Application of Artificial Intelligence Methods for Predicting the Compressive Strength of Green Concretes with Rice Husk Ash

Kovačević, Miljan; Hadzima-Nyarko, Marijana; Grubeša, Ivanka Netinger; Radu, Dorin; Lozančić, Silva

doi:10.3390/math12010066

Cited by 4 publications

(2 citation statements)

References 60 publications

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“…Discussing the results obtained and comparing them with the results previously obtained by other authors, it should be noted that the chosen question is unconditionally novel. The fact is that predicting the properties of concrete was previously known and described in works [6,11,15,17,18,20,21,[23][24][25][26][27][28]30,33,36,40,41,55]. The same study touches upon the topic of predicting the properties of special concretes, that is, variatropic concretes obtained using vibrocentrifuge technology.…”

Section: Resultsmentioning

confidence: 93%

“…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]. For example, in the research presented in [15], the authors applied one of the machine forecasting methods-the random forest method.…”

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: Resultsmentioning

confidence: 93%

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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Predictive Modelling of Flexural Strength in Recycled Aggregate-Based Concrete: A Comprehensive Approach with Machine Learning and Global Sensitivity Analysis

Singh,

Tipu,

Mir

et al. 2024

Iran J Sci Technol Trans Civ Eng

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Comparative Study on Housing Defect Repair Cost through Linear Regression Model

Park,

Seo

2024

Eng

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Despite stiff competition in the construction industry, housing quality remains a problem. From the consumer’s perspective, these quality problems are called defects. Homeowners experience inconvenience and suffering due to home defects, and developers and builders also experience severe damage in time, costs, and reputation due to defect repairs. In Korea, lawsuits are increasing due to the rise in housing defects, and the cost of repairing defects determined by lawsuits is of great concern. Litigation is a burden to consumers and producers, requiring a hefty court fee, as well as attorneys and specialist firms, and takes some years. Suppose it is possible to predict the repair costs based on the outcome of a lawsuit and present it as objective supporting data. In that case, it can be of great help in bringing a settlement between consumers and producers. According to previous studies on housing repair costs, linear regression models were mainly used. Accordingly, in this study, a linear regression model was adopted as a method to predict housing repair costs. We analyzed the defect repair costs in 100 cases in which lawsuits were filed and the verdict was finalized for housing complexes in Korea. Previous studies investigated using the following independent variables: elapsed period, litigation period, claim amount, home warranty deposit, total floor area, households, and main building’s quantity, construction cost, region, and highest floor. Among these, the floor area, elapsed period, and litigation period were determined to be valid independent variables. In addition, the construction period was discovered as a valid independent variable. The present research model, which combines these independent variables, was compared with previous research models. The results showed that the earlier research model was found to have a multicollinearity issue among some independent variables. Also, the coefficients of some independent variables were not statistically significant. This research model did not have a multicollinearity problem; all independent variables’ coefficients were statistically significant, and the coefficient of determination was higher than other linear research models. Our proposed regression model, which accounts for the interaction of each independent variable, is a significant step forward in our research. This model, using the number of households multiplied by the construction period, the construction period multiplied by the litigation period, and the litigation period multiplied by the litigation period as independent variables, has been rigorously tested and found to have no multicollinearity issue. The coefficients of all independent variables are statistically significant, further bolstering the model’s reliability. Additionally, the explanatory power of this model is comparable to the previous model, suggesting its potential to be used in conjunction with the existing model. Therefore, the linear regression model predicting the repair cost of housing defects following litigation in this study was considered the best. Utilizing the model proposed in this study is expected to play a major role in reconciling disputes between consumers and producers over housing defects.

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Application of Artificial Intelligence Methods for Predicting the Compressive Strength of Green Concretes with Rice Husk Ash

Cited by 4 publications

References 60 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

Predictive Modelling of Flexural Strength in Recycled Aggregate-Based Concrete: A Comprehensive Approach with Machine Learning and Global Sensitivity Analysis

Comparative Study on Housing Defect Repair Cost through Linear Regression Model

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