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capable of modifying their characteristics, intentionally, to reduce the effects of external actions. Such capability is attained through the integration of several basic components. Sensors, processors and actuators are examples of such components. The reliability of such integrated systems pose a major concern hindering their practical applicatio...
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... The main objective of this task is to outline a generic reliability assessment framework for evaluating the reliability of different types of components and ultimately the overall system reliability [17][18][19][20][21]. Being an integrated system, comprising a set of basic components, the individual component reliability is of major importance to the evaluation of the overall reliability of the system. ...
... Fuzzy controllers are known to employ fuzzy logic and fuzzy set theory in developing their control strategies and evaluating control actions [9, 10, 11, 12, 13]. Fuzzy logic has two primary advantages, as opposed to conventional mathematical algorithms, when employed in control applications. ...
... Smart structural systems, as outlined earlier, comprise sets of integrated components which provide added functionalities to the system, as opposed to conventional structural systems. Despite the fact that some of these components might have been proven reliable, in other applications, their reliable performance as an integral component of such a system needs validation and confirmation [8, 9]. In order to develop a comprehensive reliability assessment scheme for smart structural systems, a generic reliability assessment framework needs to be defined. ...
... In order to develop a comprehensive reliability assessment scheme for smart structural systems, a generic reliability assessment framework needs to be defined. The generic framework functions as a blueprint that identifies the reliability assessment procedures and underlying models, functions and measures that are necessary to perform the reliability assessment as per the nature of the problem at hand [8, 9]. Furthermore, it is crucial to develop proper reliability measures and assessment procedures, at two basic levels. ...
In this chapter, the design of fuzzy controllers, tailored for functioning as structural controllers, is outlined together with all necessary definitions of relevant variables, their membership functions, fuzzification and de-fuzzification procedures. The definition of the required inference engine and its underlying rule-base, implication functions and inference mechanisms are also presented. Knowing the importance of reliable performance of such heuristic systems and to ensure their general applicability, a reliability assessment procedure is also outlined to assess the reliability of the designed controllers. Finally, other potential applications of fuzzy inference systems are also briefly presented, such applications include, but not limited to, smart abstract deformed shape identification of structural systems under earthquake excitation.
... Zur Umgehung dieser Problematik können, ausgehend von den ursprünglichen Simulationsmodellen, Ersatzmodelle (Metamodelle) [41,54] Eine allgemeine Systematik zur Zuverlässigkeitsbewertung von adaptronischen Struktursystemen wird in [42,43] vorgestellt. Hierbei wird jede mögliche Fehlfunktion qualitativ über eine Fehlerbaumanalyse erfasst und es wird jeweils eine entsprechende Grenzfunktion zugewiesen. ...
... In order to evaluate the reliability of the MR damper, it is necessary to evaluate the supplied and required damper forces. This study fits in a series of an ongoing research aiming at the evaluation of the reliability of smart structural systems [4, 5]. Thus, the reliability of MR dampers, as one of potential smart components is sought. ...
... On the other hand, the required damper force is evaluated by employing an inverse dynamics model, as explained above. A system model should be developed, as a first step, in order to identify the inter-relations among the individual devices and/or components [4, 5]. Such model would serve as a blueprint for constructing the required reliability assessment procedure. ...
Smart control devices have gained a wide interest in the seismic research community in recent years. Such interest is triggered by the fact that these devices are capable of adjusting their characteristics and/or properties in order to counter act adverse effects. Magneto-Rheological (MR) dampers have emerged as one of a range of promising smart control devices, being considered for seismic applications. However, the reliability of such devices, as a component within a smart structural control scheme, still pause a viable question. In this paper, the reliability of MR dampers, employed as devices within a smart structural control system, is investigated. An integrated smart control setup is proposed for that purpose. The system comprises a smart controller, which employs a single MR damper to improve the seismic response of a single-degree-of-freedom system. The smart controller, in addition to, a model of the MR damper, is utilized in estimating the damper resistance force available to the system. On the other hand, an inverse dynamics model is utilized in evaluating the required damper resistance force necessary to maintain a predefined displacement pattern. The required and supplied forces are, then, utilized in evaluating the reliability of the MR damper. This is the first in a series of studies that aim to explore the effect of other smart control techniques such as, neural networks and neuro fuzzy controllers, on the reliability of MR dampers.
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.
Smart components and materials have emerged as a viable enhancement of structural systems in response to highly uncertain loading conditions such as earthquake loadings. Smart components and/or materials provide an added functionality for structural systems. Such functionality enhances their capabilities to adjust and/or alter engineering properties thus allowing the overall system to respond favourably to any uncertain and unforeseen loading condition. This notion, in effect, adopts a structural control framework in the sense that structural characteristics are adjusted, using smart algorithms and/or properties of individual components, as a result of the system’s response in a closed feedback loop algorithm. The basic components of any structural control system are present in any smart structural system, starting by sensors which are responsible for identifying the current state of the system, to the processor which comprises a state identifier and a smart controller and finally the actuators which are responsible for enhancing the system’s structural characteristics. In the presence of uncertainties, it is rather crucial that the reliability of the resulting overall integral smart control system is evaluated and ensured. If such an approach is to be used in lieu of, or in addition to, conventional structural control its reliable performance should be ensured.
In this paper, a smart structural control system is proposed together with a structured procedure for evaluating and ensuring its reliability. The system comprises three basic components, namely sensors, processors and actuators. All components operate in a closed feedback loop scheme. Such components could either behave in a smart manner and/or enclose smart features which adjust their structural and/or physical characteristics as needed. In order to evaluate, and thus ensure, the reliability of such systems, it is important that a set of steps be accomplished, starting from the identification of a generic reliability assessment framework, system modelling of all underlying components and the resulting system and finally the implementation of the reliability assessment framework at the component and the system levels. The paper outlines a comprehensive reliability assessment framework, a system model and demonstrates the implementation of the reliability assessment frame work on some of the basic components. This effort should eventually lead to the development of a reliability-based design procedure for smart structural control systems.
