Prediction of concrete compressive strength based on early-age effective conductivity measurement

Chung, Kwok L.; Wang, Lingling; Ghannam, Mohamed; Guan, Mengxin; Luo, Jianlin

doi:10.1016/j.jobe.2020.101998

Cited by 29 publications

(14 citation statements)

References 29 publications

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“…The measurement is performed by directly attaching the open-ended coaxial sensor probe (with an inner radius of 1.50 mm and outer radius of 4.90 mm) on to the surface of the LFC block. Real-time reflection coefficient parameter (S 11 ) from the open-ended probe is acquired through the Vector Network Analyzer (VNA) using an in-house build data acquisition interface for dielectric calculation. The S 11 is measured and converted to a dielectric constant using the capacitance model.…”

Section: Methodology and Implementationmentioning

confidence: 99%

“…The advantages of utilizing the electrical method provide a nondestructive approach and in situ measurement to characterize the concrete have been proposed using effective conductivity at the microwave frequency 10 and DC resistivity 11 correlated with compressive strength. It has been further extended to the foamed concrete measurement using frequency domain method recently 12 .…”

Section: Introductionmentioning

confidence: 99%

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Determination of density and compressive strength of lightweight foamed concrete using frequency domain approach

Lee

Phua

Lim

et al. 2021

Micro & Optical Tech Letters

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A fast and nondestructive method for predicting density and compressive strength of Lightweight Foamed Concrete (LFC) is proposed. The measurement is carried out using a frequency domain approach to determine the Lightweight Foamed Concrete dielectric constant from a frequency range of 150 kHz to 8 GHz. The relations of the dielectric, density, and compressive strength of lightweight foamed concrete has been investigated, and the correlation models have been formulated using Jonscher fitting model. The results show a close estimation with the relative error controlled within 2.6% and 6.0% for the density and the compression strength computation, respectively. The proposed frequency domain method is shown to be effective and can be applied to determine the mechanical attributes as well as the electrical properties of the LFC simultaneously.

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Section: Methodology and Implementationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Determination of density and compressive strength of lightweight foamed concrete using frequency domain approach

Lee

Phua

Lim

et al. 2021

Micro & Optical Tech Letters

View full text Add to dashboard Cite

show abstract

“…Before testing, the sample must be cured for a specified curing period. Non-destructive tests (such as ultrasonic or pulse velocity tests [ 7 ] and conductivity tests [ 8 ]) have also been proposed to determine the compressive strength due to the lack of correlation between the standard compression test value with the real strength of concrete in a structure. These tests, however, have disadvantages with respect to time, cost, and labor.…”

Section: Literature Reviewmentioning

confidence: 99%

Jellyfish Search-Optimized Deep Learning for Compressive Strength Prediction in Images of Ready-Mixed Concrete

Chou

Tjandrakusuma

Liu

2022

Computational Intelligence and Neuroscience

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Most building structures that are built today are built from concrete, owing to its various favorable properties. Compressive strength is one of the mechanical properties of concrete that is directly related to the safety of the structures. Therefore, predicting the compressive strength can facilitate the early planning of material quality management. A series of deep learning (DL) models that suit computer vision tasks, namely the convolutional neural networks (CNNs), are used to predict the compressive strength of ready-mixed concrete. To demonstrate the efficacy of computer vision-based prediction, its effectiveness using imaging numerical data was compared with that of the deep neural networks (DNNs) technique that uses conventional numerical data. Various DL prediction models were compared and the best ones were identified with the relevant concrete datasets. The best DL models were then optimized by fine-tuning their hyperparameters using a newly developed bio-inspired metaheuristic algorithm, called jellyfish search optimizer, to enhance the accuracy and reliability. Analytical experiments indicate that the computer vision-based CNNs outperform the numerical data-based DNNs in all evaluation metrics except the training time. Thus, the bio-inspired optimization of computer vision-based convolutional neural networks is potentially a promising approach to predict the compressive strength of ready-mixed concrete.

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“…It was found that the early-age concrete cured after exposure to high temperatures up to 800 °C can regain 80% of the designed compressive strength. According to the conductivity at microwave frequency, Chung et al [ 14 ] proposed a prediction model for the 28 day compressive strength of concrete. They found that the magnitude of the slope of strength–conductivity curves can indicate the grade of standard concrete during early ages.…”

Section: Introductionmentioning

confidence: 99%

Predictive Modeling of Compressive Strength for Concrete at Super Early Age

2022

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The compressive strength of concrete is an important parameter in construction practice. At present, there are few reports on the prediction model of the compressive strength of concrete at a super early age. For some engineering vibration analyses, it is very necessary to study the growth law of compressive strength of concrete at a super early age. To this end, a new prediction model is proposed in this work to analyze the variation of compressive strength for the concrete at a super early age. The innovations of this work mainly lie in two aspects. The first innovation is to propose a new compressive strength-age mathematical model to predict the variation of compressive strength more accurately. The second innovation is to develop a new robust regression analysis method to obtain the fitting parameters in the mathematical model more effectively. Using the experimental data of the super early age concrete, the proposed prediction model is compared with the existing power function model and the hyperbolic function model. The results of the comparative study show that the prediction model proposed in this work is more reasonable and reliable. Taking C40 under natural curing as an example, it has been shown from the comparative study that: (1) The total fitting error of the proposed model is approximately 60% of that of the power function model, and approximately 17% of that of the hyperbolic model; (2) The fitting standard deviation of the proposed model is approximately 49% of that of the power function model, and approximately 15% of that of the hyperbolic model; (3) The 28 day strength of concrete predicted by the proposed model is more in line with the actual strength growth law of concrete.

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Prediction of concrete compressive strength based on early-age effective conductivity measurement

Cited by 29 publications

References 29 publications

Determination of density and compressive strength of lightweight foamed concrete using frequency domain approach

Determination of density and compressive strength of lightweight foamed concrete using frequency domain approach

Jellyfish Search-Optimized Deep Learning for Compressive Strength Prediction in Images of Ready-Mixed Concrete

Predictive Modeling of Compressive Strength for Concrete at Super Early Age

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