In this paper, the behavior of barrel-vaulted structures undergoing rocking of the abutments and the effectiveness of a few retrofit solutions are discussed. The comprehension of the structural performance of vault-ab...In this paper, the behavior of barrel-vaulted structures undergoing rocking of the abutments and the effectiveness of a few retrofit solutions are discussed. The comprehension of the structural performance of vault-abutment systems is fundamental for their seismic vulnerability assessment, and for the design of efficient strengthening techniques. In the paper, traditional techniques such as extrados thin concrete slab or masonry spandrel walls as well as an innovative solution with an extrados thin improved lime mortar slab reinforced by means of glass fiber mesh are considered. The different strengthening solutions are studied and compared on the basis of the results of non linear numerical analyses and by reference to a simplified analytical approach. Numerical and analytical models are validated through comparison with the results of a recent experimental study focusing on the behavior of vaulted masonry structures subjected to rocking of the abutments. The validated models can be used by engineers for the seismic vulnerability assessment of masonry vaulted structures as well as for the proportioning of possible extrados strengthening solutions, and will be used in the future to explore different structural system configurations.展开更多
The paper describes results obtained in the development of adaptive fuzzy-neural navigation subsystem for mobile legged robot. In order to keep the motion sufficiently smooth, free of sharp turnings and transversal sw...The paper describes results obtained in the development of adaptive fuzzy-neural navigation subsystem for mobile legged robot. In order to keep the motion sufficiently smooth, free of sharp turnings and transversal swings when moving between closely located obstacles, fuzzy rules are updated on-line. To this end, the fuzzy rules are expressed through a layered feed-forward neural network and parameters are updated on line in two steps--the rough and fine updating. That is followed by the description of the learning fault diagnosis using binary neural network based on the Carpenter and Grossbergs＇ adaptive resonance theory.展开更多
文摘In this paper, the behavior of barrel-vaulted structures undergoing rocking of the abutments and the effectiveness of a few retrofit solutions are discussed. The comprehension of the structural performance of vault-abutment systems is fundamental for their seismic vulnerability assessment, and for the design of efficient strengthening techniques. In the paper, traditional techniques such as extrados thin concrete slab or masonry spandrel walls as well as an innovative solution with an extrados thin improved lime mortar slab reinforced by means of glass fiber mesh are considered. The different strengthening solutions are studied and compared on the basis of the results of non linear numerical analyses and by reference to a simplified analytical approach. Numerical and analytical models are validated through comparison with the results of a recent experimental study focusing on the behavior of vaulted masonry structures subjected to rocking of the abutments. The validated models can be used by engineers for the seismic vulnerability assessment of masonry vaulted structures as well as for the proportioning of possible extrados strengthening solutions, and will be used in the future to explore different structural system configurations.
文摘The paper describes results obtained in the development of adaptive fuzzy-neural navigation subsystem for mobile legged robot. In order to keep the motion sufficiently smooth, free of sharp turnings and transversal swings when moving between closely located obstacles, fuzzy rules are updated on-line. To this end, the fuzzy rules are expressed through a layered feed-forward neural network and parameters are updated on line in two steps--the rough and fine updating. That is followed by the description of the learning fault diagnosis using binary neural network based on the Carpenter and Grossbergs＇ adaptive resonance theory.
