Physical and Mechanical Characteristics of Variotropic Concrete during Cyclic and Continuous Sulfate Attack

Shcherban’, Evgenii M.; Stel’makh, Sergey A.; Beskopylny, Alexey; Mailyan, Levon R.; Meskhi, Besarion; El’shaeva, Diana; Chernil’nik, Andrei

doi:10.3390/app13074386

Cited by 5 publications

(13 citation statements)

References 44 publications

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“…After molding, the samples were kept for 24 h, and then were taken from the molds and kept for 28 days in appropriate conditions. Then, they were subjected to various types of aggressive influences, such as freeze-thaw cycles; chloride attack, sulfate attack, and wet-dry cycles [5,[8][9][10]42]. The compressive strength of vibrocentrifuged concrete was 58.2 MPa.…”

Section: Preparation Parameters and Composition Of Vibrocentrifuged C...mentioning

confidence: 99%

“…Particular attention should also be paid to predicting the strength characteristics of vibrocentrifuged concrete. Structures made from vibrocentrifuged concrete must withstand extreme operating conditions, and accurate prediction of their properties is important, first of all, to ensure the required level of safety [6][7][8][9][10]. Along with traditional methods for calculating strength, which involve the formation of a sample and its subsequent testing in laboratory conditions, modern, fast and equally predictive methods of machine learning are used today [11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

“…The review made it possible to select the most suitable methods for accurately predicting the strength of concrete with a non-uniform cross-section of elements in terms of thickness. In this regard, there is a scientific deficit, which comprises providing methods for predicting the characteristics of concrete characterized by different cross-sectional properties (the so-called variatropy) [5,[8][9][10]42,43]. Hence, the uniqueness of this work can be discerned in three dimensions: theoretical, applied, and recommendatory.…”

Section: Introductionmentioning

confidence: 99%

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Prediction of the Compressive Strength of Vibrocentrifuged Concrete Using Machine Learning Methods

Beskopylny,

Stel’makh,

Shcherban’

et al. 2024

Buildings

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The determination of mechanical properties for different building materials is a highly relevant and practical field of application for machine learning (ML) techniques within the construction sector. When working with vibrocentrifuged concrete products and structures, it is crucial to consider factors related to the impact of aggressive environments. Artificial intelligence methods can enhance the prediction of vibrocentrifuged concrete properties through the use of specialized machine learning algorithms for materials’ strength determination. The aim of this article is to establish and evaluate machine learning algorithms, specifically Linear Regression (LR), Support Vector Regression (SVR), Random Forest (RF), CatBoost (CB), for the prediction of compressive strength in vibrocentrifuged concrete under diverse aggressive operational conditions. This is achieved by utilizing a comprehensive database of experimental values obtained in laboratory settings. The following metrics were used to analyze the accuracy of the constructed regression models: Mean Absolute Error (MAE), Mean Squared Error (MSE), Root-Mean-Square Error (RMSE), Mean Absolute Percentage Error (MAPE) and coefficient of determination (R2). The average MAPE in the range from 2% (RF, CB) to 7% (LR, SVR) allowed us to draw conclusions about the possibility of using “smart” algorithms in the development of compositions and quality control of vibrocentrifuged concrete, which ultimately entails the improvement and acceleration of the construction and building materials manufacture. The best model, CatBoost, showed MAE = 0.89, MSE = 4.37, RMSE = 2.09, MAPE = 2% and R2 = 0.94.

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Section: Preparation Parameters and Composition Of Vibrocentrifuged C...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Prediction of the Compressive Strength of Vibrocentrifuged Concrete Using Machine Learning Methods

Beskopylny,

Stel’makh,

Shcherban’

et al. 2024

Buildings

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“…After molding, the samples were kept in the molds for 24 h, then removed from the molds and hardened under natural conditions until they reached 28 days of age. The manufactured ring-section elements were sawed on a stone-cutting machine into samples of standard sizes and shapes and were exposed to cycles of freezing and thawing, wetting and drying, and chloride and sulfate attacks [44][45][46][47].…”

Section: Composition Manufacturing Parameters and Properties Of Vibro...mentioning

confidence: 99%

“…As a result of laboratory tests to determine the compressive strength of vibro-centrifuged variatropic concrete, a dataset was generated that describes the impact of aggressive environmental factors on the final characteristics [44][45][46][47].…”

Section: Description and Analysis Of The Datasetmentioning

confidence: 99%

Prediction of the Properties of Vibro-Centrifuged Variatropic Concrete in Aggressive Environments Using Machine Learning Methods

Beskopylny,

Stel’makh,

Shcherban’

et al. 2024

Buildings

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In recent years, one of the most promising areas in modern concrete science and the technology of reinforced concrete structures is the technology of vibro-centrifugation of concrete, which makes it possible to obtain reinforced concrete elements with a variatropic structure. However, this area is poorly studied and there is a serious deficiency in both scientific and practical terms, expressed in the absence of a systematic knowledge of the life cycle management processes of vibro-centrifuged variatropic concrete. Artificial intelligence methods are seen as one of the most promising methods for improving the process of managing the life cycle of such concrete in reinforced concrete structures. The purpose of the study is to develop and compare machine learning algorithms based on ridge regression, decision tree and extreme gradient boosting (XGBoost) for predicting the compressive strength of vibro-centrifuged variatropic concrete using a database of experimental values obtained under laboratory conditions. As a result of laboratory tests, a dataset of 664 samples was generated, describing the influence of aggressive environmental factors (freezing–thawing, chloride content, sulfate content and number of wetting–drying cycles) on the final strength characteristics of concrete. The use of analytical techniques to extract additional knowledge from data contributed to improving the resulting predictive properties of machine learning models. As a result, the average absolute percentage error (MAPE) for the best XGBoost algorithm was 2.72%, mean absolute error (MAE) = 1.134627, mean squared error (MSE) = 4.801390, root-mean-square error (RMSE) = 2.191208 and R2 = 0.93, which allows to conclude that it is possible to use “smart” algorithms to improve the life cycle management process of vibro-centrifuged variatropic concrete, by reducing the time required for the compressive strength assessment of new structures.

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The Influence of Recipe-Technological Factors on the Resistance to Chloride Attack of Variotropic and Conventional Concrete

et al. 2023

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A current problem in the construction industry is the lack of complex, scientifically based technological materials and design solutions for universal types of building materials, products, and structures, especially in terms of structures operating under conditions of aggressive chloride exposure. The aim of the study was to compare and evaluate the differences in the durability of conventional and variotropic concretes made using three different technologies, vibrating, centrifuging, and vibro-centrifuging, modified with the addition of microsilica, under conditions of cyclic chloride attack. Laboratory experiments and analyses using scanning electron microscopy were conducted. Vibro-centrifuged concrete showed the highest resistance to cyclic aggressive chloride exposure, which was expressed by a lower percentage drop in compressive strength compared to vibrated (87%) and centrifuged concrete (24%). The use of a microsilica as a modifying additive in the amount of 2–6%, instead of as a part of the binder, had a positive effect on the resistance of concrete to cyclic chloride attack. The most effective intervention was the introduction of additives in the amount of 4%. There was a reduction in the loss of strength of vibrated, centrifuged, and vibro-centrifuged concrete after 90 “dry-wet” cycles, as a result of the use of a modifying additive, in an amount between 45% and 55%, depending on the type of technology being used for producing a composite. The combined effect of the use of vibro-centrifuged concrete and microsilica led to a 188% decrease in strength loss resulting from cyclic chloride exposure.

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Physical and Mechanical Characteristics of Variotropic Concrete during Cyclic and Continuous Sulfate Attack

Cited by 5 publications

References 44 publications

Prediction of the Compressive Strength of Vibrocentrifuged Concrete Using Machine Learning Methods

Prediction of the Compressive Strength of Vibrocentrifuged Concrete Using Machine Learning Methods

Prediction of the Properties of Vibro-Centrifuged Variatropic Concrete in Aggressive Environments Using Machine Learning Methods

The Influence of Recipe-Technological Factors on the Resistance to Chloride Attack of Variotropic and Conventional Concrete

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