Observability analysis for structural system identification based on state estimation

Ahmad Alahmad, Rocío Porras, José Antonio Lozano Galant and José Turmo

DES - Working Papers. Statistics and Econometrics. WS from Universidad Carlos III de Madrid. Departamento de Estadística

Abstract: The concept of observability analysis (OA) has garnered substantial attention in the field of Structural System Identification. Its primary aim is to identify a specific set of structural characteristics, such as Young's modulus, area, inertia, and possibly their combinations (e.g., flexural or axial stiffness). These characteristics can be uniquely determined when provided with a suitable subset of deflections, forces, and/or moments at the nodes of the structure. This problem is particularly intricate within the realm of Structural System Identification, mainly due to the presence of nonlinear unknown variables, such as the product of vertical deflection and flexural stiffness, in accordance with modern methodologies. Consequently, the mechanical and geometrical properties of the structure are intricately linked with node deflections and/or rotations. The paper at hand serves a dual purpose: firstly, it introduces the concept of State Estimation (SE), specially tailored for the identification of structural systems; and secondly, it presents a novel OA method grounded in SE principles, designed to overcome the aforementioned challenges. Computational experiments shed light on the algorithm's potential for practical Structural System Identification applications, demonstrating significant advantages over the existing state-of-the-art methods found in the literature. It is noteworthy that these advantages could potentially be further amplified by addressing the SE problem, which constitutes a subject for future research. Solving this problem would help address the additional challenge of developing efficient techniques that can accommodate redundancy and uncertainty when estimating the current state of the structure.

Keywords: Structural; Health; Monitoring; Structural; System; Identification; State; Estimation; Observability; Analysis (search for similar items in EconPapers)
Date: 2023-11-23
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