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Structural Engineering and Mechanics Volume 23, Number 5, July30 2006 , pages 455-468 DOI: https://doi.org/10.12989/sem.2006.23.5.455 |
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Maguid H. M. Hassan
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| Abstract | ||
| Smart structural systems are defined as ones that demonstrate the ability to modify their characteristics and/or properties in order to respond favorably to unexpected severe loading conditions. The performance of such a task requires a set of additional components to be integrated within such systems. These components belong to three major categories, sensors, processors and actuators. It is well-known that all structural systems entail some level of uncertainty, because of their extremely complex nature, lack of complete information, simplifications and modeling. Similarly, sensors, processors and actuators are expected to reflect a similar uncertain behavior. As it is imperative to be able to evaluate the impact of such components on the behavior of the system, it is as important to ensure, or at least evaluate, the reliability of such components. In this paper, a system model for reliability assessment of smart structural systems is outlined. The presented model is considered a necessary first step in the development of a reliability assessment algorithm for smart structural systems. The system model outlines the basic components of the system, in addition to, performance functions and inter-relations among individual components. A fault tree model is developed in order to aggregate the individual underlying component reliabilities into an overall system reliability measure. Identification of appropriate limit states for all underlying components are beyond the scope of this paper. However, it is the objective of this paper to set up the necessary framework for identifying such limit states. A sample model for a three-story single bay smart rigid frame, is developed in order to demonstrate the proposed framework. | ||
| Key Words | ||
| control; fuzzy; MR-dampers; neural; reliability; smart; system; uncertainty. | ||
| Address | ||
| Department of Civil & Environmental Engineering, 1173 Glenn L. Martin Hall, University of Maryland, Collage Park, MD. 20742, USA | ||