Damage identification in concrete using multiscale computational modeling and convolutional neural networks

Abstract

AbstractConcrete is a composite material with heterogeneities across multiple length scales. Degradation of concrete due to external loadings starts with diffuse microcracking, followed by damage localization that eventually leads to structural failure. Identification of damage at an early stage of degradation reduces the costs associated with maintenance of the structure. Weak changes can be detected using diffuse ultrasonic waves (so‐called Coda waves). In this contribution, a virtual testing environment for the assessment of concrete damage using coda waves is presented. The virtual test environment combines multiscale computational modeling of concrete damage, modeling of ultrasonic wave propagation, and supervised learning. At the scale of mortar material, microcrack growth is modelled using a combination of continuum micromechanics and linear elastic fracture mechanics. The micromechanics model is incorporated into a reduced‐order Lippmann‐Schwinger based mesomodel for concrete. Synthetic concrete specimens at various damage levels are generated using the multiscale damage model and subsequently these specimens are subjected to wave propagation analysis using the rotated staggered‐grid‐finite‐difference scheme. A convolutional neural network (CNN) based supervised learning framework is further employed to classify damage given the coda signals.