Understanding the fracture behavior of rocks subjected to temperature and accounting for the rock's texture is vital for safe and efficient design.Prior studies have often focused on isolated aspects of rock fract...Understanding the fracture behavior of rocks subjected to temperature and accounting for the rock's texture is vital for safe and efficient design.Prior studies have often focused on isolated aspects of rock fracture behavior,neglecting the combined influence of grain size and temperature on fracture behavior.This study employs specimens based on the particle flow code-grain based model to scrutinize the influence of temperature and grain size discrepancies on the fracture characteristics of sandstone.In pursuit of this goal,we manufactured ninety-six semi-circular bend specimens with grain sizes spanning from 0.5 mm to 1.5 mm,predicated on the mineral composition of sandstone.Recognizing the significance of intra-granular and inter-granular fractures,the grains were considered deformable and susceptible to breakage.The numerical model was calibrated using the results of uniaxial compressive strength(UCS)and Brazilian tests.We implemented thermo-mechanical coupled analysis to simulate mode Ⅰ,mode Ⅱ,and mixed mode(Ⅰ-Ⅱ)fracture toughness tests and subsequently studied alterations in the fracture behavior of sandstone at temperatures from 25℃ to 700℃.Our findings revealed increased fracture toughness as the temperature escalated from 25℃ to 200℃.However,beyond the threshold of 200℃,we noted a decline in fracture toughness.More specifically,the drop in mode Ⅰ fracture toughness was more pronounced in specimens with finer grains than those with coarser grains.Contrarily,the trend was reversed for mode Ⅱ fracture toughness.In contrast,the reduction of mixed mode(Ⅰ-Ⅱ)fracture toughness seemed almost linear across all grain sizes.Furthermore,we identified a correlation between temperature and grain size and their collective impact on crack propagation patterns.Comparing our results with established theoretical benchmarks,we confirmed that both temperature and grain size variations influence the fracture envelopes of sandstone.展开更多
The goal of this research is to develop mine-scale discrete fracture network(DFN)models in which the influence of the spatial heterogeneity of fracture distributions may be investigated on the rock wedge stability of ...The goal of this research is to develop mine-scale discrete fracture network(DFN)models in which the influence of the spatial heterogeneity of fracture distributions may be investigated on the rock wedge stability of an open pit slope.For this purpose,spatially conditioned DFN models were developed for the pit walls at Tasiast mine using comprehensive structural data from the mine.Using Sequential Gaussian Simulation(SGS),volumetric fracture intensities(P32)were modeled across the entire mine site in the form of 3D block models.The simulated P32 block models were used as the input constraints for conditional DFN fracture generation,where the DFN grid dimension is the same as the SGS 3D blocks.The spatially constrained DFN models were further calibrated using aerial fracture intensities(P21)data from the pit walls,obtained by a survey of the pit walls using an unmanned aerial vehicle(UAV)and measured traces of joints from 3D point cloud data.The final DFN model is expected to honor the fracture intensities gathered through different means with optimal model accuracy.Finally,bench-scale and interramp scale rock wedge slope stability analyses were conducted using the calibrated conditional DFN models.This work proves the significance of conditioned DFN models in rock wedge stability analysis.Such models provide detailed information regarding rock wedge stability so that site monitoring and prevention plans can be conducted with higher efficiency.展开更多
Directive(EU)2017/164 establishes a fourth list of indicative occupational exposure limit values(IOELVs)to protect workers from risks of exposure to hazardous chemicals.It states that in underground mining and tunnell...Directive(EU)2017/164 establishes a fourth list of indicative occupational exposure limit values(IOELVs)to protect workers from risks of exposure to hazardous chemicals.It states that in underground mining and tunnelling,Member States may benefit from a transitional period regarding IOELVs for nitrogen monoxide,nitrogen dioxide,and carbon monoxide,during which the existing established IOELVs may be applied.The European Advisory Committee on Health and Safety at Work questions the technical feasibility of the proposed IOELVs in underground mining(CO,NO and NO2)and tunnelling(NO and NO2).Challenges arise concerning the availability of measurement methodologies for compliance with proposed IOELVs(NO2)in underground mining and tunnelling environments.展开更多
The purpose of this research is to study the effect of longwall mining on the stability of main roadway in the underground coal mine. The PT GDM （Gerbang Daya Mandiri） underground coal mine in Indonesia, where the r...The purpose of this research is to study the effect of longwall mining on the stability of main roadway in the underground coal mine. The PT GDM （Gerbang Daya Mandiri） underground coal mine in Indonesia, where the rocks are weak, was selected as a representative study site. To accomplish the objective of the research, the finite difference code software FLAC3D was used as a tool for the numerical simulations. The longwall mining of several panel and barrier pillar widths at various depths was simulated and discussed. Based on the simulation results, it indicates that the effect of coal panel extraction on the main roadway stability depends on the width of panel and barrier pillar. The greatest effect occurs when the large panel width and the small barrier pillar width are applied, whereas the smallest effect happens when the narrow panel width and the large barrier pillar width are adopted. In this paper, therefore, to maintain the stability of the main roadway with the aim of maximizing the coal recovery, the appropriate size of panel and barrier pillar width is proposed for each mining depth for this underground coal mine.展开更多
Ignimbrites have been widely used as building materials in many historical and touristic structures in the Kayseri region of Türkiye. Their diverse colours and textures make them a popular choice for modern const...Ignimbrites have been widely used as building materials in many historical and touristic structures in the Kayseri region of Türkiye. Their diverse colours and textures make them a popular choice for modern construction as well. However, ignimbrites are particularly vulnerable to atmospheric conditions, such as freeze-thaw cycles, due to their high porosity, which is a result of their formation process. When water enters the pores of the ignimbrites, it can freeze during cold weather. As the water freezes and expands, it generates internal stress within the stone, causing micro-cracks to develop. Over time, repeated freeze-thaw (F-T) cycles lead to the growth of these micro-cracks into larger cracks, compromising the structural integrity of the ignimbrites and eventually making them unsuitable for use as building materials. The determination of the long-term F-T performance of ignimbrites can be established after long F-T experimental processes. Determining the long-term F-T performance of ignimbrites typically requires extensive experimental testing over prolonged freeze-thaw cycles. To streamline this process, developing accurate predictive equations becomes crucial. In this study, such equations were formulated using classical regression analyses and artificial neural networks (ANN) based on data obtained from these experiments, allowing for the prediction of the F-T performance of ignimbrites and other similar building stones without the need for lengthy testing. In this study, uniaxial compressive strength, ultrasonic propagation velocity, apparent porosity and mass loss of ignimbrites after long-term F-T were determined. Following the F-T cycles, the disintegration rate was evaluated using decay function approaches, while uniaxial compressive strength (UCS) values were predicted with minimal input parameters through both regression and ANN analyses. The ANN and regression models created for this purpose were first started with a single input value and then developed with two and three combinations. The predictive performance of the models was assessed by comparing them to regression models using the coefficient of determination (R2) as the evaluation criterion. As a result of the study, higher R2 values (0.87) were obtained in models built with artificial neural network. The results of the study indicate that ANN usage can produce results close to experimental outcomes in predicting the long-term F-T performance of ignimbrite samples.展开更多
Geo-engineering problems are known for their complexity and high uncertainty levels,requiring precise defini-tions,past experiences,logical reasoning,mathematical analysis,and practical insight to address them effecti...Geo-engineering problems are known for their complexity and high uncertainty levels,requiring precise defini-tions,past experiences,logical reasoning,mathematical analysis,and practical insight to address them effectively.Soft Computing(SC)methods have gained popularity in engineering disciplines such as mining and civil engineering due to computer hardware and machine learning advancements.Unlike traditional hard computing approaches,SC models use soft values and fuzzy sets to navigate uncertain environments.This study focuses on the application of SC methods to predict backbreak,a common issue in blasting operations within mining and civil projects.Backbreak,which refers to the unintended fracturing of rock beyond the desired blast perimeter,can significantly impact project timelines and costs.This study aims to explore how SC methods can be effectively employed to anticipate and mitigate the undesirable consequences of blasting operations,specifically focusing on backbreak prediction.The research explores the complexities of backbreak prediction and highlights the potential benefits of utilizing SC methods to address this challenging issue in geo-engineering projects.展开更多
An organic geochemical,petrographical,and palynological evaluation was conducted on 30 claystone outcrop samples of the Toraja Formation,along with a geochemical analysis of an oil seep in the Enrekang Sub-basin.The a...An organic geochemical,petrographical,and palynological evaluation was conducted on 30 claystone outcrop samples of the Toraja Formation,along with a geochemical analysis of an oil seep in the Enrekang Sub-basin.The aim of the study was to determine the correlation between oil and source rock in terms of age,depositional environment,organic material sources,and maturity level.The total organic carbon content of the claystone samples varies from 0.03 to 4.52 wt%,which are classified as poor to excellent.The claystones are immature to post-mature with a mixture of TypeⅡandⅢkerogen.Their vitrinite reflectance values range from 0.47 to 4.52%Ro.The samples of Toraja Formation rock and the oil seep source rock might have a similar depositional environment,a deltaic marine depositional setting with high oxidizing conditions.Organic material sources for rock and oil samples are dominated by terrestrial input.The oil is inferred to have originated from the Paleogene source rocks,which correlates in age with the Toraja Formation.The recovered palynomorphs from the studied rock samples suggest a late Eocene to Oligocene age with a strong terrestrial influence of shallow marine depositional setting.The biomarker analysis shows that the rock samples have a more substantial contribution from the terrigenous higher plants,while the oil sample indicates a higher degree of marine influence.The maturity levels are also different between the oil(peak mature)and the analyzed rock samples(immature).It is inferred that the oil seep source rock is equivalent to the analyzed rock sample but more mature,having been deposited under more marine conditions.The oil seep source rock is not exposed and is located in the deeper part of the basin.A deeper marine facies(i.e.distal delta front and prodelta facies)in front of the distributary mouth bar within a delta is interpreted as the source rock of the oil seep sample.展开更多
Sweat contains numerous vital biomarkers such as metabolites,electrolytes,proteins,nucleic acids and antigens that reflect hydration status,exhaustion,nutrition,and physiological changes.Conventional healthcare diagno...Sweat contains numerous vital biomarkers such as metabolites,electrolytes,proteins,nucleic acids and antigens that reflect hydration status,exhaustion,nutrition,and physiological changes.Conventional healthcare diagnosis relies on disease diagnostics in sophisticated centralized laboratories with invasive sample collection(e.g.,chemical analyses,plasma separation via centrifugation,tissue biopsy,etc.).Cutting-edge point-of-care diagnostics for sweat biomarker analysis allow for non-invasive monitoring of physiologically related biomarkers in sweat and real-time health status tracking.Moreover,using advanced nanoarchitectures,including nanostructured platforms and nanoparticles,can enhance the specificity,sensitivity,wearability and widen the sensing modality of sweat biosensors.Herein,we comprehensively review the secretory mechanisms,clinical uses of sweat biomarkers,and the design,principle,and latest technologies of sweat biosensors.With an emphasis on cutting-edge technologies for sweat biomarker analysis,this review chronicles the issues associated with the current sweat biomarkers analysis of sweat biomarkers and provides insights into strategies for enhancing the translation of such biosensors into routine clinical practice.展开更多
Hyperaccumulators concentrate trace metals and heavy metals in their shoots when grown in metal-contaminated soils and these trace metal-loaded plants may be removed by harvesting the fields. Studies exploring the ben...Hyperaccumulators concentrate trace metals and heavy metals in their shoots when grown in metal-contaminated soils and these trace metal-loaded plants may be removed by harvesting the fields. Studies exploring the beneficial role of these hyperaccumulators to clean up the environment have led to the development of phytoextraction. The success of phytoextraction depends upon the high biomass of plant species and bioavailability of metals for plant uptake. The phytoavailability of metals is influenced by soil- associated factors, such as pH, redox potential, cation exchange capacity, soil type, and soil texture, and by plant-associated factors, such as root exudates and root rhizosphere processes （microorganisms）. Efficiency of phytoextraction can be improved by advanced agronomic practices including soil and crop management by application of genetic engineering to enhance the metal tolerance, shoot translocation, accumulation, and sequestration and by application of chelate treatments to enhance metal bioavailability. Application of microorganisms including bacteria and mycorrhiza may facilitate the phytoextraction application at commercially large scale.展开更多
Rockburst is defined as a phenomenon with immediate dynamic instability under excavation unloading conditions of deep or high geostress areas.Inadequate knowledge and lack of characterizing information prevent enginee...Rockburst is defined as a phenomenon with immediate dynamic instability under excavation unloading conditions of deep or high geostress areas.Inadequate knowledge and lack of characterizing information prevent engineers and experts from achieving appropriate prediction results related to the rockburst behaviour.In this study,a data set including 220 rockburst instances was collected for rockburst classification via the geostatistical method.An update of the 2D graph,the tunnel rockburst classification(TRC)chart,was introduced based on analysing three indicators,namely,elastic energy index(Wet),tangential stress in rock mass(σ_(0)),and uniaxial compressive strength(σ_(c)).Distribution and correlation of data were drawn on 2D plot,and the boundaries of rockburst were distinguished according to the achieved interpolate points by kriging method.Hierarchically,the validation phase was performed using an additional set of 28 case histories obtained from several projects around the world.The results showed that the TRC chart with an average error percentage of 3.6%in the prediction of rockburst had a significant and effective implementation in comparison to the exiting heuristic systems.Despite the initial character of the prediction,the described chart may be a helpful tool in the first steps of design and construction.展开更多
Blasting is the live wire of mining and its operations,with air overpressure(AOp)recognised as an end product of blasting.AOp is known to be one of the most important environmental hazards of mining.Further research i...Blasting is the live wire of mining and its operations,with air overpressure(AOp)recognised as an end product of blasting.AOp is known to be one of the most important environmental hazards of mining.Further research in this area of mining is required to help improve on safety of the working environment.Review of previous studies has shown that many empirical and artificial intelligence(AI)methods have been proposed as a forecasting model.As an alternative to the previous methods,this study proposes a new class of advanced artificial neural network known as brain inspired emotional neural network(BIENN)to predict AOp.The proposed BI-ENN approach is compared with two classical AOp predictors(generalised predictor and McKenzie formula)and three established AI methods of backpropagation neural network(BPNN),group method of data handling(GMDH),and support vector machine(SVM).From the analysis of the results,BI-ENN is the best by achieving the least RMSE,MAPE,NRMSE and highest R,VAF and PI values of 1.0941,0.8339%,0.1243%,0.8249,68.0512%and 1.2367 respectively and thus can be used for monitoring and controlling AOp.展开更多
Dissolution kinetics of nickel from lateritic ore in nitric acid solution was investigated. Experimental parameters used were stirring speed(100-600 r/min), temperature(40-96 °C), nitric acid concentration(0.1-2 ...Dissolution kinetics of nickel from lateritic ore in nitric acid solution was investigated. Experimental parameters used were stirring speed(100-600 r/min), temperature(40-96 °C), nitric acid concentration(0.1-2 mol/L) and particle size(<106 μm). The shrinking core model was applied to the results of experiments investigating the effects of leaching temperature in the range of 40-90 °C and nitric acid concentration in range of 0.1-2 mol/L on nickel dissolution rate. The kinetic analysis shows that the nickel dissolution from lateritic ore could be described by diffusion model. The activation energy(E_a) for the dissolution reaction is calculated as 79.52 kJ/mol.展开更多
One of the most important factors influencing on a tunnel blast efficiency is the proper design of blasting pattern. Among blasting parameters, blasthole diameter and tunnel face area are more significant so that any ...One of the most important factors influencing on a tunnel blast efficiency is the proper design of blasting pattern. Among blasting parameters, blasthole diameter and tunnel face area are more significant so that any change in these parameters could finally affect on specific charge and specific drilling. There are mainly two groups of methods for tunnel blast design categorized based on the parallel cuts and angular cuts. In this research, a software for tunnel blast design was developed to analyze the effect and sensitiveness of blasthole diameter and the tunnel face area on blasting results in different blast design models. Using the software, it is quickly possible to determine specific charge, specific drilling and number of blastholes for each blast design model. The relations between both of blasthole diameters and the tunnel face area with the above parameters in different blast design models were then investigated to yield a set of equations with the highest correlations to compare the methods. The results showed that angular method requires more blasthole numbers than parallel method in similar condition(blasthole diameter and tunnel face area). Moreover, the specific charge values yielded by the two methods are approximately the same and very close together.展开更多
Our study was carried out to assess the level of noise generated and ground vibrations induced during blasting operations at the Ewekoro limestone quarry in Nigeria.To achieve this objective,vibro monitor equipment wa...Our study was carried out to assess the level of noise generated and ground vibrations induced during blasting operations at the Ewekoro limestone quarry in Nigeria.To achieve this objective,vibro monitor equipment was used to take readings related to noise generated and ground vibrations during all blasting operations that took place in the quarry for a period of one month.As well,a digital camera was used to take photographs of residential structures within villages near the quarry.The results obtained indicate that the ground vibration readings fall between 0.5 mm/s and 2.1 mm/s and the noise generated during the blasting operations between 82 dB and 89 dB.These readings when compared with the limits set by FEPA(Federal Environmental Protection Agency) of 5.0 mm/s and 150 dB) all fall within the permissible limits.However the photographs of most structures near the quarry reveal cracks and dilapidated building walls.Recommendations are made on how to sustain and improve current blasting techniques.展开更多
The effects of cyanidation conditions on gold dissolution were studied by artificial neural network （ANN） modeling. Eighty-five datasets were used to estimate the gold dissolution. Six input parameters, time, solid ...The effects of cyanidation conditions on gold dissolution were studied by artificial neural network （ANN） modeling. Eighty-five datasets were used to estimate the gold dissolution. Six input parameters, time, solid percentage, P50 of particle, NaCN content in cyanide media, temperature of solution and pH value were used. For selecting the best model, the outputs of models were compared with measured data. A fourth-layer ANN is found to be optimum with architecture of twenty, fifteen, ten and five neurons in the first, second, third and fourth hidden layers, respectively, and one neuron in output layer. The results of artificial neural network show that the square correlation coefficients （R2） of training, testing and validating data achieve 0.999 1, 0.996 4 and 0.9981, respectively. Sensitivity analysis shows that the highest and lowest effects on the gold dissolution rise from time and pH, respectively It is verified that the predicted values of ANN coincide well with the experimental results.展开更多
The main aspects that require attention in tunnel design in terms of safety and economy are the precise estimation of probable ground conditions and ground behavior during construction. The variation in rock mass beha...The main aspects that require attention in tunnel design in terms of safety and economy are the precise estimation of probable ground conditions and ground behavior during construction. The variation in rock mass behavior due to tunnel excavation sequence plays an important role during the construction stage.The purpose of this research is to numerically evaluate the effect of excavation sequence on the ground behavior for the Lowari tunnel project, Pakistan. For the tunnel stability, the ground behavior observed during the actual partial face excavation sequence is compared with the top heading and bench excavation sequence. For this purpose, the intact rock parameters are used along with the characterization of rock mass joints related parameters to provide input for numerical modelling via FLAC 2D. The in-situ stresses for the numerical modelling are obtained using empirical equations. From the comparison of the two excavation sequences, it was observed that the actual excavation sequence used for Lowari tunnel construction utilized more support than the top heading and bench method. However, the actual excavation sequence provided good results in terms of stability.展开更多
Drilling and blasting play vital roles in opencast mining. These operations not only affect the cost of production directly but as well and significantly, the overall operational costs. This research was carried out t...Drilling and blasting play vital roles in opencast mining. These operations not only affect the cost of production directly but as well and significantly, the overall operational costs. This research was carried out to find a possible way of optimizing the drilling and blasting operations in an open pit mine of Somair (Société des Mines de l’Air), in the Niger Republic. In order to optimize the drilling operation, the time taken by two drilling machines to accomplish the same task was analyzed statistically. The result indicates that the Down the Hole Hammer Drilling Rig (DMNo406) is more efficient than the Drill Master (DM405). The relative unit consumption of two explosives (Explus and Nitram 9), when used under the same operating conditions, were also considered and the results indicate Explus to be more economical per unit consumption with a range of 0.15 g/t–0.183 g/t, when compared with Nitram 9 with a unit consumption range of 0.19 g/t-0.24 g/t in the study area.展开更多
Prediction of mode I fracture toughness(KIC) of rock is of significant importance in rock engineering analyses. In this study, linear multiple regression(LMR) and gene expression programming(GEP)methods were used to p...Prediction of mode I fracture toughness(KIC) of rock is of significant importance in rock engineering analyses. In this study, linear multiple regression(LMR) and gene expression programming(GEP)methods were used to provide a reliable relationship to determine mode I fracture toughness of rock. The presented model was developed based on 60 datasets taken from the previous literature. To predict fracture parameters, three mechanical parameters of rock mass including uniaxial compressive strength(UCS), Brazilian tensile strength(BTS), and elastic modulus(E) have been selected as the input parameters. A cluster of data was collected and divided into two random groups of training and testing datasets.Then, different statistical linear and artificial intelligence based nonlinear analyses were conducted on the training data to provide a reliable prediction model of KIC. These two predictive methods were then evaluated based on the testing data. To evaluate the efficiency of the proposed models for predicting the mode I fracture toughness of rock, various statistical indices including coefficient of determination(R2),root mean square error(RMSE), and mean absolute error(MAE) were utilized herein. In the case of testing datasets, the values of R2, RMSE, and MAE for the GEP model were 0.87, 0.188, and 0.156,respectively, while they were 0.74, 0.473, and 0.223, respectively, for the LMR model. The results indicated that the selected GEP model delivered superior performance with a higher R2value and lower errors.展开更多
文摘Understanding the fracture behavior of rocks subjected to temperature and accounting for the rock's texture is vital for safe and efficient design.Prior studies have often focused on isolated aspects of rock fracture behavior,neglecting the combined influence of grain size and temperature on fracture behavior.This study employs specimens based on the particle flow code-grain based model to scrutinize the influence of temperature and grain size discrepancies on the fracture characteristics of sandstone.In pursuit of this goal,we manufactured ninety-six semi-circular bend specimens with grain sizes spanning from 0.5 mm to 1.5 mm,predicated on the mineral composition of sandstone.Recognizing the significance of intra-granular and inter-granular fractures,the grains were considered deformable and susceptible to breakage.The numerical model was calibrated using the results of uniaxial compressive strength(UCS)and Brazilian tests.We implemented thermo-mechanical coupled analysis to simulate mode Ⅰ,mode Ⅱ,and mixed mode(Ⅰ-Ⅱ)fracture toughness tests and subsequently studied alterations in the fracture behavior of sandstone at temperatures from 25℃ to 700℃.Our findings revealed increased fracture toughness as the temperature escalated from 25℃ to 200℃.However,beyond the threshold of 200℃,we noted a decline in fracture toughness.More specifically,the drop in mode Ⅰ fracture toughness was more pronounced in specimens with finer grains than those with coarser grains.Contrarily,the trend was reversed for mode Ⅱ fracture toughness.In contrast,the reduction of mixed mode(Ⅰ-Ⅱ)fracture toughness seemed almost linear across all grain sizes.Furthermore,we identified a correlation between temperature and grain size and their collective impact on crack propagation patterns.Comparing our results with established theoretical benchmarks,we confirmed that both temperature and grain size variations influence the fracture envelopes of sandstone.
基金Kinross Gold and MITACS for their financial support(Grant No.FR42880).
文摘The goal of this research is to develop mine-scale discrete fracture network(DFN)models in which the influence of the spatial heterogeneity of fracture distributions may be investigated on the rock wedge stability of an open pit slope.For this purpose,spatially conditioned DFN models were developed for the pit walls at Tasiast mine using comprehensive structural data from the mine.Using Sequential Gaussian Simulation(SGS),volumetric fracture intensities(P32)were modeled across the entire mine site in the form of 3D block models.The simulated P32 block models were used as the input constraints for conditional DFN fracture generation,where the DFN grid dimension is the same as the SGS 3D blocks.The spatially constrained DFN models were further calibrated using aerial fracture intensities(P21)data from the pit walls,obtained by a survey of the pit walls using an unmanned aerial vehicle(UAV)and measured traces of joints from 3D point cloud data.The final DFN model is expected to honor the fracture intensities gathered through different means with optimal model accuracy.Finally,bench-scale and interramp scale rock wedge slope stability analyses were conducted using the calibrated conditional DFN models.This work proves the significance of conditioned DFN models in rock wedge stability analysis.Such models provide detailed information regarding rock wedge stability so that site monitoring and prevention plans can be conducted with higher efficiency.
文摘Directive(EU)2017/164 establishes a fourth list of indicative occupational exposure limit values(IOELVs)to protect workers from risks of exposure to hazardous chemicals.It states that in underground mining and tunnelling,Member States may benefit from a transitional period regarding IOELVs for nitrogen monoxide,nitrogen dioxide,and carbon monoxide,during which the existing established IOELVs may be applied.The European Advisory Committee on Health and Safety at Work questions the technical feasibility of the proposed IOELVs in underground mining(CO,NO and NO2)and tunnelling(NO and NO2).Challenges arise concerning the availability of measurement methodologies for compliance with proposed IOELVs(NO2)in underground mining and tunnelling environments.
文摘The purpose of this research is to study the effect of longwall mining on the stability of main roadway in the underground coal mine. The PT GDM （Gerbang Daya Mandiri） underground coal mine in Indonesia, where the rocks are weak, was selected as a representative study site. To accomplish the objective of the research, the finite difference code software FLAC3D was used as a tool for the numerical simulations. The longwall mining of several panel and barrier pillar widths at various depths was simulated and discussed. Based on the simulation results, it indicates that the effect of coal panel extraction on the main roadway stability depends on the width of panel and barrier pillar. The greatest effect occurs when the large panel width and the small barrier pillar width are applied, whereas the smallest effect happens when the narrow panel width and the large barrier pillar width are adopted. In this paper, therefore, to maintain the stability of the main roadway with the aim of maximizing the coal recovery, the appropriate size of panel and barrier pillar width is proposed for each mining depth for this underground coal mine.
文摘Ignimbrites have been widely used as building materials in many historical and touristic structures in the Kayseri region of Türkiye. Their diverse colours and textures make them a popular choice for modern construction as well. However, ignimbrites are particularly vulnerable to atmospheric conditions, such as freeze-thaw cycles, due to their high porosity, which is a result of their formation process. When water enters the pores of the ignimbrites, it can freeze during cold weather. As the water freezes and expands, it generates internal stress within the stone, causing micro-cracks to develop. Over time, repeated freeze-thaw (F-T) cycles lead to the growth of these micro-cracks into larger cracks, compromising the structural integrity of the ignimbrites and eventually making them unsuitable for use as building materials. The determination of the long-term F-T performance of ignimbrites can be established after long F-T experimental processes. Determining the long-term F-T performance of ignimbrites typically requires extensive experimental testing over prolonged freeze-thaw cycles. To streamline this process, developing accurate predictive equations becomes crucial. In this study, such equations were formulated using classical regression analyses and artificial neural networks (ANN) based on data obtained from these experiments, allowing for the prediction of the F-T performance of ignimbrites and other similar building stones without the need for lengthy testing. In this study, uniaxial compressive strength, ultrasonic propagation velocity, apparent porosity and mass loss of ignimbrites after long-term F-T were determined. Following the F-T cycles, the disintegration rate was evaluated using decay function approaches, while uniaxial compressive strength (UCS) values were predicted with minimal input parameters through both regression and ANN analyses. The ANN and regression models created for this purpose were first started with a single input value and then developed with two and three combinations. The predictive performance of the models was assessed by comparing them to regression models using the coefficient of determination (R2) as the evaluation criterion. As a result of the study, higher R2 values (0.87) were obtained in models built with artificial neural network. The results of the study indicate that ANN usage can produce results close to experimental outcomes in predicting the long-term F-T performance of ignimbrite samples.
文摘Geo-engineering problems are known for their complexity and high uncertainty levels,requiring precise defini-tions,past experiences,logical reasoning,mathematical analysis,and practical insight to address them effectively.Soft Computing(SC)methods have gained popularity in engineering disciplines such as mining and civil engineering due to computer hardware and machine learning advancements.Unlike traditional hard computing approaches,SC models use soft values and fuzzy sets to navigate uncertain environments.This study focuses on the application of SC methods to predict backbreak,a common issue in blasting operations within mining and civil projects.Backbreak,which refers to the unintended fracturing of rock beyond the desired blast perimeter,can significantly impact project timelines and costs.This study aims to explore how SC methods can be effectively employed to anticipate and mitigate the undesirable consequences of blasting operations,specifically focusing on backbreak prediction.The research explores the complexities of backbreak prediction and highlights the potential benefits of utilizing SC methods to address this challenging issue in geo-engineering projects.
基金provided by Universitas Muslim Indonesia tothe first author(AAB).
文摘An organic geochemical,petrographical,and palynological evaluation was conducted on 30 claystone outcrop samples of the Toraja Formation,along with a geochemical analysis of an oil seep in the Enrekang Sub-basin.The aim of the study was to determine the correlation between oil and source rock in terms of age,depositional environment,organic material sources,and maturity level.The total organic carbon content of the claystone samples varies from 0.03 to 4.52 wt%,which are classified as poor to excellent.The claystones are immature to post-mature with a mixture of TypeⅡandⅢkerogen.Their vitrinite reflectance values range from 0.47 to 4.52%Ro.The samples of Toraja Formation rock and the oil seep source rock might have a similar depositional environment,a deltaic marine depositional setting with high oxidizing conditions.Organic material sources for rock and oil samples are dominated by terrestrial input.The oil is inferred to have originated from the Paleogene source rocks,which correlates in age with the Toraja Formation.The recovered palynomorphs from the studied rock samples suggest a late Eocene to Oligocene age with a strong terrestrial influence of shallow marine depositional setting.The biomarker analysis shows that the rock samples have a more substantial contribution from the terrigenous higher plants,while the oil sample indicates a higher degree of marine influence.The maturity levels are also different between the oil(peak mature)and the analyzed rock samples(immature).It is inferred that the oil seep source rock is equivalent to the analyzed rock sample but more mature,having been deposited under more marine conditions.The oil seep source rock is not exposed and is located in the deeper part of the basin.A deeper marine facies(i.e.distal delta front and prodelta facies)in front of the distributary mouth bar within a delta is interpreted as the source rock of the oil seep sample.
基金supported by the JSPS fellowship to M.K.M(Grant Number P20039)support from JST-ERATO Yamauchi Materials Space-Tectonics Project(JPMJER2003)+1 种基金the funding from the Queensland government through the Advance Queensland Fellowship Program(AQIRF043-2020-CV)supported by the National Health and Medical Research Council(NHMRC,1195451).
文摘Sweat contains numerous vital biomarkers such as metabolites,electrolytes,proteins,nucleic acids and antigens that reflect hydration status,exhaustion,nutrition,and physiological changes.Conventional healthcare diagnosis relies on disease diagnostics in sophisticated centralized laboratories with invasive sample collection(e.g.,chemical analyses,plasma separation via centrifugation,tissue biopsy,etc.).Cutting-edge point-of-care diagnostics for sweat biomarker analysis allow for non-invasive monitoring of physiologically related biomarkers in sweat and real-time health status tracking.Moreover,using advanced nanoarchitectures,including nanostructured platforms and nanoparticles,can enhance the specificity,sensitivity,wearability and widen the sensing modality of sweat biosensors.Herein,we comprehensively review the secretory mechanisms,clinical uses of sweat biomarkers,and the design,principle,and latest technologies of sweat biosensors.With an emphasis on cutting-edge technologies for sweat biomarker analysis,this review chronicles the issues associated with the current sweat biomarkers analysis of sweat biomarkers and provides insights into strategies for enhancing the translation of such biosensors into routine clinical practice.
文摘Hyperaccumulators concentrate trace metals and heavy metals in their shoots when grown in metal-contaminated soils and these trace metal-loaded plants may be removed by harvesting the fields. Studies exploring the beneficial role of these hyperaccumulators to clean up the environment have led to the development of phytoextraction. The success of phytoextraction depends upon the high biomass of plant species and bioavailability of metals for plant uptake. The phytoavailability of metals is influenced by soil- associated factors, such as pH, redox potential, cation exchange capacity, soil type, and soil texture, and by plant-associated factors, such as root exudates and root rhizosphere processes （microorganisms）. Efficiency of phytoextraction can be improved by advanced agronomic practices including soil and crop management by application of genetic engineering to enhance the metal tolerance, shoot translocation, accumulation, and sequestration and by application of chelate treatments to enhance metal bioavailability. Application of microorganisms including bacteria and mycorrhiza may facilitate the phytoextraction application at commercially large scale.
文摘Rockburst is defined as a phenomenon with immediate dynamic instability under excavation unloading conditions of deep or high geostress areas.Inadequate knowledge and lack of characterizing information prevent engineers and experts from achieving appropriate prediction results related to the rockburst behaviour.In this study,a data set including 220 rockburst instances was collected for rockburst classification via the geostatistical method.An update of the 2D graph,the tunnel rockburst classification(TRC)chart,was introduced based on analysing three indicators,namely,elastic energy index(Wet),tangential stress in rock mass(σ_(0)),and uniaxial compressive strength(σ_(c)).Distribution and correlation of data were drawn on 2D plot,and the boundaries of rockburst were distinguished according to the achieved interpolate points by kriging method.Hierarchically,the validation phase was performed using an additional set of 28 case histories obtained from several projects around the world.The results showed that the TRC chart with an average error percentage of 3.6%in the prediction of rockburst had a significant and effective implementation in comparison to the exiting heuristic systems.Despite the initial character of the prediction,the described chart may be a helpful tool in the first steps of design and construction.
基金This work was supported by the Ghana National Petroleum Corporation(GNPC)through the GNPC Professorial Chair in Mining Engineering at the University of Mines and Technology(UMaT),GhanaThe authors thank the Ghana National Petroleum Corporation(GNPC)for providing funding to support this work through the GNPC Professorial Chair in Mining Engineering at the University of Mines and Technology(UMaT),Ghana.
文摘Blasting is the live wire of mining and its operations,with air overpressure(AOp)recognised as an end product of blasting.AOp is known to be one of the most important environmental hazards of mining.Further research in this area of mining is required to help improve on safety of the working environment.Review of previous studies has shown that many empirical and artificial intelligence(AI)methods have been proposed as a forecasting model.As an alternative to the previous methods,this study proposes a new class of advanced artificial neural network known as brain inspired emotional neural network(BIENN)to predict AOp.The proposed BI-ENN approach is compared with two classical AOp predictors(generalised predictor and McKenzie formula)and three established AI methods of backpropagation neural network(BPNN),group method of data handling(GMDH),and support vector machine(SVM).From the analysis of the results,BI-ENN is the best by achieving the least RMSE,MAPE,NRMSE and highest R,VAF and PI values of 1.0941,0.8339%,0.1243%,0.8249,68.0512%and 1.2367 respectively and thus can be used for monitoring and controlling AOp.
基金supported by The Research Foundation of the Selcuk University under the Project No:06101021
文摘Dissolution kinetics of nickel from lateritic ore in nitric acid solution was investigated. Experimental parameters used were stirring speed(100-600 r/min), temperature(40-96 °C), nitric acid concentration(0.1-2 mol/L) and particle size(<106 μm). The shrinking core model was applied to the results of experiments investigating the effects of leaching temperature in the range of 40-90 °C and nitric acid concentration in range of 0.1-2 mol/L on nickel dissolution rate. The kinetic analysis shows that the nickel dissolution from lateritic ore could be described by diffusion model. The activation energy(E_a) for the dissolution reaction is calculated as 79.52 kJ/mol.
文摘One of the most important factors influencing on a tunnel blast efficiency is the proper design of blasting pattern. Among blasting parameters, blasthole diameter and tunnel face area are more significant so that any change in these parameters could finally affect on specific charge and specific drilling. There are mainly two groups of methods for tunnel blast design categorized based on the parallel cuts and angular cuts. In this research, a software for tunnel blast design was developed to analyze the effect and sensitiveness of blasthole diameter and the tunnel face area on blasting results in different blast design models. Using the software, it is quickly possible to determine specific charge, specific drilling and number of blastholes for each blast design model. The relations between both of blasthole diameters and the tunnel face area with the above parameters in different blast design models were then investigated to yield a set of equations with the highest correlations to compare the methods. The results showed that angular method requires more blasthole numbers than parallel method in similar condition(blasthole diameter and tunnel face area). Moreover, the specific charge values yielded by the two methods are approximately the same and very close together.
文摘Our study was carried out to assess the level of noise generated and ground vibrations induced during blasting operations at the Ewekoro limestone quarry in Nigeria.To achieve this objective,vibro monitor equipment was used to take readings related to noise generated and ground vibrations during all blasting operations that took place in the quarry for a period of one month.As well,a digital camera was used to take photographs of residential structures within villages near the quarry.The results obtained indicate that the ground vibration readings fall between 0.5 mm/s and 2.1 mm/s and the noise generated during the blasting operations between 82 dB and 89 dB.These readings when compared with the limits set by FEPA(Federal Environmental Protection Agency) of 5.0 mm/s and 150 dB) all fall within the permissible limits.However the photographs of most structures near the quarry reveal cracks and dilapidated building walls.Recommendations are made on how to sustain and improve current blasting techniques.
文摘The effects of cyanidation conditions on gold dissolution were studied by artificial neural network （ANN） modeling. Eighty-five datasets were used to estimate the gold dissolution. Six input parameters, time, solid percentage, P50 of particle, NaCN content in cyanide media, temperature of solution and pH value were used. For selecting the best model, the outputs of models were compared with measured data. A fourth-layer ANN is found to be optimum with architecture of twenty, fifteen, ten and five neurons in the first, second, third and fourth hidden layers, respectively, and one neuron in output layer. The results of artificial neural network show that the square correlation coefficients （R2） of training, testing and validating data achieve 0.999 1, 0.996 4 and 0.9981, respectively. Sensitivity analysis shows that the highest and lowest effects on the gold dissolution rise from time and pH, respectively It is verified that the predicted values of ANN coincide well with the experimental results.
基金supported by the National Research Foundation of Korea Grant funded by the Korean Government (NRF-2019R1A2C2003636)
文摘The main aspects that require attention in tunnel design in terms of safety and economy are the precise estimation of probable ground conditions and ground behavior during construction. The variation in rock mass behavior due to tunnel excavation sequence plays an important role during the construction stage.The purpose of this research is to numerically evaluate the effect of excavation sequence on the ground behavior for the Lowari tunnel project, Pakistan. For the tunnel stability, the ground behavior observed during the actual partial face excavation sequence is compared with the top heading and bench excavation sequence. For this purpose, the intact rock parameters are used along with the characterization of rock mass joints related parameters to provide input for numerical modelling via FLAC 2D. The in-situ stresses for the numerical modelling are obtained using empirical equations. From the comparison of the two excavation sequences, it was observed that the actual excavation sequence used for Lowari tunnel construction utilized more support than the top heading and bench method. However, the actual excavation sequence provided good results in terms of stability.
文摘Drilling and blasting play vital roles in opencast mining. These operations not only affect the cost of production directly but as well and significantly, the overall operational costs. This research was carried out to find a possible way of optimizing the drilling and blasting operations in an open pit mine of Somair (Société des Mines de l’Air), in the Niger Republic. In order to optimize the drilling operation, the time taken by two drilling machines to accomplish the same task was analyzed statistically. The result indicates that the Down the Hole Hammer Drilling Rig (DMNo406) is more efficient than the Drill Master (DM405). The relative unit consumption of two explosives (Explus and Nitram 9), when used under the same operating conditions, were also considered and the results indicate Explus to be more economical per unit consumption with a range of 0.15 g/t–0.183 g/t, when compared with Nitram 9 with a unit consumption range of 0.19 g/t-0.24 g/t in the study area.
文摘Prediction of mode I fracture toughness(KIC) of rock is of significant importance in rock engineering analyses. In this study, linear multiple regression(LMR) and gene expression programming(GEP)methods were used to provide a reliable relationship to determine mode I fracture toughness of rock. The presented model was developed based on 60 datasets taken from the previous literature. To predict fracture parameters, three mechanical parameters of rock mass including uniaxial compressive strength(UCS), Brazilian tensile strength(BTS), and elastic modulus(E) have been selected as the input parameters. A cluster of data was collected and divided into two random groups of training and testing datasets.Then, different statistical linear and artificial intelligence based nonlinear analyses were conducted on the training data to provide a reliable prediction model of KIC. These two predictive methods were then evaluated based on the testing data. To evaluate the efficiency of the proposed models for predicting the mode I fracture toughness of rock, various statistical indices including coefficient of determination(R2),root mean square error(RMSE), and mean absolute error(MAE) were utilized herein. In the case of testing datasets, the values of R2, RMSE, and MAE for the GEP model were 0.87, 0.188, and 0.156,respectively, while they were 0.74, 0.473, and 0.223, respectively, for the LMR model. The results indicated that the selected GEP model delivered superior performance with a higher R2value and lower errors.
