levels

AbstractNonionic contrast media are suggested to cause increased thromboembolism (in vivo), platelet aggregation and procoagulant effect (in vitro) as compared with ionic contrast media. To study these effects in vivo, 30 consecutive patients undergoing routine angiography were prospectively randomized to three groups of 10 patients each. Group A received diatrizoate (ionic, high osmolality), Group B ioxaglate (ionic, low osmolality) and Group C iohexol (nonionic, low osmolality). In vivo platelet alpha-granule release and fibrin-1 formation were measured by radioimmunoassay of beta-thromboglobulin and fibrinopeptide A in peripheral venous samples.Levels were estimated at three stages daring the procedure: before and after left ventriculography and after coronary angiography. No differences were noted (p = NS) when the ratios of beta-thromboglobulin and fibrinopeptide A were compared among the three groups. These data suggest that the newer nonionic contrast media do not demonstrate enhanced systemic platelet activation or fibrin formation as compared with standard ionic contrast media. However, larger randomized clinical studies are necessary to conclusively establish the suggested thromboembolic potential of nonionic contrast media

A case study of grinding coarse 5 mm particles into sand grade particles less than 2.36 mm

This paper presents the viability study of utilising a rod or ball mill to grind a ‘5 mm grit’ to 100% passing 2.36 mm and fit in with a desired particle size analysis. The aim is to introduce this grit into the concrete grade sand produced at the Hanson owned Axedale Sand & Gravel quarry to reduce generated waste and improve the process efficiency. A ball mill and rod mill were used to grind the samples at an interval of 5 and 10 minutes. From the laboratory experimental analysis, it was found that a ball mill with 5 minutes grinding time in closed-circuit using a classifier to remove undersize and reintroduce oversize to the mill would be a viable option in an industrial setting. A Bond Ball Mill Grindability Test was undertaken to determine the grindability of the 5 mm grit, which served to determine the power (kWh/t) required to grind it to 100% passing 2.36 mm. The bond ball mill grindability test showed that the grit had a work index value of 17.66 kWh/t. This work index gives an actual work input of 13.54 kWh/t, meaning that for every ton of feed material introduced to the mill, 13.54 kWh of work input is required to grind it to 150 microns. © 2021 Vietnam Academy of Science and Technology

Prediction of blast-induced air overpressure using a hybrid machine learning model and gene expression programming (GEP) : a case study from an iron ore mine

Mine blasting can have a destructive effect on the environment. Among these effects, air overpressure (AOp) is a major concern. Therefore, a careful assessment of the AOp intensity should be conducted before any blasting operation in order to minimize the associated environmental detriment. Several empirical models have been established to predict and control AOp. However, the current empirical methods have many limitations, including low accuracy, poor generalizability, consideration only of linear relationships among influencing parameters, and investigation of only a few influencing parameters. Thus, the current research presents a hybrid model which combines an extreme gradient boosting algorithm (XGB) with grey wolf optimization (GWO) for accurately predicting AOp. Furthermore, an empirical model and gene expression programming (GEP) were used to assess the validity of the hybrid model (XGB-GWO). An analysis of 66 blastings with their corresponding AOp values and influential parameters was conducted to achieve the goals of this research. The efficiency of AOp prediction methods was evaluated in terms of mean absolute error (MAE), coefficient of determination (R 2 ), and root mean square error (RMSE). Based on the calculations, the XGB-GWO model has performed as well as the empirical and GEP models. Next, the most significant parameters for predicting AOp were determined using a sensitivity analysis. Based on the analysis results, stemming length and rock quality designation (RQD) were identified as two variables with the greatest influence. This study showed that the proposed XGB-GWO method was robust and applicable for predicting AOp driven by blasting operations

A comparative study on the application of various artificial neural networks to simultaneous prediction of rock fragmentation and backbreak

AbstractIn blasting operation, the aim is to achieve proper fragmentation and to avoid undesirable events such as backbreak. Therefore, predicting rock fragmentation and backbreak is very important to arrive at a technically and economically successful outcome. Since many parameters affect the blasting results in a complicated mechanism, employment of robust methods such as artificial neural network may be very useful. In this regard, this paper attends to simultaneous prediction of rock fragmentation and backbreak in the blasting operation of Tehran Cement Company limestone mines in Iran. Back propagation neural network (BPNN) and radial basis function neural network (RBFNN) are adopted for the simulation. Also, regression analysis is performed between independent and dependent variables. For the BPNN modeling, a network with architecture 6-10-2 is found to be optimum whereas for the RBFNN, architecture 6-36-2 with spread factor of 0.79 provides maximum prediction aptitude. Performance comparison of the developed models is fulfilled using value account for (VAF), root mean square error (RMSE), determination coefficient (R2) and maximum relative error (MRE). As such, it is observed that the BPNN model is the most preferable model providing maximum accuracy and minimum error. Also, sensitivity analysis shows that inputs burden and stemming are the most effective parameters on the outputs fragmentation and backbreak, respectively. On the other hand, for both of the outputs, specific charge is the least effective parameter

Mineral texture identification using local binary patterns equipped with a Classification and Recognition Updating System (CARUS)

In this paper, a rotation-invariant local binary pattern operator equipped with a local contrast measure (riLBPc) is employed to characterize the type of mineral twinning by inspecting the texture properties of crystals. The proposed method uses photomicrographs of minerals and produces LBP histograms, which might be compared with those included in a predefined database using the Kullback–Leibler divergence-based metric. The paper proposes a new LBP-based scheme for concurrent classification and recognition tasks, followed by a novel online updating routine to enhance the locally developed mineral LBP database. The discriminatory power of the proposed Classification and Recognition Updating System (CARUS) for texture identification scheme is verified for plagioclase, orthoclase, microcline, and quartz minerals with sensitivity (TPR) near 99.9%, 87%, 99.9%, and 96%, and accuracy (ACC) equal to about 99%, 97%, 99%, and 99%, respectively. According to the results, the introduced CARUS system is a promising approach that can be applied in a variety of different fields dealing with classification and feature recognition tasks. © 2022 by the authors

Utilization methods and practice of abandoned mines and related rock mechanics under the ecological and double carbon strategy in china—a comprehensive review

Governance of abandoned mines has become a pressing issue for China. The utilization of abandoned mines is a technology that can solve the problem of governance and recreate the value of mines, which is in line with the current strategic goals of ecological protection and double carbon in China. In this paper, the various utilization models and the advances in rock mechanics of abandoned mines across the globe are summarized and reviewed. The utilization models of abandoned mines can be categorized into four aspects: Energy storage, Waste treatment, Ecological restoration, and carbon dioxide (CO2) sequestration. There are a number of applications and uses of abandoned mines, such as pumped storage, compressed air storage, salt cavern gas/oil storage construction, carbon dioxide storage and utilization, radioactive waste disposal and treatment, and tourism development. Various progress practices of abandoned mines are discussed in detail with emphasis on the national conditions of China. The basic rock mechanics problems and advances involved in the construction of the facilities related to the utilization of abandoned mines are discussed and evaluated. The establishment of relevant research and experimental platforms will contribute to the sustainable development of China’s mining industry and the improvement of clean technologies. © 2022 by the authors

Proposing a novel comprehensive evaluation model for the coal burst liability in underground coal mines considering uncertainty factors

Coal burst is a severe hazard that can result in fatalities and damage of facilities in underground coal mines. To address this issue, a robust unascertained combination model is proposed to study the coal burst hazard based on an updated database. Four assessment indexes are used in the model, which are the dynamic failure duration (DT), elastic energy index (WET), impact energy index (KE) and uniaxial compressive strength (RC). Four membership functions, including linear (L), parabolic (P), S and Weibull (W) functions, are proposed to measure the uncertainty level of individual index. The corresponding weights are determined through information entropy (EN), analysis hierarchy process (AHP) and synthetic weights (CW). Simultaneously, the classification criteria, including unascertained cluster (UC) and credible identification principle (CIP), are analyzed. The combination algorithm, consisting of P function, CW and CIP (P-CW-CIP), is selected as the optimal classification model in function of theory analysis and to train the samples. Ultimately, the established ensemble model is further validated through test samples with 100% accuracy. The results reveal that the hybrid model has a great potential in the coal burst hazard evaluation in underground coal mines. © 202