Measure the Application of Pre-Stressed CFRP Laminates Using Deep Learning for Computer Vision

Strengthening of reinforced concrete (RC) structures with pre-stressed Carbon Fiber Reinforced Polymer (CFRP) laminates is a well-known application. The development of vision-based approaches for monitoring the strain imposed during the pre-stress application, with the required precision and accuracy, represents an important contribution for the state of the art. A new system, named Strain- Vision, was design and developed tacking into account three main modules: (i) development of a customized high precision strain monitoring CFRP laminates (hpsm-CFRP); (ii) definition of a set-up for image acquisition during pre-stress application; (iii) design of computer vision architecture based on deep learning to measure the strain. The pre-processing of data, to be analysed with an architecture previously training, is herein discussed, aiming to improve the quality and performance of the system without the need for large datasets, usually required in deep learning applications.

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