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

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

doi:10.3390/infrastructures8070108

Cited by 5 publications

(9 citation statements)

References 51 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%

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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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“…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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Study of the resistance of concrete of anisotropic and varitropic structures to alternating wetting and drying

Mailyan,

Stel'makh,

Shcherban'

et al. 2024

Proceedings of Petersburg Transport University

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Objective: study of the influence of a number of operational factors on the final resistance of varitropic concrete to alternating cycles of wetting and drying. This work identifies the problem of concrete resistance to cyclic wetting and drying, and also formulates a scientific deficiency consisting in the inadequacy of the existing knowledge base regarding the relationship between the type of concrete structure and its resistance to cyclic influences, including alternating wetting and drying. Methods: test samples were made using three different technologies: vibration, centrifugation, and vibration centrifugation. The following main characteristics were studied: compressive strength, tensile strength in bending, water resistance. Experimental studies have shown that concretes with a varitropic structure (centrifugation and vibration centrifugation) have higher resistance to alternating wetting and drying than concretes with an anisotropic structure (vibration). After 500 cycles of wetting-drying and a high degree of aggressiveness of the saturated aqueous environment in terms of the content of sulfate, chloride and nitrate salts, the loss of compressive and tensile strength in bending for vibrated concrete was 28.7 and 32.7 respectively, for centrifuged concrete — 26.2 and 27.8%, respectively, and for vibrocentrifuged ones — 19.6 and 21.4 %, respectively. Results: Vibrocentrifuged varitropic concretes showed the greatest resistance to the effects of alternating wetting in an aqueous environment with varying degrees of aggressiveness and drying compared to centrifuged varitropic and vibrated anisotropic concretes.

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

Cited by 5 publications

References 51 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

Study of the resistance of concrete of anisotropic and varitropic structures to alternating wetting and drying

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