Current research study consists of determining the optimum location of the shear wall to get the maximum structural efficiency of a reinforced concrete frame building. It consists of a detailed analysis and design rev...Current research study consists of determining the optimum location of the shear wall to get the maximum structural efficiency of a reinforced concrete frame building. It consists of a detailed analysis and design review of a seven-story reinforced concrete building to understand the effect of shear wall location on the response of reinforced concrete structures when subjected to different earthquake forces. Three trail locations of shear walls are selected and their performance is monitored in terms of structural response under different lateral loads. Required objectives are achieved by obtaining design and construction drawings of an existing reinforced concrete structure and modeling it on Finite Element Method (FEM) based computer software. The structure is redesigned and discussed with four different configurations (one without shear wall and three with shear walls). Main framing components (Beams, Columns and Shear walls) of the superstructure are designed using SAP 2000 V. 19.0 whereas substructure (foundation) of RC building was?designed using SAFE. American Concrete Institute (ACI) design specifications were used to calculate the cracked section stiffness or non-linear geometrical properties of the cracked section. Uniform Building Code (UBC-97) procedures were adopted to calculate the lateral earthquake loading on the structures. Structural response of the building was monitored at each story level for each earthquake force zone described by the UBC-97. The earthquake lateral forces were considered in both X and Y direction of the building. Each configuration of shear wall is carefully analyzed and effect of its location is calibrated by the displacement response of the structure. Eccentricity to the lateral stiffness of the building is imparted by changing the location of shear walls. Results of the study have shown that the location of shear wall significantly affects the lateral response of the structure under earthquake forces. It also motivates to carefully decide the center of lateral stiffness of building prior to deciding the location of shear walls.展开更多
Automatic target recognition （ATR） is an important issue for military applications, the topic of the ATR system belongs to the field of pattern recognition and classification. In the paper, we present an approach fo...Automatic target recognition （ATR） is an important issue for military applications, the topic of the ATR system belongs to the field of pattern recognition and classification. In the paper, we present an approach for building an ATR system with improved artificial neural network to recog- nize and classify the typical targets in the battle field. The invariant features of Hu invariant moments and roundness were selected to be the inputs of the neural network because they have the invari- ances of rotation, translation and scaling. The pictures of the targets are generated by the 3-D mod- els to improve the recognition rate because it is necessary to provide enough pictures for training the artificial neural network. The simulations prove that the approach can be implement ed in the ATR system and it has a high recognition rate and can be applied in real time.展开更多
文摘Current research study consists of determining the optimum location of the shear wall to get the maximum structural efficiency of a reinforced concrete frame building. It consists of a detailed analysis and design review of a seven-story reinforced concrete building to understand the effect of shear wall location on the response of reinforced concrete structures when subjected to different earthquake forces. Three trail locations of shear walls are selected and their performance is monitored in terms of structural response under different lateral loads. Required objectives are achieved by obtaining design and construction drawings of an existing reinforced concrete structure and modeling it on Finite Element Method (FEM) based computer software. The structure is redesigned and discussed with four different configurations (one without shear wall and three with shear walls). Main framing components (Beams, Columns and Shear walls) of the superstructure are designed using SAP 2000 V. 19.0 whereas substructure (foundation) of RC building was?designed using SAFE. American Concrete Institute (ACI) design specifications were used to calculate the cracked section stiffness or non-linear geometrical properties of the cracked section. Uniform Building Code (UBC-97) procedures were adopted to calculate the lateral earthquake loading on the structures. Structural response of the building was monitored at each story level for each earthquake force zone described by the UBC-97. The earthquake lateral forces were considered in both X and Y direction of the building. Each configuration of shear wall is carefully analyzed and effect of its location is calibrated by the displacement response of the structure. Eccentricity to the lateral stiffness of the building is imparted by changing the location of shear walls. Results of the study have shown that the location of shear wall significantly affects the lateral response of the structure under earthquake forces. It also motivates to carefully decide the center of lateral stiffness of building prior to deciding the location of shear walls.
基金Supported by the Ministerial Level Advanced Research Foundation(9140A01010411BQ01)the National Twelfth Five-Year Project(40405050303)
文摘Automatic target recognition （ATR） is an important issue for military applications, the topic of the ATR system belongs to the field of pattern recognition and classification. In the paper, we present an approach for building an ATR system with improved artificial neural network to recog- nize and classify the typical targets in the battle field. The invariant features of Hu invariant moments and roundness were selected to be the inputs of the neural network because they have the invari- ances of rotation, translation and scaling. The pictures of the targets are generated by the 3-D mod- els to improve the recognition rate because it is necessary to provide enough pictures for training the artificial neural network. The simulations prove that the approach can be implement ed in the ATR system and it has a high recognition rate and can be applied in real time.
