<p>Recent
advances in sensor technologies and data acquisition platforms have led to the
era of Big Data. The rapid growth of artificial intelligence (AI), computing
power and machine learning (ML) algorithms allow Big Data to be processed within
affordable time constraints. This opens abundant opportunities to develop novel
and efficient approaches to enhance the sustainability and resilience of Smart
Cities. This work, by starting with a review of the state-of-the-art data
fusion and ML techniques, focuses on the development of advanced solutions to
structural health monitoring (SHM) and metamaterial design and discovery
strategies. A deep convolutional neural network (CNN) based approach that is
more robust against noisy data is proposed to perform structural response
estimation and system identification. To efficiently detect surface defects
using mobile devices with limited training data, an approach that incorporates
network pruning into transfer learning is introduced for crack and corrosion
detection. For metamaterial design, a reinforcement learning (RL) and a neural
network based approach are proposed to reduce the computation efforts for the
design of periodic and non-periodic metamaterials, respectively. Lastly, a
physics-constrained deep auto-encoder (DAE) based approach is proposed to
design the geometry of wave scatterers that satisfy user-defined downstream
acoustic 2D wave fields. The robustness of the proposed approaches as well as
their limitations are demonstrated and discussed through experimental data
or/and numerical simulations. A roadmap for future works that may benefit the
SHM and material design research communities is presented at the end of this
dissertation.</p><br>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/12858245 |
| Date | 26 August 2020 |
| Creators | Rih-Teng Wu (9293561) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Relation | https://figshare.com/articles/thesis/Development_and_Application_of_Big_Data_Analytics_and_Artificial_Intelligence_for_Structural_Health_Monitoring_and_Metamaterial_Design/12858245 |
Page generated in 0.0025 seconds