Based on the similarity theory,a tunnel excavation simulation testing system under typical unsymmetrical loading conditions was established.Using this system,the failure mechanism of surrounding rock of shallow-bias t...Based on the similarity theory,a tunnel excavation simulation testing system under typical unsymmetrical loading conditions was established.Using this system,the failure mechanism of surrounding rock of shallow-bias tunnels with small clear distance was analyzed along with the load characteristics.The results show that:1) The failure process of surrounding rock of shallow-bias tunnels with small clear distance consists of structural and stratum deformation induced by tunnel excavation; Microfracture surfaces are formed in the tunnel surrounding rock and extend deep into the rock mass in a larger density; Tensile cracking occurs in shallow position on the deep-buried side,with shear slip in deep rock mass.In the meantime,rapid deformation and slip take place on the shallow-buried side until the surrounding rocks totally collapse.The production and development of micro-fracture surfaces in the tunnel surrounding rock and tensile cracking in the shallow position on the deep-buried side represent the key stages of failure.2) The final failure mode is featured by an inverted conical fracture with tunnel arch as its top and the slope at tunnel entrance slope as its bottom.The range of failure on the deep-buried side is significantly larger than that on the shallow-buried side.Such difference becomes more prominent with the increasing bias angle.What distinguishes it from the "linear fracture surface" model is that the model proposed has a larger fracture angle on the two sides.Moreover,the bottom of the fracture is located at the springing line of tunnel arch.3) The total vertical load increases with bias angle.Compared with the existing methods,the unsymmetrical loading effect in measurement is more prominent.At last,countermeasures are proposed according to the analysis results: during engineering process,1) The surrounding rock mass on the deep-buried side should be reinforced apart from the tunnel surrounding rock for shallow-buried tunnels with small clear distance; moreover,the scope of consolidation should go beyond the midline of tunnel(along the direction of the top of slope) by 4 excavation spans of single tunnel.2) It is necessary to modify the load value of shallow-bias tunnels with small clear distance.展开更多
Prediction of tunneling-induced ground settlements is an essential task,particularly for tunneling in urban settings.Ground settlements should be limited within a tolerable threshold to avoid damages to aboveground st...Prediction of tunneling-induced ground settlements is an essential task,particularly for tunneling in urban settings.Ground settlements should be limited within a tolerable threshold to avoid damages to aboveground structures.Machine learning(ML)methods are becoming popular in many fields,including tunneling and underground excavations,as a powerful learning and predicting technique.However,the available datasets collected from a tunneling project are usually small from the perspective of applying ML methods.Can ML algorithms effectively predict tunneling-induced ground settlements when the available datasets are small?In this study,seven ML methods are utilized to predict tunneling-induced ground settlement using 14 contributing factors measured before or during tunnel excavation.These methods include multiple linear regression(MLR),decision tree(DT),random forest(RF),gradient boosting(GB),support vector regression(SVR),back-propagation neural network(BPNN),and permutation importancebased BPNN(PI-BPNN)models.All methods except BPNN and PI-BPNN are shallow-structure ML methods.The effectiveness of these seven ML approaches on small datasets is evaluated using model accuracy and stability.The model accuracy is measured by the coefficient of determination(R2)of training and testing datasets,and the stability of a learning algorithm indicates robust predictive performance.Also,the quantile error(QE)criterion is introduced to assess model predictive performance considering underpredictions and overpredictions.Our study reveals that the RF algorithm outperforms all the other models with the highest model prediction accuracy(0.9)and stability(3.0210^(-27)).Deep-structure ML models do not perform well for small datasets with relatively low model accuracy(0.59)and stability(5.76).The PI-BPNN architecture is proposed and designed for small datasets,showing better performance than typical BPNN.Six important contributing factors of ground settlements are identified,including tunnel depth,the distance between tunnel face and surface monitoring points(DTM),weighted average soil compressibility modulus(ACM),grouting pressure,penetrating rate and thrust force.展开更多
Purpose:To evaluate the clinical efficacy and safety of sulcus transscleral intraocular lens suture fixation with small incision through scleral tunnel in eyes the with posterior capsule defect or insufficient zonula ...Purpose:To evaluate the clinical efficacy and safety of sulcus transscleral intraocular lens suture fixation with small incision through scleral tunnel in eyes the with posterior capsule defect or insufficient zonula support. Methods:Thirty nine eyes with severe posterior capsule defect and zonula damages caused by small-incision cataract surgery,and those with capsule absence or intraocular lens dislocation were selected in this investigation from February 2007 to December 2009.Sulcus transscleral intraocular lens suture combined with puncture needle-guided external approach and."one- or two-point fixation" method in the small sclera tunnel incision were employed. Results:The mean follow-up was 12.1 months (range from 3 to 28 months). Six eyes were complicated by some eye diseases postoperatively.The best-corrected visual acuity was 20/40 or better in other 34 eyes.(87.17%).All eyes with secondary IOL fixation presented equal or better naked visual acuity than best-corrected visual acuity best-corrected preoperatively.No intraoperative and postoperative complications such as hemorrhage, retinal detachment, intraocular lens tilt and decentration occurred. Conclusion:Sulcus transscleral intraocular lens suture fixation via small sclera tunnel incision was easy to operate and master,required less operative time,and made primary intraocular lens fixation more effective in eyes with posterior capsule defect or insufficient zonula support in small sclera tunnel incision surgery.In addition,the technique was safe and effcacious for secondary intraocular lens fixation.展开更多
针对高速公路隧道内光线昏暗、图像受灯光影响及远距离小目标检测困难等问题,提出了一种改进的YOLOv5高速公路隧道车辆和人员检测算法。首先,使用高斯混合聚类来获得更加匹配数据集目标的一组锚框,提高了模型的检测精度;其次,在特征融...针对高速公路隧道内光线昏暗、图像受灯光影响及远距离小目标检测困难等问题,提出了一种改进的YOLOv5高速公路隧道车辆和人员检测算法。首先,使用高斯混合聚类来获得更加匹配数据集目标的一组锚框,提高了模型的检测精度;其次,在特征融合部分引入内容感知重组特征(content-aware ReAssembly of FEatures, CARAFE)上采样算子,扩大感受野,降低上采样过程特征细节损失;最后,通过向网络中插入坐标注意力(coordinate attention, CA),进一步增强模型对图像各位置特征的提取能力。为验证算法的有效性,在浙江温丽高速公路隧道数据集上进行实验,结果表明:所提算法的平均检测精度(mean average precision, mAP)达到了95.7%,较原模型提升3.8%,对于远距离小目标和受严重灯光影响的目标能够实现更加精准检测,为复杂环境下高速公路隧道内车辆和人员检测提供了一种有效的解决方案。展开更多
依托南通轨道交通1号线孩环区间(孩儿巷站—环城东路站)三线并行小净距盾构隧道工程,采用离散元法(Discrete Element Method,DEM)—有限差分法(Finite Difference Method,FDM)耦合的数值仿真,分析桩基础长度、桩隧距对桩基础变形的影响...依托南通轨道交通1号线孩环区间(孩儿巷站—环城东路站)三线并行小净距盾构隧道工程,采用离散元法(Discrete Element Method,DEM)—有限差分法(Finite Difference Method,FDM)耦合的数值仿真,分析桩基础长度、桩隧距对桩基础变形的影响。结果表明:三线小净距隧道施工会引起邻近桩基沉降;桩底和隧道顶部位于同一深度时,桩基水平位移最大值出现在桩底,桩基变形以倾斜为主;桩底与隧道底部位于同一深度和桩底位于隧道底部以下时桩基水平位移最大值出现在隧道轴线位置,桩基变形以弯曲为主,但桩底位于隧道底部以下时桩基对隧道施工表现出更强的敏感性;三种长度的桩基最大拉应力所在截面靠近隧道一侧和远离隧道一侧拉压应力状态均完全相反;桩长一定时,随桩隧距增大,桩基的沉降、最大水平位移、最大轴向拉应力均减小。展开更多
基金Project(51508575)supported by the National Natural Science Foundation of ChinaProject(2011CB013802)supported by the National Basic Research Program of China+1 种基金Projects(2014M560652,2016T90764)supported by the China Postdoctoral Science FoundationProject(2015RS4006)supported by the Innovative Talents of Science and Technology Plan of Hunan Province,China
文摘Based on the similarity theory,a tunnel excavation simulation testing system under typical unsymmetrical loading conditions was established.Using this system,the failure mechanism of surrounding rock of shallow-bias tunnels with small clear distance was analyzed along with the load characteristics.The results show that:1) The failure process of surrounding rock of shallow-bias tunnels with small clear distance consists of structural and stratum deformation induced by tunnel excavation; Microfracture surfaces are formed in the tunnel surrounding rock and extend deep into the rock mass in a larger density; Tensile cracking occurs in shallow position on the deep-buried side,with shear slip in deep rock mass.In the meantime,rapid deformation and slip take place on the shallow-buried side until the surrounding rocks totally collapse.The production and development of micro-fracture surfaces in the tunnel surrounding rock and tensile cracking in the shallow position on the deep-buried side represent the key stages of failure.2) The final failure mode is featured by an inverted conical fracture with tunnel arch as its top and the slope at tunnel entrance slope as its bottom.The range of failure on the deep-buried side is significantly larger than that on the shallow-buried side.Such difference becomes more prominent with the increasing bias angle.What distinguishes it from the "linear fracture surface" model is that the model proposed has a larger fracture angle on the two sides.Moreover,the bottom of the fracture is located at the springing line of tunnel arch.3) The total vertical load increases with bias angle.Compared with the existing methods,the unsymmetrical loading effect in measurement is more prominent.At last,countermeasures are proposed according to the analysis results: during engineering process,1) The surrounding rock mass on the deep-buried side should be reinforced apart from the tunnel surrounding rock for shallow-buried tunnels with small clear distance; moreover,the scope of consolidation should go beyond the midline of tunnel(along the direction of the top of slope) by 4 excavation spans of single tunnel.2) It is necessary to modify the load value of shallow-bias tunnels with small clear distance.
基金funded by the University Transportation Center for Underground Transportation Infrastructure(UTC-UTI)at the Colorado School of Mines under Grant No.69A3551747118 from the US Department of Transportation(DOT).
文摘Prediction of tunneling-induced ground settlements is an essential task,particularly for tunneling in urban settings.Ground settlements should be limited within a tolerable threshold to avoid damages to aboveground structures.Machine learning(ML)methods are becoming popular in many fields,including tunneling and underground excavations,as a powerful learning and predicting technique.However,the available datasets collected from a tunneling project are usually small from the perspective of applying ML methods.Can ML algorithms effectively predict tunneling-induced ground settlements when the available datasets are small?In this study,seven ML methods are utilized to predict tunneling-induced ground settlement using 14 contributing factors measured before or during tunnel excavation.These methods include multiple linear regression(MLR),decision tree(DT),random forest(RF),gradient boosting(GB),support vector regression(SVR),back-propagation neural network(BPNN),and permutation importancebased BPNN(PI-BPNN)models.All methods except BPNN and PI-BPNN are shallow-structure ML methods.The effectiveness of these seven ML approaches on small datasets is evaluated using model accuracy and stability.The model accuracy is measured by the coefficient of determination(R2)of training and testing datasets,and the stability of a learning algorithm indicates robust predictive performance.Also,the quantile error(QE)criterion is introduced to assess model predictive performance considering underpredictions and overpredictions.Our study reveals that the RF algorithm outperforms all the other models with the highest model prediction accuracy(0.9)and stability(3.0210^(-27)).Deep-structure ML models do not perform well for small datasets with relatively low model accuracy(0.59)and stability(5.76).The PI-BPNN architecture is proposed and designed for small datasets,showing better performance than typical BPNN.Six important contributing factors of ground settlements are identified,including tunnel depth,the distance between tunnel face and surface monitoring points(DTM),weighted average soil compressibility modulus(ACM),grouting pressure,penetrating rate and thrust force.
基金Science and technology project of Fujian Province(2008F3031)
文摘Purpose:To evaluate the clinical efficacy and safety of sulcus transscleral intraocular lens suture fixation with small incision through scleral tunnel in eyes the with posterior capsule defect or insufficient zonula support. Methods:Thirty nine eyes with severe posterior capsule defect and zonula damages caused by small-incision cataract surgery,and those with capsule absence or intraocular lens dislocation were selected in this investigation from February 2007 to December 2009.Sulcus transscleral intraocular lens suture combined with puncture needle-guided external approach and."one- or two-point fixation" method in the small sclera tunnel incision were employed. Results:The mean follow-up was 12.1 months (range from 3 to 28 months). Six eyes were complicated by some eye diseases postoperatively.The best-corrected visual acuity was 20/40 or better in other 34 eyes.(87.17%).All eyes with secondary IOL fixation presented equal or better naked visual acuity than best-corrected visual acuity best-corrected preoperatively.No intraoperative and postoperative complications such as hemorrhage, retinal detachment, intraocular lens tilt and decentration occurred. Conclusion:Sulcus transscleral intraocular lens suture fixation via small sclera tunnel incision was easy to operate and master,required less operative time,and made primary intraocular lens fixation more effective in eyes with posterior capsule defect or insufficient zonula support in small sclera tunnel incision surgery.In addition,the technique was safe and effcacious for secondary intraocular lens fixation.
文摘针对高速公路隧道内光线昏暗、图像受灯光影响及远距离小目标检测困难等问题,提出了一种改进的YOLOv5高速公路隧道车辆和人员检测算法。首先,使用高斯混合聚类来获得更加匹配数据集目标的一组锚框,提高了模型的检测精度;其次,在特征融合部分引入内容感知重组特征(content-aware ReAssembly of FEatures, CARAFE)上采样算子,扩大感受野,降低上采样过程特征细节损失;最后,通过向网络中插入坐标注意力(coordinate attention, CA),进一步增强模型对图像各位置特征的提取能力。为验证算法的有效性,在浙江温丽高速公路隧道数据集上进行实验,结果表明:所提算法的平均检测精度(mean average precision, mAP)达到了95.7%,较原模型提升3.8%,对于远距离小目标和受严重灯光影响的目标能够实现更加精准检测,为复杂环境下高速公路隧道内车辆和人员检测提供了一种有效的解决方案。
文摘依托南通轨道交通1号线孩环区间(孩儿巷站—环城东路站)三线并行小净距盾构隧道工程,采用离散元法(Discrete Element Method,DEM)—有限差分法(Finite Difference Method,FDM)耦合的数值仿真,分析桩基础长度、桩隧距对桩基础变形的影响。结果表明:三线小净距隧道施工会引起邻近桩基沉降;桩底和隧道顶部位于同一深度时,桩基水平位移最大值出现在桩底,桩基变形以倾斜为主;桩底与隧道底部位于同一深度和桩底位于隧道底部以下时桩基水平位移最大值出现在隧道轴线位置,桩基变形以弯曲为主,但桩底位于隧道底部以下时桩基对隧道施工表现出更强的敏感性;三种长度的桩基最大拉应力所在截面靠近隧道一侧和远离隧道一侧拉压应力状态均完全相反;桩长一定时,随桩隧距增大,桩基的沉降、最大水平位移、最大轴向拉应力均减小。
