A comparative study of selected bridge damage due to the Wenchuan, Northridge, Loma Prieta and San Fernando earthquakes is described in this paper. Typical ground motion effects considered include large ground fault d...A comparative study of selected bridge damage due to the Wenchuan, Northridge, Loma Prieta and San Fernando earthquakes is described in this paper. Typical ground motion effects considered include large ground fault displacement, liquefaction, landslide, and strong ground shaking. Issues related to falling spans, inadequate detailing for structural ductility and complex bridge configurations are discussed within the context of the recent seismic design codes of China and the US. A significant lesson learned from the Great Wenchuan earthquake, far beyond the opportunities to improve the seismic design provisions for bridges, is articulated.展开更多
Transfer learning could reduce the time and resources required by the training of new models and be therefore important for generalized applications of the trainedmachine learning algorithms.In this study,a transfer l...Transfer learning could reduce the time and resources required by the training of new models and be therefore important for generalized applications of the trainedmachine learning algorithms.In this study,a transfer learningenhanced convolutional neural network(CNN)was proposed to identify the gross weight and the axle weight of moving vehicles on the bridge.The proposed transfer learning-enhanced CNN model was expected to weigh different bridges based on a small amount of training datasets and provide high identification accuracy.First of all,a CNN algorithm for bridge weigh-in-motion(B-WIM)technology was proposed to identify the axle weight and the gross weight of the typical two-axle,three-axle,and five-axle vehicles as they crossed the bridge with different loading routes and speeds.Then,the pre-trained CNN model was transferred by fine-tuning to weigh themoving vehicle on another bridge.Finally,the identification accuracy and the amount of training data required were compared between the two CNN models.Results showed that the pre-trained CNN model using transfer learning for B-WIM technology could be successfully used for the identification of the axle weight and the gross weight for moving vehicles on another bridge while reducing the training data by 63%.Moreover,the recognition accuracy of the pre-trained CNN model using transfer learning was comparable to that of the original model,showing its promising potentials in the actual applications.展开更多
目的观察分析脑卒中患者实施双、单桥式运动对相关核心肌群肌电活动的影响,探讨发现其规律和特征。方法选取2020年3月至2021年5月在安徽省合肥市第二人民医院康复医学科住院的脑卒中患者40例作为观察对象,进行前瞻性队列研究。应用表面...目的观察分析脑卒中患者实施双、单桥式运动对相关核心肌群肌电活动的影响,探讨发现其规律和特征。方法选取2020年3月至2021年5月在安徽省合肥市第二人民医院康复医学科住院的脑卒中患者40例作为观察对象,进行前瞻性队列研究。应用表面肌电图(surface electromyography,sEMG)仪采集患者双、单桥式运动时竖脊肌、腹直肌、臀大肌和股二头肌表面肌电信号,分析其时域指标均方根值(root mean squar,RMS)和肌电积分值(integrate electromyography,IEMG)。符合正态分布计量资料以x¯±s表示;非正态分布数据以M(Q_(1),Q_(3))表示,采用Wilcoxon秩和检验比较两组相关肌群差异有无统计学意义。结果双桥运动时健、患侧臀大肌RMS[30.0(21.3,45.5)μV与24.0(14.0,35.8)μV]和IEMG[15.5.(10.0,23.0)μV·s与9.0(5.0,13.0)μV·s]值比较差异均有统计学意义(Z值分别为2.07、4.19,P值分别为0.039、<0.001);双桥运动时健、患侧股二头肌RMS[31.0(15.3,70.0)μV与17.0(11.0,28.8)μV]和IEMG[14.5(8.0,26.5)μV·s与7.0(5.0,10.8)μV·s]值比较差异均有统计学意义(Z值分别为3.44、3.64,P值分别为0.001、<0.001);单桥运动时健、患侧臀大肌RMS[38.5(32.3,46.0)μV与35.0(22.3,43.0)μV]和IEMG[16.5(12.0,22.8)μV·s与12.0(7.0,21.0)μV·s]值比较差异均有统计学意义(Z值分别为2.24、2.45,P值分别为0.025、0.014);单桥运动时健、患侧股二头肌RMS[38.0(15.3,70.0)μV与19.0(12.0,35.5)μV]和IEMG[16.0(10.0,27.0)μV·s与6.5(5.0,12.5)μV·s]值比较差异均有统计学意义(Z值分别为2.98、4.34,P值分别为0.003、<0.001);患侧双、单桥臀大肌的RMS[24.0(14.0,35.8)μV与35.0(22.3,43.0)μV]和IEMG[9.0(5.0,13.0)μV·s与12.0(7.0,21.0)μV·s]值比较差异均有统计学意义(Z值分别为2.24、1.99,P值分别为0.025、0.047)。结论双、单桥式运动对脑卒中偏瘫患者相关核心肌群有一定改善作用,且对于臀大肌激活程度单桥更优于双桥。展开更多
文摘A comparative study of selected bridge damage due to the Wenchuan, Northridge, Loma Prieta and San Fernando earthquakes is described in this paper. Typical ground motion effects considered include large ground fault displacement, liquefaction, landslide, and strong ground shaking. Issues related to falling spans, inadequate detailing for structural ductility and complex bridge configurations are discussed within the context of the recent seismic design codes of China and the US. A significant lesson learned from the Great Wenchuan earthquake, far beyond the opportunities to improve the seismic design provisions for bridges, is articulated.
基金the financial support provided by the National Natural Science Foundation of China(Grant No.52208213)the Excellent Youth Foundation of Education Department in Hunan Province(Grant No.22B0141)+1 种基金the Xiaohe Sci-Tech Talents Special Funding under Hunan Provincial Sci-Tech Talents Sponsorship Program(2023TJ-X65)the Science Foundation of Xiangtan University(Grant No.21QDZ23).
文摘Transfer learning could reduce the time and resources required by the training of new models and be therefore important for generalized applications of the trainedmachine learning algorithms.In this study,a transfer learningenhanced convolutional neural network(CNN)was proposed to identify the gross weight and the axle weight of moving vehicles on the bridge.The proposed transfer learning-enhanced CNN model was expected to weigh different bridges based on a small amount of training datasets and provide high identification accuracy.First of all,a CNN algorithm for bridge weigh-in-motion(B-WIM)technology was proposed to identify the axle weight and the gross weight of the typical two-axle,three-axle,and five-axle vehicles as they crossed the bridge with different loading routes and speeds.Then,the pre-trained CNN model was transferred by fine-tuning to weigh themoving vehicle on another bridge.Finally,the identification accuracy and the amount of training data required were compared between the two CNN models.Results showed that the pre-trained CNN model using transfer learning for B-WIM technology could be successfully used for the identification of the axle weight and the gross weight for moving vehicles on another bridge while reducing the training data by 63%.Moreover,the recognition accuracy of the pre-trained CNN model using transfer learning was comparable to that of the original model,showing its promising potentials in the actual applications.
文摘目的观察分析脑卒中患者实施双、单桥式运动对相关核心肌群肌电活动的影响,探讨发现其规律和特征。方法选取2020年3月至2021年5月在安徽省合肥市第二人民医院康复医学科住院的脑卒中患者40例作为观察对象,进行前瞻性队列研究。应用表面肌电图(surface electromyography,sEMG)仪采集患者双、单桥式运动时竖脊肌、腹直肌、臀大肌和股二头肌表面肌电信号,分析其时域指标均方根值(root mean squar,RMS)和肌电积分值(integrate electromyography,IEMG)。符合正态分布计量资料以x¯±s表示;非正态分布数据以M(Q_(1),Q_(3))表示,采用Wilcoxon秩和检验比较两组相关肌群差异有无统计学意义。结果双桥运动时健、患侧臀大肌RMS[30.0(21.3,45.5)μV与24.0(14.0,35.8)μV]和IEMG[15.5.(10.0,23.0)μV·s与9.0(5.0,13.0)μV·s]值比较差异均有统计学意义(Z值分别为2.07、4.19,P值分别为0.039、<0.001);双桥运动时健、患侧股二头肌RMS[31.0(15.3,70.0)μV与17.0(11.0,28.8)μV]和IEMG[14.5(8.0,26.5)μV·s与7.0(5.0,10.8)μV·s]值比较差异均有统计学意义(Z值分别为3.44、3.64,P值分别为0.001、<0.001);单桥运动时健、患侧臀大肌RMS[38.5(32.3,46.0)μV与35.0(22.3,43.0)μV]和IEMG[16.5(12.0,22.8)μV·s与12.0(7.0,21.0)μV·s]值比较差异均有统计学意义(Z值分别为2.24、2.45,P值分别为0.025、0.014);单桥运动时健、患侧股二头肌RMS[38.0(15.3,70.0)μV与19.0(12.0,35.5)μV]和IEMG[16.0(10.0,27.0)μV·s与6.5(5.0,12.5)μV·s]值比较差异均有统计学意义(Z值分别为2.98、4.34,P值分别为0.003、<0.001);患侧双、单桥臀大肌的RMS[24.0(14.0,35.8)μV与35.0(22.3,43.0)μV]和IEMG[9.0(5.0,13.0)μV·s与12.0(7.0,21.0)μV·s]值比较差异均有统计学意义(Z值分别为2.24、1.99,P值分别为0.025、0.047)。结论双、单桥式运动对脑卒中偏瘫患者相关核心肌群有一定改善作用,且对于臀大肌激活程度单桥更优于双桥。
