  Structural health monitoring (SHM) tasks like damage detection are crucial
for decision-making regarding maintenance and deterioration. For example, crack
detection in SHM is crucial for bridge maintenance as crack progression can
lead to structural instability. However, most AI/ML models in the literature
have low latency and late inference time issues while performing in real-time
environments. This study aims to explore the integration of edge-AI in the SHM
domain for real-time bridge inspections. Based on edge-AI literature, its
capabilities will be valuable integration for a real-time decision support
system in SHM tasks such that real-time inferences can be performed on physical
sites. This study will utilize commercial edge-AI platforms, such as Google
Coral Dev Board or Kneron KL520, to develop and analyze the effectiveness of
edge-AI devices. Thus, this study proposes an edge AI framework for the
structural health monitoring domain. An edge-AI-compatible deep learning model
is developed to validate the framework to perform real-time crack
classification. The effectiveness of this model will be evaluated based on its
accuracy, the confusion matrix generated, and the inference time observed in a
real-time setting.
