Structural damage identification is a challenging subject in the structural health monitoring research. The piezoelectric impedance-based damage identification, which usually utilizes the matrix inverse-based optimization, may in theory identify the damage location and damage severity. However, the sensitivity matrix is oftentimes ill-conditioned in practice, since the number of unknowns may far exceed the useful measurements/inputs. In this research, a new method based on intelligent inference framework for damage identification is presented. Bayesian inference is used to directly predict damage location and severity using impedance measurement through forward prediction and comparison. Gaussian process is employed to enrich the forward analysis result, thereby reducing computational cost. Case study is carried out to illustrate the identification performance.
Proc. SPIE. 9063, Nondestructive Characterization for Composite Materials, Aerospace Engineering, Civil Infrastructure, and Homeland Security 2014
KEYWORDS: Data modeling, Monte Carlo methods, Analytical research, Probability theory, Stochastic processes, Statistical modeling, Structural dynamics, Process modeling, Mendelevium, Bayesian inference
Currently, the deviation between the model and an actual structure is generally identified through a so-called model updating process, in which a set of experimental measurement of structural dynamic response is used in combination with the model prediction to facilitate an inverse analysis that is usually deterministic. In reality, however, structural properties, such as mass and stiffness, are inevitably subject to variation/uncertainties. As such, the identification of property variations in a probabilistic manner can truly reveal the underlying physical characteristics of the structure involved. In this research, we adopt the Bayesian probabilistic framework to conduct stochastic model updating using measured vibratory response. Furthermore, this paper proposes an efficient scheme to facilitate such procedures by incorporating the Gaussian process and Markov Chain Monte Carlo (MCMC) into the Bayesian framework. The feasibility of this presented methodology is validated by case studies.
Cyclically periodic structures, such as blade-disk assembly in turbo-machinery, are widely used in engineering practice. While these structures are generally designed to be periodic with identical substructures, it is well-known that small random uncertainties exist among substructures which in certain cases may cause drastic change in the dynamic responses, a phenomenon known as vibration localization. Previous studies have illustrated that the introduction of small design modifications, i.e., intentional mistuning, may alleviate such vibration localization. The design objective here thus is to identify proper deign modification that can reduce the response variation under uncertainties. In this research, we first develop a perturbation-based approach to efficiently quantify the variation of forced response of a periodic structure, without and with the design modification, under uncertainties. We then propose a Gaussian process emulation which enables us to evaluate the objective function over the design space by using only a small number of design candidates. The combination of these algorithms allows us to perform effective design modification to minimize the response variation in nearly periodic structures.
This paper presents a new methodology that is built upon existing hardware such as shaker force generator and
accelerometers that are both portable and convenient to use for a variety of civil and mechanical structures. Our key
idea is to use a moving load that is placed successively at a number of locations on the structure, and measure the
corresponding frequency responses. These frequency response measurements will then be used to extract the
structural properties. Our new methodology so called mass response method enables the direct extraction of the
equivalent stiffness and mass of the critical members of a structure without using a priori information of the structure.
A number of case studies are carried out to demonstrate the accuracy and efficiency of its usage in structural health
monitoring applications. Furthermore, the uncertainty introduces to this methodology is also investigated and discussed.