No abstract is provided for this article.
No abstract is provided for this article.
No abstract is provided for this article.
{Fibre-optic Bragg Grating (FBG) strain sensors hold a great potential for vibration monitoring of civil structures because of their exceptional stability and accuracy; however, the accurate measurement of the very small strains levels occurring during ambient, or operational, excitation has been so far problematic. There are two ways to improve the measurement resolution: employing a strain-enhancing sensor package, and applying an improved wavelength detection algorithm. In this work, the potential of an improved wavelength detection algorithm for the identification of modal characteristics based on sub-microstrain data is investigated. The strategy is illustrated for a steel beam to which a chain of multiplexed FBG sensors has been attached at the top side of the beam. The raw FBG data are processed into strain values with an algorithm that is based on detecting the peak shifts in the wavelength spectrum by correlation analysis rather than by simply tracking the peak values. Subsequently, the strain sequences are used for identification of modal characteristics of the beam (natural frequencies and strain mode shapes) with ``Covariance driven Stochastic Subspace Identification (SSI/cov){''}. Computational results of a Finite Element Model are used to validate the experimental results.}
In this paper, a novel approach to model updating for a large-scale railway bridge using orthogonal diagonalization (OD) coupled with an improved particle swarm optimization (IPSO) is proposed. Particle swarm optimization (PSO) is a well-known and widely applied evolutionary algorithm. However, as other evolutionary algorithms (EAs), PSO has two main drawbacks that may reduce its capability to tackle optimization issues. A fundamental shortcoming of PSO is premature convergence. On the other hand, since PSO employs all populations to seek the best solution through iterations, it is very time-consuming. This makes PSO as well as EAs difficult to apply for optimization problems of large-scale structural models. In order to overcome those drawbacks, we propose coupling OD with IPSO (ODIPSO). OD is applied to arrange the position of particles and to select only particles with the best solution for next iterations, which helps to reduce the computational cost dramatically. There are several significant features of ODIPSO: (1) IPSO is employed to tackle the problem of premature convergence of PSO; (2) only one guide is used to update the velocity of particles instead of utilizing both guides, consisting of the local best and the global best; and (3) in each iteration, only the velocity and the position of the best particles are updated. In order to assess the effectiveness of the proposed approach, a large-scale railway bridge calibrated on the field is employed. This paper also introduces the use of wireless triaxial sensors (replacing classical wired systems) to obtain structural dynamic characteristics. The appearance of wireless triaxial transducers increases significantly the freedom in designing an ambient vibration test. The results show that ODIPSO not only outperforms PSO, IPSO and OD combined with PSO (ODPSO) in terms of accuracy, but also dramatically reduces the computational time compared to PSO and IPSO.
Structural health monitoring refers to the process of measuring damage-sensitive variables to assess the functionality of a structure. In principle, vibration data can capture the dynamics of the structure and reveal possible failures, but environmental and operational variability can mask this information. Thus, an effective outlier detection algorithm can be applied only after having performed data normalization (i.e. filtering) to eliminate external influences. Instead, in this article we propose a technique which unifies the data normalization and damage detection steps. The proposed algorithm, called adaptive kernel spectral clustering (AKSC), is initialized and calibrated in a phase when the structure is undamaged. The calibration process is crucial to ensure detection of early damage and minimize the number of false alarms. After the calibration, the method can automatically identify new regimes which may be associated with possible faults. These regimes are discovered by means of two complementary damage (i.e. outlier) indicators. The proposed strategy is validated with a simulated example and with real-life natural frequency data from the Z24 pre-stressed concrete bridge, which was progressively damaged at the end of a one-year monitoring period.
No abstract is provided for this article.
No abstract is provided for this article.
