High strain rate compressive strength behavior of cemented paste backfill using split Hopkinson pressure bar

The stability of cemented paste backfill(CPB)is threatened by dynamic disturbance,but the conventional low strain rate laboratory pressure test has difficulty achieving this research purpose.Therefore,a split Hopkinson pressure bar(SHPB)was utilized to investigate the high strain rate compressive behavior of CPB with dynamic loads of 0.4,0.8,and 1.2 MPa.And the failure modes were determined by macro and micro analysis.CPB with different cement-to-tailings ratios,solid mass concentrations,and curing ages was prepared to conduct the SHPB test.The results showed that increasing the cement content,tailings content,and curing age can improve the dynamic compressive strength and elastic modulus.Under an impact load,a higher strain rate can lead to larger increasing times of the dynamic compressive strength when compared with static loading.And the dynamic compressive strength of CPB has an exponential correlation with the strain rate.The macroscopic failure modes indicated that CPB is more seriously damaged under dynamic loading.The local damage was enhanced,and fine cracks were formed in the interior of the CPB.This is because the CPB cannot dissipate the energy of the high strain rate stress wave in a short loading period.

Rock mass structural recognition from drill monitoring technology in underground mining using discontinuity index and machine learning techniques

A procedure to recognize individual discontinuities in rock mass from measurement while drilling(MWD)technology is developed,using the binary pattern of structural rock characteristics obtained from in-hole images for calibration.Data from two underground operations with different drilling technology and different rock mass characteristics are considered,which generalizes the application of the methodology to different sites and ensures the full operational integration of MWD data analysis.Two approaches are followed for site-specific structural model building:a discontinuity index(DI)built from variations in MWD parameters,and a machine learning(ML)classifier as function of the drilling parameters and their variability.The prediction ability of the models is quantitatively assessed as the rate of recognition of discontinuities observed in borehole logs.Differences between the parameters involved in the models for each site,and differences in their weights,highlight the site-dependence of the resulting models.The ML approach offers better performance than the classical DI,with recognition rates in the range 89%to 96%.However,the simpler DI still yields fairly accurate results,with recognition rates 70%to 90%.These results validate the adaptive MWD-based methodology as an engineering solution to predict rock structural condition in underground mining operations.

Prediction of blasting mean fragment size using support vector regression combined with five optimization algorithms

The main purpose of blasting operation is to produce desired and optimum mean size rock fragments.Smaller or fine fragments cause the loss of ore during loading and transportation,whereas large or coarser fragments need to be further processed,which enhances production cost.Therefore,accurate prediction of rock fragmentation is crucial in blasting operations.Mean fragment size(MFS) is a crucial index that measures the goodness of blasting designs.Over the past decades,various models have been proposed to evaluate and predict blasting fragmentation.Among these models,artificial intelligence(AI)-based models are becoming more popular due to their outstanding prediction results for multiinfluential factors.In this study,support vector regression(SVR) techniques are adopted as the basic prediction tools,and five types of optimization algorithms,i.e.grid search(GS),grey wolf optimization(GWO),particle swarm optimization(PSO),genetic algorithm(GA) and salp swarm algorithm(SSA),are implemented to improve the prediction performance and optimize the hyper-parameters.The prediction model involves 19 influential factors that constitute a comprehensive blasting MFS evaluation system based on AI techniques.Among all the models,the GWO-v-SVR-based model shows the best comprehensive performance in predicting MFS in blasting operation.Three types of mathematical indices,i.e.mean square error(MSE),coefficient of determination(R^(2)) and variance accounted for(VAF),are utilized for evaluating the performance of different prediction models.The R^(2),MSE and VAF values for the training set are 0.8355,0.00138 and 80.98,respectively,whereas 0.8353,0.00348 and 82.41,respectively for the testing set.Finally,sensitivity analysis is performed to understand the influence of input parameters on MFS.It shows that the most sensitive factor in blasting MFS is the uniaxial compressive strength.

A comprehensive comparison of different regression techniques and nature-inspired optimization algorithms to predict carbonation depth of recycled aggregate concrete

The utilization of recycled aggregates(RA)for concrete production has the potential to offer substantial environmental and economic advantages.However,RA concrete is plagued with considerable durability concerns,particularly carbonation.To advance the application of RA concrete,the establishment of a reliable model for predicting the carbonation is needed.On the one hand,concrete carbonation is a long and slow process and thus consumes a lot of time and energy to monitor.On the other hand,carbonation is influenced by many factors and is hard to predict.Regarding this,this paper proposes the use of machine learning techniques to establish accurate prediction models for the carbonation depth(CD)of RA concrete.Three types of regression techniques and meta-heuristic algorithms were employed to provide more alternative predictive tools.It was found that the best prediction performance was obtained from extreme gradient boosting-multi-universe optimizer(XGB-MVO)with R^(2) value of 0.9949 and 0.9398 for training and testing sets,respectively.XGB-MVO was used for evaluating physical laws of carbonation and it was found that the developed XGB-MVO model could provide reasonable predictions when new data were investigated.It also showed better generalization capabilities when compared with different models in the literature.Overall,this paper emphasizes the need for sustainable solutions in the construction industry to reduce its environmental impact and contribute to sustainable and low-carbon economies.

Compressive strength prediction and optimization design of sustainable concrete based on squirrel search algorithm-extreme gradient boosting technique

Concrete is the most commonly used construction material.However,its production leads to high carbon dioxide(CO_(2))emissions and energy consumption.Therefore,developing waste-substitutable concrete components is necessary.Improving the sustainability and greenness of concrete is the focus of this research.In this regard,899 data points were collected from existing studies where cement,slag,fly ash,superplasticizer,coarse aggregate,and fine aggregate were considered potential influential factors.The complex relationship between influential factors and concrete compressive strength makes the prediction and estimation of compressive strength difficult.Instead of the traditional compressive strength test,this study combines five novel metaheuristic algorithms with extreme gradient boosting(XGB)to predict the compressive strength of green concrete based on fly ash and blast furnace slag.The intelligent prediction models were assessed using the root mean square error(RMSE),coefficient of determination(R^(2)),mean absolute error(MAE),and variance accounted for(VAF).The results indicated that the squirrel search algorithm-extreme gradient boosting(SSA-XGB)yielded the best overall prediction performance with R^(2) values of 0.9930 and 0.9576,VAF values of 99.30 and 95.79,MAE values of 0.52 and 2.50,RMSE of 1.34 and 3.31 for the training and testing sets,respectively.The remaining five prediction methods yield promising results.Therefore,the developed hybrid XGB model can be introduced as an accurate and fast technique for the performance prediction of green concrete.Finally,the developed SSA-XGB considered the effects of all the input factors on the compressive strength.The ability of the model to predict the performance of concrete with unknown proportions can play a significant role in accelerating the development and application of sustainable concrete and furthering a sustainable economy.