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Dynamic fuzzy wavelet neural network for system identification, damage detection and active control of highrise buildings

Jiang, Xiaomo
http://rave.ohiolink.edu/etdc/view?acc_num=osu1110266591

Abstract Details

2005, Doctor of Philosophy, Ohio State University, Civil Engineering.
A multi-paradigm nonparametric model, dynamic fuzzy wavelet neural network (WNN) model, is developed for structural system identification of three dimensional highrise buildings. The model integrates chaos theory (nonlinear dynamics theory), a signal processing method (wavelets), and two complementary soft computing methods (fuzzy logic and neural network). An adaptive Levenberg-Marquardt-least-squares learning algorithm is developed for adjusting parameters of the dynamic fuzzy WNN model. The methodology is applied to one five-story test frame and two highrise moment-resisting building structures. Results demonstrate that the methodology incorporates the imprecision existing in the sensor data effectively and balances the global and local influences of the training data. It therefore provides more accurate system identifications and nonlinear approximation with a fast training convergence. A nonparametric system identification-based model is developed for damage detection of highrise building structures subjected to seismic excitations using the dynamic fuzzy WNN model. The model does not require complete measurements of the dynamic responses of the whole structure. A damage evaluation method is proposed based on a power density spectrum method. The multiple signal classification method is employed to compute the pseudospectrum from the structural response time series. The methodology is validated using experimental data obtained for a 38-story concrete test model. It is demonstrated that the WNN model together with the pseudospectrum method is effective for damage detection of highrise buildings based on a small amount of sensed data. A nonlinear control model is developed for active control of highrise three dimensional building structures including geometrical and material nonlinearities, coupling action between lateral and torsional motions, and actuator dynamics. A dynamic fuzzy wavelet neuroemulator is developed for predicting the structural response in future time steps. A neuro-genetic algorithm is developed for finding the optimal control forces without the pre-training required in a neural network-based controller. Both neuroemulator and neuro-genetic algorithm are validated using two irregular three-dimensional steel building structures, a twelve-story structure with vertical setbacks and an eight-story structure with plan irregularity. Numerical validations demonstrate that the control methodology can significantly reduce the structural displacements of three-dimensional buildings subjected to various seismic excitations.
Hojjat Adeli (Advisor)
239 p.
Engineering, Civil
Structural System Identification; Damage Detection; Structural Health Monitoring; Active Control; Dynamic Neural Network; Wavelets; Fuzzy Logic; Genetic Algorithm; Nonlinear Optimization

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Citations

  • Jiang, X. (2005). Dynamic fuzzy wavelet neural network for system identification, damage detection and active control of highrise buildings [Doctoral dissertation, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1110266591

    APA Style (7th edition)

  • Jiang, Xiaomo. Dynamic fuzzy wavelet neural network for system identification, damage detection and active control of highrise buildings. 2005. Ohio State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1110266591.

    MLA Style (8th edition)

  • Jiang, Xiaomo. "Dynamic fuzzy wavelet neural network for system identification, damage detection and active control of highrise buildings." Doctoral dissertation, Ohio State University, 2005. http://rave.ohiolink.edu/etdc/view?acc_num=osu1110266591

    Chicago Manual of Style (17th edition)

osu1110266591
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© 2005, all rights reserved.
This open access ETD is published by The Ohio State University and OhioLINK.