High-Performance Concrete Strength Prediction Based on Machine Learning

Liu, Yanning

doi:10.1155/2022/5802217

Cited by 16 publications

(7 citation statements)

References 21 publications

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“…Ref. [21] applied Random Forest, Support Vector Regression, and XGBoost to predict the resistance of high-performance concrete, finding XGBoost to be the most accurate. The artificial neural network, decision trees, and random forest methods for predicting tensile splitting strength were described in the study by [22], where concrete is used as a recycled aggregate, obtaining a database divided into training and validation.…”

Section: Related Workmentioning

confidence: 99%

Multivariate Analysis for Prediction of Splitting Tensile Strength in Concrete Paving Blocks

Benalcázar-Rojas,

Yambay-Vallejo,

Herrera-Granda

2023

Applied Sciences

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Paving blocks are concrete pieces exposed to the weather and subjected to loads and wear. Hence, quality control in their manufacture is essential to guarantee their properties and durability. In Ecuador, the requirements are described in the technical standard “NTE INEN 3040”, and tensile splitting strength is a fundamental requirement to guarantee product quality. The objective of the study is to predict the tensile splitting strength using two groups of predictor variables. The first group is the thickness in mm, width in mm, length in mm, mass of the fresh paving block in g, and percentage of water absorption; the second group of predictor variables is the density of the fresh paving block in kg/m3 and the percentage of water absorption. The data were obtained from a company that can produce 30,000 units per day of rectangular paving blocks with 6 cm thickness. The research involves sampling, analysis of outliers, descriptive and inferential statistics, and the analysis of multivariate models such as multiple linear regression, regression trees, random forests, and neural networks. It is concluded that the multiple linear regression method performs better in predicting the first group of predictor variables with a mean square error (MSE) of 0.110086, followed by the neural network without hidden layers, resulting in an MSE of 0.112198. The best method for the second set of predictors was the neural network without hidden layers, with a mean square error (MSE) of 0.112402, closely followed by the multiple linear regression model, with an MSE of 0.115044.

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Section: Related Workmentioning

confidence: 99%

Multivariate Analysis for Prediction of Splitting Tensile Strength in Concrete Paving Blocks

Benalcázar-Rojas,

Yambay-Vallejo,

Herrera-Granda

2023

Applied Sciences

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“…Experimental prediction results show that the developed data-driven neural network prediction model can provide rapid prediction and design for FRP-constrained composites. Moreover, some researchers [38][39][40] also used machine learning methods to predict the strength and self-healing behavior of concrete and have obtained good prediction results.…”

Section: Introductionmentioning

confidence: 99%

Prediction of the Frost Resistance of Iron Ore Tailings Concrete Based‐BP Neural Network

Fu,

Li,

Han

et al. 2023

Journal of Sensors

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In order to predict the relative dynamic elastic modulus (RDEM), which is used to reflect the frost resistance of iron ore tailings concrete, the backpropagation neural network (BPNN) was used in this study. Here, one hidden layer was chosen in the structure of BPNN. It is well known that the number of neurons in the hidden layer is the key of BPNN; hence, checking the features of overfitting was chosen in this paper to determine the number of neurons in the hidden layer. According to the actual conditions of freeze–thaw cycle test, a BPNN model of 2D input vector and 1D output vector was established. Thirty datasets from the test were used for training and test the proposed ANN. The results showed that the predicted values were in good agreement with the measured ones, and the correlation coefficient between them reached 0.9505. It showed that the BPNN, with the ability to solve nonlinear problems, had great advantages in predicting RDEM, and it can be used as reliable and simple forecasting tools for the prediction of frost resistance of iron ore tailings concrete.

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“…They concluded that the RF method was able to predict compressive strength with high accuracy. Liu [ 41 ] investigated the prediction of the mechanical properties of HPC using extreme gradient boosting (XGBoost), SVR, and RF. He showed that the XGBoost algorithm has appropriate performance in predicting the compressive strength of HPC.…”

Section: Introductionmentioning

confidence: 99%

Prediction of the Mechanical Properties of Basalt Fiber Reinforced High-Performance Concrete Using Machine Learning Techniques

et al. 2022

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In this research, we present an efficient implementation of machine learning (ML) models that forecast the mechanical properties of basalt fiber-reinforced high-performance concrete (BFHPC). The objective of the present study was to predict compressive, flexural, and tensile strengths of BFHPC through ML techniques and propose some correlations between these properties. Moreover, the modulus of elasticity (ME) values and compressive stress–strain curves were simulated using ML techniques. In this regard, three predictive algorithms, including linear regression (LR), support vector regression (SVR), and polynomial regression (PR), were considered. LR, SVR, and PR were utilized to forecast the compressive, flexural, and tensile strengths of BFHPC, and the PR technique was employed to simulate the compressive stress–strain curves. The performance of the models was also determined by the coefficient of determination (R2), mean absolute errors (MAE), and root mean square errors (RMSE). According to the obtained values of R2, MAE, and RMSE, the performance of PR was better than other types of algorithms in estimating the compressive, tensile, and flexural strengths. For example, R2 values were 0.99, 0.94, and 0.98 in predicting the compressive, flexural, and tensile strengths using PR, respectively. This shows the higher accuracy and reliability of the PR technique compared with other predictive algorithms. Finally, we concluded that ML techniques can be appropriately applied to assess the mechanical characteristics of BFHPC.

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High-Performance Concrete Strength Prediction Based on Machine Learning

Cited by 16 publications

References 21 publications

Multivariate Analysis for Prediction of Splitting Tensile Strength in Concrete Paving Blocks

Multivariate Analysis for Prediction of Splitting Tensile Strength in Concrete Paving Blocks

Prediction of the Frost Resistance of Iron Ore Tailings Concrete Based‐BP Neural Network

Prediction of the Mechanical Properties of Basalt Fiber Reinforced High-Performance Concrete Using Machine Learning Techniques

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