Abstract
The civil engineering industry faces a critical need for innovative non-destructive evaluation methods, particularly for ageing critical infrastructure, such as bridges, where current techniques fall short. Muography, a non-invasive imaging technique, constructs three-dimensional density maps by detecting interactions of naturally occurring cosmic-ray muons within the scanned volume. Cosmic-ray muons provide deep penetration and inherent safety due to their high momenta and natural source. However, the technology's reliance on this source results in constrained muon flux, leading to prolonged acquisition times, noisy reconstructions and image interpretation challenges. To address these limitations, we developed a two-model deep learning approach. First, we employed a conditional Wasserstein generative adversarial network with gradient penalty (cWGAN-GP) to perform predictive upsampling of undersampled muography images. Using the structural similarity index measure (SSIM), 1-day sampled images matched the perceptual qualities of a 21-day image, while the peak signal-to-noise ratio (PSNR) indicated noise improvement equivalent to 31 days of sampling. A second cWGAN-GP model, trained for semantic segmentation, quantitatively assessed the upsampling model's impact on concrete sample features. This model achieved segmentation of rebar grids and tendon ducts, with Dice-Sørensen accuracy coefficients of 0.8174 and 0.8663. Notably, it could mitigate or remove z-plane smearing artifacts caused by muography's inverse imaging problem. Both models were trained on a comprehensive Geant4 Monte-Carlo simulation dataset reflecting realistic civil infrastructure scenarios. Our results demonstrate significant improvements in acquisition speed and image quality, marking a substantial step toward making muography more practical for reinforced concrete infrastructure monitoring applications.
Abstract (translated)
土木工程行业迫切需要创新的无损检测方法，尤其是在评估老化关键基础设施（如桥梁）方面，当前技术存在不足。缪子成像是一种非侵入性成像技术，通过探测扫描体积内自然存在的宇宙射线缪子相互作用来构建三维密度图。由于其高动量和天然来源，宇宙射线缪子提供了深层穿透能力以及固有的安全性。然而，该技术依赖于这种天然源会导致缪子通量受限，从而导致较长的采集时间、噪声重建问题及图像解释挑战。 为了解决这些问题，我们开发了一种双模型深度学习方法。首先，我们使用带梯度惩罚（cWGAN-GP）的条件瓦瑟斯坦生成对抗网络对采样不足的缪子成像进行预测插值处理。通过结构相似性指数测量（SSIM），一天采集的数据图像在感知质量上可以与21天采集的数据相匹配；峰值信噪比（PSNR）则表明噪声水平等同于31天采集的效果有所改善。 第二个cWGAN-GP模型经过训练用于语义分割，它可以定量评估插值处理对混凝土样本特征的影响。该模型能够实现钢筋网和预应力筋管道的分割，Dice-Sørensen准确度系数分别为0.8174和0.8663。值得注意的是，它还可以减轻或消除由缪子成像反向问题导致的Z平面模糊伪影。 这两种模型都是基于全面的Geant4蒙特卡洛模拟数据集进行训练的，该数据集反映了现实中的土木基础设施场景。我们的结果表明，在采集速度和图像质量方面取得了显著改进，这标志着缪子成像技术在增强混凝土结构监测应用中更加实用的关键一步。
URL
https://arxiv.org/abs/2502.02624
