Predicting material properties of concrete from ground-penetrating radar attributes

2020 ◽  
pp. 147592172097699
Author(s):  
Isabel M Morris ◽  
Vivek Kumar ◽  
Branko Glisic
Keyword(s):  
Compressive Strength ◽  
Physical Properties ◽  
Ground Penetrating Radar ◽  
Material Properties ◽  
Regression Models ◽  
Physical Property ◽  
Portland Cement Concrete ◽  
Instantaneous Amplitude ◽  
Data Set ◽  
Ground Penetrating

We present here a laboratory-based experimental protocol that seeks to establish and characterize the relationship between ground-penetrating radar attributes and the mechanical properties (density, porosity, and compressive strength) of typical industry concrete mixes. The experimental data consist of ground-penetrating radar attributes from 900 MHz radargrams that correspond to simultaneously measured physical properties of Portland cement concrete, alkali-activated concrete, and cement mortar. Appropriate regression models are trained and tested on this data set to predict each physical property from ground-penetrating radar attributes. From a small selection of individual attributes, including total phase and intensity, trained random forest regression models predict porosity ( R2 = 0.83 from the instantaneous amplitude), density ( R2 = 0.67 from the intensity attribute), and compressive strength ( R2 = 0.51 from instantaneous amplitude). These novel relationships between physical properties and ground-penetrating radar attributes indicate that material properties could be predicted from the attributes of ordinary ground-penetrating radar scans of concrete.

Experimental approaches to estimate concrete properties with ground penetrating radar

2019 ◽  
Author(s):  
Isabel M. Morris ◽  
Vivek Kumar ◽  
Claire E. White ◽  
Branko Glisic
Keyword(s):  
Compressive Strength ◽  
Physical Properties ◽  
Ground Penetrating Radar ◽  
Feature Detection ◽  
Assessment Tool ◽  
Physical Property ◽  
Learning Approaches ◽  
Destructive Testing ◽  
Physical Property Data ◽  
Ground Penetrating

<p>When faced with the problems of aging infrastructure and historic constructions, there are many unknowns such as physical properties and arrangements of materials. This information is necessary for estimating the capacity, safety, and overall condition and for ensuring successful maintenance or repair of the structure. Often, this information is only available through invasive means, which can be unsightly, legally prohibited, or too expensive. Ground penetrating radar (GPR) is a noninvasive assessment tool successful at infrastructure inspection, feature detection, and condition assessments. An experiment was designed to investigate the ability of GPR to predict the physical properties (compressive strength, young’s modulus, and porosity) of concrete samples. A set of samples with variable properties and mix designs was fabricated. The samples were tested both with traditional methods (physical destructive testing) and by noninvasive GPR scanning at 7, 14, 28, and 56 days. A variety of machine learning approaches were used to investigate correlations between the physical property data and the GPR data, resulting in a model that predicts the density, compressive strength, and porosity of concrete with some success (R<span>2</span>-values between 0.4 and 0.8). This predictive model is currently being further developed and tested on several case studies.</p>

scholarly journals Identifying Spatial and Temporal Variations in Concrete Bridges with Ground Penetrating Radar Attributes

2021 ◽  
Vol 13 (9) ◽  
pp. 1846
Author(s):  
Vivek Kumar ◽  
Isabel M. Morris ◽  
Santiago A. Lopez ◽  
Branko Glisic
Keyword(s):  
Compressive Strength ◽  
Ground Penetrating Radar ◽  
Material Properties ◽  
Temporal Variations ◽  
Concrete Bridges ◽  
Spatial And Temporal Variation ◽  
Reflected Waves ◽  
Ground Penetrating ◽  
Over Time

Estimating variations in material properties over space and time is essential for the purposes of structural health monitoring (SHM), mandated inspection, and insurance of civil infrastructure. Properties such as compressive strength evolve over time and are reflective of the overall condition of the aging infrastructure. Concrete structures pose an additional challenge due to the inherent spatial variability of material properties over large length scales. In recent years, nondestructive approaches such as rebound hammer and ultrasonic velocity have been used to determine the in situ material properties of concrete with a focus on the compressive strength. However, these methods require personnel expertise, careful data collection, and high investment. This paper presents a novel approach using ground penetrating radar (GPR) to estimate the variability of in situ material properties over time and space for assessment of concrete bridges. The results show that attributes (or features) of the GPR data such as raw average amplitudes can be used to identify differences in compressive strength across the deck of a concrete bridge. Attributes such as instantaneous amplitudes and intensity of reflected waves are useful in predicting the material properties such as compressive strength, porosity, and density. For compressive strength, one alternative approach of the Maturity Index (MI) was used to estimate the present values and compare with GPR estimated values. The results show that GPR attributes could be successfully used for identifying spatial and temporal variation of concrete properties. Finally, discussions are presented regarding their suitability and limitations for field applications.

Clinical Performance and Physical Properties of Twelve Amalgam Alloys

1978 ◽  
Vol 57 (11-12) ◽  
pp. 983-988 ◽  
Author(s):  
J.W. Osborne ◽  
E.N. Gale ◽  
C.L. Chew ◽  
B.F. Rhodes ◽  
R.W. Phillips
Keyword(s):  
Compressive Strength ◽  
Physical Properties ◽  
Failure Rate ◽  
Clinical Performance ◽  
Physical Property ◽  
One Year

An assessment of the marginal failure rate of 1,041 restorations of twelve alloys was made at one year. In addition, physical property tests were conducted. A correlation was found between the clinical performance and creep (.79), flow (.62) and 24-hour compressive strength (.60).

The Effect of Substituting Rdf on the Physical and Environmental Properties of Coal Fly Ash

1988 ◽  
Vol 136 ◽  
Author(s):  
Ashaari B. Mohamad ◽  
David L. Gress
Keyword(s):  
Compressive Strength ◽  
Fly Ash ◽  
Physical Properties ◽  
Activity Index ◽  
Coal Fly Ash ◽  
Pozzolanic Activity ◽  
Portland Cement Concrete ◽  
Fly Ash Concrete ◽  
Refuse Derived Fuel ◽  
Cadmium And Lead

ABSTRACTRefuse-derived-fuel (RDF) consisting mainly of waste paper and plastics is a viable fuel source for the production of power. An experimental test burn partially substituting coal with RDF was undertaken by the Public Service of New Hampshire at the Merrimack Power Station.Five percent and ten percent RDF were substituted, on a BTU basis, for coal in the test bums. The chemical and physical properties of the resulting fly ash were determined. Twelve test burn days were run with 4 days of 5% RDF and 8 days of 10% RDF. Emphasis was placed on investigating the effect of the RDF fly ash on Portland cement concrete.Most of the chemical and physical properties of the coal-RDF fly ash were found to be comparable with ordinary coal fly ash except for the amount of cadmium and lead, the pozzolanic activity index and the compressive strength of fly ash concrete. Cadmium and lead were at average levels of 5.1 ppm and 102.6 ppm for the 5% RDF, and 7.8 ppm and 198.3 ppm for the 10% RDF, respectively. Although the pozzolanic activity index of coal-RDF fly ash increases over normal coal fly ash, preliminary results show that the 28-day compressive strength of concrete with direct replacement of cement and sand decreases by up to 30%. Leaching tests on crushed concrete were conducted to evaluate the environmental effect of acid rain.

scholarly journals Particle Center Supported Plane for Subsurface Target Classification based on Full Polarimetric Ground Penetrating Radar

2019 ◽  
Vol 11 (4) ◽  
pp. 405
Author(s):  
Xuan Feng ◽  
Haoqiu Zhou ◽  
Cai Liu ◽  
Yan Zhang ◽  
Wenjing Liang ◽  
...  
Keyword(s):  
Ground Penetrating Radar ◽  
Decomposition Methods ◽  
Support Vector ◽  
Classification Methods ◽  
Target Classification ◽  
Data Set ◽  
Particle Center ◽  
Ground Penetrating ◽  
New Criterion

The subsurface target classification of ground penetrating radar (GPR) is a popular topic in the field of geophysics. Among the existing classification methods, geometrical features and polarimetric attributes of targets are primarily used. As polarimetric attributes contain more information of targets, polarimetric decomposition methods, such as H-Alpha decomposition, have been developed for target classification of GPR in recent years. However, the classification template used in H-Alpha classification is preset depending on the experience of synthetic aperture radar (SAR); therefore, it may not be suitable for GPR. Moreover, many existing classification methods require excessive human operation, particularly when outliers exist in the sample (the data set containing the features of targets); therefore, they are not efficient or intelligent. We herein propose a new machine learning method based on sample centers, i.e., particle center supported plane (PCSP). The sample center is defined as the point with the smallest sum of distances from all points in the same sample, which is considered as a better representation of the sample without significant effect of the outliers. In this proposed method, particle swarm optimization (PSO) is performed to obtain the sample centers; the new criterion for subsurface target classification is achieved. We applied this algorithm to full polarimetric GPR data measured in the laboratory and outdoors. The results indicate that, comparing with support vector machine (SVM) and classical H-Alpha classification, this new method is more efficient and the accuracy is relatively high.

Helicopter-borne ground-penetrating radar investigations on temperate alpine glaciers: A comparison of different systems and their abilities for bedrock mapping

Geophysics ◽  
2016 ◽  
Vol 81 (1) ◽  
pp. WA119-WA129 ◽  
Author(s):  
Anja Rutishauser ◽  
Hansruedi Maurer ◽  
Andreas Bauder
Keyword(s):  
Ground Penetrating Radar ◽  
Low Frequency ◽  
Large Data ◽  
Internal Reflection ◽  
Swiss Alps ◽  
Reflection Band ◽  
Data Set ◽  
Investigation Area ◽  
Attractive Option ◽  
Ground Penetrating

On the basis of a large data set, comprising approximately 1200 km of profile lines acquired with different helicopter-borne ground-penetrating radar (GPR) systems over temperate glaciers in the western Swiss Alps, we have analyzed the possibilities and limitations of using helicopter-borne GPR surveying to map the ice-bedrock interface. We have considered data from three different acquisition systems including (1) a low-frequency pulsed system hanging below the helicopter (BGR), (2) a stepped frequency system hanging below the helicopter (Radar Systemtechnik GmbH [RST]), and (3) a commercial system mounted directly on the helicopter skids (Geophysical Survey Systems Incorporated [GSSI]). The systems showed considerable differences in their performance. The best results were achieved with the BGR system. On average, the RST and GSSI systems yielded comparable results, but we observed significant site-specific differences. A comparison with ground-based GPR data found that the quality of helicopter-borne data is inferior, but the compelling advantages of airborne surveying still make helicopter-borne data acquisition an attractive option. Statistical analyses concerning the bedrock detectability revealed not only large differences between the different acquisition systems but also between different regions within our investigation area. The percentage of bedrock reflections identified (with respect to the overall profile length within a particular region) varied from 11.7% to 68.9%. Obvious factors for missing the bedrock reflections included large bedrock depths and steeply dipping bedrock interfaces, but we also observed that internal features within the ice body may obscure bedrock reflections. In particular, we identified a conspicuous “internal reflection band” in many profiles acquired with the GSSI system. We attribute this feature to abrupt changes of the water content within the ice, but more research is required for a better understanding of the nature of this internal reflection band.

On the use of dispersive APVO GPR curves for thin-bed properties estimation: Theory and application to fracture characterization

Geophysics ◽  
2009 ◽  
Vol 74 (1) ◽  
pp. J1-J12 ◽  
Author(s):  
Jacques Deparis ◽  
Stéphane Garambois
Keyword(s):  
Ground Penetrating Radar ◽  
Inverse Method ◽  
Phase Variation ◽  
Fractured Media ◽  
Data Set ◽  
Fracture Characterization ◽  
Propagation Effects ◽  
Phase Liquid ◽  
Ground Penetrating ◽  
Homogeneous Formation

The presence of a thin layer embedded in any formation creates complex reflection patterns caused by interferences within the thin bed. The generated reflectivity amplitude variations with offset have been increasingly used in seismic interpretation and more recently tested on ground-penetrating radar (GPR) data to characterize nonaqueous-phase liquid contaminants. Phase and frequency sensitivities of the reflected signals are generally not used, although they contain useful information. The present study aims to evaluate the potential of these combined properties to characterize a thin bed using GPR data acquired along a common-midpoint (CMP) survey, carried out to assess velocity variations in the ground. It has been restricted to the simple case of a thin bed embedded within a homogeneous formation, a situation often encountered in fractured media. Dispersive properties ofthe dielectric permittivity of investigated materials (homogeneous formation, thin bed) are described using a Jonscher parameterization, which permitted study of the dependency of amplitude and phase variation with offset (APVO) curves on frequency and thin-bed properties (filling nature, aperture). In the second part, we discuss and illustrate the validity of the thin-bed approximation as well as simplify assumptions and make necessary careful corrections to convert raw CMP data into dispersive APVO curves. Two different strategies are discussed to correct the data from propagation effects: a classical normal-moveout approach and an inverse method. Finally, the proposed methodology is applied to a CMP GPR data set acquired along a vertical cliff. It allowed us to extract the characteristics of a subvertical fracture with satisfying resolution and confidence. The study motivates interest to use dispersion dependency of the reflection coefficient variations for thin-bed characterization.

Interpolating wide-aperture ground-penetrating radar beyond aliasing

Geophysics ◽  
2015 ◽  
Vol 80 (2) ◽  
pp. H13-H22 ◽  
Author(s):  
Saulo S. Martins ◽  
Jandyr M. Travassos
Keyword(s):  
Ground Penetrating Radar ◽  
Real Data ◽  
Velocity Model ◽  
Data Sets ◽  
Inversion Problem ◽  
Data Set ◽  
Isolated Points ◽  
Antarctic Continent ◽  
Ground Penetrating ◽  
The Antarctic

Most of the data acquisition in ground-penetrating radar is done along fixed-offset profiles, in which velocity is known only at isolated points in the survey area, at the locations of variable offset gathers such as a common midpoint. We have constructed sparse, heavily aliased, variable offset gathers from several fixed-offset, collinear, profiles. We interpolated those gathers to produce properly sampled counterparts, thus pushing data beyond aliasing. The interpolation methodology estimated nonstationary, adaptive, filter coefficients at all trace locations, including at the missing traces’ corresponding positions, filled with zeroed traces. This is followed by an inversion problem that uses the previously estimated filter coefficients to insert the new, interpolated, traces between the original ones. We extended this two-step strategy to data interpolation by employing a device in which we used filter coefficients from a denser variable offset gather to interpolate the missing traces on a few independently constructed gathers. We applied the methodology on synthetic and real data sets, the latter acquired in the interior of the Antarctic continent. The variable-offset interpolated data opened the door to prestack processing, making feasible the production of a prestack time migrated section and a 2D velocity model for the entire profile. Notwithstanding, we have used a data set obtained in Antarctica; there is no reason the same methodology could not be used somewhere else.

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