为研究负载水平对FRP(纤维增强复合材料)约束混凝土柱峰值应力和峰值应变的影响,根据32个CFRP(碳纤维增强复合材料)约束混凝土圆柱构件和16个CFRP约束混凝土方柱构件的试验结果,引入负载影响因子,对J G Teng提出的CFRP约束混凝土柱峰值...为研究负载水平对FRP(纤维增强复合材料)约束混凝土柱峰值应力和峰值应变的影响,根据32个CFRP(碳纤维增强复合材料)约束混凝土圆柱构件和16个CFRP约束混凝土方柱构件的试验结果,引入负载影响因子,对J G Teng提出的CFRP约束混凝土柱峰值应力和峰值应变计算公式进行修正.在此基础上分析了CFRP约束混凝土柱构件轴向-侧向应变关系,以J G Teng本构模型为主动约束关系,建立了负载下CFRP约束混凝土应力-应变分析型模型.研究结果表明:模型理论曲线与试验曲线接近,修正后的峰值应力和峰值应变与试验结果较吻合,圆柱构件的误差约10%,方柱构件的误差在15%左右.展开更多
In order to improve the prediction accuracy and test the generalization ability of the dam deformation analysis model, the back-propagation（BP） neural network model for dam deformation analysis is studied, and the m...In order to improve the prediction accuracy and test the generalization ability of the dam deformation analysis model, the back-propagation（BP） neural network model for dam deformation analysis is studied, and the merging model is built based on the neural network BP algorithm and the traditional statistical model. The three models mentioned above are calculated and analyzed according to the long-term deformation observation data in Chencun Dam. The analytical results show that the average prediction accuracies of the statistical model and the BP neural network model are ~ 0.477 and ＋- 0.390 mm, respectively, while the prediction accuracy of the merging model is ~0. 318 mm, which is improved by 33% and 18% compared to the other two models, respectively. And the merging model has a better generalization ability and broad applicability.展开更多
文摘为研究负载水平对FRP(纤维增强复合材料)约束混凝土柱峰值应力和峰值应变的影响,根据32个CFRP(碳纤维增强复合材料)约束混凝土圆柱构件和16个CFRP约束混凝土方柱构件的试验结果,引入负载影响因子,对J G Teng提出的CFRP约束混凝土柱峰值应力和峰值应变计算公式进行修正.在此基础上分析了CFRP约束混凝土柱构件轴向-侧向应变关系,以J G Teng本构模型为主动约束关系,建立了负载下CFRP约束混凝土应力-应变分析型模型.研究结果表明:模型理论曲线与试验曲线接近,修正后的峰值应力和峰值应变与试验结果较吻合,圆柱构件的误差约10%,方柱构件的误差在15%左右.
基金The Scientific Innovation Research of College Graduates in Jiangsu Province(No.CXLX11_0143)
文摘In order to improve the prediction accuracy and test the generalization ability of the dam deformation analysis model, the back-propagation（BP） neural network model for dam deformation analysis is studied, and the merging model is built based on the neural network BP algorithm and the traditional statistical model. The three models mentioned above are calculated and analyzed according to the long-term deformation observation data in Chencun Dam. The analytical results show that the average prediction accuracies of the statistical model and the BP neural network model are ~ 0.477 and ＋- 0.390 mm, respectively, while the prediction accuracy of the merging model is ~0. 318 mm, which is improved by 33% and 18% compared to the other two models, respectively. And the merging model has a better generalization ability and broad applicability.
