Prediction of Static Modulus and Compressive Strength of Concrete from Dynamic Modulus Associated with Wave Velocity and Resonance Frequency Using Machine Learning Techniques

Park, Jong Yil; Sim, Sung‐Han; Yoon, Young-Geun; Oh, Tae Keun

doi:10.3390/ma13132886

Cited by 9 publications

(6 citation statements)

References 25 publications

(29 reference statements)

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“…The next graph (Figure 4) again shows the same electrical resistivity values but adds the static and dynamic modulus values at the given times. The literature indicates that the static modulus corresponds to approximately 60% of the dynamic modulus [40]. This fact is also observed here.…”

Section: Time-dependent Resultssupporting

confidence: 87%

Relationship of Time-Dependent Parameters from Destructive and Non-Destructive Tests of Structural Concrete

Lehner

Hrabová

2022

Mathematics

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Reinforced concrete structures are typically exposed to a combination of aggressive substances and mechanical stresses, which contribute to fast degradation. The present research was conducted to evaluate five time-dependent parameters from several different tests, namely compressive strength, static modulus, dynamic modulus, surface, and bulk electrical resistance. Some parameters were obtained using destructive testing (DT) and some using non-destructive testing (NDT). Due to the correlation and calculation of regression curves, it was possible to compare the correlation of parameters important for estimating the durability of reinforced concrete structures in relation to degradation and corrosion. Concrete of C40/50 grade was examined in several time periods, and the parameter relationships were analysed. At the same time, a statistical evaluation was carried out, and therefore the study contains the average values and standard deviations of all measured parameters. The results show that the compressive strength and the electrical resistivity of the surface and bulk have a high correlation. In contrast, the dynamic modulus and electrical resistivity have low linear correlation, but it was possible to apply a quadratic curve with a high degree of fit. For the comparison of static elastic modulus and electrical resistance, the quality of the quadratic regression model was low but sufficient. The results show that, for structural concrete, the presented NDT methods can be used to estimate other parameters obtained from the DT methods.

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Section: Time-dependent Resultssupporting

confidence: 87%

Relationship of Time-Dependent Parameters from Destructive and Non-Destructive Tests of Structural Concrete

Lehner

Hrabová

2022

Mathematics

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“…It can be seen from Figure 10, that compared to

E_{dp}

E_{ds}

values obtained from the model gives conservative results. The experimental results of Park 24 et al (2020) also depicted similar behavior. Ravindrarahaj and Tam 44 model shows very close aggreement with the present model predicting

E_{dp}

, although Ravindrarahaj and Tam have not mentioned about whether their dynamic modulus is P wave or S wave.…”

Section: Analytical Investigationsupporting

confidence: 57%

“…It can be seen from Figure 10, that compared to E dp , E ds values obtained from the model gives conservative results. The experimental results of Park 24…”

Section: Correlation Of Compressive Strength and P-wave And S-wave 1d...mentioning

confidence: 99%

Development of statistical model for prediction of modulus of elasticity of concrete

Jajodia

Gadve

2022

Structural Concrete

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Modulus of elasticity of concrete is one of the important parameters in structural design of Reinforced Concrete (RC) members. For design and analysis purposes destructive/laboratory test are performed, however, for structural health monitoring and forensic investigation non-destructive tests are preferred. Hence, it is essential to have correlations that can yield accurate results. Standard codes of different nations propose different methodologies for the estimation of modulus of elasticity. Moreover, in available literature no consensus is observed for ideal methodology. In view of this, the present paper experimentally investigates secant modulus of elasticity, initial tangent modulus, P-wave dynamic modulus and S-wave dynamic modulus for eight different concrete mixes. The regression analysis is conducted on experimental data and statistical models are proposed for reliable assessment of modulus of elasticity. Further, a correlation between static and dynamic modulus of elasticity is also proposed. Lastly, the experimental results are compared to the results obtained from developed models. It has been discovered that the proposed models accurately predict the modulus of elasticity of concrete.

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“…The static elastic modulus ( Ec ) and compressive strength ( fc ) are critical properties of concrete [ 30 ]. When determining Ec and fc , concrete cores are collected and subjected to destructive tests.…”

Section: Description Of the Articles Presented In The Issuementioning

confidence: 99%

“…Using ML, the prediction accuracy of Ec and fc was improved by 2.5–5% and 7–9%, respectively, compared with the accuracy obtained using classical or normal-regression equations. By combining ML methods, the accuracy of the predicted Ec and fc was improved by 0.5% and 1.5%, respectively, compared with the optimal single variable results [ 30 ].…”

Section: Description Of the Articles Presented In The Issuementioning

confidence: 99%

Testing of Materials and Elements in Civil Engineering

Schabowicz

2021

Materials

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This issue is proposed and organized as a means to present recent developments in the field of testing of materials in civil engineering. For this reason, the articles highlighted in this issue should relate to different aspects of testing of different materials in civil engineering, from building materials and elements to building structures. The current trend in the development of materials testing in civil engineering is mainly concerned with the detection of flaws and defects in elements and structures using destructive, semi-destructive, and nondestructive testing. The trend, as in medicine, is toward designing test equipment that allows one to obtain a picture of the inside of the tested element and materials. Very interesting results with significance for building practices of testing of materials and elements in civil engineering were obtained.

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Prediction of Static Modulus and Compressive Strength of Concrete from Dynamic Modulus Associated with Wave Velocity and Resonance Frequency Using Machine Learning Techniques

Cited by 9 publications

References 25 publications

Relationship of Time-Dependent Parameters from Destructive and Non-Destructive Tests of Structural Concrete

Relationship of Time-Dependent Parameters from Destructive and Non-Destructive Tests of Structural Concrete

Development of statistical model for prediction of modulus of elasticity of concrete

Testing of Materials and Elements in Civil Engineering

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