随着城市快速发展与土地资源日益紧缺,地下空间的高效开发愈发关键。盾构法因对地面交通影响小、适应性强,已成为主流的地下工程施工方法。然而,实际地层条件往往复杂多变,当隧道同时穿越软土与硬岩地层时,传统单一模式的盾构设备难以兼...随着城市快速发展与土地资源日益紧缺,地下空间的高效开发愈发关键。盾构法因对地面交通影响小、适应性强,已成为主流的地下工程施工方法。然而,实际地层条件往往复杂多变,当隧道同时穿越软土与硬岩地层时,传统单一模式的盾构设备难以兼顾,若进行工法切换则工期长、成本高。为此,兼具土压平衡式盾构机(earth pressure balance type shield machine,EPB)与硬岩隧道掘进机(hard rock tunnel boring machine,TBM)功能的双模盾构机应运而生。针对大源站—太和站区间双模盾构机的模式转换过程开展研究,通过有限元软件模拟转换全过程,并分析其引发的地表沉降规律。研究结果表明,模式转换对转换区正上方地表横向沉降影响显著,而对沿隧道纵向的地表沉降影响较小。因此,施工中应加强对转换段上方横向沉降的监测与控制。展开更多
目的探讨蛋白质EPB41(erythrocyte membrane protein band 4.1)对食管癌细胞恶性表型的影响,并基于Wnt/β-catenin信号通路介导的脂肪酸氧化(fatty acid oxidation,FAO)探讨可能的潜在机制。方法将对数生长期的KYSE30细胞及KYSE150细胞...目的探讨蛋白质EPB41(erythrocyte membrane protein band 4.1)对食管癌细胞恶性表型的影响,并基于Wnt/β-catenin信号通路介导的脂肪酸氧化(fatty acid oxidation,FAO)探讨可能的潜在机制。方法将对数生长期的KYSE30细胞及KYSE150细胞各分为5组,检测mRNA和蛋白质表达、细胞增殖和迁移率、ATP水平、NADPH/NADP+比率、活性氧(ROS)和GSH水平。结果在KYSE30细胞中,转染EPB41 siRNA组细胞克隆数、细胞迁移率、ATP水平、NADPH/NADP+比率、ROS水平、细胞中Wnt3a、β-catenin和核β-catenin蛋白质表达均明显增加(P<0.05),且脂肪酸氧化抑制剂和Wnt通路抑制剂可逆转EPB41的作用;在KYSE150细胞中,转染EPB41过表达载体oe-EPB41组细胞克隆数、细胞迁移率、ATP水平、NADPH/NADP+比率、ROS水平、细胞中Wnt3a、β-catenin和核β-catenin蛋白质表达均明显降低(P<0.05),且脂肪酸氧化诱导剂和Wnt通路激活剂可逆转EPB41的作用。结论EPB41在食管癌细胞中的表达下调,EPB41过表达可通过抑制FAO途径来抑制食管癌细胞的增殖和迁移,这可能是通过调控Wnt/β-catenin信号通路转导来实现。展开更多
In situ recycling is one of the most effective methods to dispose of earth pressure balance(EPB)shield waste muck with residual foaming agents with high moisture content.In this context,response surface methodology(RS...In situ recycling is one of the most effective methods to dispose of earth pressure balance(EPB)shield waste muck with residual foaming agents with high moisture content.In this context,response surface methodology(RSM)was employed to quantify the effects of independent variables,including flocculant dosage,defoamer dosage,and muck drying mass(MDM)and their interactions on defoaming-flocculation-dewatering indices.The polymeric aluminum chloride(PACL)and hydroxy silicone oil-glycerol polypropylene ether(H-G)were selected as the flocculant and defoamer.The contents of surfactants and foam stabilizers in residual foaming agents were determined using the proposed empirical equation.The defoaming ratio,antifoaming ratio,turbidity,moisture content,filtration loss ratio,and fall cone penetration depth were considered as dependent variables.The accuracy of developed RSM models was verified by the analysis results of variance,residuals,and paired t-test.Combined with the desirability approach,an optimal mixing ratio of 0.078 wt%PACL,0.016 wt%H-G,and 27.882 wt%MDM was recommended,leading to a defoaming ratio of 98.34 vol%for residual foams and a moisture content of 56.72 wt%for pressure-filtration cakes.Our findings were demonstrated to be able to provide useful guidance for prediction and optimization of the in situ recycling indicators of EPB shield waste muck in metro tunnel construction sites.展开更多
This paper introduces an intelligent framework for predicting the advancing speed during earth pressure balance(EPB)shield tunnelling.Five artificial intelligence(AI)models based on machine and deep learning technique...This paper introduces an intelligent framework for predicting the advancing speed during earth pressure balance(EPB)shield tunnelling.Five artificial intelligence(AI)models based on machine and deep learning techniques-back-propagation neural network(BPNN),extreme learning machine(ELM),support vector machine(SVM),long-short term memory(LSTM),and gated recurrent unit(GRU)-are used.Five geological and nine operational parameters that influence the advancing speed are considered.A field case of shield tunnelling in Shenzhen City,China is analyzed using the developed models.A total of 1000 field datasets are adopted to establish intelligent models.The prediction performance of the five models is ranked as GRU>LSTM>SVM>ELM>BPNN.Moreover,the Pearson correlation coefficient(PCC)is adopted for sensitivity analysis.The results reveal that the main thrust(MT),penetration(P),foam volume(FV),and grouting volume(GV)have strong correlations with advancing speed(AS).An empirical formula is constructed based on the high-correlation influential factors and their corresponding field datasets.Finally,the prediction performances of the intelligent models and the empirical method are compared.The results reveal that all the intelligent models perform better than the empirical method.展开更多
摘要随着城市快速发展与土地资源日益紧缺,地下空间的高效开发愈发关键。盾构法因对地面交通影响小、适应性强,已成为主流的地下工程施工方法。然而,实际地层条件往往复杂多变,当隧道同时穿越软土与硬岩地层时,传统单一模式的盾构设备难以兼顾,若进行工法切换则工期长、成本高。为此,兼具土压平衡式盾构机(earth pressure balance type shield machine,EPB)与硬岩隧道掘进机(hard rock tunnel boring machine,TBM)功能的双模盾构机应运而生。针对大源站—太和站区间双模盾构机的模式转换过程开展研究,通过有限元软件模拟转换全过程,并分析其引发的地表沉降规律。研究结果表明,模式转换对转换区正上方地表横向沉降影响显著,而对沿隧道纵向的地表沉降影响较小。因此,施工中应加强对转换段上方横向沉降的监测与控制。
摘要目的探讨蛋白质EPB41(erythrocyte membrane protein band 4.1)对食管癌细胞恶性表型的影响,并基于Wnt/β-catenin信号通路介导的脂肪酸氧化(fatty acid oxidation,FAO)探讨可能的潜在机制。方法将对数生长期的KYSE30细胞及KYSE150细胞各分为5组,检测mRNA和蛋白质表达、细胞增殖和迁移率、ATP水平、NADPH/NADP+比率、活性氧(ROS)和GSH水平。结果在KYSE30细胞中,转染EPB41 siRNA组细胞克隆数、细胞迁移率、ATP水平、NADPH/NADP+比率、ROS水平、细胞中Wnt3a、β-catenin和核β-catenin蛋白质表达均明显增加(P<0.05),且脂肪酸氧化抑制剂和Wnt通路抑制剂可逆转EPB41的作用;在KYSE150细胞中,转染EPB41过表达载体oe-EPB41组细胞克隆数、细胞迁移率、ATP水平、NADPH/NADP+比率、ROS水平、细胞中Wnt3a、β-catenin和核β-catenin蛋白质表达均明显降低(P<0.05),且脂肪酸氧化诱导剂和Wnt通路激活剂可逆转EPB41的作用。结论EPB41在食管癌细胞中的表达下调,EPB41过表达可通过抑制FAO途径来抑制食管癌细胞的增殖和迁移,这可能是通过调控Wnt/β-catenin信号通路转导来实现。
基金supported by the National Youth Top-notch Talent Support Program of China(Grant No.00389335)the National Natural Science Foundation of China(Grant No.52378392)the“Foal Eagle Program”Youth Top-notch Talent Project of Fujian Province(Grant No.00387088).
摘要In situ recycling is one of the most effective methods to dispose of earth pressure balance(EPB)shield waste muck with residual foaming agents with high moisture content.In this context,response surface methodology(RSM)was employed to quantify the effects of independent variables,including flocculant dosage,defoamer dosage,and muck drying mass(MDM)and their interactions on defoaming-flocculation-dewatering indices.The polymeric aluminum chloride(PACL)and hydroxy silicone oil-glycerol polypropylene ether(H-G)were selected as the flocculant and defoamer.The contents of surfactants and foam stabilizers in residual foaming agents were determined using the proposed empirical equation.The defoaming ratio,antifoaming ratio,turbidity,moisture content,filtration loss ratio,and fall cone penetration depth were considered as dependent variables.The accuracy of developed RSM models was verified by the analysis results of variance,residuals,and paired t-test.Combined with the desirability approach,an optimal mixing ratio of 0.078 wt%PACL,0.016 wt%H-G,and 27.882 wt%MDM was recommended,leading to a defoaming ratio of 98.34 vol%for residual foams and a moisture content of 56.72 wt%for pressure-filtration cakes.Our findings were demonstrated to be able to provide useful guidance for prediction and optimization of the in situ recycling indicators of EPB shield waste muck in metro tunnel construction sites.
基金funded by“The Pearl River Talent Recruitment Program”in 2019(Grant No.2019CX01G338),。
摘要This paper introduces an intelligent framework for predicting the advancing speed during earth pressure balance(EPB)shield tunnelling.Five artificial intelligence(AI)models based on machine and deep learning techniques-back-propagation neural network(BPNN),extreme learning machine(ELM),support vector machine(SVM),long-short term memory(LSTM),and gated recurrent unit(GRU)-are used.Five geological and nine operational parameters that influence the advancing speed are considered.A field case of shield tunnelling in Shenzhen City,China is analyzed using the developed models.A total of 1000 field datasets are adopted to establish intelligent models.The prediction performance of the five models is ranked as GRU>LSTM>SVM>ELM>BPNN.Moreover,the Pearson correlation coefficient(PCC)is adopted for sensitivity analysis.The results reveal that the main thrust(MT),penetration(P),foam volume(FV),and grouting volume(GV)have strong correlations with advancing speed(AS).An empirical formula is constructed based on the high-correlation influential factors and their corresponding field datasets.Finally,the prediction performances of the intelligent models and the empirical method are compared.The results reveal that all the intelligent models perform better than the empirical method.