The crosshole ground-penetrating radar (GPR) technique is widely used to characterize subsurface structures, yet the interpretation of crosshole GPR data involves solving non-linear and ill-posed inverse problems. In this work, we developed a generative adversarial network (GAN)-based inversion framework to translate crosshole GPR images to their corresponding 2D defect reconstruction images automatically. This approach uses fully connected layers to extract global features from crosshole GPR images and employs a series of cascaded U-Net structures to produce high-resolution defect reconstruction results. The feasibility of the proposed framework was demonstrated on a synthetic crosshole GPR dataset created with the finite-difference time-domain (FDTD) method and real-world data from a field experiment. Our inversion network obtained recognition accuracy of 91.36%, structural similarity index measure (SSIM) of 0.93, and RAscore of 91.77 on the test dataset. Furthermore, comparisons with ray-based tomography and full-waveform inversion (FWI) suggest that the proposed method provides a good balance between inversion accuracy and efficiency and has the best generalization when inverting actual measured crosshole GPR data.

数值积分算法的稳定性和精度是保证实时子结构试验顺利开展的重要因素,而试验中加载时滞的存在会对算法的数值特性产生不利影响。为此,本文选取了三种基于模型的显式积分算法（Chang法、CR法和RST法）,对比分析了三种算法在考虑时滞的线性系统和非线性系统中的稳定性和精度。由Lyapunov稳定性分析可以得到:在线性系统和具有刚度软化特性的非线性系统中原本是无条件稳定的三种算法,引入时滞效应后退化为有条件稳定,而在具有刚度硬化特性的非线性系统中稳定界限大幅降低。精度分析表明,引入时滞后算法的数值阻尼比和周期失真率绝对值均增大;算例结果也表明,时滞将显著降低算法的计算精度,在实时子结构试验中不容忽视。

经过改革开放四十多年的快速建设,各大中型城市的地表空间已极为有限,因此针对地下空间的开发便成为了未来城市建设的重点发展方向。地下工程施工时,需要对开挖的深基坑进行及时支护,地下连续墙凭借其可靠的结构形式和高效的施工工艺,已成为地下工程中常用的支护结构。然而,地下连续墙在施工时也不能避免在墙体内部产生夹泥、裂缝、渗漏水等病害。如果这些病害不能得到及时处理,在基坑开挖后,会演变成致使基坑失稳的安全隐患。跨孔雷达方法可通过地下连续墙内的测管,向地下连续墙结构内部激发高频电磁波,利用电磁波在不同介质间的传播特性,在基坑开挖前便可以对连续墙内部病害进行定位。但跨孔雷达图像并非地下介质的直接成像,数据解释一直是跨孔雷达方法的难点所在,限制了该方法的广泛应用。针对上述问题,本文在国家自然科学基金（41904095）和深圳市中央引导地方科技发展资金自由探索类基础研究项目（2021Szvup020）的支持下,借助深度学习网络强大的特征提取与分类能力,首先构建了跨孔雷达数据的二维反演网络,建立起跨孔雷达数据与其对应的介电常数剖面图之间的非线性映射关系。在二维反演网络的基础上,通过替换三维模块的方法,实现由跨孔雷达数据到连续墙及内部缺陷三维实体模型的反演过程。同时,由于网络的训练需要大量的数据,但实测数据难以大量获取,故本文基于时域有限差分法构建了连续墙数值模拟数据集,来满足反演网络对训练数据量的要求。最后,设计了跨孔雷达探测连续墙缺陷的可视化模型试验系统,该系统可对任意形态缺陷进行模拟,并可通过三轴滑台系统实现跨孔雷达数据的自动采集。所提出的二维和三维反演网络的有效性和泛化性能也在该试验模型上得到验证。本文取得的主要研究成果如下:（1）针对现有的跨孔雷达数据解释方法反演精度低,计算成本高等问题,本文在生成式对抗网络的基础上提出了跨孔雷达数据二维反演算法。该网络可以自动提取低分辨率下的跨孔雷达数据内全局特征信息,进行特征分类和归纳,最后重建为高分辨率的介电常数分布图。与层析成像算法相比,所提出的二维反演网络在精度上提升82.30%;在二维反演网络的基础上,引入部分三维计算模块,提出了三维反演网络。该网络可以接收跨孔雷达数据,提取数据内介电常数三维分布特征,重建出地下连续墙结构及其内部缺陷的三维介电常数模型,实现三维反演。与层析成像算法相比,所提出的三维反演网络在精度上提升81.73%,并且可以额外重建出墙体和缺陷的三维模型。（2）针对实际工程中跨孔雷达实测数据获取成本高,但是深度学习反演算法又需要大量数据进行训练的问题,本文基于电磁波时域有限差分法开展了地下连续墙缺陷检测的数值模拟,共建立仿真实验模型1100个,获得仿真雷达图像39600张作为跨孔雷达正演模拟数据集。（3）本文在正演模拟数据集上对反演网络进行训练和测试,利用训练集,通过分析网络内不同的超参数和训练数据格式的影响,完成了网络的调优工作。随后将测试集数据输入网络,二维反演网络的缺陷识别准确率可以达到91.36%,结构相似度可以达到0.93,三维反演网络的缺陷识别准确率可以达到92.02%,结构相似度可以达到0.88。另外,反演网络在识别从未出现在训练集的,包含小尺寸和不同分布情况缺陷的跨孔雷达数据时,也具有一定的泛化能力。（4）最后,本文设计了一套跨孔雷达探测地下连续墙缺陷的室内缩尺可视化模型试验系统。试验模型以钢化玻璃子母箱作为载体,采用丙酸二甲酯和纯净水分别模拟地下连续墙结构和土体介质,实现了探测过程可视化和可重复设置缺陷的目的。该系统使用步进电机驱动的三轴同步带滑台作为天线的移动控制系统,配合矢量网络分析仪的自动激发功能,实现了跨孔雷达数据的自动采集,极大简化了常规跨孔雷达的试验流程。利用所提出的二维和三维反演网络对试验数据进行反演,验证了反演算法的有效性。

Cross-hole radar is a popular method to characterize subsurface structures, yet traditional data interpretation methods have limitations: tomography method has poor inversion accuracy, while the full waveform inversion method costs huge computing time. In order to solve the above problems, an automatic inversion algorithm based on generative adversarial networks (GAN) is developed in this paper to interpret the permittivity from cross-hole radar B-scan images. This algorithm uses a low-resolution GAN to extract the global features in the cross-hole radar data to reconstruct the dielectric constant distribution map with low resolution, and then adopts a high-resolution GAN to enhance the resolution of the inversion results. The algorithm is trained on 1000 pairs of cross-hole radar data obtained from the finite-difference time-domain (FDTD) method. Finally, 100 pairs of similar data which has never been shown in the network are used to verify the inversion performance of the algorithm. The results show that the inversion accuracy of is greater than 90%, and the structural similarity index measure (SSIM) of the reconstructed image reaches 0.9. In addition, the proposed method also has rapid computing speed. © Published under licence by IOP Publishing Ltd.

Ground penetrating radar (GPR) has been extensively used in the routine inspection of reinforced concrete structures. However, the signatures in GPR images are reflected electromagnetic waves rather than their actual shapes. The interpretation of GPR data is a mandatory but time- and labor-consuming task. Furthermore, the rebars in the near-surface of concrete cause clutter in the GPR images, which hinders the interpretation of GPR data. This work presents a deep learning network to invert GPR B-scan images to permittivity maps of subsurface structures. The proposed network has a multi-path encoder which enables the network to leverage three kinds of GPR data: the original, migrated, and encoder-decoder-processed GPR data. Each type of processing method is designed to serve a different purpose: the original GPR images retain all the waveforms; the migration method intensifies the vertices of the subsurface anomalies; the encoder-decoder network suppresses rebar clutter and enhances the visibility of the defect echoes. The outputs of three processing methods are jointly used to interpret GPR B-scan images. We demonstrated the superiority of the proposed network by comparing it with a network with a single-path encoder. We also validated the proposed network with synthetic and experimental GPR data. The results indicate that the proposed network effectively reconstructs the defects in the reinforced concrete.

地下连续墙是保障深大基坑施工安全的关键载体，但由于缺乏有效手段在基坑开挖前对地下连续墙进行检测，导致因地下连续墙缺陷造成的基坑安全事故时有发生。本项目基于跨孔雷达方法，从电磁波在地下连续墙复杂介质中的传播规律出发，挖掘各类缺陷的跨孔雷达数据特征；研究跨孔雷达数据贝叶斯概率反演理论，提高对地下连续墙介电参数的反演精度，实现对地下连续墙缺陷的准确识别。具体研究内容及相关成果包括：(1)分析了跨孔雷达电磁波在复杂地下介质中的传播特性，得出跨孔雷达方法可用于地下连续墙病害检测的条件。一是地下连续墙结构为低损耗介质件，电磁波能在地下连续墙结构内传播；二是病害部位由于含水量增大导致介电常数显著增加，使得病害部位和完整结构出现明显的电性参数差异，这种差异使电磁波发生反射、折射和散射等现象，因此可通过对电磁波的分析获得病害信息。(2)通过数值模拟总结了地下连续墙缺陷的跨孔雷达数据特征。层状病害可通过其在零高差数据中的特征，即直达波走时不改变但振幅显著减小，来判断层状病害的发生范围。块状病害的零高差初至走时在病害处发生延迟、直达波振幅明显减小，病害具体位置和分布可由多高差数据反演得出。接头楔形间隙零高差初至走时随深度增大逐渐增加，直达波振幅随深度增大逐渐减小。槽底淤泥零高差初至走时突然发生较大延迟，直达波振幅出现“断崖式”减小，淤泥的具体分布范围可由多高差数据反演得出。(3)建立了基于贝叶斯概率反演的地下连续墙缺陷识别方法。该方法基于贝叶斯定理，建立观测数据和模型参数间的概率联系。采用马尔科夫链蒙特卡罗(MCMC)模拟结合DREAM(ZS)算法来实现参数的后验分布采样，该算法构造马尔科夫链，并通过差异进化算法和Metropolis-Hastings(MH)准则使马尔科夫链收敛于后验分布。与确定性反演方法相比，该方法能够显式处理测量误差，并且对反演结果的不确定性做出定量判断，反演结果更加客观和可信。

Ground penetrating radar (GPR) is one of the most recommended tools for routine inspection of tunnel linings. However, the rebars in the reinforced concrete produce a strong shielding effect on the electromagnetic waves, which may hinder the interpretation of GPR data. In this work, we proposed a method to improve the identification of tunnel lining voids by designing a generative adversarial network-based rebar clutter elimination network (RCE-GAN). The designed network has two sets of generators and discriminators, and by introducing the cycle-consistency loss, the network is capable of learning high-level features between unpaired GPR images. In addition, an attention module and a dilation center part were designed in the network to improve the network performance. Validation of the proposed method was conducted on both synthetic and real-world GPR images, collected from the implementation of finite-difference time-domain (FDTD) simulations and a controlled physical model experiment, respectively. The results demonstrate that the proposed method is promising for its lower demand on the training dataset and the improvement in the identification of tunnel lining voids.

The ground penetrating radar (GPR) is an effective tool to detect the grouting layer behind shield tunnel linings, yet to estimate the thickness from GPR data is always difficult. We herein present a probabilistic inversion method to infer the grouting layer thickness together with its relative permittivity and electric conductivity values from GPR waveform data. This method uses a sliding window and Markov chain Monte Carlo (MCMC) simulation with Bayesian inference to explore the posterior distribution of model parameters. The inversion results of a synthetic example demonstrate that the proposed method successfully estimates the grouting layer thickness. We also investigate the impact of the modeling error on the inversion results, and use a modified likelihood function to eliminate the modeling error. With the modeling error corrected, the posterior model parameters converge correctly to their true values, and the associated uncertainties are quantified.

设计了跨孔雷达检测地下结构缺陷的实验系统,由地下连续墙模型和跨孔雷达系统2部分组成。内置缺陷的地下连续墙模型为钢筋混凝土结构,尺寸5.0 m×3.0 m×4.0 m,为跨孔雷达检测提供测试条件。跨孔雷达系统硬件部分采用步进频率体