Prediction of flyrock induced by mine blasting using a novel kernel-based extreme learning machine

Blasting is a common method of breaking rock in surface mines.Although the fragmentation with proper size is the main purpose,other undesirable effects such as flyrock are inevitable.This study is carried out to evaluate the capability of a novel kernel-based extreme learning machine algorithm,called kernel extreme learning machine(KELM),by which the flyrock distance(FRD) is predicted.Furthermore,the other three data-driven models including local weighted linear regression(LWLR),response surface methodology(RSM) and boosted regression tree(BRT) are also developed to validate the main model.A database gathered from three quarry sites in Malaysia is employed to construct the proposed models using 73 sets of spacing,burden,stemming length and powder factor data as inputs and FRD as target.Afterwards,the validity of the models is evaluated by comparing the corresponding values of some statistical metrics and validation tools.Finally,the results verify that the proposed KELM model on account of highest correlation coefficient(R) and lowest root mean square error(RMSE) is more computationally efficient,leading to better predictive capability compared to LWLR,RSM and BRT models for all data sets.

A systematic framework of constructing surrogate model for slider track peeling strength prediction

Peeling strength can comprehensively reflect slider track safety and is crucial in car seat safety assessments.Current methods for determining slider peeling strength are primarily physical testing and numerical simulation.However,these methods encounter the potential challenges of high costs and overlong time consumption which have not been adequately addressed.Therefore,the efficient and low-cost surrogate model emerges as a promising solution.Nevertheless,currently used surrogate models suffer from inefficiencies and complexity in data sampling,lack of robustness in local model predictions,and isolation between data sampling and model prediction.To overcome these challenges,this paper aims to set up a systematic framework for slider track peeling strength prediction,including sensitivity analysis,dataset sampling,and model prediction.Specifically,the interpretable linear regression is performed to identify the sensitivity of various geometric variables to peeling strength.Based on the variable sensitivity,a distance metric is constructed to measure the disparity of different variable groups.Then,the sparsity-targeted sampling(STS)is proposed to formulate a representative dataset.Finally,the sequentially selected local weighted linear regression(SLWLR)is designed to achieve accurate track peeling strength prediction.Additionally,a quantitative cost assessment of the supplementary dataset is proposed by utilizing the minimum adjacent sample distance as a mediator.Experimental results validate the efficacy of sequential selection and the weighting mechanism in enhancing localization robustness.Furthermore,the proposed SLWLR method surpasses similar approaches and other common surrogate methods in terms of prediction performance and data quantity requirements,achieving an average absolute error of 3.3 kN in the simulated test dataset.