Tunnel boring machines(TBMs)have been widely utilised in tunnel construction due to their high efficiency and reliability.Accurately predicting TBM performance can improve project time management,cost control,and risk...Tunnel boring machines(TBMs)have been widely utilised in tunnel construction due to their high efficiency and reliability.Accurately predicting TBM performance can improve project time management,cost control,and risk management.This study aims to use deep learning to develop real-time models for predicting the penetration rate(PR).The models are built using data from the Changsha metro project,and their performances are evaluated using unseen data from the Zhengzhou Metro project.In one-step forecast,the predicted penetration rate follows the trend of the measured penetration rate in both training and testing.The autoregressive integrated moving average(ARIMA)model is compared with the recurrent neural network(RNN)model.The results show that univariate models,which only consider historical penetration rate itself,perform better than multivariate models that take into account multiple geological and operational parameters(GEO and OP).Next,an RNN variant combining time series of penetration rate with the last-step geological and operational parameters is developed,and it performs better than other models.A sensitivity analysis shows that the penetration rate is the most important parameter,while other parameters have a smaller impact on time series forecasting.It is also found that smoothed data are easier to predict with high accuracy.Nevertheless,over-simplified data can lose real characteristics in time series.In conclusion,the RNN variant can accurately predict the next-step penetration rate,and data smoothing is crucial in time series forecasting.This study provides practical guidance for TBM performance forecasting in practical engineering.展开更多
目的探讨治疗前中性粒细胞与淋巴细胞比值(NLR)和血小板与淋巴细胞比值(PLR)对于胃肠胰神经内分泌肿瘤(GEP-NETs)的预后价值。方法对中国知网、万方数据知识服务平台、PubMed、EMBase、Web of Science、The Cochrane Library等中英文数...目的探讨治疗前中性粒细胞与淋巴细胞比值(NLR)和血小板与淋巴细胞比值(PLR)对于胃肠胰神经内分泌肿瘤(GEP-NETs)的预后价值。方法对中国知网、万方数据知识服务平台、PubMed、EMBase、Web of Science、The Cochrane Library等中英文数据库进行系统性检索。检索时间截止为2022年2月,语言为中英文。对检索到的文献根据纳排标准进行筛选及质量评价,确定最终纳入文献。运用Revman 5.4软件进行统计学分析,评价治疗前NLR和PLR对于GEP-NETs患者的预后影响。结果最终纳入12项研究共2040例患者,分析结果如下:高NLR患者组的无进展生存期(PFS)/无疾病复发期(RFS)和总生存期(OS)明显缩短(P<0.01)。进一步对OS进行亚组分析结果表明,不论国家、样本数量、治疗方式、NLR临界值的确定方式及肿瘤原发部位,治疗前的高NLR水平均与GEP-NETs患者较短的OS有关(P<0.01);高PLR患者与较差的OS有关(P<0.01);高NLR组患者与较差的肿瘤分级、分期、淋巴结转移、无功能性GEP-NETs有关(P<0.01)。结论治疗前NLR、PLR水平对GEP-NETs患者的生存具有预测价值,NLR、PLR的检测可能在GEP-NETs患者分层管理及预后评估中起重要作用,可协助GEPNETs患者个体化管理。展开更多
文摘Tunnel boring machines(TBMs)have been widely utilised in tunnel construction due to their high efficiency and reliability.Accurately predicting TBM performance can improve project time management,cost control,and risk management.This study aims to use deep learning to develop real-time models for predicting the penetration rate(PR).The models are built using data from the Changsha metro project,and their performances are evaluated using unseen data from the Zhengzhou Metro project.In one-step forecast,the predicted penetration rate follows the trend of the measured penetration rate in both training and testing.The autoregressive integrated moving average(ARIMA)model is compared with the recurrent neural network(RNN)model.The results show that univariate models,which only consider historical penetration rate itself,perform better than multivariate models that take into account multiple geological and operational parameters(GEO and OP).Next,an RNN variant combining time series of penetration rate with the last-step geological and operational parameters is developed,and it performs better than other models.A sensitivity analysis shows that the penetration rate is the most important parameter,while other parameters have a smaller impact on time series forecasting.It is also found that smoothed data are easier to predict with high accuracy.Nevertheless,over-simplified data can lose real characteristics in time series.In conclusion,the RNN variant can accurately predict the next-step penetration rate,and data smoothing is crucial in time series forecasting.This study provides practical guidance for TBM performance forecasting in practical engineering.
