Finite element (FE) model updating belongs to the class of inverse problems in mechanics and is a constrained optimization problem. In FE model updating, the difference between the modal parameters (the frequencies, damping ratios and the mode shapes) obtained from the FE model of the structure and those from the vibration measurements are minimized within an optimization algorithm. The design variables of the optimization problem are the stiffness reduction factors, which represent the damage. In this study, the Genetic Algorithms (GA), the Parallel GA, the local search algorithms, the Trust Region Gauss Newton, the Sequential Quadratic Programming, the Levenberg–Marquardt Techniques and the hybrid versions of these methods are applied within the FE Model Updating Technique for updating the Young's modulus of different FEs of a reinforced concrete beam. Different damage scenarios and different noise levels are taken into account. The results of the study show that the local search algorithms cannot detect, locate and quantify damage in reinforced concrete beam type structures while the GA together with the hybrid and the parallel versions detect, localize and identify the damage very accurately. It is apparent that the hybrid GA & Trust Region Gauss Newton Technique is best in terms of the computation speed as well as accuracy.
It is known that the structural health monitoring (SHM) applications rely on the physical data obtained from in-situ measurements. In this view, various instruments such as accelerometers, strain gauges, displacement sensors are used to collect data, which are to be used in structural identification applications. Considering the basic requirements like precision, accuracy and applicability for reliable data post-processing, a new scheme for a vibration sensing device is introduced in this study. The proposed sensor scheme is a vibration transducer which combines the fundamental sensing principles of conventional accelerometers and the computer vision techniques. Basically, the transducer consists of a mechanical system as the primary sensor and a camera as the secondary sensor. In conventional piezoelectric (PE) accelerometers, the PE material generates charge or voltage which is proportional to the acceleration applied to the sensor. Subsequently, this charge or voltage is measured and used to determine the imposed accelerations. However, in the proposed vibration transducer, the motion of the seismic mass is directly tracked by a camera and the displacements are extracted using computer vision algorithms. Afterwards, displacement of the seismic mass can be related to the imposed acceleration, velocity and displacement. In this study, the transducer concept was realized practically using two different primary sensors consist of a spring-mass system and a cantilever beam together with a smartphone's camera. The concept was tested on a laboratory structure in order to verify its capabilities in modal identification, damage detection and localization applications. The comparison of the results obtained by the proposed transducer and the conventional accelerometers has shown that the proposed vibration transducer is capable of both identifying modal parameters and detecting damage despite its crude design. Although the sensitivity of the transducer is lower than the conventional accelerometers in its current state, the concept is prone to further improvements.
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