Leveraging Acoustic Emission and Machine Learning for Concrete Materials Damage Classification on Embedded Devices

• For the field of structural health monitoring (SHM), acoustic emission (AE) technology is important as a damage identification technique that does not cause secondary damage to concrete. Nowadays, applications of non-destructive concrete damage identification are mostly limited to commercial software or identification algorithms running on desktop computers. It has so far not been deployed in low-power embedded devices. In this study, a lightweight convolutional neural network (CNN) model for online non-destructive damage type recognition of concrete materials is presented and deployed on a resource-constrained microcontroller unit as a tiny machine learning (TinyML) application. The CNN model uses raw acoustic emission signals as input and damage recognition types as output. 15,000 acoustic emission signals are used as data sets divided into training, validation, and test sets in the ratio of 8:1:1. The experimental results show that an accuracy of 99.6% is achieved on the nRF52840 microcontroller (ARM Cortex M4) with only 166.822 ms and 0.555mJ for a single inference using only 20K parameters and 30.5KB model size. This work demonstrates the effectiveness and feasibility of the proposed model, which achieves a trade-off between high classification accuracy and deployability on resource-constrained MCUs. Consequently, it provides strong support for online continuous non-destructive structural health monitoring. 

Ämnesord

TEKNIK OCH TEKNOLOGIER  -- Elektroteknik och elektronik (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Electrical Engineering, Electronic Engineering, Information Engineering (hsv//eng)

Nyckelord

Acoustic emission

acoustic emissions

Convolution

Convolutional neural networks

damage classification

Data models

embedded systems

Monitoring

Non-destructive testing

structural health monitoring

Testing

TinyML

Training

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