A new method for structural diagnostics with muon tomography and deep learning
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arXiv
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| Hauptverfasser: | , , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866913913219579904 |
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| author | Pezzotti, Lorenzo Cifarelli, Davide Corradetti, Daniele Costa, José Paulo Gabrielli, Giorgio Galante, Lorenzo Gallerati, Antonio Gnesi, Ivan Jouve, Andrea Marrani, Alessio |
| author_facet | Pezzotti, Lorenzo Cifarelli, Davide Corradetti, Daniele Costa, José Paulo Gabrielli, Giorgio Galante, Lorenzo Gallerati, Antonio Gnesi, Ivan Jouve, Andrea Marrani, Alessio |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_03339 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A new method for structural diagnostics with muon tomography and deep learning Pezzotti, Lorenzo Cifarelli, Davide Corradetti, Daniele Costa, José Paulo Gabrielli, Giorgio Galante, Lorenzo Gallerati, Antonio Gnesi, Ivan Jouve, Andrea Marrani, Alessio High Energy Physics - Experiment High Energy Physics - Phenomenology Computational Physics Geophysics Instrumentation and Detectors 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. |
| title | A new method for structural diagnostics with muon tomography and deep learning |
| topic | High Energy Physics - Experiment High Energy Physics - Phenomenology Computational Physics Geophysics Instrumentation and Detectors |
| url | https://arxiv.org/abs/2502.03339 |