Faculty Advisor or Committee Member

Zhikun Hou, Advisor

Faculty Advisor or Committee Member

Mikhail Dimentberg, Committee Member

Faculty Advisor or Committee Member

John R Hall, Committee Member

Faculty Advisor or Committee Member

John Sullivan, Committee Member

Identifier

etd-010509-093209

Abstract

Support vector machines (SVMs) are a set of supervised learning methods that have recently been applied for structural damage detection due to their ability to form an accurate boundary from a small amount of training data. During training, they require data from the undamaged and damaged structure. The unavailability of data from the damaged structure is a major challenge in such methods due to the irreversibility of damage. Recent methods create data for the damaged structure from finite element models. In this thesis we propose a new method to derive the dataset representing the damage structure from the dataset measured on the undamaged structure without using a detailed structural finite element model. The basic idea is to reduce the values of a copy of the data from the undamaged structure to create the data representing the damaged structure. The performance of the method in the presence of measurement noise, ambient base excitation, wind loading is investigated. We find that SVMs can be used to detect small amounts of damage in the structure in the presence of noise. The ability of the method to detect damage at different locations in a structure and the effect of measurement location on the sensitivity of the method has been investigated. An online structural health monitoring method has also been proposed to use the SVM boundary, trained on data measured from the damaged structure, as an indicator of the structural health condition.

Publisher

Worcester Polytechnic Institute

Degree Name

MS

Department

Mechanical Engineering

Project Type

Thesis

Date Accepted

2009-01-05

Accessibility

Unrestricted

Subjects

Statistical Pattern Recognition, Online Health Monitoring, Support Vector Machines, Structural analysis (Engineering), Machine learning

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