Accurate detection and localization of moisture damage in asphalt pavements using Ground Penetrating Radars (GPR) has been attracting more and more interest in research. Existing approaches rely ...heavily on human efforts and expert experience and are thus both time and cost consuming and are also subject to accuracy issues caused by stochastic human errors. To address this issue, this paper presents an automated moisture damage detection and localization method by leveraging the state-of-the-art deep learning approach and newly proposed incremental random sampling (IRS) approach. First, 2.3 GHz Ground coupled GPR system was used to survey moisture damages on 16 asphalt pavement bridges to create three moisture damage datasets with different resolutions including 2135 moisture damages and 474 steel joints. On this basis, we propose mixed deep convolutional neural networks (CNN) including ResNet50 network, for feature extraction, and YOLO v2 network, for recognition, to detect and localize moisture damages. In addition, to prepare the input for the deep learning models, an IRS algorithm is proposed to generate suitable GPR images from GPR data to feed the CNN. Comprehensive experimental testing, analysis, and comparison of the proposed approaches are conducted. Experimental results demonstrated the promising performance and superiority of the proposed approaches in detecting and localizing moisture damages in asphalt pavements.
•Moisture damage in bridge deck asphalt pavement is successfully detected and visualized in GPR image.•Moisture damage dataset is constructed from 16 asphalt pavement bridges.•Proposed CNN model is applied to detect moisture damage with F1 score (91.97%), Recall (94.53%) and Precision (91.00%).•A novel IRS algorithm for selecting GPR image with suitable plot scale for deep learning is proposed.•Experimental results demonstrate promising performance in detection and localization of moisture damages with IRS and CNN.
Multistatic ground-penetrating radar (GPR) signals can be imaged tomographically to produce 3-D distributions of image intensities. In the absence of objects of interest, these intensities can be ...considered to be estimates of clutter. These clutter intensities spatially vary over several orders of magnitude and vary across different arrays, which makes a direct comparison of these raw intensities difficult. However, by gathering statistics on these intensities and their spatial variation, a variety of metrics can be determined. In this study, the clutter distribution is found to fit better to a two-parameter Weibull distribution than Gaussian or log-normal distributions. Based on the spatial variation of the two Weibull parameters, scale and shape, more information may be gleaned from these data. How well the GPR array is illuminating various parts of the ground, in depth and cross track, may be determined from the spatial variation of the Weibull scale parameter, which may in turn be used to estimate an effective attenuation coefficient in the soil. The transition in depth from clutter- to noise-limited conditions (which is one possible definition of GPR penetration depth) can be estimated from the spatial variation of the Weibull shape parameter. Lastly, the underlying clutter distributions also provide an opportunity to standardize image intensities to determine when a statistically significant deviation from background (clutter) has occurred, which is convenient for buried threat detection algorithm development that needs to be robust across multiple different arrays.
Ground penetrating radar (GPR) has been used for non-destructive inspection of civil infrastructure systems such as bridges and pipelines. Manually extracting useful data from a large amount of ...non-intuitive GPR scans is tedious and error-prone. To address this challenge, a generalizable end-to-end framework is developed and implemented to simultaneously detect and segment object signatures in GPR scans. The proposed approach improves the Mask Region-based Convolutional Neural Network (R-CNN) by incorporating a novel distance guided intersection over union (DGIoU) as a new loss function for detection and segmentation. The DGIoU considers the center distance between two bounding boxes and overcomes the weakness of intersection over union (IoU) in training and evaluation. In addition, a new method is proposed to extract data points from the segmented mask patches containing both object signatures and background noises. The extracted data points can be further processed for object localization and characterization. Experiments were conducted using GPR scans collected from a concrete bridge deck. The hyperbolic signatures of rebars can be accurately detected and segmented using the proposed method. It was demonstrated that using DGIoU improves the regression effect of bounding box and mask. The improved Mask R-CNN achieved an average accuracy (AP) of 58.64% and 47.64% for the detection and segmentation task, respectively.
•A new loss function is computed based on distance guided intersection over union.•Mask R-CNN is enhanced with the new loss function to segment GPR signatures.•A new method is proposed to automate data extraction from segmented GPR signatures.•The method achieves high average accuracy in field experiments.
•Project presents an application of convolutional neural networks (CNN) in cracks.•Different CNNs are established by the processes of structure design, training and testing.•The crack feature points ...are extracted by feature extraction CNN to establish 3D model.•CNN is able to recognize concealed cracks from other damage in GPR images with zero error.•CNNs could be accurately used for the recognition, location of concealed crack of asphalt pavement.
Concealed cracks in asphalt pavement are the cracks that originate below the surface of the pavement. These cracks are a major contributing factor to pavement damage, in addition to being a major contributing factor to the formation of reflection cracks. The detection of a concealed crack is considered challenging because the location of the crack is, by definition, difficult to find. Therefore, the research on the utilization of ground penetrating radar (GPR) to locate concealed cracks has gained significant interest in recent years. However, the manually processed GPR image used for the recognition, location, and measurement of concealed cracks is inefficient and inaccurate. This project presents an application of convolutional neural networks (CNNs) to GPR images that automatically recognizes, locates, measures, and produces a 3D reconstruction of concealed cracks. In this project, three different CNNs (recognition, location, and feature extraction) were established to accomplish the aforementioned tasks automatically. Each CNN is developed through processes of structural design, training, and testing. The recognition CNN was designed to distinguish concealed cracks from other types of damage in a GPR image, the location CNN determined the location and length measurement of concealed crack images based on the results provided by the recognition CNN, and crack feature points were extracted by the feature extraction CNN to establish the 3D reconstruction models of the concealed cracks. The 3D reconstruction models were then used to calculate crack volume and predict the growth tendency of cracks. The results indicated that the recognition CNN is able to distinguish concealed cracks from other types of damages in 6482 GPR images with zero errors. In addition, the length recognition results calculated from the location CNN possess a 0.2543cm mean squared error, a 0.978cm maximum length error, and a 0.504cm average error in the test samples. Meanwhile, the feature extraction CNN is able to provide feature points for a 3D reconstruction model. The results of this study suggest that the CNNs could be accurately used for the recognition, location, and 3D reconstruction of concealed cracks in asphalt pavement in real-world applications.
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•Proposed a parameter for quantitative analysis of pipeline orientation on hyperbolic fitting.•Introduced an optimization method for estimating pipeline orientation, depth and wave ...velocity.•Analyzed the influence of depth and pipe diameter on hyperbolas and the proposed algorithm.
Ground-penetrating radar (GPR) is commonly employed as a non-destructive technique for detecting subsurface cylindrical objects such as pipes, cables, rebars, and tree roots. Hyperbolic features generated in GPR radargrams are often used to estimate key parameters like burial depth, object radius, and electromagnetic wave velocity. However, traditional approaches frequently rely on the assumption that GPR traverses are perpendicular to the alignment of target—an assumption or a negligence that is not always valid in real-world scenarios. To address this limitation, a novel method is introduced for simultaneously estimating the orientation and burial depth of pipes, as well as wave velocity, from the hyperbolic patterns observed in GPR data. The method innovates by incorporating an angle correction index into the classical hyperbolic fitting model. This modified model is then formulated as an optimization problem, which is solved using a hybrid approach combining the Multi-Verse Optimizer (MVO) and Gradient Descent (GD) algorithms. A unique index, termed the “C-value,” is introduced to quantitatively analyse the influence of oblique angles on the hyperbolic fitting models. Two distinct fitting models are validated through both simulation and field experiments. The study also scrutinizes the impact of varying pipe radius and burial depth on the accuracy of parameter estimation at different pipe orientation. The methodology presented herein enables the simultaneous estimation of burial depth, wave velocity, and pipe orientation directly from hyperbolic fitting—a significant advancement, as orientation is typically assumed as a known input yet is often challenging to ascertain obtain in practical field situations.
Maintaining and upgrading underground pipelines are major undertakings in urban operations, where accurately locating buried pipelines has long been an issue. In this paper, we propose a pipeline ...mapping method based on integrating multi-positional pipeline data, which includes the Multi-sensor Data Acquisition (MDA) platform and the Scalable Probability-based Pipeline Mapping (SP-PM) model. To effectively collect pipeline data at multiple positions, several pipeline detecting and positioning sensors are equipped in the MDA platform. Different types of sensor data are synchronously collected and processed to obtain manageable pipeline data at multiple positions within the detected area, such as the radius, depth, and positioned points of underground pipelines. The SP-PM model is then proposed, where the obtained pipeline data is probabilistically described and classified into classes. Each class contains the pipeline data possibly generated by the same pipeline. The classified data is then iteratively integrated to estimate the pipeline map with the maximum probability. The SP-PM model probabilistically estimates the degree of correlation between the multi-positional pipeline data and each potential pipeline, and it has no strict requirement on existing statutory records or limits on the number of detections per pipeline. Unrecorded pipelines could be identified and involved into the generated pipeline map, along with continuous adjusting of the pipelines' number. We conducted experiments on real-world environments. The experimental results verify the accuracy and efficiency of the proposed method for buried pipeline mapping.
Monitoring underground fluid infiltration and estimating soil water content (SWC) is essential for hydrogeology and soil science research. Conventional hydrological survey methods, such as trenching, ...soil sampling, or time domain reflectometry (TDR), provide in-situ, static, and discrete measurements. However, these methods have limitations in capturing the spatiotemporal dynamics of fluid infiltration. Ground penetrating radar (GPR) offers a noninvasive and high spatiotemporal measurement approach for near-surface applications. Nevertheless, the complexity of GPR data and its weak response to small-scale fluid infiltration pose challenges in instantaneous attribute analysis and SWC estimation using the travel-time method. In this paper, we propose a time-lapse GPR data full waveform inversion (FWI) method to effectively monitor the spatial distribution of small-scale fluid transport and estimate SWC with improved accuracy. Firstly, we combine velocity spectral analysis and the structural similarity index method (SSIM) to construct a permittivity model that solves the dependence of FWI on the initial model. Subsequently, to simultaneously invert the permittivity and conductivity, we introduce gradient normalization to balance the convergence rate of the two parameters during the inversion process. The typical GPR time-lapse infiltration GPR field data example confirms that the proposed method can accurately characterize small-scale fluid infiltration distribution and estimate SWC parameters. The total estimated error in SWC is found to be less than 5%. The proposed time-lapse GPR data processing protocol provides a quantitative means for monitoring small-scale fluid infiltration.
The Laacher See volcano (LSV) is located at the western margin of the Neuwied Basin, the central part of the Middle Rhine Basin of Germany. Its paroxysmal Plinian eruption c. 13 ka ago (Laacher See ...event; LSE) deposited a complex tephra sequence in the Neuwied Basin, whilst the distal ashes became one of the most important chronostratigraphic markers in Central Europe. However, some other impacts on landscape formation have thus far been largely neglected, such as buried gully structures in the proximity of the LSV. In this contribution, we map and discuss the spatial extent of these landforms at the site Lungenkärchen c. 4 km south of the LSV based on geophysical prospection as well as contrasting pedo‐sedimentary characteristics of the gully infill (particle‐size distribution, bulk‐sediment density, thin‐section analysis, saturated hydraulic conductivity) and the surrounding soils and tephra layers. These data are combined with a luminescence‐ and carbon‐14 (14C)‐based age model that relates them to the LSE. It is demonstrated how these gullies seem to have been formed and rapidly infilled by rainfall and surface discharge both during and subsequent to the eruptive phase, with modern analog processes documented for the 1980 Mount St Helens eruption (Washington State, USA). Given the density of the gullies at the site and their deviating pedo‐sedimentary properties compared to the surrounding soils, we propose a significant influence on agricultural production in the proximity of the LSV, which remains to be tested in future studies. Finally, in contrast, gullies of similar lateral and vertical dimensions identified in post‐LSE reworked loess and tephra deposits of the Wingertsbergwand (close to the main study site and proximal to the LSV) have shown to be unrelated to the LSE and can either be attributed to periglacial processes at the Younger Dryas‐Preboreal transition or to linear incision during the early Holocene.
Linear subsurface gullies were identified close to the Laacher See volcano in magnetometer and ground‐penetrating radar prospection.
Optically stimulated luminescence data indicate they incised during or shortly after the Laacher See event 13 ka ago.
Pedo‐sedimentary characteristics of the gully infill differs from the surrounding regosols and brown earths, possibly influencing regional agricultural land use.
Ground-penetrating radar (GPR) has been widely used as a nondestructive tool for the investigation of the subsurface, but it is challenging to automatically process the generated GPR B-scan images. ...In this paper, an automatic GPR B-scan image interpreting model is proposed to interpret GPR B-scan images and estimate buried pipes, which consists of the preprocessing method, the open-scan clustering algorithm (OSCA), the parabolic fitting-based judgment (PFJ) method, and the restricted algebraic-distance-based fitting (RADF) algorithm. First, a thresholding method based on the gradient information transforms the B-scan image to the binary image, and the opening and closing operations remove discrete noisy points. Then, OSCA scans the preprocessed binary image progressively to identify the point clusters<xref ref-type="fn" rid="fn1"> 1 with downward-opening signatures, and PFJ further validates whether the point clusters with downward-opening signatures are hyperbolic. By utilizing OSCA and PFJ, point clusters with hyperbolic signatures could be classified and segmented from other regions even if there are some connections and intersections between them. Finally, the validated point clusters are fitted into the lower parts of hyperbolas by RADF that solves fitting problems with additional constraints related to the hyperbolic central axis. By integrating these methods, the proposed model is able to extract information from GPR B-scan images automatically and efficiently. The experiments on simulated and real-world data sets demonstrate the effectiveness of the proposed model. 1
A point cluster is a collection of points with the same class identification.
Drainage reorganization on restricted temporal and spatial scales is poorly‐documented. We attempt to decode the relatively complicated mechanism of drainage realignment involving two small rivers ...that show structurally controlled, highly anomalous channel networks. We provide geomorphic and shallow subsurface evidence using ground‐penetrating radar (GPR) for the presence of a buried paleo‐valley flowing northward through the wind gap and surface faulting along the range bounding Katrol Hill Fault (KHF) which correlates with the previously known three surface faulting events in last ~30 ka bp. Most of the present river channels and the KHF zone are occupied by aeolian miliolite (local name) which is stratigraphic and lithologic equivalent of the Late Quaternary carbonate rich aeolianite deposits occurring in several parts of the globe. The history of drainage evolution in the study area comprises pre‐miliolite, syn‐miliolite and post‐miliolite phases. Geomorphic evidences show that the paleo‐Gangeshwar River flowed north through the wind gap and paleo‐valley, while the short paleo‐Gunawari occupied the saddle zone to the east of Ler dome prior to and during the phase of miliolite deposition which ended by ~40 ka bp. Southward tilting of the Katrol Hill Range (KHR) due to surface faulting cut off the catchment of the paleo‐Gangeshwar River. The abandoned catchment stream extended its channel eastward along the strike through top‐down process while the paleo‐Gunawari River extended its course westward by headward erosion (bottom‐up process). As the channels advanced towards each other they joined to produce the “S”‐shaped bend which formed the capture point. We conclude that multiple surface faulting events along the KHF in the last ~30 ka bp, resulted in uplift and tilting of the KHR which caused drainage realignment by river diversion, beheading and river capture. Our study shows that the complexity of drainage reorganization processes is more explicit on shorter rather than longer timescales.
Two independent drainages experienced realignment induced by tectonic uplift and tilting giving rise to the present‐day Gunawari River channel; as evidenced by the presence of anomalous channel reaches, knickpoints, buried paleo‐valley and wind gap. The study shows the absolute influence of tectonic tilting on the processes of drainage rearrangement.
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