Investigation of feature contribution to shield tunneling-induced settlement using Shapley additive explanations method

Accurate prediction of shield tunneling-induced settlement is a complex problem that requires consideration of many influential parameters.Recent studies reveal that machine learning(ML)algorithms can predict the settlement caused by tunneling.However,well-performing ML models are usually less interpretable.Irrelevant input features decrease the performance and interpretability of an ML model.Nonetheless,feature selection,a critical step in the ML pipeline,is usually ignored in most studies that focused on predicting tunneling-induced settlement.This study applies four techniques,i.e.Pearson correlation method,sequential forward selection(SFS),sequential backward selection(SBS)and Boruta algorithm,to investigate the effect of feature selection on the model’s performance when predicting the tunneling-induced maximum surface settlement(S_(max)).The data set used in this study was compiled from two metro tunnel projects excavated in Hangzhou,China using earth pressure balance(EPB)shields and consists of 14 input features and a single output(i.e.S_(max)).The ML model that is trained on features selected from the Boruta algorithm demonstrates the best performance in both the training and testing phases.The relevant features chosen from the Boruta algorithm further indicate that tunneling-induced settlement is affected by parameters related to tunnel geometry,geological conditions and shield operation.The recently proposed Shapley additive explanations(SHAP)method explores how the input features contribute to the output of a complex ML model.It is observed that the larger settlements are induced during shield tunneling in silty clay.Moreover,the SHAP analysis reveals that the low magnitudes of face pressure at the top of the shield increase the model’s output。

基于复杂网络技术的耳穴疗法治疗单纯性肥胖处方分析（英文）

目的:探索耳穴疗法治疗单纯性肥胖的核心穴位与配伍规律,进一步分析耳穴治疗单纯性肥胖处方特点。方法:检索PubMed、中国生物医学文献数据库(CBM)、中文科技期刊全文数据库(CNKI)、万方数据库(Wan Fang)、维普数据库(VIP)及中医药在线数据库(TCM)30余年来相关临床研究文献,录人符合要求的文献,构建耳穴治疗单纯性肥胖处方数据库。基于复杂网络技术,对耳穴治疗单纯性肥胖的耳穴核心穴位和核心配伍穴组进行分析,并对耳穴治疗单纯性肥胖处方特点展开全面分析。结果:耳穴网络节点共46个,耳穴治疗单纯性肥胖的前16个核心穴位依次为内分泌、脾、胃、三焦、饥点、神门、大肠、皮质下、肺、肾、交感、口、肝、小肠、脑。耳穴配伍主要根据穴位主治作用进行配伍,耳穴配伍分析提示胃与内分泌配伍应用最多,其次是脾与内分泌、脾与胃。耳穴刺激方法的分析中,以使用王不留行籽耳压居多,其次是磁珠及揿针。耳穴联合针刺治疗单纯性肥胖最多,其次是耳穴联合埋线及单纯耳穴治疗。结论:本研究有效挖掘出耳穴治疗单纯性肥胖的核心穴位及配伍穴组,总结分析压材及主要联合的干预方法,为临床耳穴治疗单纯性肥胖的选穴用方提供参考依据和治疗思路。