Adaptīva segmentētas regresijas metode ārējās vides faktoru ietekmes samazināšanai būvju dinamisko parametru ilgtermiņa tehniskā monitoringa datos
Doctoral Thesis
Description
The Doctoral Thesis develops and experimentally validates a new method for long-term structural health monitoring that effectively reduces the impact of environmental factors on monitoring data and significantly improves the accuracy of structural damage detection. The developed method is based on nonlinear and segmented regression algorithms that apply an individual correction approach to each environmental factor, considering the nature of its impact under different climatic conditions. The method was validated using long-term monitoring data from a two-storey timber frame and the Filomena Delli Castelli Bridge.
About the author
Additional information
| Publication type | |
|---|---|
| Defence date | 09.10.2026. |
| Format | |
| Pages | 127 |
| Publication date | |
| Published online | |
| Publication language | |
| Publisher | RTU Press |
| Country of Publication | Latvia |
| Funding source |
