Faculty Advisor

Yeesock Kim

Faculty Advisor

Leonard D. Albano

Faculty Advisor

Tahar El-Korchi


"Maintaining an efficient and reliable infrastructure requires continuous monitoring and control. In order to accomplish these tasks, algorithms are needed to process large sets of data and for modeling based on these processed data sets. For this reason, computationally efficient and accurate modeling algorithms along with data compression techniques and optimal yet practical control methods are in demand. These tools can help model structures and improve their performance. In this thesis, these two aspects are addressed separately. A principal component analysis based adaptive neuro-fuzzy inference system is proposed for fast and accurate modeling of time-dependent behavior of a structure integrated with a smart damper. Since a smart damper can only dissipate energy from structures, a challenge is to evaluate the dissipativity of optimal control methods for smart dampers to decide if the optimal controller can be realized using the smart damper. Therefore, a generalized deterministic definition for dissipativity is proposed and a commonly used controller, LQR is proved to be dissipative. Examples are provided to illustrate the effectiveness of the proposed modeling algorithm and evaluating the dissipativity of LQR control method. These examples illustrate the effectiveness of the proposed modeling algorithm and dissipativity of LQR controller."


Worcester Polytechnic Institute

Degree Name



Civil & Environmental Engineering

Project Type


Date Accepted





Magnetorheological Damper, Neural network, Adaptive Neuro-Fuzzy Inference System, Earthquake, System Identification, Dissipativity, Linear Quadratic Regulator, Structural Control, Smart Structures, Fuzzy Logic, Principal Component Analysis