This work investigates the production of high-resolution images of typical support elements in concrete structures by means of muon tomography (muography). By exploiting detailed Monte Carlo radiation-matter simulations, we demonstrate the feasibility of reconstructing 1 cm-thick iron bars inside 30 cm-deep concrete blocks, regarded as an important testbed within the structural diagnostics community. In addition, we present a new method for integrating simulated data with advanced deep learning techniques in order to improve the muon imaging of concrete structures. Through deep learning enhancement techniques, this results in a dramatic improvement in image quality and a significant reduction in data acquisition time, which are two critical limitations within the usual practice of muography for civil engineering diagnostics.
@article{arxiv.2502.03339,
title = {A new method for structural diagnostics with muon tomography and deep learning},
author = {Lorenzo Pezzotti and Davide Cifarelli and Daniele Corradetti and José Paulo Costa and Giorgio Gabrielli and Lorenzo Galante and Antonio Gallerati and Ivan Gnesi and Andrea Jouve and Alessio Marrani},
journal= {arXiv preprint arXiv:2502.03339},
year = {2025}
}