• Steel and Composite Structures
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• v.52 no.6
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• pp.621-626
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• 2024

The work researched the application of artificial intelligence to the design and analysis of advanced nanoplates, with a particular emphasis on size and surface effects. Employing an integrated theoretical framework, this study developed a more accurate model of complex nanoplate behavior. The following analysis considers nanoplates embedded in a Pasternak viscoelastic fractional foundation and represents the important step in understanding how nanoscale structures may respond under dynamic loads. Surface effects, significant for nanoscale, are included through the Gurtin-Murdoch theory in order to better describe the influence of surface stresses on the overall behavior of nanoplates. In the present analysis, the modified couple stress theory is utilized to capture the size-dependent behavior of nanoplates, while the Kelvin-Voigt model has been incorporated to realistically simulate the structural damping and energy dissipation. This paper will take a holistic approach in using sinusoidal shear deformation theory for the accurate replication of complex interactions within the nano-structure system. Addressing different aspectsof the dynamic behavior by considering the length scale parameter of the material, this work aims at establishing which one of the factors imposes the most influence on the nanostructure response. Besides, the surface stresses that become increasingly critical in nanoscale dimensions are considered in depth. AI algorithms subsequently improve the prediction of the mechanical response by incorporating other phenomena, including surface energy, material inhomogeneity, and size-dependent properties. In these AI- enhanced solutions, the improvement of precision becomes considerable compared to the classical solution methods and hence offers new insights into the mechanical performance of nanoplates when applied in nanotechnology and materials science.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.1A
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• pp.9-18
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• 2009

This paper introduces the application of DE (Differential Evolutionary) method for the estimation of tensile force of the externally prestressed tendon. The proposed technique, a SI (System Identification) method using the DE algorithm, can make global solution search possible as opposed to classical gradient-based optimization techniques. The numerical tests show that the proposed technique employing DE algorithm is a useful method which can detect the effective nominal diameters as well as estimate the exact tensile forces of the externally prestressed tendon with an estimation error less than 1% although there is no a priori information about the identification variables. In addition, the validity of the proposed technique is experimentally proved using a scale-down model test considering the serviceability state condition without and with the loss of the prestressed force. The test results prove that the technique is a feasible and effective method that can not only estimate the exact tensile forces and detect the effective nominal diameters but also inspect the damping properties of test model irrespective of the loss of the prestressed force. The 2% error of the estimated effective nominal diameter is due to the difference between the real tendon diameter with a wired section and the FE model diameter with a full-section. Finally, The accuracy and superiority of the proposed technique using the DE algorithm are verified through the comparative study with the existing theories.