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

Abstract

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.

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

“…The prediction error plots presented in Figure 10 reflect the distribution of actual strength values for samples from the test set, in comparison with the values obtained as a result of prediction using the implemented machine learning algorithms. The red lines show the boundary ∆ = ±5 MPa [10]. The worst fit into a given "tube" of acceptable values, as well as a low value of the coefficient of determination, is observed in the RD model trained on the original dataset.…”

“…X 1 -number of freezing-thawing cycles; X 2 -chloride content, mg/dm 3 ; X 3 -sulfate content, mg/dm 3 ; X 4 -number of moistening-drying cycles. All of the above factors directly affect the strength of the building material under study [10,48,49]. From the point of view of construction, these parameters are sufficient to assess strength under the influence of an aggressive environment, since the data obtained have undergone mathematical statistical processing and the number of tests performed corresponds to the methods of regulatory and technical documents and, moreover, exceeds the standard quantity.…”

“…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.…”

Numerical simulation of the defect's behavior in building structure elements during ultrasonic diagnostics

“…The YOLOv5 software package is widely used to solve the problem of finding defects in buildings and structures made of concrete products using machine vision technologies and artificial intelligence instead of manual determination [4][5][6][7]. For this purpose, the lightweight model You Only Look Once V5 (YOLOv5) and adaptive spatial fusion of capabilities (Adaptively Spatial Feature Fusion, ASFF) are used.…”