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arXiv · 2502.03339

A new method for structural diagnostics with muon tomography and deep learning

Lorenzo Pezzotti·Davide Cifarelli·Daniele Corradetti·José Paulo Costa·Giorgio Gabrielli·Lorenzo Galante·Antonio Gallerati·Ivan Gnesi·Andrea Jouve·Alessio Marrani
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Abstract

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.

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BibTeXRIS

Lorenzo Pezzotti, Davide Cifarelli, Daniele Corradetti, José Paulo Costa, Giorgio Gabrielli, Lorenzo Galante, Antonio Gallerati, Ivan Gnesi, Andrea Jouve, Alessio Marrani. 2025-02-05. A new method for structural diagnostics with muon tomography and deep learning. https://doi.org/10.1088/1748-0221/20/06/p06034

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