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。

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.

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

地下连续墙的接头是基坑围护结构的薄弱部位之一,在施工中容易出现渗漏、夹泥、开裂等病害,危害基坑本身和周边环境的安全。为实现在基坑开挖前对病害进行检测,针对跨孔雷达检测方法展开了研究,分析了电磁波在地下连续墙内的传播特点,

Direct integration algorithms are effective methods to solve the temporally discretized differential equations of motion for structural dynamics. Numerous researchers have worked out various algorithms to achieve desirable properties of explicit expression, unconditional stability, and controllable numerical dissipation. However, studies involving the numerical dispersion of integration algorithms are limited. In this paper, a precorrected bilinear transformation from a continuous domain to a discrete domain associating with pole-matching based on the control theory is utilized to develop a new family of explicit structure-dependent integration algorithms, referred to as TL-phi algorithms. In contrast to the existing algorithms, the significant improvement of the proposed method is that it can control the amount of numerical dispersion by an additional parameter related to the critical frequency of the structure. Stability, energy dissipation, and numerical dispersion properties of the proposed algorithms for both linear and nonlinear systems are fully studied. It is shown that the proposed family of algorithms is unconditionally stable for linear systems while only conditionally stable for nonlinear systems. Though the numerical dissipation property of the TL-phi algorithms is quite similar to that of other well-developed methods, its ability to minimize the period errors when compared with other methods makes it beneficial to the accuracy of the numerical simulation of dynamic responses. Four numerical examples are used to investigate the improved performance of the new method, and the results show that the proposed algorithms can be potentially used to solve linear and nonlinear structural dynamic problems with desirable numerical dispersion performance. (c) 2021 American Society of Civil Engineers.

The crosshole ground penetrating radar (GPR) is a widely used tool to map subsurface properties, and inversion methods are used to derive electrical parameters from crosshole GPR data. In this paper, a probabilistic inversion algorithm that uses Markov chain Monte Carlo (MCMC) simulations within the Bayesian framework is implemented to infer the posterior distribution of the relative permittivity of the subsurface medium. Close attention is paid to the critical elements of this method, including the forward model, data type and prior information, and their influence on the inversion results are investigated. First, a uniform prior distribution is used to reflect the lack of prior knowledge of model parameters, and inversions are performed using the straight-ray model with first-arrival traveltime data, the finite-difference time-domain (FDTD) model with first-arrival traveltime data, and the FDTD model with waveform data, respectively. The cases using first-arrival traveltime data require an unreasonable number of model evaluations to converge, yet are not able to recover the real relative permittivity field. In contrast, the inversion using the FDTD model with waveform data successfully infers the correct model parameters. Then, the smooth constraint of model parameters is employed as the prior distribution. The inversion results demonstrate that the prior information barely affects the inversion results using the FDTD model with waveform data, but significantly improves the inversion results using first-arrival traveltime data by decreasing the computing time and reducing uncertainties of the posterior distribution of model parameters.