STRUCTURAL HEALTH MONITORING: CONDITION ASSESSMENT OF BRIDGE STRUCTURES WITH THE DEFORMATION AREA DIFFERENCE (DAD) METHOD USING MOST MODERN MEASUREMENT TECHNIQUES SUCH AS PHOTOGRAMMETRY AND UAV

The implementation of Structural Health Monitoring (SHM) is of paramount importance for the assurance of the safety and durability of bridge infrastructure, particularly in light of the advancing age of existing structures and the concurrent increase in traffic loads. The Deformation Area Difference (DAD) method has emerged as a promising approach for detecting and assessing structural damage, offering a cost-effective alternative to traditional monitoring techniques. However, its real-world applicability has been limited by challenges such as sensitivity to noise, measurement point density, and performance under low-deflection scenarios. It is, therefore, essential to address these issues to advance the reliability and scalability of SHM solutions.
This doctoral thesis has the following objectives: firstly, to address the limitations of the DAD method; secondly, to enhance its precision and applicability through complementary techniques; and thirdly, to extend its functionality to support higher SHM levels. By systematically evaluating the method's performance and integrating innovative approaches, this work seeks to establish a comprehensive framework for accurate and practical SHM solutions.
The initial study examines the constraints of the DAD method through a comprehensive parametric numerical analysis, introducing the Damage Detection Range (DDR) to quantify its efficacy. The study reveals the impact of various factors, including deflection values, noise levels, and measurement point distances, on the performance of the DAD method. Additionally, it explores the influence of local cross-sectional damage for the first time. The second study addresses the method's noise sensitivity by introducing the Strain Area Difference (SAD) method, which leverages strain-based curvature analysis to enhance damage detection precision, particularly in low-deflection scenarios, and demonstrates resilience to noise through laboratory validation. The third study integrates low-complexity Model Updating (MU) techniques to enable damage level assessment.
The findings demonstrate that the limitations of the DAD method can be effectively mitigated through the implementation of innovative approaches. The DDR and local cross-sectional analysis provide new insights into the method's applicability across diverse scenarios. The SAD method significantly enhances damage detection precision and noise resilience, while MU techniques enable damage severity assessments. Collectively, these contributions expand the capabilities and applicability of the DAD method for SHM.
This dissertation addresses pivotal research questions pertaining to the constraints, improvements, and applications of the DAD method. By incorporating cutting-edge techniques such as the SAD method and model updating methodologies, it establishes a robust foundation for precise, scalable, and pragmatic SHM solutions for bridge structures, paving the way for future advancements in the field.