In many seismically active regions of the world there are large numbers of masonry buildings. Most of these buildings have not been designed for seismic loads. Recent earthquakes have shown that many of these building...In many seismically active regions of the world there are large numbers of masonry buildings. Most of these buildings have not been designed for seismic loads. Recent earthquakes have shown that many of these buildings are seismically vulnerable and should be considered for retrofitting. Different conventional and unconventional retrofitting techniques are available to increase the strength and/or ductility of unreinforced masonry （URM） walls. This paper reviews and discusses seismic retrofitting of masonry walls with emphasis on the conventional techniques. Retrofitting procedures are discussed with regard to a case study： a stone masonry building in lrpinia region, damaged by the 1980 earthquake. The interventions are evaluated by means of finite elements with a macroelement obtained with a homogenization technique. Linear and nonlinear procedures are compared, and peculiarities of each procedure are shown.展开更多
Aimed at the problem that the traditional ART-2 neural network can not recognize a gradually changing course, an eternal term memory (ETM) vector is introduced into ART-2 to simulate the function of human brain, i.e. ...Aimed at the problem that the traditional ART-2 neural network can not recognize a gradually changing course, an eternal term memory (ETM) vector is introduced into ART-2 to simulate the function of human brain, i.e. the deep remembrance for the initial impression.. The eternal term memory vector is determined only by the initial vector that establishes category neuron node and is used to keep the remembrance for this vector for ever. Two times of vigilance algorithm are put forward, and the posterior input vector must first pass the first vigilance of this eternal term memory vector, only succeeded has it the qualification to begin the second vigilance of long term memory vector. The long term memory vector can be revised only when both of the vigilances are passed. Results of recognition examples show that the improved ART-2 overcomes the defect of traditional ART-2 and can recognize a gradually changing course effectively.展开更多
文摘In many seismically active regions of the world there are large numbers of masonry buildings. Most of these buildings have not been designed for seismic loads. Recent earthquakes have shown that many of these buildings are seismically vulnerable and should be considered for retrofitting. Different conventional and unconventional retrofitting techniques are available to increase the strength and/or ductility of unreinforced masonry （URM） walls. This paper reviews and discusses seismic retrofitting of masonry walls with emphasis on the conventional techniques. Retrofitting procedures are discussed with regard to a case study： a stone masonry building in lrpinia region, damaged by the 1980 earthquake. The interventions are evaluated by means of finite elements with a macroelement obtained with a homogenization technique. Linear and nonlinear procedures are compared, and peculiarities of each procedure are shown.
基金Sponsored by the National Natural Science Foundation of China (Grant No. 50305005)
文摘Aimed at the problem that the traditional ART-2 neural network can not recognize a gradually changing course, an eternal term memory (ETM) vector is introduced into ART-2 to simulate the function of human brain, i.e. the deep remembrance for the initial impression.. The eternal term memory vector is determined only by the initial vector that establishes category neuron node and is used to keep the remembrance for this vector for ever. Two times of vigilance algorithm are put forward, and the posterior input vector must first pass the first vigilance of this eternal term memory vector, only succeeded has it the qualification to begin the second vigilance of long term memory vector. The long term memory vector can be revised only when both of the vigilances are passed. Results of recognition examples show that the improved ART-2 overcomes the defect of traditional ART-2 and can recognize a gradually changing course effectively.
