The prediction of durability to freeze–thaw of limestone aggregates using machine-learning techniques

Kahraman, Esma; Özdemir, Ali Can

doi:10.1016/j.conbuildmat.2022.126678

Cited by 20 publications

(6 citation statements)

References 52 publications

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“…The model's performance is evaluated by comparing the experimental data with the predicted data. Four evaluation criteria for measuring the performance of the proposed model are selected: mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) [14,15]. The calculation equations of the selected criteria are shown in Table 1.…”

Section: Prophet Modelmentioning

confidence: 99%

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Corrosion prediction of iron-based metals by the prophet model

Shi,

WU,

Shen

et al. 2023

Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023)

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This paper proposes the prophet model for corrosion prediction of iron-based materials. The corrosive degree of iron-based materials under different conditions is assessed though the weight loss method and the experimental results are fed to the proposed model as the training data. It indicated that HT200, Q345B, and 45 steel are easily and rapidly corroded in the periodic immersion condition. The predicted results from the Prophet model are compared with the experimental results to evaluate the performance of the prediction model. The highest RMSE is 2.35×10-4 and the largest MAPE is 2.90×10- 4%. That means the proposed model is a reliable machine learning model for corrosion prediction of iron-based materials.

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Section: Prophet Modelmentioning

confidence: 99%

“…The calculation equations of the selected criteria are shown in Table 1. In theory, if the proposed model could predict the corrosive degree of different metals, the values of these evaluation criteria will be approach to zero [14,15].…”

Section: Prophet Modelmentioning

confidence: 99%

Corrosion prediction of iron-based metals by the prophet model

Shi,

WU,

Shen

et al. 2023

Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023)

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“…Their results show that different features can provide a better understanding of the problem and that the techniques can be applied to the assessment of wood quality. As part of the freeze-thaw prediction of aggregate by Kahraman et al [20], gaussian process regression (GPR-Exponential and GPR-Matern5/2), SVM, and regression trees (RT) were applied. In their study, ML techniques were successfully applied to freeze-thaw resistance prediction.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Mechanical Strength Prediction of BFRC Based on Stacking Ensemble Learning and Genetic Algorithm Optimization

et al. 2023

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Split Hopkinson pressure bar (SHPB) tests are usually used to determine the dynamic mechanical strength of basalt-fiber-reinforced concrete (BFRC), but this test method is time-consuming and expensive. This paper makes predictions about the dynamic mechanical strength of BFRC by employing machine learning (ML) algorithms and feature sets drawn from experimental data from prior works. However, there is still the problem of improving the accuracy of the dynamic mechanical strength prediction by the BFRC, which remains a challenge. Using stacking ensemble learning and genetic algorithms (GA) to optimize parameters, this study proposes a prediction method that combines these two techniques for obtaining accurate predictions. This method is composed of three parts: (1) the training uses multiple base learners, and the algorithms employed by the learners include extreme gradient boosting (XGBoost), gradient boosting (GB), random forest (RF), and support vector regression (SVR); (2) multi-base learners are combined using a stacking strategy to obtain the final prediction; and (3) using GA, the parameters are optimized in the prediction model. An experiment was conducted to compare the proposed approach with popular techniques for machine learning. In the study, the stacking ensemble algorithm integrated the base learner prediction results to improve the model’s performance and the GA further improved prediction accuracy. As a result of the application of the method, the dynamic mechanical strength of BFRC can be predicted with high accuracy. A SHAP analysis was also conducted using the stacking model to determine how important the contributing properties are and the sensitivity of the stacking model. Based on the results of this study, it was found that in the SHPB test, the strain rate had the most significant influence on the DIF, followed by the specimen diameter and the compressive strength.

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“…Significant importance today is given to improving the durability of concrete, especially cement compositions. For various operating environments, the durability of concrete is achieved by increasing the entrained air content, reducing the W/C, and increasing the strength class of concrete, as well as cement consumption, using a limited number of types of cement and normalizing their mineralogical composition [1][2][3].…”

Section: Introductionmentioning

confidence: 99%

Both Plasticizing and Air-Entraining Effect on Cement-Based Material Porosity and Durability

Tolegenova

Skripkiūnas

Rishko³

et al. 2022

Materials

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The influence of a complex application of both plasticizing and air-entraining effects on concrete with polycarboxylate ether superplasticizer (PCE), air-entraining admixture (AIR), or an anti-foaming agent (AF) is analyzed in this paper with considerations for on the air content, workability, flexural and compressive strength, and freezing–thawing resistance of hardened cement mixtures. The effect of the complex behavior of PCE, AIR, and AF on the porosity of hardened cement mortar (HCM) and freezing–thawing resistance was investigated; freezing–thawing resistance prediction methodology for plasticized mortar was also evaluated. The results presented in the article demonstrate the beneficial influence of entrained air content on consistency and stability of cement mortar, closed porosity, and durability of concrete. Freezing–thawing factor KF depending on porosity parameters can be used for freezing–thawing resistance prediction. With both plasticizing (decrease in the water–cement ratio) and air-entraining effects (increase in the amount of entrained air content), the frost resistance of concrete increases, scaling decreases exponentially, and it is possible to obtain great frost resistance for cement-based material.

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The prediction of durability to freeze–thaw of limestone aggregates using machine-learning techniques

Cited by 20 publications

References 52 publications

Corrosion prediction of iron-based metals by the prophet model

Corrosion prediction of iron-based metals by the prophet model

Dynamic Mechanical Strength Prediction of BFRC Based on Stacking Ensemble Learning and Genetic Algorithm Optimization

Both Plasticizing and Air-Entraining Effect on Cement-Based Material Porosity and Durability

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