System Identification and Damage Detection Solutions: Sensor Fusion, Algorithms and Physics-Guided Learning
| dc.contributor.advisor | Nagarajaiah, Satish | en_US |
| dc.creator | Pal, Ashish | en_US |
| dc.date.accessioned | 2024-08-30T16:33:48Z | en_US |
| dc.date.created | 2024-08 | en_US |
| dc.date.issued | 2024-08-08 | en_US |
| dc.date.submitted | August 2024 | en_US |
| dc.date.updated | 2024-08-30T16:33:48Z | en_US |
| dc.description | EMBARGO NOTE: This item is embargoed until 2025-08-01 | en_US |
| dc.description.abstract | The underlying behavior of physical systems is commonly modeled through mathematical equations. Most systems inherently show some amount of nonlinearity in their response. Depending on the system characteristics, surroundings, and application, a variety of nonlinearities can be found. The coefficients of linear and nonlinear functions in the mathematical description of the system provide additional insights. Physical systems such as buildings and bridges change with time because of aging, environmental factors, and extreme events such as earthquakes and storms. Identifying these changes using system identification (SI) and damage detection (DD) methods is crucial. The identification of the correct type of nonlinearity indicates changes in the intrinsic behavior of the system, while the corresponding coefficients help track the changes in the physical properties. The SI and DD methods rely on the system's measured response for inference. The quality of the measurements often guides the quality of the results. Using sensors that provide high-quality data can be expensive and, in some situations, have limited use based on the physical restrictions. Incorporating additional information such as the system's numerical model, governing equation, and data fusion from multiple cheaper/average quality sensors can generate superior-quality data can be used to get reliable and accurate SI and DD results. The advancements in machine learning methods, computational power, and novel full-field strain sensing methods such as strain sensing smart skin can be useful tools for the purpose. In this thesis, several algorithms have been proposed for SI and DD. These include (i) surface DD from fusing the measured full-field strain data and the finite element model of the structure, (ii) deep learning-based subsurface DD from full-field strain data, (iii) fusing coarse displacements and finite element model of the structure to obtain dense accurate displacements, (iv) Kalman filter-based high-quality displacement estimation from data fusion of average-quality displacement and acceleration, (v) Physics-informed SI of nonlinear dynamic systems via deep learning or Kalman filter methods. The proposed algorithms leverage the additional information from the physics of the system or multiple sensors to obtain results that are otherwise not possible to achieve. For example, the finite element model of the structure provides physically consistent information that is used to construct dense displacement data from coarse data, which is far superior to interpolation techniques. The fusion of displacement and acceleration data can find the physical inconsistencies in the measured data, which can be corrected to generate superior-quality displacement and acceleration data. Incorporating physical constraints into the measured data can help identify the unknown governing equation of motion of dynamic systems from field measurements only. Using information from multiple sources in a physically consistent manner can help unfold answers that cannot be pursued individually. | en_US |
| dc.embargo.lift | 2025-08-01 | en_US |
| dc.embargo.terms | 2025-08-01 | en_US |
| dc.format.mimetype | application/pdf | en_US |
| dc.identifier.citation | Pal, Ashish. System Identification and Damage Detection Solutions: Sensor Fusion, Algorithms and Physics-Guided Learning. (2024). PhD diss., Rice University. https://hdl.handle.net/1911/117793 | en_US |
| dc.identifier.uri | https://hdl.handle.net/1911/117793 | en_US |
| dc.language.iso | eng | en_US |
| dc.rights | Copyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder. | en_US |
| dc.subject | System Identification | en_US |
| dc.subject | Damage detection | en_US |
| dc.subject | data fusion | en_US |
| dc.subject | physics-guided machine learning | en_US |
| dc.subject | strain-sensing smart skin | en_US |
| dc.subject | Unscented Kalman filter | en_US |
| dc.title | System Identification and Damage Detection Solutions: Sensor Fusion, Algorithms and Physics-Guided Learning | en_US |
| dc.type | Thesis | en_US |
| dc.type.material | Text | en_US |
| thesis.degree.department | Civil and Environmental Engineering | en_US |
| thesis.degree.discipline | Engineering | en_US |
| thesis.degree.grantor | Rice University | en_US |
| thesis.degree.level | Doctoral | en_US |
| thesis.degree.name | Doctor of Philosophy | en_US |