The Mechanical Properties of Centrifuged Concrete in Reinforced Concrete Structures

Kliukas, Romualdas; Lukoševičienė, Ona; Jaras, Arūnas; Jonaitis, Bronius

doi:10.3390/app10103570

Cited by 12 publications

(10 citation statements)

References 25 publications

(41 reference statements)

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“…Vibrations make it possible to form more uniform and dense outer and middle layers. The internal part of the product comprises a mortar part with a large amount of squeezed out water from the top and middle layers and is the least durable [4,5]. In addition to thetechnological solutions aimed at improving physical and mechanical characteristics, there are inexpensive and effective formulation solutions that can improve the properties of vibrocentrifuged concrete [5].…”

Section: Introductionmentioning

confidence: 99%

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: Introductionmentioning

confidence: 99%

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

Beskopylny,

Stel’makh,

Shcherban’

et al. 2024

Buildings

View full text Add to dashboard Cite

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“…Lately, the technology of vibrocentrifugation, specifically of concrete, has emerged as one of the most promising areas in modern concrete science and technology, allowing for reinforced concrete elements with a variatropic structure. Quite a lot of relevant and interesting scientific works are devoted to this issue from the points of view of materials [3][4][5], design solutions [6,7] and forecasting methods [8][9][10]. However, this area is poorly studied and there is a serious deficiency in both scientific and practical terms, which is expressed in the absence of systematic knowledge about the life cycle management processes of vibro-centrifuged variatropic concrete.…”

Section: Introductionmentioning

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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“…Combining the topics of concrete heterogeneity and the technology of its productioncentrifugation, we will focus on the works [29,[65][66][67]. In [65], the authors studied the effect of a steel shell on the bearing capacity and mechanical characteristics of concrete piles reinforced with a steel pipe.…”

Section: Introductionmentioning

confidence: 99%

“…In particular, the steel shell significantly increased the bearing capacity of the pile shaft, but reduced the strength of the laboratory sample heads, which led to an increase in the overall bearing capacity of the pile, but created potential difficulties for the installation of such piles [65]. The study [66] is devoted to the study of the effect of helical transverse and high-strength longitudinal reinforcement on the physical and mechanical properties of centrifuged reinforced concrete elements of an annular section. Reinforcement parameters and concrete strength varied.…”

Section: Introductionmentioning

confidence: 99%

Modeling and Calculation of Improved Centrifuged Reinforced Concrete Columns with Variotropic Structure

et al. 2023

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The use of vibro-centrifugation technology allows the manufacture of variotropic structures that are inhomogeneous in the annular section and have different characteristics along the section thickness. Hardening of the outer layers allows the structure to better resist bending conditions, however, the behavior of the variotropic column under central and eccentric compression remains unexplored. This article considers the problem of compression of hollow columns made of homogeneous concrete that is non-uniform in the annular section (variotropic), and is reinforced with steel reinforcing bars at different values of the load application eccentricity. Variotropic concrete obtained by vibro-centrifugation technology has a stronger outer part and a less durable inner part. The strength of a homogeneous column corresponds to the strength of the middle part of variotropic concrete. The problem was solved numerically in the ANSYS environment for a vertical column rigidly clamped at the bottom edge and loaded with eccentricity at the top edge. Three types of eccentricity are considered; e/r = 0, 0.16 and 0.32 (respectively 0 mm, 0.24 mm and 48 mm). The results of the solution in the form of stress fields, deformations and a pattern of crack development in a spatial setting are obtained. The results showed that for central compression, a homogeneous column has a better bearing capacity of 3.6% than a variotropic one. With the values of eccentricity e/r = 0.16 and 0.32, the variotropic column has a higher bearing capacity (by 5.5% and 6.2%) than the homogeneous one and better resists the development of cracks. The significance of the study lies in the practical application of the proposed approach, developed on a research basis, for non-trivial and complicated operating conditions of columns. This study influences the development of reinforced concrete structures and applies scientific findings to engineering practice.

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The Mechanical Properties of Centrifuged Concrete in Reinforced Concrete Structures

Cited by 12 publications

References 25 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

Modeling and Calculation of Improved Centrifuged Reinforced Concrete Columns with Variotropic Structure

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