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In the process of open-pit bench blasting for many mines, rock fragments move in the direction of loose space after fragmentation under explosive energy, leading to ore distribution differences before and after blasting. Considering that there is no simple and inexpensive method for post-blast ore boundary determination, a machine-learning-aided determination method and a corresponding evaluation system are proposed. In this regard, 95 datasets with nine variables were investigated using different kinds of predictive models: support vector regression, the Gaussian process (GP), extreme learning machine, and two metaheuristic algorithms. By evaluating the predictive performance using three performance metrics, absolute error comparison, and Taylor diagram, a hybrid model composed of whale optimization algorithm (WOA) and GP, namely WOA–GP, was found to be the best precision model. Then, a blast block with 55 blast holes was assumed and was analyzed using the WOA–GP model and the established evaluation system. It was found that using the post-blast ore boundary to guide the shovel can decrease the ore loss rate by 33.1% and the ore dilution rate by 57.9%, meaning that the methodology has great significance for improving resource recovery and increasing economic benefits. In addition, the mixing of high-grade and low-grade rock fragments during the geological modeling process may also cause ore loss and dilution. This article provides a new methodology for post-blast ore boundary determination, which can inspire development of other ore loss and dilution management techniques.

To reduce the difficulty of obtaining the unconfined compressive strength (UCS) value of fiber-reinforced cemented paste backfill (CPB) and analyze the comprehensive impact of conventional and fiber variables on the compressive property, a new artificial intelligence model was proposed by combining a newly invented meta-heuristics algorithm (salp swarm algorithm, SSA) and extreme learning machine (ELM) technology. Aiming to test the reliability of that model, 720 UCS tests with different cement-to-tailing mass ratio, solid mass concentration, fiber content, fiber length, and curing time were carried out, and a strength evaluation database was collected. The obtained results show that the optimized SSA−ELM model can accurately predict the uniaxial compressive strength of the fiber-reinforced CPB, and the model performance of SSA−ELM model is better than ANN, SVR and ELM models. Variable sensitivity analysis indicates that fiber content and fiber length have a significant effect on the UCS of fiber-reinforced CPB.

To test the impact of different mixture ratios on backfilling strength in Fankou lead–zinc mine, various mixture ratio designs have been conducted. Meanwhile, to improve the strength of ultra-fine tailings-based cement paste backfill (CPB), two kinds of fibers were utilized in this study, namely polypropylene (PP) fibers and straw fibers. To achieve these, a total of 144 CPB backfilling scenarios with different combinations of influenced factors were tested by uniaxial compressive tests. The test results indicated that polypropylene fibers improve the strength of CPB, while in some scenarios the addition of straw fibers decreases the strength of CPB. In this research, the support vector machine (SVM) technique coupled with three heuristic algorithms, namely genetic algorithms, particle swarm optimization and salp swarm algorithm (SSA), was developed to predict the strength of fiber-reinforced CPB. Also, the optimal performance of metaheuristic algorithms was compared with one fundamental search method, i.e., grid search (GS). The overall performance of four optimal algorithms was calculated by the ranking system. It can be found that these four approaches all presented satisfactory predictive capability. But the metaheuristic algorithms can capture better hyper-parameters for SVM prediction models compared with GS-SVM method. The robustness and generalization of SSA-SVM methods were the most prominent with the R2 values of 0.9245 and 0.9475 for training sets and testing sets. Therefore, SSA-SVM will be recommended to model the complexity of interactions for fiber-reinforced CPB and predict fiber-reinforced CPB strength.

To reduce the workload and misjudgment of manually discriminating microseismic events and blasts in mines, an artificial intelligence model called PSO-ELM, based on the extreme learning machine (ELM) optimized by the particle swarm optimization (PSO) algorithm, was applied in this study. Firstly, based on the difference between microseismic events and mine blasts and previous research results, 22 seismic parameters were selected as the discrimination feature parameters and their correlation was analyzed. Secondly, 1600 events were randomly selected from the database of the microseismic monitoring system in Fankou Lead-Zinc Mine to form a sample dataset. Then, the optimal discrimination model was established by investigating the model parameters. Finally, the performance of the model was tested using the sample dataset, and it was compared with the performance of the original ELM model and other commonly used intelligent discrimination models. The results indicate that the discrimination performance of PSO-ELM is the best. The values of the six evaluation indicators are close to the optimal value, which shows that PSO-ELM has great potential for discriminating microseismic events and blasts. The research results obtained can provide a new method for discriminating microseismic events and blasts, and it is of great significance to ensure the safe and smooth operation of mines.

The determination of the impacts of marble exploitation on the surface and underground water at SAKALALINA-MADAGASCAR was determined by assessing the physico-chemical properties of the water samples obtained from SAKALALINA and its environs. The concentration of Fe, Mn, Cu, Zn, Pb, Cd, Hg, As and selected major ions in water samples were analyzed in the laboratory to assess their role in the contamination of both surface and ground water. The water in this Study area is mainly used for drinking and industrial purposes followed by agriculture to some extent.

In order to reduce the undesired phenomenon including brow damage, over crushing and bulking yield in the ring blasting which are owning to the unreasonable distribution of blasting energy, a new optimization method is proposed to improve the charge scheme based on the Scaled Heelan (SH) model. First, the traversal algorithm for an arrangement of regular holes (holes charged to the collar) is proposed based on the analysis of the current interval charge design method in ring blasting. Second, a new developed mathematical model based on the SH model is introduced to predict the fragment size of the column charge. Then, the optimization method according to two key parameters of the blasted fragment prediction, D 50 and D 90 − D 10 , is discussed. Finally, a case study is introduced to illustrate the usage and optimization procedure of the program. Sixty trial blasts were conducted in the Tonglvshan copper mine to verify the feasibility and effectiveness of the optimization algorithm. The frequency of occurrence of brow damage was reduced from 36.7% to 16.7% using the proposed method in our sixty field tests, and the loading efficiency was also improved by 11.3% due to the more uniform distribution of muck pile fragment size.

It is essential to control the damage to the surrounding rock and engineering structures in the process of cut blasting with a single free surface in underground mining. To reduce vibration induced by cut blasting, this paper proposes short-delay cut blasting, in which blast holes that are near each other are sequentially initiated with short-delay times. Experimental tests of cut blasting were conducted in a roadway in the Shaxi copper mine to compare the peak particle velocity (PPV) and frequency characteristics of simultaneous blasting and short-delay blasting. Numerical modelling was then developed to study the influence of short-delay times on blast vibration. The accuracy of the numerical simulation was verified by the comparison of the test and simulated data of single-hole blasting. The results show that the amplitude reduction ratio (ARR) value increases gradually with the increase in delay intervals, and the vibration reduction for delay intervals smaller than 6 ms is very limited, particularly in the near field. The principal frequencies (PFs) for short-delay blasting are similar to those for simultaneous blasting, which implies that the frequencies do not increase directly with the decrease of the delay intervals. The experimental tests also show that the mean frequencies (MFs) for the 8 ms delay are slightly higher than those for the 0 ms delay blast. In the case of ensuring the rock breaking of cut blasting, longer delay intervals of 8∼10 ms are beneficial to further reduce PPV in practical blasting.

Objective Pulmonary cryptococcosis (PC) is a pulmonary fungal disease caused by Cryptococcus neoformans infection, the clinical manifestations are pneumonia or pulmonary nodules, or acute respiratory distress syndrome (ARDS) in severe cases. But its clinical manifestations lack specificity, so it is often misdiagnosed or missed in recent years. Pulmonary cryptococcosis is common in patients with human immunodeficiency virus (HIV) and non-HIV-associated immunosuppression, it has been found that pulmonary cryptococcosis is also common in people with sound immune function. The purpose of this paper is to report the diagnosis and treatment process of a patient with pulmonary cryptococcosis in our hospital, so as to provide reference for the diagnosis and treatment of the disease and reduce the misdiagnosis rate and missed diagnosis rate of the disease. Methods The diagnosis and treatment process of a case of pulmonary cryptococcosis in our hospital were reviewed. Results Combined with the patient's lung CT Imaging manifestations, and environmental exposure history and immune status, pulmonary cryptococcosis was considered. After empirical anti-cryptococcosis treatment, the patient's symptoms improved, and pulmonary nodules disappeared according to lung CT review. Conclusion Clinicians should identify the imaging characteristics of patients with cryptococcus pneumonicum and combine them with their environmental exposure history and immune status to avoid missed diagnosis and delayed disease.

Owing to the complex features of blasting vibration damage assessment systems, a gradient boosted machine (GBM) model is developed for the classification of residential structure damage (RSD) due to blasting vibrations of open pit mining. Twelve indicators are defined as the indices for the prediction of RSD in the proposed model. These are: peak particle velocity, dominant frequency, dominant frequency duration, distance, maximum safe charge per delay, compressive strength of mortar joints, ratio of brick area to house area, height of residential house, roof structures, beam-column frames, quality of construction, and site conditions. The GBM model is achieved by training 108 sets of measured data of blasting vibration. A 10-fold cross-validation procedure was applied to determine the optimal parameter values during modeling, and an external testing set was employed to validate the prediction performance of the model. Two performance measures – classification accuracy rate and Cohen’s kappa – have been employed. The analysis of accuracy together with kappa for the dataset demonstrate that the GBM model has high credibility as it achieves a comparable median classification accuracy rate and Cohen’s kappa values of 91.7% and 0.875 for the prediction of RSD, respectively.

The database of 254 rockburst events was examined for rockburst damage classification using stochastic gradient boosting (SGB) methods. Five potentially relevant indicators including the stress condition factor, the ground support system capacity, the excavation span, the geological structure and the peak particle velocity of rockburst sites were analyzed. The performance of the model was evaluated using a 10 folds cross-validation (CV) procedure with 80% of original data during modeling, and an external testing set (20%) was employed to validate the prediction performance of the SGB model. Two accuracy measures for multi-class problems were employed: classification accuracy rate and Cohen's Kappa. The accuracy analysis together with Kappa for the rockburst damage dataset reveals that the SGB model for the prediction of rockburst damage is acceptable.

The accurate identification of delay time in millisecond blasting plays an important role in the optimization of blasting design and reduction of vibration effect. Through a case study of a surge shaft blasting project, the capability of the EMD (empirical mode decomposition) method in identifying the delay time of short millisecond blasting with precise initiation was compared with the instantaneous energy method based on Hilbert-Huang transform (HHT). The recognition rate of the EMD method was more than 80%, while the instantaneous energy method was less than 25%. By analyzing the instantaneous energy of single-hole blasting signal, it was found that the instantaneous energy method was adaptable to millisecond blasting with delay time longer than half of the energy peak period. The EMD method was used to identify delay time of millisecond blasting in Zijinshan open-pit mine. According to the identification results, the blasting parameters were optimized for controlling the blast-induced vibration and reducing the large block ratio. The field data showed that the velocity peak of ground vibration was reduced by more than 30% under almost the same maximum charge per delay by the optimization of delay time and detonating detonators. Combining with slag-remaining blasting and burden optimization of the first row, the large block ratio was reduced to less than 3%. The research results proved that the identification method based on HHT was feasible to optimize the blasting design. The identification method is also of certain reference value for design optimization of other similar blasting projects.

Ground vibrations originating from bench blasting may cause damage to slopes, structures, and underground workings in close proximity to an operating open-pit mine. It is important to monitor and predict ground vibration levels induced by blasting and to take measures to reduce their hazardous effects. The aims of this paper are to determine the weaker protection objects by comparatively studying bench blasting induced vibrations obtained at surface and in an underground tunnel in an open-pit mine and thus to seek vibration control methods to protect engineering objects at the site. Vibrations arising from measurement devices at surface and in an underground tunnel at the Zijinshan Open-Pit Mine were obtained. Comparative analysis of the peak particle velocities shows that, in the greatest majority of cases, surface values are higher than underground values for the same vibration distance. The transmission laws of surface and underground vibrations were established depending on the type of rock mass, the explosive charge, and the distance. Compared with the Chinese Safety Regulations for Blasting (GB6722-2014), the bench blasting induced vibrations would not currently cause damage to the underground tunnel. According to the maximum allowable peak particle velocities for different objects, the permitted maximum charges per delay are obtained to reduce damage to these objects at different distances.

The charge structure is an important factor in the adjustment of the energy distribution of explosives and the control of the blasting effect, especially in an underground large-diameter longhole, for interval charge millisecond blasting design. To achieve a reasonable charge structure, a multi-planar detection optimization (MDO) algorithm for charge structures is proposed based on the Harries mathematical model and the superposition method. The vector size of a fragment is transformed into a scalar ruler, and the definition of the spatial non-uniformity evaluation factor is used in the MDO algorithm. A detailed methodology is implemented using a Visual LISP program and validated for a case study of the Fankou lead–zinc mine in China. The results reveal that the MDO algorithm can improve the rock fragmentation and increase the energy utilization rate of explosives in large-diameter longhole blasting operations. This algorithm could be extremely helpful in the process of underground mine blast optimization.

Ground settlements above a tunnel as a result of tunnel construction can be predicted with the help of input variables that have direct physical significance. Several empirical and artificial intelligence methods for estimating ground settlements have been established by researchers. However, these methods have some limitations because the large number of influential factors involved makes tunnel–ground interaction complicated. In this work, a random forest (RF) was developed and employed to predict ground settlements above tunnels. To achieve this goal, tunnel geometry, geological properties, and construction parameters were investigated as input variables to utilize in the RF modeling, resulting in the maximum surface settlement value (Smax) and trough width (i) as the ground surface settlement index. To demonstrate the applicability of the RF model, two data sets associated with different features, which were obtained from a detailed investigation of different tunnel projects published in litera...

Rockburst possibility prediction is an important activity in many underground openings design and construction as well as mining production. Due to the complex features of rockburst hazard assessment systems, such as multivariables, strong coupling and strong interference, this study employs support vector machines (SVMs) for the determination of classification of long-term rockburst for underground openings. SVMs is firmly based on the theory of statistical learning algorithms, uses classification technique by introducing radial basis function (RBF) kernel function. The inputs of models are buried depth H , rocks’ maximum tangential stress σ θ , rocks’ uniaxial compressive strength σ c , rocks’ uniaxial tensile strength σ t , stress coefficient σ θ / σ c , rock brittleness coefficient σ c / σ t and elastic energy index W et . In order to improve predictive accuracy and generalization ability, the heuristic algorithms of genetic algorithm (GA) and particle swarm optimization algorithm (PSO) are adopted to automatically determine the optimal hyper-parameters for SVMs. The performance of hybrid models (GA + SVMs = GA-SVMs) and (PSO + SVMs = PSO-SVMs) have been compared with the grid search method of support vector machines (GSM-SVMs) model and the experimental values. It also gives variance of predicted data. A rockburst dataset, which consists of 132 samples, was employed to evaluate the current method for predicting rockburst grade, and the good results of overall success rate were obtained. The results indicated that the heuristic algorithms of GA and PSO can speed up SVMs parameter optimization search, the proposed method is robust model and might hold a high potential to become a useful tool in rockburst prediction research.

Aiming at the problems of the traditional method of assessing distribution of particle size in bench blasting, a support vector machines (SVMs) regression methodology was used to predict the mean particle size (X50) resulting from rock blast fragmentation in various mines based on the statistical learning theory. The data base consisted of blast design parameters, explosive parameters, modulus of elasticity and in-situ block size. The seven input independent variables used for the SVMs model for the prediction of X50 of rock blast fragmentation were the ratio of bench height to drilled burden (H/B), ratio of spacing to burden (S/B), ratio of burden to hole diameter (B/D), ratio of stemming to burden (T/B), powder factor (Pf), modulus of elasticity (E) and in-situ block size (XB). After using the 90 sets of the measured data in various mines and rock formations in the world for training and testing, the model was applied to 12 another blast data for validation of the trained support vector regression (SVR) model. The prediction results of SVR were compared with those of artificial neural network (ANN), multivariate regression analysis (MVRA) models, conventional Kuznetsov method and the measured X50 values. The proposed method shows promising results and the prediction accuracy of SVMs model is acceptable.

The purpose of this study is to apply some statistical and soft computing methods such as Fisher discriminant analysis (FDA) and support vector machines (SVMs) methodology to the determination of pillar stability for underground mines selected from various coal and stone mines by using some index and mechanical properties, including the width, the height, the ratio of the pillar width to its height, the uniaxial compressive strength of the rock and pillar stress. The study includes four main stages: sampling, testing, modeling and assessment of the model performances. During the modeling stage, two pillar stability prediction models were investigated with FDA and SVMs methodology based on the statistical learning theory. After using 40 sets of measured data in various mines in the world for training and testing, the model was applied to other 6 data for validating the trained proposed models. The prediction results of SVMs were compared with those of FDA as well as the measured field values. The general performance of models developed in this study is close; however, the SVMs exhibit the best performance considering the performance index with the correct classification rate Prs by re-substitution method and Pcv by cross validation method. The results show that the SVMs approach has the potential to be a reliable and practical tool for determination of pillar stability for underground mines.

A three-dimensional backfill pipeline transport model is developed using the computational fluid dynamics (CFD) technique, which is applied to study the pipeline transport properties of three-phase foam slurry backfill (TFSB). Based on rheological property tests and CFD simulations, the foam phase, pressure, and velocity in the pipeline system are investigated using the CFD mixture method for different bubble volume fractions and bubble diameters. The simulation results indicate that TFSB can maintain a steady state during pipeline transport, experience a markedly reduced pipeline transport resistance, and exhibit better liquidity than conventional cement slurry. Furthermore, as the bubble volume fraction increases, the resistance of the pipeline decreases and the fluidity improves. By contrast, the bubble diameter has little effect on the transport properties of TFSB. The combined results of CFD simulations, slump tests, and strength tests indicate that, when the bubble volume fraction is 15–20 vol %, TFSB can satisfy the necessary strength requirements and exhibit self-flowing transport. The CFD technique provides an intuitive and accurate basis for pipeline transport research and has the potential for wider application in studies of mine backfill.

Deep underground mines are highly energy consuming due to the need to overcome the growing airflow resistance. The multi-fan station ventilation system (MFSVS), formed by several parallel fans at different locations in an underground mine generally, has greatly reduced energy costs by using high-quantity and low-pressure energy-saving fans. However, experimental data still indicates that 30–70% of the fan pressure is used to overcome the severe shock losses in a parallel fan station (PFS), in spite of more than 80% operating efficiency, and the shock losses greatly weaken the superiority and the service capacity of PFS. Based on the investigation and measured data of several PFSs in a MFSVS in an underground mine, a three-dimensional PFS model was developed by computational fluid dynamics (CFD) to demonstrate airflow performance and variation characteristics of velocity, pressure and turbulence. First, the fan characteristic in the PFS was discussed and compared with the fan operating performance under standard conditions; the shock losses were then presented from both sides of the inlet shock losses and the outlet shock losses in the PFS; meanwhile, the effects of blade angle variation and airflow mutual interference were conducted to determine whether they exert a significant influence on the shock losses. The results show that the shock losses are primarily generated in the range of 0 to 3.0 m from the fans’ exits, due to the intensely change in air velocity in the PFS. The study also provides several directions and references for recovering air pressure and reducing energy consumption in the parallel fans’ structure.

According to optimization on safety thickness of roof for underground mining, which is a complex problem affected by multiple factors. On the basis of Genetic Algorithm (GA), each influencing factor on safety thickness of roof is considered as different gene and used to express individual composition factor of population. Therefore, each arbitrary group composed by different influencing factors can be used as a population. Then, by constructing objective function, designing optimization variables and determining constraint condition, the mathematical model of optimization on safety thickness of roof can be constructed. By using the Evolver software, which is based on the thought of GA, the optimization on safety thickness of roof for stoping the orebody No.92, under the overlap zones in Tongkeng Mine, is studied. The optimized results show that more conformable and accurate of the safety thickness of roof compared with the results obtained by numerical simulation and mathematical prediction.