New multi-objective optimization to evaluate the compressive strength and variability of concrete by combining non-destructive techniques

Kouddane, Bouchra; Sbartaï, Zoubir Mehdi; Elachachi, Sidi Mohammed; Lamdouar, Nouzha

doi:10.1016/j.jobe.2023.107526

Cited by 8 publications

(4 citation statements)

References 15 publications

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“…To address this issue, the use of nondestructive testing/evaluation/inspection (NDT/E/I) techniques to determine the compressive strength of concrete in an in-situ configuration has been a promising solution, including ground penetrating radar (GPR) [1,7,12], rebound hammer (RH), ultrasonic testing (UT) [2], four-probe electrical resistivity method [9], open-ended coaxial probes [15], and synthetic aperture radar (SAR) [6,7]. The nondestructive nature of these techniques allows engineers to repeatedly monitor the development of concrete compressive strength at the same locations over time without causing additional concerns or needs for repair (e.g., patching).…”

Section: Introductionmentioning

confidence: 99%

“…They also proposed a scaling calibrating method to improve the prediction accuracy for individual or combined use of RH and UT. Kouddane et al (2023) [15] investigated RH (type N) and UT (54 kHz) and the combination (SonReb method) with the Monte Carlo method to predict the compressive strength of 205 concrete cores, using a multi-objective approach. In their experimental data, the rebound number showed a linear correlation with the compressive strength of concrete, while the UPV showed a nonlinear correlation with the compressive strength of concrete.…”

Section: Introductionmentioning

confidence: 99%

“…15, the relation between the normalized integrated SAR amplitude 𝐼 𝑖𝑛𝑡 /𝐼 𝑚𝑎𝑥 and 𝑓 𝑐 of concrete cylinders is shown. It is evident that as the curing time increased, both the compressive strength 𝑓 𝑐 and 𝐼 𝑖𝑛𝑡 /𝐼 𝑚𝑎𝑥 increased.…”

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confidence: 99%

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Multiphysical Characterization for Predicting Compressive Strength of Portland Cement Concrete using Synthetic Aperture Radar, Ultrasonic Testing, and Rebound Hammer

Abazarsa,

2024

Preprint

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Portland cement concrete (PCC) is a versatile and widely used construction material renowned for its strength and durability. The mechanical properties of PCC, including compressive strength, flexural strength, and splitting tensile strength, play a pivotal role in ensuring the safety and sustainability of structures such as buildings, bridges, and dams. Traditionally, the determination of PCC's compressive strength involves destructive testing of standard-size concrete cylinders until they fail. While nondestructive evaluation (NDE) techniques are available for assessing these properties, they often require direct contact between the sensor and the concrete surface, making them less efficient and practical compared to remote sensing techniques. In this paper, we applied three NDE techniques for estimating the mechanical properties of concrete, including synthetic aperture radar (SAR), ultrasonic testing (UT), and a rebound hammer (RH). We manufactured a total of 48 laboratory concrete cylinders (diameter = 3", height = 6"). These cylinders were created with different water-to-cement ratios (0.4, 0.45, 0.5, and 0.55) with a mix design ratio of 1:2:3 for cement: sand: gravel (by mass). Before these cylinders were tested by destructive compression test, they were measured by three NDE techniques. A 10GHz SAR system, a 54kHz UT system, and a RH sensor were used to inspect those cylinders at different concrete ages (7, 14, 28, and 96 days). From our result, the performance ranking among three NDE techniques was individually UT, SAR, and RH. When combining two NDE techniques, SAR with UT delivered the best performance. Multiphysical NDE (SAR with UT) outperformed uniphysical NDE (UT with RH) on the prediction of compressive strength of concrete. This research demonstrates the promising potential of multiphysical NDE for other engineering problems.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multiphysical Characterization for Predicting Compressive Strength of Portland Cement Concrete using Synthetic Aperture Radar, Ultrasonic Testing, and Rebound Hammer

Abazarsa,

2024

Preprint

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“…Compressive stresses are best withstood by concrete due to its high compressive strength [1], which is in fact the basis for the design of concrete elements under failure conditions [2][3][4], regardless of the nature of the reinforcement used [5]. Moreover, compressive strength is the most readily measurable mechanical property of concrete in the quality control of a concrete plant [6]; this is because its determination only requires a press with the appropriate load capacity, without the need for any other specific equipment [7,8]. The modulus of elasticity is also a key property in the design of concrete elements in the context of serviceability conditions [9], whose objective is to ensure that the deformation or deflection of the concrete element is within adequate limits in order to ensure comfortable and reliable use [10].…”

Section: Introductionmentioning

confidence: 99%

Analyzing the Relationship between Compressive Strength and Modulus of Elasticity in Concrete with Ladle Furnace Slag

Revilla-Cuesta,

Serrano-López,

Espinosa

et al. 2023

Buildings

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The addition of Ladle Furnace Slag (LFS) to concrete modifies its compressive strength and modulus of elasticity and consequently impacts their relationship. This research evaluated both properties at 28, 90, and 180 days in concrete mixes produced with 5%, 10%, and 20% of two LFS types, both stabilized and non-stabilized. The relationship between them was then analyzed through these experimental results by adopting a statistical approach. A three-way analysis of variance revealed that both properties were affected by LFS differently. Thus, the effect of each LFS content on both features varied depending on its composition and pre-treatment. Furthermore, the effect of the LFS content on the compressive strength was also influenced by the age of the concrete. These facets implied that when analyzing the relationship between both mechanical properties, the monotonic correlations were stronger than the linear ones, reaching values between 0.90 and 1.00. Therefore, the double reciprocal regression models were the most precise ones for expressing the modulus of elasticity as a function of compressive strength. The model accuracy was further enhanced when discriminating based on the LFS type and introducing concrete age as a predictive variable. With all these considerations, the average deviations between the estimated and experimental values of 1–3% and the maximum deviations of 4–7% were reached, as well as R2 coefficients of up to 97%. These aspects are central to the further development of LFS concrete models.

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Development of a machine learning model for on-site evaluation of concrete compressive strength by SonReb

Alavi,

Noel,

Moradi

et al. 2024

Journal of Building Engineering

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New multi-objective optimization to evaluate the compressive strength and variability of concrete by combining non-destructive techniques

Cited by 8 publications

References 15 publications

Multiphysical Characterization for Predicting Compressive Strength of Portland Cement Concrete using Synthetic Aperture Radar, Ultrasonic Testing, and Rebound Hammer

Multiphysical Characterization for Predicting Compressive Strength of Portland Cement Concrete using Synthetic Aperture Radar, Ultrasonic Testing, and Rebound Hammer

Analyzing the Relationship between Compressive Strength and Modulus of Elasticity in Concrete with Ladle Furnace Slag

Development of a machine learning model for on-site evaluation of concrete compressive strength by SonReb

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