An Improved Prediction Model for Bond Strength of Deformed Bars in Rc Using Upv Test and Artificial Neural Network

Concha, Nolan C.; Oreta, Andres Winston C.

doi:10.21660/2020.65.9139

Cited by 11 publications

(4 citation statements)

References 10 publications

(16 reference statements)

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“…The concrete strength varied from 24.82 to 40.10 MPa with a mean strength fcm of 33.30 MPa. A linear relationship between fc and n with ST = 1.72 MPa was found as: 7) and (9).…”

Section: Ultimate Strength Of Rc Column In Combined Bending and Compr...mentioning

confidence: 80%

“…Sanchez et al used observation data from rebound hammer tests on a RC bridge beam to update a probabilistic prediction of flexural failure of the beam due to carbonation [8]. Concha and Oreta utilized data acquired from UPV testing of concrete specimens to develop a neural network model for the prediction of the bond strength of rebars in concrete structures [9]. Using a rebound hammer together with UPV and compression tests, Qasrawi observed variation in strength among concrete cores taken from different positions along column length [10].…”

Section: Introductionmentioning

confidence: 99%

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Verification of Design Bearing Capacity of Existing Reinforced Concrete Columns

Nguyen¹

2022

GEOMATE

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Most of the case studies reported in the literature on rebound hammer testing focus on the assessment of the compressive strength of concrete. The objective of this paper is to extend the use of rebound hammer data to assess the overall bearing capacity of existing reinforced concrete columns subject to combined compression and bending. This research concentrates on typical columns of two buildings under construction, with site-mixed concrete used for one building and ready-mixed concrete used for the other. Concrete specimens were taken from the construction sites for rebound hammer test and compression test whereby a correlation between the rebound index and concrete strength was developed. On-site rebound hammer tests were then performed on the columns to estimate the in-place concrete strength using the correlation established previously. In addition, the actual strength of reinforcement was determined via tensile testing of reinforcement specimens. Compared with the site-mixed concrete, the ready-mixed concrete exhibited an 18% lower coefficient of variation in strength. The in-place concrete strength was 24-31% greater than the standardized concrete strength. The moment-axial load interaction diagrams of the columns were calculated based on the determined strengths of concrete and steel reinforcement. It was found that the actual bearing capacity in combined compression and bending of the existing columns was 34-47% greater than the design bearing capacity. The proposed non-invasive procedure successfully verified the design strength of the surveyed columns. However, using only one non-destructive test method to estimate the concrete strength may somewhat limit the accuracy of the prediction.

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“…The concrete strength varied from 24.82 to 40.10 MPa with a mean strength fcm of 33.30 MPa. A linear relationship between fc and n with ST = 1.72 MPa was found as: 7) and (9).…”

Section: Ultimate Strength Of Rc Column In Combined Bending and Compr...mentioning

confidence: 80%

Section: Introductionmentioning

confidence: 99%

Verification of Design Bearing Capacity of Existing Reinforced Concrete Columns

Nguyen¹

2022

GEOMATE

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show abstract

“…Researchers tried to develop various relationships between the measured indicators and the mechanical properties of concrete, by using different techniques, such as response surface (RS) [27][28][29][30][31][32][33][34], data fusion (DF) [35][36][37], and artificial neural networks (ANN) [38][39][40][41][42][43]. The empirical relationships developed over the years have a different structure: linear (LN) [40,41], polynomial (PL) [44,45], and power (PW) [46,47].…”

Section: Introductionmentioning

confidence: 99%

Concrete Compressive Strength by Means of Ultrasonic Pulse Velocity and Moduli of Elasticity

Bolborea¹,

Baeră²,

Dan³

et al. 2021

Statistical Inference for Ergodic Algorithmic Model (EAM), Applied to Hydrophobic Hydration Processes

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Developing non-destructive methods (NDT) that can deliver faster and more accurate results is an objective pursued by many researchers. The purpose of this paper is to present a new approach in predicting the concrete compressive strength through means of ultrasonic testing for non-destructive determination of the dynamic and static modulus of elasticity. For this study, the dynamic Poisson's coefficient was assigned values provided by technical literature. Using ultra-sonic pulse velocity (UPV) the apparent density and the dynamic modulus of elasticity were determined. The viability of the theoretical approach proposed by Salman, used for the air-dry density determination (predicted density), was experimentally confirmed (measured density). The calculated accuracy of the Salman method ranged between 98 and 99% for all the four groups of specimens used in the study. Furthermore, the static modulus of elasticity was deducted through a linear relationship between the two moduli of elasticity. Finally, the concrete compressive strength was mathematically determined by using the previously mentioned parameters. The accuracy of the proposed method for concrete compressive strength assessment ranged between 92 and 94%. The precision was established with respect to the destructive testing of concrete cores. For this research, the experimental part was performed on concrete cores extracted from different elements of different structures and divided into four distinct groups. The high rate of accuracy in predicting the concrete compressive strength, provided by this study, exceeds 90% with respect to the reference, and makes this method suitable for further investigations related to both the optimization of the procedure and = the domain of applicability (in terms of structural aspects and concrete mix design, environmental conditions, etc.).

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mentioning

confidence: 99%

Concrete Compressive Strength by Means of Ultrasonic Pulse Velocity and Moduli of Elasticity

et al. 2021

View full text Add to dashboard Cite

Developing non-destructive methods (NDT) that can deliver faster and more accurate results is an objective pursued by many researchers. The purpose of this paper is to present a new approach in predicting the concrete compressive strength through means of ultrasonic testing for non-destructive determination of the dynamic and static modulus of elasticity. For this study, the dynamic Poisson’s coefficient was assigned values provided by technical literature. Using ultra-sonic pulse velocity (UPV) the apparent density and the dynamic modulus of elasticity were determined. The viability of the theoretical approach proposed by Salman, used for the air-dry density determination (predicted density), was experimentally confirmed (measured density). The calculated accuracy of the Salman method ranged between 98 and 99% for all the four groups of specimens used in the study. Furthermore, the static modulus of elasticity was deducted through a linear relationship between the two moduli of elasticity. Finally, the concrete compressive strength was mathematically determined by using the previously mentioned parameters. The accuracy of the proposed method for concrete compressive strength assessment ranged between 92 and 94%. The precision was established with respect to the destructive testing of concrete cores. For this research, the experimental part was performed on concrete cores extracted from different elements of different structures and divided into four distinct groups. The high rate of accuracy in predicting the concrete compressive strength, provided by this study, exceeds 90% with respect to the reference, and makes this method suitable for further investigations related to both the optimization of the procedure and = the domain of applicability (in terms of structural aspects and concrete mix design, environmental conditions, etc.).

show abstract

An Improved Prediction Model for Bond Strength of Deformed Bars in Rc Using Upv Test and Artificial Neural Network

Cited by 11 publications

References 10 publications

Verification of Design Bearing Capacity of Existing Reinforced Concrete Columns

Verification of Design Bearing Capacity of Existing Reinforced Concrete Columns

Concrete Compressive Strength by Means of Ultrasonic Pulse Velocity and Moduli of Elasticity

Concrete Compressive Strength by Means of Ultrasonic Pulse Velocity and Moduli of Elasticity

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