Integrating Edge-AI in Structural Health Monitoring domain
This work addresses real-time decision-making for bridge maintenance in the structural health monitoring domain, but it appears incremental as it applies existing edge-AI platforms to a specific task.
This study tackles the problem of low latency and late inference times in real-time structural health monitoring by integrating edge-AI for bridge inspections, proposing a framework with a deep learning model to achieve real-time crack classification and evaluating its effectiveness based on accuracy, confusion matrix, and inference time.
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.