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arXiv 2502.03339hep-exhep-phphysics.comp-phphysics.geo-phphysics.ins-det
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利用μ子断层扫描和深度学习进行结构诊断的新方法

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

AI总结:

本研究提出结合蒙特卡罗模拟与深度学习技术提升μ子断层扫描成像质量,实现了30厘米深混凝土块内1厘米厚铁棒的重建,显著提升了图像质量并缩短了数据采集时间。

摘要:

本研究探讨了利用μ子断层扫描(muography)技术生成混凝土结构中典型支撑元件高分辨率图像的方法。通过利用详细的蒙特卡罗辐射-物质模拟,我们证明了在30厘米深的混凝土块内部重建1厘米厚铁棒的可行性,这被视为结构诊断领域的重要测试平台。此外,我们提出了一种将模拟数据与先进深度学习技术相结合的新方法,以改善混凝土结构的μ子成像。通过深度学习增强技术,图像质量得到了显著提升,数据采集时间也大幅减少,而这两者正是土木工程诊断中μ子断层扫描常规应用的两个关键限制。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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