Predicting the compressive strength of fly ash concrete with the Particle Model

Kang, Shinhyu; Lloyd, Zane; Kim, Tae‐Hwan; Ley, M. Tyler

doi:10.1016/j.cemconres.2020.106218

Cited by 34 publications

(11 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…SCMs used in the cement industry can be industrial and agricultural waste products, which includes olive oil, bagasse ash, sugarcane, rice husk ash, palm oil fuel ash, etc. However, commonly adopted and used in the construction industry are silica fume, fly ash, and ground granulated blast furnace slag [ 7 , 8 , 9 ]. Their utilization in concrete reduces the malignant effect on the environment [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Compressive Strength of Fly Ash Based Concrete Using Individual and Ensemble Algorithm

et al. 2021

View full text Add to dashboard Cite

Machine learning techniques are widely used algorithms for predicting the mechanical properties of concrete. This study is based on the comparison of algorithms between individuals and ensemble approaches, such as bagging. Optimization for bagging is done by making 20 sub-models to depict the accurate one. Variables like cement content, fine and coarse aggregate, water, binder-to-water ratio, fly-ash, and superplasticizer are used for modeling. Model performance is evaluated by various statistical indicators like mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE). Individual algorithms show a moderate bias result. However, the ensemble model gives a better result with R2 = 0.911 compared to the decision tree (DT) and gene expression programming (GEP). K-fold cross-validation confirms the model’s accuracy and is done by R2, MAE, MSE, and RMSE. Statistical checks reveal that the decision tree with ensemble provides 25%, 121%, and 49% enhancement for errors like MAE, MSE, and RMSE between the target and outcome response.

show abstract

Section: Introductionmentioning

confidence: 99%

Prediction of Compressive Strength of Fly Ash Based Concrete Using Individual and Ensemble Algorithm

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Among them, the use of fly ash as an auxiliary cementitious material for the production of a large amount of fly ash concrete is one of the important ways to reduce environmental pollution and realize the resourcefulness of fly ash. Moreover, it is also an effective means for concrete producers to enhance and improve the performance of concrete in all aspects, reduce the use of cement, and lower the cost of concrete [6,7]. e incorporation of fly ash not only ensures the quality of concrete and reduces the cost of manufacturing concrete but also improves the compatibility, durability, and strength, thus becoming the most widely used alternative and receiving great attention [8].…”

Section: Introductionmentioning

confidence: 99%

Hybridizing Grid Search and Support Vector Regression to Predict the Compressive Strength of Fly Ash Concrete

Tang

Zhou

2022

Advances in Civil Engineering

View full text Add to dashboard Cite

Support vector regression (SVR) has been applied to the prediction of mechanical properties of concrete, but the selection of its hyperparameters has been a key factor affecting the prediction accuracy. To this end, hybrid machine learning combines the SVR model and grid search (GS), namely, the GS-SVR model was proposed to predict the compressive strength of concrete and sensitivity analysis in this work. The hybrid model was trained and tested on a total of 98 datasets retrieved from literature, and the model performance was compared with the original SVR model under the same datasets. The obtained results in terms of R of 0.981, MSE of 3.44, RMSE of 1.85, MAE of 1.17, and MAPE of 0.05 demonstrate that the GS-SVR model proposed can be a candidate method for compressive strength prediction in subsequent related studies. Additionally, a graphical user interface (GUI) was developed to conveniently provide some initial estimates of the outcomes before performing extensive laboratory or fieldwork. Finally, the effect of each variable on the compressive strength in a random environment was analyzed.

show abstract

“…Papadakis [8,9] analyzed the strength of composite concrete using the content of calcium silicate hydrate, which was determined from compound compositions of cement and mineral admixtures. Using a particle model, Kang et al [10] predicted the strength of fly ash composite concrete between 3 and 180 days. Wu et al [11] proposed a composite hydration model and evaluated the gel-space ratio and strength development of cement-fly ash-silica fume ternary composites.…”

Section: Introductionmentioning

confidence: 99%

Compressive Strength Estimation and CO2 Reduction Design of Fly Ash Composite Concrete

2022

View full text Add to dashboard Cite

Fly ash is broadly utilized to produce concrete materials. This study presents a strength estimation model and a CO2 reduction design method for concrete with fly ash. First, a hydration-based strength (HBS) model is proposed for the evaluation of strength development at different ages of fly ash composite concrete with different mix proportions. Second, CO2 emissions for 1 MPa strength were evaluated. The analysis results show that, as the fly ash-to-binder ratio (FA/B) increased, the CO2 emissions for 1 MPa strength decreased. For concrete with a low water-to-binder ratio (W/B), the addition of high content of fly ash had an obvious dilution effect, which increased the reaction degree of cement and reduced CO2 emissions for 1 MPa strength. Moreover, the extension of the design age could reduce CO2 emissions for 1 MPa strength. Third, a genetic-algorithm-based optimal design model is proposed to find the individual mass of cement and fly ash of low-CO2 concrete. The analysis results show that, as the water contents increased from 160 to 170 kg/m3, to obtain the same strength, cement mass and fly ash mass increased, while the water/binder ratio and fly ash/binder ratio did not change. This means that the reduction in mixed water is one feasible way to lower CO2 emissions. In summary, the proposed strength–emission integrated analysis method is useful for designing sustainable fly ash composite concrete with the desired strength and low levels of CO2 emissions.

show abstract

Predicting the compressive strength of fly ash concrete with the Particle Model

Cited by 34 publications

References 27 publications

Prediction of Compressive Strength of Fly Ash Based Concrete Using Individual and Ensemble Algorithm

Prediction of Compressive Strength of Fly Ash Based Concrete Using Individual and Ensemble Algorithm

Hybridizing Grid Search and Support Vector Regression to Predict the Compressive Strength of Fly Ash Concrete

Compressive Strength Estimation and CO2 Reduction Design of Fly Ash Composite Concrete

Contact Info

Product

Resources

About