Comprehensive utilization of solid waste resources: Development of wet shotcrete for mines

The development of solid waste resources as constituent materials for wet shotcrete has significant economic and environmental advantages. In this study, the concept of using tailings as aggregate and fly ash and slag powder as auxiliary cementitious material is proposed and experiments are carried out by response surface methodology(RSM). Multivariate nonlinear response models are constructed to investigate the effect of factors on the uniaxial compressive strength(UCS) of tailings wet shotcrete(TWSC). The UCS of TWSC is predicted and optimized by constructing Gaussian process regression(GPR) and genetic algorithm(GA). The UCS of TWSC is gradually enhanced with the increase of slag powder dosage and fineness modulus, and it is enhanced first and then decreased with the increase of fly ash dosage. The microstructure of TWSC has the highest gray value and the highest UCS when the fly ash dosage is about 120 kg·m^(-3). The GPR–GA model constructed in this study achieves high accuracy prediction and optimization of the UCS of TWSC under multi-factor conditions.

Development of Wet Shotcrete with Solid Waste as Aggregate:Strength Optimization and Mix Proportion Design

The super-fine particle size of tailings is its drawback as a recycled resource,which is reflected in the low strength of the new construction and industrial materials formed when it is mixed with cement and other cementitious materials.Therefore,it is crucial to study the effect of tailings particle size and cementitious material on the strength of tailings wet shotcrete(TWSC)and to investigate the optimal mix proportion.In this paper,a multivariate nonlinear response model was constructed by conducting central composite experiments to investigate the effect of different factors on the strength of TWSC.The strength prediction and mix proportion optimization of TWSC are carried out by machine learning techniques.The results show that the response model has R^(2)>0.94 and P<0.01,which indicates that the model has high reliability.Moreover,the strength of TWSC increases with the increase of tailings fineness modulus and decrease of water-binder ratio,while it also increases and then decreases with the increase of replacement rate of slag powder to cement(SRC rate).The extreme learning machine(ELM)constructed in this paper predicts the strength of TWSC with an accuracy of more than 98%and achieves rapid prediction under multi-factor conditions.It is worth mentioning that the ELM combined with the genetic algorithm(ELM-GA)collaboratively solved to obtain the mix proportion for C15 and C20 strength grades of TWSC and the maximum error is verified by experiments to be less than 2%.

Mechanism of wet shotcrete interacting with rock in support systems

In order to have a good understanding of the behavior of wet shotcrete as a support element interacting with the rock mass,mechanism of wet shotcrete interacting with rock in support systems was analyzed through theoretical,numerical study and analytical analysis.A new model of distribution of rock stress state after wet shotcrete was applied,which includes shotcrete layer,composite layer,strengthening layer,plastic layer and elastic layer,and a full illustration of the rock mass stress state was given after shotcrete interacting with rock mass.At the same time,numerical analysis with FLAC gives a stress distribution along the monitor line,respectively,at the sidewall and roof of the tunnel.The displacement obviously decreases with the depth of rock,the tangential stress for tunnel supported by shotcrete is lower than that without shotcrete,and radial stress for tunnel supported by shotcrete is higher than that without shotcrete.It has been demonstrated by AIRY'S stress function,which gives a reasonable solution.Finally,the application of wet shotcrete in Jinfeng Gold Mine shows that the displacement of tunnel decreases obviously in sidewall and roof.

Fly Ash and Slag as Partial Replacement of Cement for the Synthesis of Low Carbon Cementitious Materials

Tailings known as solid waste are generated by the mining industry.The development of tailings as wet shotcrete aggregates has significant economic and environmental benefits.The fine particle size of the tailings results in a large consumption of traditional cement as a cementitious material and insignificant improvement in strength.Therefore,a composite cementitious system of cement and solid waste resources(fly ash and slag powder)is explored for this study.In this paper,the response surface methodology(RSM)is used to optimize the experimental design and a multivariate nonlinear response model with cement,fly ash and slag powder contents as variables are constructed,which can investigate the effect of the composite cementitious system on the strength of tailing wet shotcrete(TWSC).In addition,the information entropy(IE)is introduced and combined with the RSM to evaluate the composite cementitious system.Finally,the desirability function(DF)combined with RSM is used to optimize the composite cementitious system.The results show that the response model constructed in this paper has R^(2)=0.96 and P-value<0.01(the test result of the model is P-value<0.01),which indicates that the model has high reliability.The higher the content of slag powder and cement in the composite cementitious system,the higher the strength and comprehensive score of the TWSC.There is a critical value of fly ash content,which makes the maximum cementation of the composite cementing system.The optimal mix proportion of the composite cementitious system is obtained based on RSM-DF,which leads to the strength of TWSC at different curing time to achieve the expected index.