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Abstract
This study follows a novel path toward alarming the dangerous conditions of the buildings by means of piezoelectricity. piezoelectric materials have the ability to translate electrical voltage and displacement to each other interchangeably. This feature is employed in this research to define a critical threshold for the displacements. The case study of this research is a graphene-platelets reinforced functionally-graded piezoelectric (GPLR-FGP) rectangular wall with one free edged at its top. The voltage would be gradually increased until the natural frequency approaches to zero. This circumstance puts the wall in the direct danger of failing its stability. Accordingly, displacements of the wall at this condition can be considered as the critical deflections of the system. The wall’s governing equations are established in the background of higher-order shear deformation theory (HOSDT). Differential quadrature approach (DQA) is employed to obtain the natural frequency of the wall. Deep-learning is utilized to accelerate the computation process by means of training a deep network of neurons through the instances determined by the explained numerical approach. Efficiency of the innovative implemented solution in this research is verified through a comparative analysis with the results of the published articles. This research can be considered as the main stone of developing innovative solutions for alarming safety issues in the smart cities of near future.
Acknowledgment
This research was supported by the Researchers Supporting Project number (RSP2023R206), King Saud University, Riyadh, Saudi Arabia.