Prediction of Rapid Chloride Penetration Resistance to Assess the Influence of Affecting Variables on Metakaolin-Based Concrete Using Gene Expression Programming

Amin, Muhammad; Raheel, Muhammad; Iqbal, Mudassir; Khan, Kaffayatullah; Qadir, Muhammad Ghulam; Jalal, Fazal E.; Alabdullah, Anas Abdulalim; Ajwad, Ali; Al-Faiad, Majdi Adel; Abu-Arab, Abdullah M.

doi:10.3390/ma15196959

Cited by 6 publications

(3 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Also, it was deduced from their ML model that using metakaolin has a vital influence on chloride penetration resistance with the optimum dosage of 15%. In this regard, Amin et al (2022) 51 used the same dataset and input features of metakaolin-based concrete to develop a GEP model of predicting RCPT. Their model showed that the concrete age is the most noteworthy factor, along with aggregate content.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Hosseinzadeh,

Mousavi,

Hosseinzadeh

et al. 2023

Sci Rep

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%

“…Accordingly, most developed AI models ignored this variable as an input feature. Also, only a few studies used concrete compressive strength as an input variable in AI methods 4 , 40 , 50 , 51 . Similarly, only Taffese et al (2022) 55 considered concrete density as one of the input variables to predict chloride resistance.…”

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

View full text Add to dashboard Cite

show abstract

“…Input features included cement, fly ash, silica fume, fine and soft aggregate contents, water-to-cement ratio, and temperature. Amin et al [31] used a gene expression programming algorithm to investigate the effects of fine and coarse aggregate contents, water-to-binder ratio, compressive strength, and metakaolin content on rapid chloride penetration. Aggarwal et al [32] developed predictive models using random forest, random tree, multilayer perceptron, M5P, and support vector regression algorithms, based on the contents of cement, fine and coarse aggregates, metakaolin, rice husk ash, water, and superplasticizers as input features to predict the 28-day compressive strength of SCC.…”

Section: Introductionmentioning

confidence: 99%

Explainable Ensemble Learning Models for the Rheological Properties of Self-Compacting Concrete

et al. 2022

View full text Add to dashboard Cite

Self-compacting concrete (SCC) has been developed as a type of concrete capable of filling narrow gaps in highly reinforced areas of a mold without internal or external vibration. Bleeding and segregation in SCC can be prevented by the addition of superplasticizers. Due to these favorable properties, SCC has been adopted worldwide. The workability of SCC is closely related to its yield stress and plastic viscosity levels. Therefore, the accurate prediction of yield stress and plastic viscosity of SCC has certain advantages. Predictions of the shear stress and plastic viscosity of SCC is presented in the current study using four different ensemble machine learning techniques: Light Gradient Boosting Machine (LightGBM), Extreme Gradient Boosting (XGBoost), random forest, and Categorical Gradient Boosting (CatBoost). A new database containing the results of slump flow, V-funnel, and L-Box tests with the corresponding shear stress and plastic viscosity values was curated from the literature to develop these ensemble learning models. The performances of these algorithms were compared using state-of-the-art statistical measures of accuracy. Afterward, the output of these ensemble learning algorithms was interpreted with the help of SHapley Additive exPlanations (SHAP) analysis and individual conditional expectation (ICE) plots. Each input variable’s effect on the predictions of the model and their interdependencies have been illustrated. Highly accurate predictions could be achieved with a coefficient of determination greater than 0.96 for both shear stress and plastic viscosity.

show abstract

Evaluating the rapid chloride permeability of self-compacting concrete containing fly ash and silica fume exposed to different temperatures: An artificial intelligence framework

Kazemi,

Gholampour

2023

Construction and Building Materials

View full text Add to dashboard Cite

Prediction of Rapid Chloride Penetration Resistance to Assess the Influence of Affecting Variables on Metakaolin-Based Concrete Using Gene Expression Programming

Cited by 6 publications

References 48 publications

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

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

Explainable Ensemble Learning Models for the Rheological Properties of Self-Compacting Concrete

Evaluating the rapid chloride permeability of self-compacting concrete containing fly ash and silica fume exposed to different temperatures: An artificial intelligence framework

Contact Info

Product

Resources

About