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论文范文
1. Introduction The complexity concept is very difficult to define for engineering applications. Considering practical necessity, the users often initially attempt to solve a problem using common sense. However, the solution strategy evolves over a very long period of time. It is not uncommon to find that the initial solution strategy was developed without using any mathematics-based concept. The same basic concept is being developed at present using the current state-of-the-art advanced mathematical concepts embedded in highly powerful computational frame work. To assess health of an earthen utensil, our forefather tapped it and listened to the sound it produced. The world communities at present are trying to assess the health of civil infrastructures to help maintain our way of life. Some of these infrastructures were designed a long time ago and their design life may have expired. They need to be replaced; however, we do not have resources to replace them. Their design lives need to be extended. One attractive economical approach has attracted the attention of the profession is to inspect them as comprehensively as possible to identify the location, type, and severity of defects if any and then repair them in the most cost effective way to bring them back to the original state when initially designed. Because of its relevance and importance, the structural health assessment (SHA) of civil infrastructures has attracted multidisciplinary research interest from all over the world. The basic health assessment problem of earthen utensils has now extended to infrastructures. As the health assessment concept started maturing over thousands of years, the users are now demanding different types of capabilities. Essentially, a simple concept has become very complex to satisfy the current needs. Similar challenges are faced by physicians to assess human health. They now have access to numerous equipment and test protocols with various degrees of sophistication. They need to use them very judiciously using information on cost and benefit. Looking back to the chronology of events for the development of SHA techniques for civil infrastructures, the engineering profession also followed strategies similar to the physicians. To identify the location, type, and severity of defects in large infrastructures, it is necessary to represent them in mathematical or algorithmic form, for example, representing them by finite elements. By tracking the structural properties of the elements and comparing them with the previous values if available from past inspections or changes from the original values mentioned in design drawings, deviation from the expected values, or variation with other similar elements, the location and the severity of defect in elements can be assessed. This model-based advancement replaced the non-model-based approaches used to check health of earthen utensils. The engineers also realized that to assess the current health, structural behavior at the time of the inspection needs to be used. This requirement adds another layer of complexity. Because of its simplicity, the profession started measuring responses by exciting the structures statically. Although it is relatively simple, it has major deficiencies [1–3] and may not be an attractive option. Measuring responses by exciting the structures dynamically removes some of the deficiencies in static approaches; it requires dynamic responses to be measured at all dynamic degrees of freedom (DDOFs) of infrastructures. It will be practically impossible to instrument large civil infrastructures. Response information may be measured at a limited number of DDOFs. To add fuel to the fire, dynamic responses are always expected to be noise-contaminated and this information needs to be mitigated appropriately. Dynamic response information can be treated in the frequency or time domain. Since a large number of modes are difficult to evaluate [4] and the presence of defects may force the structure behave nonlinearly, the frequency domain approach may not be appropriate. Although it is relatively complicated but efficient in mitigating uncertainty related issues in the measured dynamic responses, the time domain approach has attracted the attention of the research team of the University of Arizona. Time domain approaches will be discussed in this paper. 
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