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- 저자 : Zhanzhan Hu (검색결과 2 건)
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Case study on stability performance of asymmetric steel arch bridge with inclined arch ribs
Xinke Hu,Xu Xie,Zhanzhan Tang,Yonggang Shen,Pu Wu,Lianfeng Song 국제구조공학회 2015 Steel and Composite Structures, An International J Vol.18 No.1
As one of the most common failure types of arch bridges, stability is one of the critical aspects for the design of arch bridges. Using 3D finite element model in ABAQUS, this paper has studied the stability performance of an arch bridge with inclined arch ribs and hangers, and the analysis also took the effects of geometrical and material nonlinearity into account. The impact of local buckling and residual stress of steel plates on global stability and the applicability of fiber model in stability analysis for steel arch bridges were also investigated. The results demonstrate an excellent stability of the arch bridge because of the transverse constraint provided by transversely-inclined hangers. The distortion of cross section, local buckling and residual stress of ribs has an insignificant effect on the stability of the structure, and the accurate ultimate strength may be obtained from a fiber model analysis. This study also shows that the yielding of the arch ribs has a significant impact on the ultimate capacity of the structure, and the bearing capacity may also be approximately estimated by the initial yield strength of the arch rib.
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Modeling of a paper-making wastewater treatment process using a fuzzy neural network
Mingzhi Huang,유창규,Jinquan Wan,Yan Wang,Yongwen Ma,Huiping Zhang,류홍빈,Zhanzhan Hu 한국화학공학회 2012 Korean Journal of Chemical Engineering Vol.29 No.5
An intelligent system that includes a predictive model and a control was developed to predict and control the performance of a wastewater treatment plant. The predictive model was based on fuzzy C-means clustering, fuzzy inference and neural networks. Fuzzy C-means clustering was used to identify model’s architecture, extract and optimize fuzzy rule. When predicting, MAPE was 4.7582% and R was 0.8535. The simulative results indicate that the learning ability and generalization of the model was good, and it can achieve a good predication of effluent COD. The control model was based on a fuzzy neural network model, taking into account the difference between the predicted value of COD and the setpoint. When simulating, R was 0.9164, MAPE was 5.273%, and RMSE was 0.0808, which showed that the FNN control model can effectively change the additive dosages. The control of a paper-making wastewater treatment process in the laboratory using the developed predictive control model and MCGS (monitor and control generated system) software shows the dosage was computed accurately to make the effluent COD remained at the setpoint,when the influent COD value or inflow flowrate was changed. The results indicate that reasonable forecasting and control performances were achieved through the developed system; the maximum error was only 3.67%, and the average relative error was 2%.
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