A comparative assessment of tree-based predictive models to estimate geopolymer concrete compressive strength

Nguyen, May Huu; Thi, Hai‐Van; Trinh, Son Hoang; Ly, Haï‐Bang

doi:10.1007/s00521-022-08042-2

Cited by 18 publications

(3 citation statements)

References 77 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The compressive strength is critical as the primary mechanical index used to evaluate the GMS performance [ 64 , 69 ]. The changes in the compressive strength of the GMS samples produced with different GBFS contents are indicated in Figure 8 .…”

Section: Resultsmentioning

confidence: 99%

Incorporating Industrial By-Products into Geopolymer Mortar: Effects on Strength and Durability

Лам

Nguyen

2023

Materials

Self Cite

View full text Add to dashboard Cite

In recent years, the reuse of industrial waste has become increasingly important for sustainable development. Therefore, this study investigated the application of granulated blast furnace slag (GBFS) as a cementitious replacement material in fly-ash-based geopolymer mortar containing silica fume (GMS). The performance changes in the GMS samples manufactured with different GBFS ratios (0–50 wt%) and alkaline activators were evaluated. The results indicated that GBFS replacement from 0 wt% to 50 wt% significantly affects GMS performance, including improving the bulk density from 2235 kg/m3 to 2324 kg/m3, flexural-compressive strength from 5.83 MPa to 7.29 MPa and 63.5 MPa to 80.2 MPa, respectively; a decrease in water absorption and chloride penetration, and an improvement in the corrosion resistance of GMS samples. The GMS mixture containing 50 wt% GBFS demonstrated the best performances with notable results regarding strength and durability. Owing to the increased production of C-S-H gel, the microstructure of the GMS sample containing more GBFS was denser, as obtained via the scanning electron micrograph analysis results. Incorporating the three industrial by-products into geopolymer mortars was verified when all samples were determined to be in accordance with the relevant Vietnamese standards. The results demonstrate a promising method to manufacture geopolymer mortars that aid sustainable development.

show abstract

Section: Resultsmentioning

confidence: 99%

Incorporating Industrial By-Products into Geopolymer Mortar: Effects on Strength and Durability

Лам

Nguyen

2023

Materials

Self Cite

View full text Add to dashboard Cite

show abstract

“…Machine learning algorithms [10,31,32] Using machine learning algorithms, such as decision trees [33], random forests [34], neural networks [35], etc., the training of this model requires the input of relevant data of influencing factors and the characteristic data of known materials. Through the input of a large number of data sets, the model can predict the compressive strength of geopolymer concrete [36,37].…”

Section: Prediction Methods Detailed Approach Featuresmentioning

confidence: 99%

Towards a Reliable Design of Geopolymer Concrete for Green Landscapes: A Comparative Study of Tree-Based and Regression-Based Models

Wang,

Zhang,

et al. 2024

Buildings

View full text Add to dashboard Cite

The design of geopolymer concrete must meet more stringent requirements for the landscape, so understanding and designing geopolymer concrete with a higher compressive strength challenging. In the performance prediction of geopolymer concrete compressive strength, machine learning models have the advantage of being more accurate and faster. However, only a single machine learning model is usually used at present, there are few applications of ensemble learning models, and model optimization processes is lacking. Therefore, this paper proposes to use the Firefly Algorithm (AF) as an optimization tool to perform hyperparameter tuning on Logistic Regression (LR), Multiple Logistic Regression (MLR), decision tree (DT), and Random Forest (RF) models. At the same time, the reliability and efficiency of four integrated learning models were analyzed. The model was used to analyze the influencing factors of geopolymer concrete and determine the strength of their influencing ability. According to the experimental data, the RF-AF model had the lowest RMSE value. The RMSE value of the training set and test set were 4.0364 and 8.7202, respectively. The R value of the training set and test set were 0.9774 and 0.8915, respectively. Therefore, compared with the other three models, RF-AF has a stronger generalization ability and higher prediction accuracy. In addition, the molar concentration of NaOH was the most important influencing factors, and its influence was far greater than the other possible factors including NaOH content. Therefore, it is necessary to pay more attention to NaOH molarity when designing geopolymer concrete.

show abstract

“…Their study demonstrated that GEP exhibited superior performance in predicting the splitting tensile strength compared to other approaches. Nguyen et al [19] used RF, decision trees (DTs), and XGBoost for the prediction of CS on fly-ash-based polymer concrete and concluded that the XGBoost model outperformed the other two models. In a study by Gupta et al [20], the optimal proportions of concrete mixes were determined using the Gaussian process, M5P model, random forest (RF), and random tree (RT) techniques and different applied models were evaluated.…”

Section: Introductionmentioning

confidence: 99%

Compressive Strength Prediction of BFRC Based on a Novel Hybrid Machine Learning Model

Zheng,

Yao,

Yue

et al. 2023

Buildings

View full text Add to dashboard Cite

Basalt fiber-reinforced concrete (BFRC) represents a form of high-performance concrete. In structural design, a 28-day resting period is required to achieve compressive strength. This study extended an extreme gradient boosting tree (XGBoost) hybrid model by incorporating genetic algorithm (GA) optimization, named GA-XGBoost, for the projection of compressive strength (CS) on BFRC. GA optimization may reduce many debugging efforts and provide optimal parameter combinations for machine learning (ML) algorithms. The XGBoost is a powerful integrated learning algorithm with efficient, accurate, and scalable features. First, we created and provided a common dataset using test data on BFRC strength from the literature. We segmented and scaled this dataset to enhance the robustness of the ML model. Second, to better predict and evaluate the CS of BFRC, we simultaneously used five other regression models: XGBoost, random forest (RF), gradient-boosted decision tree (GBDT) regressor, AdaBoost, and support vector regression (SVR). The analysis results of test sets indicated that the correlation coefficient and mean absolute error were 0.9483 and 2.0564, respectively, when using the GA-XGBoost model. The GA-XGBoost model demonstrated superior performance, while the AdaBoost model exhibited the poorest performance. In addition, we verified the accuracy and feasibility of the GA-XGBoost model through SHAP analysis. The findings indicated that the water–binder ratio (W/B), fine aggregate (FA), and water–cement ratio (W/C) in BFRC were the variables that had the greatest effect on CS, while silica fume (SF) had the least effect on CS. The results demonstrated that GA-XGBoost exhibits exceptional accuracy in predicting the CS of BFRC, which offers a valuable reference for the engineering domain.

show abstract

A comparative assessment of tree-based predictive models to estimate geopolymer concrete compressive strength

Cited by 18 publications

References 77 publications

Incorporating Industrial By-Products into Geopolymer Mortar: Effects on Strength and Durability

Incorporating Industrial By-Products into Geopolymer Mortar: Effects on Strength and Durability

Towards a Reliable Design of Geopolymer Concrete for Green Landscapes: A Comparative Study of Tree-Based and Regression-Based Models

Compressive Strength Prediction of BFRC Based on a Novel Hybrid Machine Learning Model

Contact Info

Product

Resources

About