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

TL;DR

This paper introduces a novel approach combining muon tomography and deep learning to produce high-resolution images of concrete structures, significantly enhancing image quality and reducing data acquisition time.

Contribution

It presents a new method integrating simulated data with deep learning to improve muon imaging of concrete structures, demonstrating feasibility with detailed simulations.

Findings

Successful reconstruction of 1 cm iron bars inside concrete

Deep learning significantly improves image quality

Reduction in data acquisition time

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.