Kinetic Study of Portland Cement Hydration with Ground Penetrating Radar

2013 ◽  
Vol 539 ◽  
pp. 25-29
Author(s):  
Wei Chen ◽  
Pei Liang Shen ◽  
Jian Xin Lu ◽  
Wan Ru Zhang
Keyword(s):  
Dielectric Constant ◽  
Dielectric Properties ◽  
Ground Penetrating Radar ◽  
Cement Hydration ◽  
Relative Dielectric Constant ◽  
Gradual Decline ◽  
Hardening Process ◽  
Ground Penetrating ◽  
Kinetics Of ◽  
Portland Cement Hydration

The variations of dielectric constant and the amplitude of reflected EM wave of concrete during the first 3 days are measured with Ground Penetrating Radar (GPR) at 20 oC. The amplitude decreases sharply after mixing with water, and then increases till a stabilized stage, followed by a gradual decline. The relative dielectric constant decreases with increasing hydrating time. The results show that the dielectric properties of concrete can be used as an effective way of studying the kinetics of concrete setting and hardening process at early ages.

Monitoring Early Hydration of Concrete with Ground Penetrating Radar

2017 ◽  
Vol 726 ◽  
pp. 115-119
Author(s):  
Ya Po Tian ◽  
Ding Ce Huang ◽  
Bo Li
Keyword(s):  
Dielectric Constant ◽  
Dielectric Properties ◽  
Portland Cement ◽  
Ground Penetrating Radar ◽  
Relative Dielectric Constant ◽  
Electric Resistivity ◽  
Time Curve ◽  
Early Hydration ◽  
Concrete Mix ◽  
Ground Penetrating

A nondestructive method is designed and employed to monitoring the early hydration of concrete mixes. The concrete mixes are prepared with Portland cement content and water to cement ratio 0.45. Retarder (citric acid) and accelerator (triethanolamine) are used to investigate the effects on the hydration process. The variations of dielectric constant and the intensity of reflected EM wave (amplitude) are measured within the frequency of 1.5GHz using Ground Penetrating Radar (GPR) at 25 °C for each sample from 10min to 3 days after mixing with water. The electric resistivity of Portland cement paste samples is measured using a non-contacting electric resistivity device at 25 °C.The amplitude tested changes sharply corresponding to the changes of microstructure and hydration products in the concrete mix during early hydration. The relative dielectric constant decreases with increasing hydrating time. The electrical resistivity changes in agreement with the dielectric properties. The dielectric properties mainly depend on the electrical properties and water content. The test results of samples in presence of retarder and accelerator shows that the relative dielectric constant decrease slower and faster respectively compared to the plain sample. The effect of these two admixtures can be reflected on the amplitude-time curve. The results demonstrate that the dielectric properties of concrete mix can be used as an effective ways of studying the hydration progress of concrete during hydration.

The Research on the Piezoelectric and Dielectric Properties of Bi0.5(Nak)0.5-BaTiO3 Lead-Free Ceramics

2014 ◽  
Vol 1035 ◽  
pp. 422-425
Author(s):  
Jian Yong Guo ◽  
Tao Sheng Zhou ◽  
Ji Hong Liao
Keyword(s):  
Dielectric Constant ◽  
Dielectric Properties ◽  
Perovskite Structure ◽  
Relative Dielectric Constant ◽  
Lead Free ◽  
Reactive Sintering ◽  
Lead Free Ceramics ◽  
Xrd Patterns ◽  
Sintering Method

The Bi0.5(Na1-xKx)0.5-yBaTiO3(BNK-BT) lead-free ceramics have been prepared by the solild reactive sintering method. XRD patterns show the BNK-BT ceramics had a perovskite structure. Piezoelectric and dielectric properties of the ceramics also have been studied. The results show that the samples had the best piezoelectric and dielectric properties when x=0.20, y=0.10. And the maximum of d33is 149 pC/N, while the relative dielectric constant is 1087.

Imaging the internal structure of trunks via multiscale phase inversion of ground-penetrating radar data

2021 ◽  
pp. 1-53
Author(s):  
Lei Fu ◽  
Lanbo Liu
Keyword(s):  
Dielectric Constant ◽  
Internal Structure ◽  
Ground Penetrating Radar ◽  
Phase Inversion ◽  
Multiscale Approach ◽  
White Oak ◽  
Near Surface ◽  
Oak Tree ◽  
Ground Penetrating ◽  
Constant Distribution

Ground-penetrating radar (GPR) is a geophysical technique widely used in near-surface non-invasive detecting. It has the ability to obtaining a high-resolution internal structure of living trunks. Full wave inversion (FWI) has been widely used to reconstruct the dielectric constant and conductivity distribution for cross-well application. However, in some cases, the amplitude information is not reliable due to the antenna coupling, radiation pattern and other effects. We present a multiscale phase inversion (MPI) method, which largely matches the phase information by normalizing the magnitude spectrum; in addition, a natural multiscale approach by integrating the input data with different times is implemented to partly mitigate the local minimal problem. Two synthetic GPR datasets generated from a healthy oak tree trunk and from a decayed trunk are tested by MPI and FWI. Field GPR dataset consisting of 30 common shot GPR data are acquired on a standing white oak tree (Quercus alba); the MPI and FWI methods are used to reconstruct the dielectric constant distribution of the tree cross-section. Results indicate that MPI has more tolerance to the starting model, noise level and source wavelet. It can provide a more accurate image of the dielectric constant distribution compared to the conventional FWI.

Bone permittivity and its effect on using ground-penetrating radar

Geophysics ◽  
2018 ◽  
Vol 83 (1) ◽  
pp. H1-H11
Author(s):  
Blair B. Schneider ◽  
Georgios Tsoflias ◽  
Don W. Steeples ◽  
Rolfe Mandel ◽  
Jack Hofman
Keyword(s):  
Dielectric Properties ◽  
Dielectric Property ◽  
Ground Penetrating Radar ◽  
Relative Permittivity ◽  
Water Saturation ◽  
Mixing Models ◽  
Bone Properties ◽  
Animal Bone ◽  
Bone Minerals ◽  
Ground Penetrating

Ground-penetrating radar (GPR) is a powerful tool that is still being developed for archaeological investigations. We investigated the dielectric properties of mammoth bone and bone from modern bison, cow, deer, and elk as a proxy for applying GPR for detecting prehistoric animal remains. Sample dielectric properties (relative permittivity, loss factor, and loss-tangent values) were measured with an impedance analyzer over frequencies ranging from 10 MHz to 1 GHz. Bone-sample porosity, bulk density, water saturation, and volumetric water content of the specimens were also measured. The measured sample-relative permittivity values were then compared with modeled relative permittivity values using common dielectric-mixing models to determine which parameters control the best-fit predictions of relative permittivity of animal bone. We observe statistically significant dielectric-property differences among different animal fauna, as well as variation as a function of frequency. In addition, we determine that the relative permittivity values of 8–9 for similar minerals, such as apatite, are not suitable as a proxy for predicting animal bone properties. We estimate new relative permittivity values of 3–5 for dry animal bone minerals in the frequency range of 100–1000 MHz using these common dielectric-mixing models. We postulate that differences in bone microstructure contribute to dielectric-property variability.

scholarly journals Mapping the Physical and Dielectric Properties of Layered Soil Using Short-Time Matrix Pencil Method-Based Ground-Penetrating Radar

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 105610-105621
Author(s):  
Nattawat Chantasen ◽  
Akkarat Boonpoonga ◽  
Krit Athikulwongse ◽  
Kamol Kaemarungsi ◽  
Prayoot Akkaraekthalin
Keyword(s):  
Dielectric Properties ◽  
Ground Penetrating Radar ◽  
Matrix Pencil ◽  
Layered Soil ◽  
Matrix Pencil Method ◽  
Ground Penetrating ◽  
Short Time

scholarly journals Soil water content estimation using ground penetrating radar data via group intelligence optimization algorithms: An application in the Northern Shaanxi Coal Mining Area

2020 ◽  
pp. 014459872097336
Author(s):  
Fan Cui ◽  
Jianyu Ni ◽  
Yunfei Du ◽  
Yuxuan Zhao ◽  
Yingqing Zhou
Keyword(s):  
Dielectric Constant ◽  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Error Rate ◽  
Ground Penetrating Radar ◽  
Volumetric Water Content ◽  
Average Error ◽  
Average Error Rate ◽  
Ground Penetrating

The determination of quantitative relationship between soil dielectric constant and water content is an important basis for measuring soil water content based on ground penetrating radar (GPR) technology. The calculation of soil volumetric water content using GPR technology is usually based on the classic Topp formula. However, there are large errors between measured values and calculated values when using the formula, and it cannot be flexibly applied to different media. To solve these problems, first, a combination of GPR and shallow drilling is used to calibrate the wave velocity to obtain an accurate dielectric constant. Then, combined with experimental moisture content, the intelligent group algorithm is applied to accurately build mathematical models of the relative dielectric constant and volumetric water content, and the Topp formula is revised for sand and clay media. Compared with the classic Topp formula, the average error rate of sand is decreased by nearly 15.8%, the average error rate of clay is decreased by 31.75%. The calculation accuracy of the formula has been greatly improved. It proves that the revised model is accurate, and at the same time, it proves the rationality of the method of using GPR wave velocity calibration method to accurately calculate the volumetric water content.

Ü-Net: Deep-Learning Schemes for Ground Penetrating Radar Data Inversion

2020 ◽  
Vol 25 (2) ◽  
pp. 287-292
Author(s):  
Longhao Xie ◽  
Qing Zhao ◽  
Chunguang Ma ◽  
Binbin Liao ◽  
Jianjian Huo
Keyword(s):  
Neural Network ◽  
Dielectric Constant ◽  
Data Compression ◽  
Ground Penetrating Radar ◽  
Quantitative Imaging ◽  
Inversion Method ◽  
Output Unit ◽  
Data Inversion ◽  
Ground Penetrating ◽  
Constant Distribution

Electromagnetic (EM) inversion is a quantitative imaging technique that can describe the dielectric constant distribution of a target based on the EM signals scattered from it. In this paper, a novel deep neural network (DNN) based methodology for ground penetrating radar (GPR) data inversion, known as the Ü-net is introduced. The proposed Ü-net consists of three parts: a data compression unit, U-net, and an output unit. The novel inversion approach, based on supervised learning, uses a neural network to generate the dielectric constant distribution from GPR data. The GPR data can be compressed and reshaped the size using data compression unit. The U-net maps the object features to the dielectric constant distribution. The output unit meshes the dielectric constant distribution more finely. A novel feature of the proposed methodology is the application of instance normalization (IN) to the DNN EM inversion method and a comparison of its performance to batch normalization (BN). The validity of this technique is confirmed by numerical simulations. The Mean-Square Error of the test data sets is 0.087. These simulations prove that the instance normalization is suitable for GPR data inversion. The proposed approach is promising for achieving quality dielectric constant images in real-time.

REFLECTIVITY OF ELECTROMAGNETIC (EM) WAVE IN SHALLOW GROUND PENETRATING RADAR (GPR) SURVEY

2016 ◽  
Vol 78 (7-3) ◽  
Author(s):  
Nur Azwin Ismail ◽  
Nordiana Mohd Muztaza ◽  
Rosli Saad
Keyword(s):  
Dielectric Properties ◽  
Ground Penetrating Radar ◽  
Geophysical Method ◽  
Wave Reflections ◽  
Material Parameters ◽  
Geophysical Surveys ◽  
Underground Utilities ◽  
Dielectric Contrast ◽  
Different Depths ◽  
Ground Penetrating

Ground Penetrating Radar (GPR) is a geophysical method that is widely used in geophysical surveys, civil engineering applications, archaeological studies and locating underground utilities or hidden objects. It works by sending electromagnetic (EM) wave into the ground by transmitter and recording the returning signals by receiver. The returning signals bring information about the materials and changes in material parameters at different depths. The changes in dielectric properties () of two adjacent media result in EM wave reflections. In this study, several types of materials with different dielectric properties () are used in order to identify the reflectivity of the EM wave. Results prove that the larger the dielectric contrast, the higher the reflection coefficient thus the stronger the reflection.

scholarly journals Using ground-penetrating radar to image previous years’ summer surfaces for mass-balance measurements

1997 ◽  
Vol 24 ◽  
pp. 355-360 ◽  
Author(s):  
Jack Kohler ◽  
John Moore ◽  
Mike Kennett ◽  
Rune Engeset ◽  
Hallgeir Elvehøy
Keyword(s):  
Dielectric Properties ◽  
Mass Balance ◽  
Ground Penetrating Radar ◽  
Radar Data ◽  
Snow Accumulation ◽  
Ice Surface ◽  
Winter Snow ◽  
Depth Scale ◽  
Ground Penetrating ◽  
Previous Summer

In traditional mass-balance measurements one estimates winter snow accumulation by identifying the depth to the previous summer’s snow or ice surface using a snow probe. This is labor-intensive and unreliable for inhomogeneous summer surfaces. Another method is to image internal reflection horizons using a ground-penetrating radar (GPR), which has advantages in speed and areal coverage over traditional probing. However, to obtain quantitative mass-balance measurements from GPR images one needs to convert the time scale to a depth scale, not a straightforward problem. We compare a GPR section with dielectric profiles and visual stratigraphy of three snow cores, manual probings, and previous mass-balance measurements. We relate changes in snow-core dielectric properties to changes in density and to the travel times of reflecting horizons in the GPR section, and correlate some of these reflecting horizons with previous summer surfaces. We conclude that GPR can be used as a complementary tool in mass-balance measurements, giving a wide areal survey of winter accumulation and net balance for preceding years. However, proper calibration is essential for identifying specific surfaces in the radar data.

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