Electronic Thesis and Dissertation Repository

Thesis Format

Integrated Article

Degree

Doctor of Philosophy

Program

Civil and Environmental Engineering

Collaborative Specialization

Scientific Computing

Supervisor

Sadhu, A

Abstract

Traditional vibration-based damage detection methods provide structural health information based on their measured data (i.e., acceleration and displacement response). Over the last few decades, various model-based and time-frequency methods have shown great promises for damage identification and localization. However, the existing methods are unable to perform satisfactorily in many situations, including the presence of limited sensor measurements and training data, detection of minor and progressive damage, and identification of multiclass damage, creating constraints to make them free of user-intervention and implemented using the modern sensors. The main objective of this thesis is to develop algorithms capable of damage identification and localization using limited measurements that can address the limitation of the traditional methods while providing a minimal to no user-intervention damage identification process.

The proposed research in this thesis involves casting damage detection problems as non-parametric and autonomous with the least user intervention. Progressive damage identification is presented using novel time-frequency methods, such as synchrosqueezing transform and multivariate empirical mode decomposition, showing improved sensitivity of identifying minor damage over traditional methods. A basis-free method, such as multivariate empirical mode decomposition, is employed for damage localization using limited sensors. The acquired vibration measurement is decomposed into its mono components, and a damage localization index based on modal energy is proposed to overcome the need for a large number of sensors. The limited measurement aspect of damage localization is explored by selecting fewer sensors, and it is shown that with limited measurements, the proposed method is as effective as a total number of measurements equals the number of degrees of freedom of the model.

To create an autonomous damage identification framework, Artificial Intelligence-based methods are explored the first time for multiclass damage classification and localization. Due to the lack of availability of a large amount of data, the acquired vibration data is augmented using windowing of the data per damage class. A novel window-based one-dimensional convolutional neural network is explored to classify sequential time-series of vibration measurements with only one hidden layer. The robustness of the proposed method is further evaluated by a suite of parametric and sensitivity analysis. Improvement of this method is further accomplished by implementing a windowed Long Short-term Memory network capable of learning long-term dependencies of the sequential data. Finally, the proposed methods are validated using a suite of experimental and full-scale studies, including a high-rate dynamics experimental testbed, a stadia prototype experimental setup, the MIT green building, and the Z24 bridge.

Summary for Lay Audience

Large-scale civil structures, such as buildings, bridges, stadiums, or roads, degrade with time due to various operational, environmental, and human-made factors. To effectively utilize the build infrastructure during their intended design life, it is crucial to monitor them in a timely manner and provide any necessary maintenance required for their efficient performance to our citizens. The proposed research of this Ph.D. thesis is focused on exploring cost-effective strategies for structural monitoring and identifying any defects in the structures using a fewer number of sensors. It is emphasized that the proposed strategies are user-intervention free and capable of creating an autonomous monitoring framework. The critical component of this research is to utilize limited sensors to reduce the financial burden on structural health monitoring communities and infrastructure owners. Advanced pattern recognition methods capable of providing information about both the time and instance of damage, and innovative artificial intelligence algorithms are evaluated for effective damage identification and localization in various types of structures using limited sensors and condition data. Through the proposed research, an autonomous infrastructure monitoring framework is developed for the health monitoring of structures subjected to a wide range of damage.

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

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