Optimization of clay material mixture ratio and filling process in gypsum mine goaf

Because there is neither waste rock nor mill tailings in the gypsum mine, and the buildings on the goaf of gypsum mine are needed to be protected, the research proposed the scheme of the clay filling technology. Gypsum, cement, lime and water glass were used as adhesive, and the strength of different material ratios were investigated in this study. The influence factors of clay strength were obtained in the order of cement, gypsum, water glass and lime. The results show that the cement content is the determinant influence factor, and gypsum has positive effects, while the water glass can enhance both clay strength and the fluidity of the filing slurry. Furthermore, combining chaotic optimization method with neural network, the optimal ratio of composite cementing agent was obtained. The results show that the optimal ratio of water glass, cement, lime and clay (in quality) is 1.17:6.74:4.17:87.92 in the process of bottom self-flow filling, while the optimal ratio is 1.78:9.58:4.71:83.93 for roof-contacted filling. A novel filling process to fill in gypsum mine goaf with clay is established. The engineering practice shows that the filling cost is low, thus, notable economic benefit is achieved.

Effect of granite gravel content on improved granular mixtures as railway subgrade fillings

The improved granular mixtures are widely used as the fillings of railway 8ubgrade, and in order to investigate the effect of coarse grain content on granular mixtures, a series of field tests were conducted. The experimental results indicate that the permeability coefficient increases significantly with the increment of granite gravel content, especially in the range of 60%-70%. Thcrc exists a coarse grain content limit defined as 53%-58.5% to reform the permeable granular skeleton. Beyond this limit, the permeable granular skeleton is efficiently formed, and the macro pores between the separate gravels are partially filled, which is the explanation lbr the permeability increase. The investigations indicate the subgrade resistance modulus （ks0, Ev2, and Evd） depends on the granite gravel content, and the resistance modulus increases significantly beyond granite gravel content of 50%. The skeletons of granitc gravel clayey sand mixture change in the long-term deformation objected to the train-induced dynamic load, which involves three main repeated and circular deformation stages. Generally, the long-time deformation is explained as the gravel crushing and filling the internal porous space with crushed gravel fragments. Through these investigations, the C40-G60 or C30-G70 is recommended as an optimum soil mixture for the good permeability and high resistance modulus.

Low Temperature Performance Prediction Model of Cold-Filled SMA-13 Asphalt Mixture

Sets of cold-filled SMA-13 asphalt mixture were designed by means of orthogonal design method. The bending and low temperature creep tests of the cold-filled SMA-13 asphalt mixture were carried out. The related models of the fractal dimension and the road performance evaluation index including low temperature bending failure strain εB and bending strength RB are established by using fractal theory. The model can be used to predict the low temperature performance of cold-filled SMA-13 asphalt mixture according to the design gradation, which can reduce the test workload and improve the working efficiency, so as to provide the reference for engineering design.

Reinforcement Effect and Permeability Assessment of Gravel-Tire Chips Mixture (GTCM) for Use in Marine Landfill

The number of marine landfills in Japan has increased over the past decade due to the lack of suitable land. For marine landfills, protection of the alluvium clay layer and improvement of the drainage performance in waste inflow are important aspects. In this paper, an economical construction method for these problems is proposed using gravel-tire chips mixture (GTCM) as the horizontal reinforcement and drainage medium beneath the waste. The content and particle size of tire chips mixed with gravel are essential factors that affect the bearing capacity and permeability of the reinforcement layer. Therefore, a series of permeability tests are conducted using newly developed large-scale triaxial compression and permeability test apparatus to investigate the effect of tire chips particle size, the mass proportion of tire chips (MPTC), and triaxial stress on the permeability of GTCM. In addition, the effectiveness of this technique is evaluated by numerical simulations. The experimental results confirm that the shear strength of GTCM is influenced by tire chips content. Furthermore, permeability coefficient of GTCM is on the order of 0.02 cm/s to 0.08 cm/s, which is higher than the tolerable level of permeability of drainage layer in landfills. GTCM sample shows excellent permeability even on higher compression. Moreover, the Non-Darcy flow properties of GTCM (non-linear) are introduced in this study, and an approximate power function relationship between the permeability coefficient and the non-Darcy flow coefficient is developed. The numerical results confirm that GTCM performs better than the sand, a traditional reinforcement material.

Mechanical Properties of Soil-Rock Mixture Filling in Fault Zone Based on Mesostructure

Soil-rock mixture(SRM)filling in fault zone is an inhomogeneous geomaterial,which is composed of soil and rock block.It controls the deformation and stability of the abutment and dam foundation,and threatens the long-term safety of high arch dams.To study the macroscopic and mesoscopic mechanical properties of SRM,the development of a viable mesoscopic numerical simulation method with a mesoscopic model generation technology,and a reasonable parametric model is crucially desired to overcome the limitations of experimental conditions,specimen dimensions,and experiment fund.To this end,this study presents a mesoscopic numerical method for simulating the mechanical behavior of SRM by proposing mesoscopic model generation technology based on its mesostructure features,and a rock parameter model considering size effect.The validity and rationality of the presented mesoscopic numerical method is experimentally verified by the triaxial compression tests with different rock block contents(RBC).The results indicate that the rock block can increase the strength of SRM,and it is proved that the random generation technique and the rock parameter model considering size effect are validated.Furthermore,there are multiple failure surfaces for inhomogeneous geomaterial of SRM,and the angle of the failure zone is no longer 45◦.The yielding zones of the specimen are more likely to occur in thin sections of soil matrix isolated by blocks with the failure path avoiding the rock block.The proposed numerical method is effective to investigate the meso-damage mechanism of SRM.

Modeling of Leachate Propagation in a Municipal Solid Waste Landfill Foundation

The study deals with the numerical modeling of leachate distribution in the porous medium located under a municipal solid waste disposal landfill(MSWLF).The considered three-layer system is based on geological data obtained from field measurements.For simplicity,the problem is investigated by assuming a two-component approach.Nevertheless,the heat produced by landfills due to biological and chemical processes and the thermal diffusion mechanism contributing to pollution transport are taken into account.The numerical modeling of the propagation of leachate in the considered layered porous medium is implemented for parameters corresponding to natural soil and for the case where disruption of the porous medium structure is due to cracks formation or erosion.The latter is accompanied by an increase in rock permeability.In this case,the emergence of multi-vortex flow in the layer of high permeability is observed to substantially influence the bottom layers and cause intensification of the leachate propagation.These effects should be taken into account when estimating the parameters and properties of materials required for the construction of solid waste disposal facilities.

Research on Alkaline Filler Flame-Retarded Asphalt Pavement

Used as flame returdant of tunnel asphalt pavement, organic bromides produce a large amount of poisons and smoke in construction and flame retardation stage. The alkaline filler was found to replace mineral filler, and the flame- retarded asphalt mixtures were produced. Experimental results show that these asphalt mixtures are smoke restrained ; the performances and construction technology of asphalt pavement are not influenced; also the alkaline filler is of low-price. So this kind of flame-retarded asphalt mixtures is suitable for tunnel patement.

UV,five wavelengths fusion and electrochemical fingerprints combined with antioxidant activity for quality control of antiviral mixture

Aiming to ensure the consistency of quality control of Traditional Chinese Medicines(TCMs),a combination method of high-performance liquid chromatography(HPLC),ultraviolet(UV),electrochemical(EC)was developed in this study to comprehensively evaluate the quality of Antiviral Mixture(AM),and Comprehensive Linear Quantification Fingerprint Method(CLQFM)was used to process the data.Quantitative analysis of three active substances in TCM was conducted.A fivewavelength fusion fingerprint(FWFF)was developed,using second-order derivatives of UV spectral data to differentiate sample levels effectively.The combination of HPLC and UV spectrophotometry,along with electrochemical fingerprinting(ECFP),successfully evaluated total active substances.Ultimately,a multidimensional profiling analytical system for TCM was developed.

Hardfill筑坝材料配合比试验研究

根据强度波动区理论,进行配合比设计,配制超贫碾压筑坝材料——Hardfill材料,按测定的混凝土VC值控制用水量;分析影响Hardfill材料强度的各种因素,深入分析混凝土压实和结构机理。结果表明,水胶比、胶凝材料用量、砂率、粉煤灰掺量等对Hardfill材料抗压强度有显著影响,由于砂率偏大、水胶比较大,造成Hardfill材料强度偏低。

Joint Effects of Multipollutant Mixtures on Mortality in Chengdu,China

Significant epidemiological research has revealed that exposure to air pollution is substantially associated with numerous detrimental health consequences^([1-3]).The negative health effects of individual air pollutants(e.g.,fine particulate matter:PM_(2.5);nitrogen dioxide:NO_(2);carbon monoxide,CO;or ozone:O_(3))have been widely explored^([4]).However,humans are constantly exposed to multipollutant mixtures in real life,and biological responses to inhaled pollutants are likely to depend on the interplay of pollutant mixtures.Therefore,it is critical and imperative to explore the joint effects of multipollutant mixtures on human beings.

Hydrogen release of NaBH_(4) below 60 ℃ with binary eutectic mixture of xylitol and erythritol additive

NaBH_(4) was widely regarded as a low-cost hydrogen storage material due to its high-mass hydrogen capacity of approximately 10.8%(mass)and high volumetric hydrogen capacity of around 115 g·L^(–1).However,it exhibits strong stability and requires temperatures above 500℃ for hydrogen release in practical applications.In this study,two polyhydric alcohols,xylitol and erythritol(XE),were prepared as a binary eutectic sugar alcohol through a grinding-melting method.This binary eutectic sugar alcohol was used as a proton-hydrogen carrier to destabilize NaBH_(4).The 19NaBH_(4)-16XE composite material prepared by ball milling could start releasing hydrogen at 57.5℃,and the total hydrogen release can reach over 88.8%(4.45%(mass))of the theoretical capacity.When the 19NaBH_(4)-16XE composite was pressed into solid blocks,the volumetric hydrogen capacity of the block-shaped composite could reach 67.2 g·L^(–1).By controlling the temperature,the hydrogen desorption capacity of the NaBH_(4)-XE composite material was controllable,which has great potential for achieving solid-state hydrogen production from NaBH_(4).

Optimization of Biofuel Formulation by Mixture Design

With the full growth of energy needs in the world, several studies are now focused on finding renewable sources. The aim of this work is to optimise biofuel formulation from a mixture design by studying physical properties, such as specific gravity and kinematic viscosity of various formulated mixtures. Optimization from the mixture plan revealed that in the chosen experimental domain, the optimal conditions are: 40% for used frying oil (UFO), 50% for bioethanol and 10% for diesel. These experimental conditions lead to a biofuel with a density of 0.84 and a kinematic viscosity of 2.97 cSt. These parameters are compliant with the diesel quality certificate in tropical areas. These density and viscosity values were determined according to respective desirability values of 0.68 and 0.75.

Probabilistic seismic inversion based on physics-guided deep mixture density network

Deterministic inversion based on deep learning has been widely utilized in model parameters estimation.Constrained by logging data,seismic data,wavelet and modeling operator,deterministic inversion based on deep learning can establish nonlinear relationships between seismic data and model parameters.However,seismic data lacks low-frequency and contains noise,which increases the non-uniqueness of the solutions.The conventional inversion method based on deep learning can only establish the deterministic relationship between seismic data and parameters,and cannot quantify the uncertainty of inversion.In order to quickly quantify the uncertainty,a physics-guided deep mixture density network(PG-DMDN)is established by combining the mixture density network(MDN)with the deep neural network(DNN).Compared with Bayesian neural network(BNN)and network dropout,PG-DMDN has lower computing cost and shorter training time.A low-frequency model is introduced in the training process of the network to help the network learn the nonlinear relationship between narrowband seismic data and low-frequency impedance.In addition,the block constraints are added to the PG-DMDN framework to improve the horizontal continuity of the inversion results.To illustrate the benefits of proposed method,the PG-DMDN is compared with existing semi-supervised inversion method.Four synthetic data examples of Marmousi II model are utilized to quantify the influence of forward modeling part,low-frequency model,noise and the pseudo-wells number on inversion results,and prove the feasibility and stability of the proposed method.In addition,the robustness and generality of the proposed method are verified by the field seismic data.

Experimental study on shear properties and resistivity change of soil-rock mixture

The deterioration of shear resistance in rock and soil masses has resulted in numerous severe natural disasters,highlighting the significance of long-term monitoring for disaster prevention and mitigation.This study explores the use of a non-destructive method to quickly and accurately evaluate the shear properties of soil-rock mixture.The shear stress,shear strain,and resistivity of the soil-rock mixture were tested simultaneously using a combination of direct shear and resistivity tests.The test results show that the resistivity of the soil-rock mixture gradually decreases with increasing shear strain.The resistivity of all specimens ranged approximately from 60 to 130Ω.m throughout the shear process.At the end of the shear test,the vertical failure resistivity showed an irregular“W”shape with increasing rock content.It exhibited a significant negative linear functional relationship with the shear strength.With reference to the determination of cohesion and internal friction angle on the shear strength envelope,the horizontal angle of the vertical failure resistivity-normal stress curve is defined as the resistivity angle,and the intercept of the curve is the resistivity at the initial moment of shear.It has been observed that the resistivity angle is negatively and linearly correlated with the internal friction angle.At the same time,there is a linear growth relationship between resistivity at the initial moment of shear and cohesion.It has been demonstrated that an increase in rock content contributes to a general escalation in both the average structure factor and average shape factor.Meanwhile,a decrease in the anisotropy coefficient has also been noted.These alterations are indicative of the extent of microstructural transformations occurring during the deformation process of the soil-rock mixture.The research results verify the feasibility of real-time deformation monitoring and characterization of shear strength parameters using resistivity.

Optimal preparation of Bose and Fermi atomic gas mixtures of ^(87)Rb and ^(40)K in a crossed optical dipole trap

We report on the optimal production of the Bose and Fermi mixtures with ^(87) Rb and ^(40)K in a crossed optical dipole trap(ODT).We measure the atomic number and lifetime of the mixtures in combination of the spin state |F=9/2,m_(F)=9/2> of^(40)K and |1,1>of ^(87) Rb in the ODT,which is larger and longer compared with the combination of the spin state |9/2,9/2> of^(40)K and 12,2) of ^(87)Rb in the ODT.We observe the atomic numbers of ^(87)Rb and ^(40)K shown in each stage of the sympathetic cooling process while gradually reducing the depth of the optical trap.By optimizing the relative loading time of atomic mixtures in the MOT,we obtain the large atomic number of ^(40)K(~6 ×10^(6)) or the mixtures of atoms with an equal number(~1.6 × 10^(6)) at the end of evaporative cooling in the ODT.We experimentally investigate the evaporative cooling in an enlarged volume of the ODT via adding a third laser beam to the crossed ODT and found that more atoms(8 × 10^(6)) and higher degeneracy(T/T_(F)=0.25) of Fermi gases are obtained.The ultracold atomic gas mixtures pave the way to explore phenomena such as few-body collisions and the Bose-Fermi Hubbard model,as well as for creating ground-state molecules of ^(87)Rb^(40)K.

Gaussian Mixture-Learned Approximate Message Passing(GM-LAMP)Based Hybrid Precoders for mmWave Massive MIMO Systems

Hybrid precoder design is a key technique providing better antenna gain and reduced hardware complexity in millimeter-wave(mmWave)massive multiple-input multiple-output(MIMO)systems.In this paper,Gaussian Mixture learned approximate message passing(GM-LAMP)network is presented for the design of optimal hybrid precoders suitable for mmWave Massive MIMO systems.Optimal hybrid precoder designs using a compressive sensing scheme such as orthogonal matching pursuit(OMP)and its derivatives results in high computational complexity when the dimensionality of the sparse signal is high.This drawback can be addressed using classical iterative algorithms such as approximate message passing(AMP),which has comparatively low computational complexity.The drawbacks of AMP algorithm are fixed shrinkage parameter and non-consideration of prior distribution of the hybrid precoders.In this paper,the fixed shrinkage parameter problem of the AMP algorithm is addressed using learned AMP(LAMP)network,and is further enhanced as GMLAMP network using the concept of Gaussian Mixture distribution of the hybrid precoders.The simula-tion results show that the proposed GM-LAMP network achieves optimal hybrid precoder design with enhanced achievable rates,better accuracy and low computational complexity compared to the existing algorithms.

Predicting carbon storage of mixed broadleaf forests based on the finite mixture model incorporating stand factors,site quality,and aridity index

The diameter distribution function(DDF)is a crucial tool for accurately predicting stand carbon storage(CS).The current key issue,however,is how to construct a high-precision DDF based on stand factors,site quality,and aridity index to predict stand CS in multi-species mixed forests with complex structures.This study used data from70 survey plots for mixed broadleaf Populus davidiana and Betula platyphylla forests in the Mulan Rangeland State Forest,Hebei Province,China,to construct the DDF based on maximum likelihood estimation and finite mixture model(FMM).Ordinary least squares(OLS),linear seemingly unrelated regression(LSUR),and back propagation neural network(BPNN)were used to investigate the influences of stand factors,site quality,and aridity index on the shape and scale parameters of DDF and predicted stand CS of mixed broadleaf forests.The results showed that FMM accurately described the stand-level diameter distribution of the mixed P.davidiana and B.platyphylla forests;whereas the Weibull function constructed by MLE was more accurate in describing species-level diameter distribution.The combined variable of quadratic mean diameter(Dq),stand basal area(BA),and site quality improved the accuracy of the shape parameter models of FMM;the combined variable of Dq,BA,and De Martonne aridity index improved the accuracy of the scale parameter models.Compared to OLS and LSUR,the BPNN had higher accuracy in the re-parameterization process of FMM.OLS,LSUR,and BPNN overestimated the CS of P.davidiana but underestimated the CS of B.platyphylla in the large diameter classes(DBH≥18 cm).BPNN accurately estimated stand-and species-level CS,but it was more suitable for estimating stand-level CS compared to species-level CS,thereby providing a scientific basis for the optimization of stand structure and assessment of carbon sequestration capacity in mixed broadleaf forests.

A Bayesian Mixture Model Approach to Disparity Testing

The topic of this article is one-sided hypothesis testing for disparity, i.e., the mean of one group is larger than that of another when there is uncertainty as to which group a datum is drawn. For each datum, the uncertainty is captured with a given discrete probability distribution over the groups. Such situations arise, for example, in the use of Bayesian imputation methods to assess race and ethnicity disparities with certain insurance, health, and financial data. A widely used method to implement this assessment is the Bayesian Improved Surname Geocoding (BISG) method which assigns a discrete probability over six race/ethnicity groups to an individual given the individual’s surname and address location. Using a Bayesian framework and Markov Chain Monte Carlo sampling from the joint posterior distribution of the group means, the probability of a disparity hypothesis is estimated. Four methods are developed and compared with an illustrative data set. Three of these methods are implemented in an R-code and one method in WinBUGS. These methods are programed for any number of groups between two and six inclusive. All the codes are provided in the appendices.

Sensing the heavy water concentration in an H_(2)O-D_(2)O mixture by solid-solid phononic crystals

A new method based on phononic crystals is presented to detect the concentration of heavy water(D_(2)O)in an H_(2)O-D_(2)O mixture.Results have been obtained and analyzed in the concentration range of 0%-10%and 90%-100%D_(2)O.A proposed structure of tungsten scatterers in an aluminum host is studied.In order to detect the target material,a cavity region is considered as a sound wave resonator in which the target material with different concentrations of D_(2)O is embedded.By changing the concentration of D_(2)O in the H_(2)O-D_(2)O mixture,the resonance frequency undergoes a frequency shift.Each 1%change in D_(2)O concentration in the H_(2)O-D_(2)O mixture causes a frequency change of about 120 Hz.The finite element method is used as the numerical method to calculate and analyze the natural frequencies and transmission spectra of the proposed sensor.The performance evaluation index shows a high Q factor up to 1475758 and a high sensitivity up to 13075,which are acceptable values for sensing purposes.The other figures of merit related to the detection performance also indicate high-quality performance of the designed sensor.