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

Abstract

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 <i>in situ</i> 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; <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow><mml:msub><mml:mi>K</mml:mi> <mml:mi>P</mml:mi></mml:msub> </mml:mrow> </mml:math> ). ML fields and their corresponding <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow><mml:msubsup><mml:mi>K</mml:mi> <mml:mi>p</mml:mi> <mml:mrow><mml:mi>M</mml:mi> <mml:mi>L</mml:mi></mml:mrow> </mml:msubsup> </mml:mrow> </mml:math> 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.