Structural Vibration Control Technique using Modified Probabilistic Neural Network
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Recently, structures are becoming longer and higher because of the developments of new materials and construction techniques. However, such modern structures are more susceptible to excessive structural vibrations which cause deterioration in serviceability and structural safety. A modified probabilistic neural network(MPNN) approach is proposed to reduce the structural vibration. In this study, the global probability density function(PDF) of MPNN is reflected by summing the heterogeneous local PDFs automatically determined in the individual standard deviation of each variable. The proposed algorithm is applied for the vibration control of a three-story shear building model under Northridge earthquake. When the control results of the MPNN are compared with those of conventional PNN to verify the control performance, the MPNN controller proves to be more effective than PNN methods in decreasing the structural responses.
Abstract
Recently, structures are becoming longer and higher because of the developments of new materials and construction techniques. However, such modern structures are more susceptible to excessive structural vibrations which cause deterioration in serviceability and structural safety. A modified probabilistic neural network(MPNN) approach is proposed to reduce the structural vibration. In this study, the global probability density function(PDF) of MPNN is reflected by summing the heterogeneous local PDFs automatically determined in the individual standard deviation of each variable. The proposed algorithm is applied for the vibration control of a three-story shear building model under Northridge earthquake. When the control results of the MPNN are compared with those of conventional PNN to verify the control performance, the MPNN controller proves to be more effective than PNN methods in decreasing the structural responses.
참고문헌 (18)
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