This paper presents a novel smart MEMS accelerometer which employs a hybrid control algorithm and an estimator. This scheme is realized by adding a sliding-mode controller to a conventional PID closed loop system to achieve higher stability and higher dynamic range and to prevent pull-in phenomena by preventing finger displacement from passing a maximum preset value as well as adding an adaptive nonlinear observer to a conventional PID closed loop system. This estimator is used for online estimation of the parameter variations for MEMS accelerometers and gives the capability of self testing to the system. The analysis of convergence and resolution show that while the proposed control scheme satisfies these criteria it also keeps resolution performance better than what is normally obtained in conventional PID controllers. The performance of the proposed hybrid controller investigated here is validated by computer simulation.

The approach to damage detection and localization adopted in this paper is based on a statistical comparison of models built from the response time histories collected at different stages during the structure lifetime. Some of these time histories are known to have been recorded when the structural system was undamaged. The consistency of the models associated to two different stages, both undamaged, is first recognized. By contrast, the method detects the discrepancies between the models from measurements collected for a damaged situation and for the undamaged reference situation. The damage detection and localization is pursued by a comparison of the SSE (sum of the squared errors) histograms. The validity of the proposed approach is tested by applying it to the analytical benchmark problem developed by the ASCE Task Group on Structural Health Monitoring (SHM). In the paper, the results of the benchmark studies are presented and the performance of the method is discussed.

An ElectroMagnetic Actuator (EMA) is designed and assessed numerically and experimentally. The EMA has the advantage to be without contact with the structure so it could be applied to light and small mechanism. Nevertheless, the open-loop instability and the nonlinear dynamic behavior with respect to the excitation frequency could limit its application field. The EMA is designed and dimensioned as a function of the experimental structure to be controlled. An inverse model of the EMA is proposed in order to implement a linear action block for the used frequency range. The control strategy is a fuzzy controller with displacements and velocities as inputs. A fuzzy controller of Takagi-Sugeno type is used. The air gap is estimated by using a modal approximation of the displacements issued from all measurements. Several configurations of control are assessed by using numerical simulations. The block diagram used for numerical simulations is implemented under Dspace$^{(R)}$ environment. The implemented controller was tested experimentally in the context of impact perturbations. The results obtained show the effectiveness of the developed procedures and the robustness of the implemented control.

Structural Control relies, with a great deal, on the ability of the control algorithm to identify the current state of the system, at any given point in time. When such algorithms are designed to perform in a smart manner, several smart technologies/devices are called upon to perform tasks that involve pattern recognition and control. Smart pattern recognition is proposed to replace/enhance traditional state identification techniques, which require the extensive manipulation of intricate mathematical equations. Smart pattern recognition techniques attempt to emulate the behavior of the human brain when performing abstract pattern identification. Since these techniques are largely heuristic in nature, it is reasonable to ensure their reliability under real life situations. In this paper, a neural network pattern recognition scheme is explored. The pattern identification of three structural systems is considered. The first is a single bay three-story frame. Both the second and the third models are variations on benchmark problems, previously published for control strategy evaluation purposes. A Neural Network was developed and trained to identify the deformed shape of structural systems under earthquake excitation. The network was trained, for each individual model system, then tested under the effect of a different set of earthquake records. The proposed smart pattern identification scheme is considered an integral component of a Smart Structural System. The Reliability assessment of such component represents an important stage in the evaluation of an overall reliability measure of Smart Structural Systems. Several studies are currently underway aiming at the identification of a reliability measure for such smart pattern recognition technique.

This paper presents the most significant results obtained within a broad-ranging experimental program aiming to evaluate both the effectiveness and the robustness of a Base Isolation (BIS) and a Tuned Mass Damper (TMD) combined control strategy (BI & TMD). Following a brief description of the experimental model set-up and the adopted kinematic scaling technique, this paper describes the identification procedures carried out to characterize the system''s model. The dynamic response of a small-scale model to recorded earthquake excitations, which has been scaled by using the Buckingham pi-theorem, are later presented and discussed. Finally, the effectiveness and robustness of the combined control strategy is evaluated by comparing the model's dynamic response. In particular, reduction in relative displacements and absolute accelerations due to the application of different mass damping systems is investigated.

The potential offered by the thermo-mechanical properties of shape memory alloys (SMA) in structural engineering applications has been the topic of many research studies during the last two decades. The main issues concern the long-term predictability of the material behaviour and the fatigue lifetime of the macro structural elements (as different from the one of wire segments). The laboratory tests reported in this paper are carried out on bar specimens and they were planned in order to pursue two objectives. First, the creep phenomenon is investigated for two different alloys, a classical Ni-Ti alloy and a Cu-based alloy. The attention is then focused on the Cu-based alloy only and its fatigue characteristics at given temperatures are investigated. Stress and thermal cycles are alternated to detect any path dependency.