Memory response in a nonlocal micropolar double porous thermoelastic medium with variable conductivity under Moore-Gibson-Thompson thermoelasticity theory

The present study enlightens the two-dimensional analysis of the thermo-mechanical response for a micropolar double porous thermoelastic material with voids (MDPTMWV) by virtue of Eringen’s theory of nonlocal elasticity. Moore-Gibson-Thompson (MGT) heat equation is introduced to the considered model in the context of memory-dependent derivative and variable conductivity. By employing the normal mode technique, the non-dimensional coupled governing equations of motion are solved to determine the analytical expressions of the displacements, temperature, void volume fractions, microrotation vector, force stress tensors, and equilibrated stress vectors. Several two-dimensional graphs are presented to demonstrate the influence of various parameters, such as kernel functions, thermal conductivity, and nonlocality. Furthermore, different generalized thermoelasticity theories with variable conductivity are compared to visualize the variations in the distributions associated with the prior mentioned variables. Some particular cases are also discussed in the presence and absence of different parameters

Detecting Danger: AI-Enabled Road Crack Detection for Autonomous Vehicles

The present article proposes the deep learning concept termed ―Faster-Region Convolutional Neural Network‖ (Faster-RCNN) technique to detect cracks on road for autonomous cars. Feature extraction, preprocessing, and classification techniques have been used in this study. Several types of image datasets, such as camera images, faster-RCNN laser images, and real-time images, have been considered. With the help of GPU (graphics processing unit), the input image is processed. Thus, the density of the road is measured and information regarding the classification of road cracks is acquired. This model aims to determine road crack precisely as compared to the existing techniques