Prediction of Ground Vibration Induced by Rock Blasting Based on Optimized Support Vector Regression Models

Accurately estimating blasting vibration during rock blasting is the foundation of blasting vibration management.In this study,Tuna Swarm Optimization(TSO),Whale Optimization Algorithm(WOA),and Cuckoo Search(CS)were used to optimize two hyperparameters in support vector regression(SVR).Based on these methods,three hybrid models to predict peak particle velocity(PPV)for bench blasting were developed.Eighty-eight samples were collected to establish the PPV database,eight initial blasting parameters were chosen as input parameters for the predictionmodel,and the PPV was the output parameter.As predictive performance evaluation indicators,the coefficient of determination(R2),rootmean square error(RMSE),mean absolute error(MAE),and a10-index were selected.The normalizedmutual information value is then used to evaluate the impact of various input parameters on the PPV prediction outcomes.According to the research findings,TSO,WOA,and CS can all enhance the predictive performance of the SVR model.The TSO-SVR model provides the most accurate predictions.The performances of the optimized hybrid SVR models are superior to the unoptimized traditional prediction model.The maximum charge per delay impacts the PPV prediction value the most.

细菌降解低分子量多环芳烃的研究进展

具有"三致"效应的多环芳烃污染造成了巨大的环境危害,威胁人类健康和生存。目前能够降解低分子量多环芳烃的细菌已有广泛的研究。细菌通过多层次的调控分析和适应性进化提高它们的降解能力。本文基于国内外文献调研,简要总结了生物修复在低分子量多环芳烃降解领域的研究进展。拟通过多层次的调控分析和适应性进化来产生多种分解代谢途径,为生物降解能力强化的未来降解技术提供支撑。