In situ health monitoring of multiscale structures and its instantaneous verification using mechanoluminescence and dual machine learning

Extensive changes in the legal, commercial and technical requirements in engineering fields have necessitated automated real-time structural health monitoring (SHM) and instantaneous verification. An integrated system with mechanoluminescence (ML) and dual artificial intelligence (AI) modules with subsidiary finite element method (FEM) simulation is designed for in situ SHM and instantaneous verification. The ML module detects the exact position of a crack tip and evaluates the significance of existing cracks with a plastic stress-intensity factor (PSIF; KP). ML fields and their corresponding KpML values are referenced and verified using the FEM simulation and bidirectional generative adversarial network (GAN). Well-trained forward and backward GANs create fake FEM and ML images that appear authentic to observers; a convolutional neural network is used to postulate precise PSIFs from fake images. Finally, the reliability of the proposed system to satisfy existing commercial requirements is validated in terms of tension, compact tension, AI, and instrumentation. [Display omitted] •Evaluated the significance of existing crack using mechanoluminescence in SrAl2O4:Eu,Dy•Trained GAN generated fake finite element method and mechanoluminescence images•CNN postulated precise plastic stress intensity factor from fake images•Demonstrated in situ SHM and its instantaneous verification using ML and dual AI Machine learning; Mechanical Phenomenon; Optical property