Ultrasonic Assessment of Damage in Concrete under Compressive and Thermal Loading Using Longitudinal and Transverse Waves

Silva, Fernando A. N.; Nogueira, Carnot L.; Silva, J. A.; Araujo, Abigail; Azevedo, António C.

doi:10.1134/s1061830919110081

Cited by 4 publications

(5 citation statements)

References 6 publications

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“…There is not a clear consensus for the relationship between the compressive strength and the UPV; for example, Güneyli et al [15] and Silva et al [16] presented a linear relationship between these variables, and several researchers [17,18] have presented an exponential relationship between compressive strength and UPV, i.e., f c = aV b p and f c = ae aV p , respectively. To verify if the results for the samples investigated followed the same trend, exponential fittings between the UPV and compressive strength of the tested specimens were performed, and Figure 2 presents the best one obtained with a strong correlation coefficient (R), i.e., close to 97%.…”

Section: Relationship Between Compressive Strength and Upvmentioning

confidence: 99%

“…Some authors [16,20,21] report that with a 5% addition of metakaolin, it is already possible to observe an increase of 12 to 15% in concrete compressive strength, and for an addition of 10%, the observed increase in strength varied from 9 to 28% [22,23]. It is important to highlight that it is well known that the aggregate size influences the Young's modulus of concrete more than its compressive strength [24], although compressive strength might also be related to concrete pore size composition-distribution and spacing [25].…”

Section: Relationship Between Compressive Strength and Upvmentioning

confidence: 99%

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Use of Nondestructive Testing of Ultrasound and Artificial Neural Networks to Estimate Compressive Strength of Concrete

et al. 2021

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The work presents the results of an experimental campaign carried out on concrete elements in order to investigate the potential of using artificial neural networks (ANNs) to estimate the compressive strength based on relevant parameters, such as the water–cement ratio, aggregate–cement ratio, age of testing, and percentage cement/metakaolin ratios (5% and 10%). We prepared 162 cylindrical concrete specimens with dimensions of 10 cm in diameter and 20 cm in height and 27 prismatic specimens with cross sections measuring 25 and 50 cm in length, with 9 different concrete mixture proportions. A longitudinal transducer with a frequency of 54 kHz was used to measure the ultrasonic velocities. An ANN model was developed, different ANN configurations were tested and compared to identify the best ANN model. Using this model, it was possible to assess the contribution of each input variable to the compressive strength of the tested concretes. The results indicate an excellent performance of the ANN model developed to predict compressive strength from the input parameters studied, with an average error less than 5%. Together, the water–cement ratio and the percentage of metakaolin were shown to be the most influential factors for the compressive strength value predicted by the developed ANN model.

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Section: Relationship Between Compressive Strength and Upvmentioning

confidence: 99%

Section: Relationship Between Compressive Strength and Upvmentioning

confidence: 99%

Use of Nondestructive Testing of Ultrasound and Artificial Neural Networks to Estimate Compressive Strength of Concrete

et al. 2021

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“…Lee and Lee [28] concluded on their studies that the presence of coarse aggregate has some significant influence at the pulse velocity on concrete at early ages, the pulse velocity was found to be 16% higher at the concrete than at the mortar. However, Silva et al [18] attested by their study that the amount of coarse aggregate has no significant influence at the pulse velocity. Temperature, if inside the serviceability range, and stress level, besides overstress situations, are variables that does not affects in a significant way the pulse velocities [15,26].…”

Section: The Ultrasonic Testing Methodsmentioning

confidence: 92%

“…2) which measures the velocity of S-waves. The S-waves have the advantage to be more statically stable than the P-waves [18].…”

Section: The Ultrasonic Testing Methodsmentioning

confidence: 99%

The Use of Ultrasonic Echo Pulse Velocity as an NDT Method to Predict the Concrete Strength and Uniformity

Filho

Moreno²,

Luso³

2022

Russ J Nondestruct Test

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The ultrasonic approach is one of the non-destructive testing methods that most technologically progressed in past few years. This study aims to validate the accuracy and capabilities of this method in the mechanical characterization of concrete elements, by using an ultrasonic tomography equipment based in echo pulse velocity (S-waves). The elastic modulus and compressive strength of concrete are estimated from the echo pulse velocity. Furthermore, the uniformity of concrete elements is evaluated. The concrete compression strength prediction was performed on cubic specimens and the reached accuracy was over 91% using the analytical approach proposed on this study. The correlation coefficient between the pulse echo velocity and the uniaxial compression strength found in this study was over 97%. Thus, the use of the ultrasonic NDT to evaluate the uniformity of concrete elements proved to be efficient.

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“…UPV was developed to reveal the tunnel lining structure of fire damaged concrete [12]. The propagation velocity of longitudinal and transverse ultrasonic waves is related to the degree of degradation in the concrete's compressive strength after a fire [14][15][16]. The relationship between compressive strength and UPV propagation has been analyzed [17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Evaluation Residual Compressive Strength of Tunnel Lining Concrete Structure after Fire Damage Based on Ultrasonic Pulse Velocity and Shear-Wave Tomography

et al. 2022

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In this study, ultrasonic pulse velocity (UPV) and ultrasonic shear-wave tomography are combined to measure the residual compressive strength (RCS) of small-scale lining concrete blocks and to detect inner defects in the lining structure. The characteristics of and variations in the RCS of test blocks after being exposed to elevated temperatures (200–800 °C) and constant heating times (2 h, 3 h, and 4 h) were studied. At 800 °C, the RCS values reduced by 64.4%, 69.2%, and 74.6% at heating times of 2 h, 3 h, and 4 h. Scanning electron microscopy (SEM) was used for the micro-phase analysis of the samples that had been exposed to high temperatures. The heating time and RCS as well as the SEM micro-structure relationship were compared. Finally, a tunnel lining slab sample was designed to simulate the post-fire damage inside the blocks. Additionally, shear-wave tomography with 32 probes was able to detect the ϕ10 mm void defects at a depth of 200 mm.

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Ultrasonic Assessment of Damage in Concrete under Compressive and Thermal Loading Using Longitudinal and Transverse Waves

Cited by 4 publications

References 6 publications

Use of Nondestructive Testing of Ultrasound and Artificial Neural Networks to Estimate Compressive Strength of Concrete

Use of Nondestructive Testing of Ultrasound and Artificial Neural Networks to Estimate Compressive Strength of Concrete

The Use of Ultrasonic Echo Pulse Velocity as an NDT Method to Predict the Concrete Strength and Uniformity

Evaluation Residual Compressive Strength of Tunnel Lining Concrete Structure after Fire Damage Based on Ultrasonic Pulse Velocity and Shear-Wave Tomography

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