Abstract

Nowadays, the use of small-scale structures in micro/nanomachines has become more and more widespread. The most important applications of such small-sized parts are in micro-electro-mechanical systems (MEMS) as well as nano-electro-mechanical systems (NEMS) as actuators, sensors, energy harvesters. For example, nanosensors are nanoscale devices that measure physical quantities and convert these to signals that can be detected and analyzed. On the applications of micro/nanosensors in civil engineering, one can state that nanosensors can be developed and used in construction to monitor and/or control the environmental conditions and the materials/structures' performance. As an example, nanosensors can be used to monitor concrete corrosion and micro-cracking. The smart sensor can also be employed for structural health monitoring in bridges and other structures. In this regard, understanding the mechanical response of such structures in various environmental and physical situations is seriously required. For the design and modelling of such a device, one can use various approaches. First, we mention straightforward experiments which need special equipment and result in high costs. Second, molecular dynamics could be used, which requires a lot of computational efforts, in general. Moreover, this method cannot be implemented for all types of nanostructures. Finally, the application of continuum models properly modified for modelling materials and structures at small scales is worth mentioning. Among various enhancements of classic mechanics of continua and structures, we mention the non-local approach related to the description of long-range interactions. In what follows, we apply the third technique based on non-local models and corresponding modelling to thin-walled structures as principal elements of MEMS and NEMS. Moreover, we consider the coupling between mechanical and electromagnetic fields. So this dissertation is based on this approach. Using it, the mechanical behavior of the MEMS and NEMS has been predicted.

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