With the increased deterioration of infrastructure in this country, it has become important to find ways to maintain the strength and integrity of a structure over its design life. Being able to control the amount a structure displaces or vibrates during a seismic event, as well as being able to model this nonlinear behavior, provides a new challenge for structural engineers. This research proposes a wavelet-based adaptive neuro- fuzzy inference system for use in system identification and structural control of civil engineering structures. This algorithm combines aspects of fuzzy logic theory, neural networks, and wavelet transforms to create a new system that effectively reduces the number of sensors needed in a structure to capture its seismic response and the amount of computation time needed to model its nonlinear behavior. The algorithm has been tested for structural control using a three-story building equipped with a magnetorheological damper for system identification, an eight-story building, and a benchmark highway bridge. Each of these examples has been tested using a variety of earthquakes, including the El-Centro, Kobe, Hachinohe, Northridge, and other seismic events.

Creator

• Mitchell, Ryan

Contributors

• Kim, Yeesock
• Albano, Leonard D.

Degree

• MS

Unit

• Civil & Environmental Engineering

Publisher

• Worcester Polytechnic Institute

Language

• English

Identifier

• etd-041812-134324

Keyword

• Earthquake
• System Identification
• Neural network
• Wavelet Transform
• Fuzzy Logic
• Structural Control

Advisor

• Kim, Yeesock

Committee

• Albano, Leonard D.

Defense date

• 2012-04-17

Year

• 2012

Date created

• 2012-04-18

Resource type

• Thesis

Rights statement

• In Copyright