In-situ recognition of moisture damage in bridge deck asphalt pavement with time-frequency features of GPR signal

Zhang, Jun; Zhang, Chao; Lu, Yaming; Zheng, Ting; Dong, Zhonghong; Tian, Yaogang; Jia, Yunyi

doi:10.1016/j.conbuildmat.2020.118295

Cited by 51 publications

(12 citation statements)

References 24 publications

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“…The method can also be used to analyse the presence of moisture due to the difference between the relative dielectric permittivity of the water and air [45]. A significant effect of moisture on the dielectric constant of bituminous mixes was observed [40,[58][59][60][61][62].…”

Section: Application Of Gpr In the Detection Of Road Failuresmentioning

confidence: 99%

“…Similar line of analysis, applied to the assessment of the pavement base, was focused on the study of water content, providing promising results and showing that the shift and peaks of the spectrum could most likely be and indicator that help in the mapping of spatial soil moisture variability [60,73]. The spectrum is also sensible to clay content in the pavement base, and several studies point to the possibility of using the peaks displacement to detect changes in clay content [74].…”

Section: The Frequency Spectrummentioning

confidence: 99%

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GPR Spectra for Monitoring Asphalt Pavements

2020

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Ground Penetrating Radar (GPR) is a prospecting method frequently used in monitoring asphalt pavements, especially as an optimal complement to the defection test that is commonly used for determining the structural condition of the pavements. Its application is supported by studies that demonstrate the existence of a relationship between the parameters determined in GPR data (usually travel time and wave amplitude) and the preservation conditions of the structure. However, the analysis of frequencies is rarely applied in pavement assessment. Nevertheless, spectral analysis is widespread in other fields such as medicine or dynamic analysis, being one the most common analytical methods in wave processing through use of the Fourier transform. Nevertheless, spectral analysis has not been thoroughly applied and evaluated in GPR surveys, specifically in the field of pavement structures. This work is focused on analyzing the behavior of the GPR data spectra as a consequence of different problems affecting the pavement. The study focuses on the determination of areas with failures in bituminous pavement structures. Results epitomize the sensitivity of frequencies to the materials and, in some cases, to the damage.

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Section: Application Of Gpr In the Detection Of Road Failuresmentioning

confidence: 99%

Section: The Frequency Spectrummentioning

confidence: 99%

GPR Spectra for Monitoring Asphalt Pavements

2020

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“…[12]. A single scatterer, such as a landmine, rebar, or pipeline, reflects a hyperbolic signature in a recorded GPR B-scan profile [13], which can be used to locate the buried objects from GPR images [14]. However, even for an experienced practitioner, interpretation of GPR data is extremely time-and labor-consuming due to complex field conditions and huge data volumes.…”

Section: Introductionmentioning

confidence: 99%

Generation of High-Precision Ground Penetrating Radar Images Using Improved Least Square Generative Adversarial Networks

et al. 2021

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Deep learning models have achieved success in image recognition and have shown great potential for interpretation of ground penetrating radar (GPR) data. However, training reliable deep learning models requires massive labeled data, which are usually not easy to obtain due to the high costs of data acquisition and field validation. This paper proposes an improved least square generative adversarial networks (LSGAN) model which employs the loss functions of LSGAN and convolutional neural networks (CNN) to generate GPR images. This model can generate high-precision GPR data to address the scarcity of labelled GPR data. We evaluate the proposed model using Frechet Inception Distance (FID) evaluation index and compare it with other existing GAN models and find it outperforms the other two models on a lower FID score. In addition, the adaptability of the LSGAN-generated images for GPR data augmentation is investigated by YOLOv4 model, which is employed to detect rebars in field GPR images. It is verified that inclusion of LSGAN-generated images in the training GPR dataset can increase the target diversity and improve the detection precision by 10%, compared with the model trained on the dataset containing 500 field GPR images.

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“…Moisture damage is one of the factors that reduce the lifespan of asphalt pavements due to the presence of water in it and, as a result, intensifies other damages such as fatigue, rutting, pothole, and so on in pavements, especially in rainy areas [1][2][3]. In general, the presence of moisture in pavement causes the bitumen membrane to separate from the surface of the aggregate, resulting in a type of failure that engineers call stripping [4,5]. e main and the most serious consequence of stripping is the loss of strength and integrity of the pavement, which by moving the bitumen over the aggregate surface or breaking the bitumen membrane creates a greater tendency in aggregates to absorb water than their tendency to be covered by bitumen [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

The Effect of Aggregate‐Forming Minerals on Thermodynamic Parameters Using Surface Free Energy Concept and Its Relationship with the Moisture Susceptibility of Asphalt Mixtures

Hamedi

Esmaeeli

Gilani

et al. 2021

Advances in Civil Engineering

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Moisture damage is known to be a key factor influencing the durability of asphalt pavements. This phenomenon will reduce the mechanical properties of asphalt mixtures due to its destructive effects on the adhesion of the bitumen-aggregate system and the cohesion of the bitumen membrane. The mineral properties of the aggregates of asphalt mixtures are one of the factors affecting moisture susceptibility. Therefore, the surface free energy (SFE) method, as one of the thermodynamic methods, and indirect tensile strength test (ITST) have been used to determine the failure mechanism and the rate of change of adhesion properties of asphalt mixtures under the influence of mineralogical characteristics. Prior to the above tests, an X-ray fluorescence spectrometry (XRF) test was performed to identify aggregate-forming minerals in eight different types of aggregates with various minerals from eight mines, and also the apparent bitumen film thickness was measured. XRF test results showed that most parts of the aggregates in mines 1– 6 were composed of SiO2, which had strong acidic properties, according to the SFE results. ITST results showed that samples constructed with aggregates of mine 7 and 8 (with high CaO mineral content) experienced a higher TSR than other mixtures in all freeze-thaw (F-T) cycles. Results of the SFE method showed that aggregates with more calcareous properties had a smaller acidic component and a larger basic component. In mines 7 and 8, where the nonpolar component of their aggregates was larger, the tendency of the aggregates to adherence and wettability by water decreased. The adhesion free energy (AFE) between bitumen and aggregates with higher CaO and less SiO2 amounts was more than AFE between bitumen and aggregates with lower CaO and higher SiO2 amounts, in dry conditions. The presence of higher amounts of CaO in aggregates increased AFE of bitumen-aggregate in presence of water from a negative value to zero. In linear regression analysis, due to the positive regression coefficient of the CaO mineral, this mineral had a positive impact on TSR; on the other hand, due to the negative regression coefficient of the SiO2 mineral, this mineral had a negative impact on the TSR of asphalt mixtures.

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In-situ recognition of moisture damage in bridge deck asphalt pavement with time-frequency features of GPR signal

Cited by 51 publications

References 24 publications

GPR Spectra for Monitoring Asphalt Pavements

GPR Spectra for Monitoring Asphalt Pavements

Generation of High-Precision Ground Penetrating Radar Images Using Improved Least Square Generative Adversarial Networks

The Effect of Aggregate‐Forming Minerals on Thermodynamic Parameters Using Surface Free Energy Concept and Its Relationship with the Moisture Susceptibility of Asphalt Mixtures

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