Estimation of rapid chloride permeability of SCC using hyperparameters optimized random forest models

Ge, Da-Ming; Zhao, Li-Cai; Esmaeili‐Falak, Mahzad

doi:10.1080/21650373.2022.2093291

Cited by 26 publications

(12 citation statements)

References 62 publications

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“…Different statistical evaluators were used to appraisal the performance of developed models for predicting the g dmax and w opt of lateritic soils [46]. In these equations, y , P t , P t , and ȳ represent the forecasted values of the Pth pattern, the target values of the Pth pattern, the averages of the target values, and the averages of the predicted values, respectively.…”

Section: Performance Evaluatorsmentioning

confidence: 99%

Comparison of SVR models for predicting the compaction properties of lateritic soils as novel hybrid methods

Zhu¹,

Zhu²,

Zhang³

2022

Eng. Res. Express

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Soil compaction and related parameters play a pivotal role in the material selection process for earth constructions. Because of time limitations and attention to completion resources, the need to develop models for predicting compaction properties is felt more than ever (i.e., maximum dry unit weight (γ_dmax) and optimum moisture content (ω_opt)) using easily measured index properties. The main purpose of this study is to evaluate the usefulness of conventional SVR, ensembled (additive regression (AD-SVR)), and hybrid SVR (imperialist competitive algorithm (ICA-SVR)) models for predicting the maximum dry unit weight (γ_dmax) and optimum water content (ω_opt) related to proctor compaction test of lateritic soils, where the used dataset was collected from published literature from specific sites. Results depict that all developed models have acceptable efficiency in predicting the γ_dmax with R^2 larger than 0.844 and 0.719, and the ω_opt with R^2 larger than 0.732 and 0.714 for the training and testing data, respectively, representing the acceptable correlation between observed and predicted values. The results of all SVR models were better than the literature, in which the hybrid ICA-SVR model outperformed other models by considering performance criteria. Overall, the hybrid ICA-SVR algorithm could get the most suitable results than others for estimating both γ_dmax and ω_opt. The ICA-SVR model outperforms other approaches and demonstrates the optimization algorithm's capacity throughout the optimization process.

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Section: Performance Evaluatorsmentioning

confidence: 99%

Comparison of SVR models for predicting the compaction properties of lateritic soils as novel hybrid methods

Zhu¹,

Zhu²,

Zhang³

2022

Eng. Res. Express

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“…[32][33][34][35][36] For instance, artificial neural networks and fuzzy logic techniques have been employed so effectively to estimate the splitting tensile and compressive mechanical behavior of concrete. [37][38][39][40][41][42][43] Also, the non-plastic state of the RAC has been modeled by artificial neural network techniques by Duan et al 44,45 It has been understood that ANN approach leads to higher performance efficiency in predicting the elasticity modulus of RAC in comparison to the correlation equation method.…”

Section: Introductionmentioning

confidence: 99%

Application of optimization‐based regression analysis for evaluation of frost durability of recycled aggregate concrete

Esmaeili‐Falak,

Sarkhani Benemaran

2024

Structural Concrete

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Concrete constructed using recycled aggregates in place of natural aggregates is an efficient approach to increase the construction sector's sustainability. To improve recycled aggregate concrete () technologies in permafrost, it is essential to certify the stability in frost‐induced conditions. The main goal of this study was to use support vector regression () methods to forecast the frost durability () of on the basis of durability agent value in cold climates. Herein, three optimization methods called Ant lion optimization (), Grey wolf optimization (), and Henry Gas Solubility Optimization () were employed for indicating optimal values of key parameters. The results depicted that all developed models have capability in predicting the of in cold regions. The values of as a comprehensive index depicted that the model has the higher value at 0.0571 as the weakest model, then at 0.0312 recognized as the second‐highest model, and finally the system at 0.0224 mentioned as outperformed model. and approaches were likewise capable of accurately forecasting the of in cold regions, but the created method outperformed them all when taking into account the explanations and justifications.

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“…They considered 5 input variables of W/B, cement, FA, GGBFS, and curing age. Regarding self-consolidating concrete (SCC) mixtures, extensive AI models were also presented in the literature 33 – 35 . In this field, Ghafoori et al (2013) 36 used statistical and ANN models on their limited experimental database (only 24 SCC mixtures) to present a predicting model for the rapid chloride penetration test (RCPT) value choosing 6 input features of cementitious materials, W/B, coarse aggregate, fine aggregates, air-entraining admixture, and high range water reducer (HRWR).…”

Section: Introductionmentioning

confidence: 99%

“…Similarly, only Taffese et al (2022) 55 considered concrete density as one of the input variables to predict chloride resistance. Most studies also ignored the temperature, while many models considered it an input feature 34 , 35 , 39 , 46 , 47 , 53 , 57 , 60 . It may be due to the missing data in the experimental research.…”

Section: Introductionmentioning

confidence: 99%

An efficient machine learning approach for predicting concrete chloride resistance using a comprehensive dataset

Hosseinzadeh,

Mousavi,

Hosseinzadeh

et al. 2023

Sci Rep

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By conducting an analysis of chloride migration in concrete, it is possible to enhance the durability of concrete structures and mitigate the risk of corrosion. In addition, the utilization of machine learning techniques that can effectively forecast the chloride migration coefficient of concrete shows potential as a financially viable and less complex substitute for labour-intensive experimental evaluations. The existing models for predicting chloride resistance encounter two primary challenges: the constraints imposed by a limited dataset and the absence of certain input variables. These factors collectively contribute to a decrease in the overall effectiveness of these models. Therefore, this study aims to propose an advanced approach for dataset cleaning, utilizing a comprehensive experimental dataset comprising 1073 pre-existing experimental outcomes. The proposed model for predicting the chloride diffusion coefficient incorporates various input variables, such as water content, cement content, slag content, fly ash content, silica fume content, fine aggregate content, coarse aggregate content, superplasticizer content, fresh density, compressive strength, age of compressive strength test, and age of migration test. The utilization of the artificial neural network (ANN) technique is also employed for the processing of missing data. The current supervised learning incorporates both regression and classification tasks. The efficacy of the proposed models for accurately predicting the chloride diffusion coefficient has been effectively validated. The findings indicate that the XGBoost and SVM algorithms exhibit superior performance compared to other regression prediction algorithms, as evidenced by their high R2 scores of 0.94 and 0.91, respectively. In relation to classification algorithms, the findings demonstrate that the Random Forest, LightGBM, and XGBoost models exhibit the highest levels of accuracy, specifically 0.93, 0.96, and 0.97, respectively. Furthermore, a website has been developed that is capable of predicting the chloride migration coefficient and chloride penetration resistance of concrete.

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Estimation of rapid chloride permeability of SCC using hyperparameters optimized random forest models

Cited by 26 publications

References 62 publications

Comparison of SVR models for predicting the compaction properties of lateritic soils as novel hybrid methods

Comparison of SVR models for predicting the compaction properties of lateritic soils as novel hybrid methods

Application of optimization‐based regression analysis for evaluation of frost durability of recycled aggregate concrete

An efficient machine learning approach for predicting concrete chloride resistance using a comprehensive dataset

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