Compressive Strength Prediction of Fly Ash Concrete Using Machine Learning Techniques

Jiang, Yimin; Li, Hangyu; Zhou, Yisong

doi:10.3390/buildings12050690

Cited by 17 publications

(6 citation statements)

References 34 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%

“…For example, in studies [19], the SVR method was used to assess the durability of high-performance basalt fiber reinforced concrete and showed fairly high accuracy. Similarly, in research [20][21][22][23][24], this machine learning method makes it possible to quite accurately predict the concrete characteristics by including various additives (fly ash, microsilica) and subject to various types of impacts. In general, the introduction of machine learning methods allows to save costs on the production of many experimental samples and the procedure for testing them, as well as significantly speeds up the process of obtaining future properties of a concrete composite [25].…”

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

show abstract

“…The regression trees serve the same purpose, and the final mean is taken as the final regression output of the random forest model. The accuracy of the random forest model can be adjusted by changing some parameters [34,35]: the number of random regression trees (n_estimators); the maximum depth of the decision tree (max_depth); the maximum number of samples randomly sampled when fitting the decision tree (max_sample); the minimum number of leaf sample nodes (min_samples_leaf); the maximum number of features when constructing a decision tree (max_features); and the minimum number of samples when splitting nodes (min_samples_split). In addition, the random forest model not only can solve the problem of the influence of input variables on output variables by setting up the regression model but also can calculate partial dependence to indicate the marginal effect of an input variable in the random forest model's regression results.…”

Section: Carbon Storage Estimation Methodologymentioning

confidence: 99%

“…However, it was difficult for the Pearson coefficients to accurately depict the relationship between the individual input variables and the output variable. This limitation was due to complex nonlinear relationships between multiple independent and dependent variables [35]. The random forest model offered an appropriate method to assess the nonlinear correlations.…”

Section: Correlations Between Key Vegetation Traits and Aboveground C...mentioning

confidence: 99%

Determinants of Aboveground Carbon Storage of Woody Vegetation in an Urban–Rural Transect in Shanghai, China

et al. 2023

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Carbon storage of urban woody vegetation is crucial for climate change mitigation. Biomass structure and species composition have been shown to be important determinants of carbon storage in woody vegetation. In this study, allometric equations were used to estimate the aboveground carbon storage of urban woody vegetation along an urban–rural transect in Shanghai. A random forest model was developed to evaluate the importance scores and influence of species diversity, canopy cover, species evenness, and tree density on aboveground carbon storage. The results showed that tree density, canopy cover, species diversity, species evenness, and aboveground carbon storage of urban woody vegetation vary with the degree of urbanization and urban–rural environment. In addition, the Bayesian optimization algorithm optimized the random forest model parameters to enhance model accuracy, and good modeling results were demonstrated in the study. The R2 was at 0.61 in the testing phase and 0.78 in the training phase. The root mean square errors (RMSEs) were 0.84 Mg/ha of carbon in the testing phase and 0.57 Mg/ha in the training phase, which is indicative of a low error of the optimized model. Tree species diversity, canopy cover, species evenness, and tree density were found to correlate with aboveground carbon storage. Tree density was the most important contributor, followed by species diversity and canopy cover, and species evenness was the least effective for aboveground carbon storage. Meanwhile, the results of the partial dependence analysis indicated the combination of factors most conducive to aboveground carbon storage at a tree density of 2200 trees/ha, canopy cover of 50%, species diversity of 1.2, and species evenness of 0.8 in the transect. The findings provided practical recommendations for urban forest managers to adjust the structure and composition of woody vegetation to increase carbon storage capacity and reduce greenhouse gas emissions.

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“…The selection of hyperparameters for SVR models has a significant impact on the identification of results [27].…”

Section: Grid Search Optimizationmentioning

confidence: 99%

Improving Cryptocurrency Price Prediction Accuracy with Multi-Kernel Support Vector Regression Approach

Thumu,

Nellore

2024

Int. Res. J. multidiscip. Technovation

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Cryptocurrencies are digital assets that have attracted a lot of investment and attention. It is challenging and essential for investors and traders to predict their stock price movements. Making accurate predictions about cryptocurrency prices is crucial for avoiding losses and gaining profits. Our research proposes a novel method for predicting the stock closed prices of three popular cryptocurrencies: Bitcoin, Ethereum and Polkadot. The SVR (Support vector regression) machine learning method can provide robust and accurate predictions for nonlinear and nonstationary data. This paper compares SVR radial basis functions (RBFs) and hybrid kernels based on cryptocurrency data characteristics. SVR parameters such as regularization, gamma, and epsilon can also be tuned using grid search. Our approach is tested on real-world cryptocurrency stock prices collected from Yahoo Finance. Prediction performance is measured using regression metrics like MAPE (Mean absolute percentage error) and R2 score. In our work, a MAPE value of 0.07772 and an R2 score of 0.9999 have been obtained. The results of our experiments indicate that our approach is significantly more accurate and reliable than existing methods.

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Compressive Strength Prediction of Fly Ash Concrete Using Machine Learning Techniques

Cited by 17 publications

References 34 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

Determinants of Aboveground Carbon Storage of Woody Vegetation in an Urban–Rural Transect in Shanghai, China

Improving Cryptocurrency Price Prediction Accuracy with Multi-Kernel Support Vector Regression Approach

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