Effectiveness of Calcium Carbonate Whisker in Cementitious Composites

Cao, Mingli; Khan, Mehran; Ahmed, S.

doi:10.3311/ppci.14288

Cited by 29 publications

(27 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Industrial waste, when incorporated in concrete, can contribute towards sustainable development in terms of environmentally friendly and economical construction materials [2,3]. The partial replacement of cement and other constituents of concrete has already been made extensively by industrial byproducts in various studies [4][5][6][7][8]. Several types of waste materials have been studied for their potential use in building materials, such as marble [9][10][11][12], super-absorbent polymer [13,14], glass [15][16][17], slag [18], bagasse ash [19], rubber [20,21], plastic [22], ceramic [23,24], natural fiber [25][26][27][28], and recycled aggregate [29][30][31][32].…”

Section: Introductionmentioning

confidence: 99%

Investigating the Ultrasonic Pulse Velocity of Concrete Containing Waste Marble Dust and Its Estimation Using Artificial Intelligence

et al. 2022

View full text Add to dashboard Cite

Researchers and engineers are presently focusing on efficient waste material utilization in the construction sector to reduce waste. Waste marble dust has been added to concrete to minimize pollution and landfills problems. Therefore, marble dust was utilized in concrete, and its prediction was made via an artificial intelligence approach to give an easier way to scholars for sustainable construction. Various blends of concrete having 40 mixes were made as partial substitutes for waste marble dust. The ultrasonic pulse velocity of waste marble dust concrete (WMDC) was compared to a control mix without marble dust. Additionally, this research used standalone (multiple-layer perceptron neural network) and supervised machine learning methods (Bagging, AdaBoost, and Random Forest) to predict the ultrasonic pulse velocity of waste marble dust concrete. The models’ performances were assessed using R2, RMSE, and MAE. Then, the models’ performances were validated using k-fold cross-validation. Furthermore, the effect of raw ingredients and their interactions using SHAP analysis was evaluated. The Random Forest model, with an R2 of 0.98, outperforms the MLPNN, Bagging, and AdaBoost models. Compared to all the other models (individual and ensemble), the Random Forest model with greater R2 and lower error (RMSE, MAE) has a superior performance. SHAP analysis revealed that marble dust content has a positive and direct influence on and relationship to the ultrasonic pulse velocity of concrete. Using machine learning to forecast concrete properties saves time, resources, and effort for scholars in the engineering sector.

show abstract

Section: Introductionmentioning

confidence: 99%

Investigating the Ultrasonic Pulse Velocity of Concrete Containing Waste Marble Dust and Its Estimation Using Artificial Intelligence

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Now, development of sustainable fiber reinforced composites is widely acknowledged to be for maximizing packing density. The calcium carbonate whisker (CW) is needle like inorganic fiber with better resistance to cracking at micro-scale [1,9]. Also, basalt fiber is non-decomposable mineral fiber extracted form volcanic rock without environmental hazards.…”

Section: Introductionmentioning

confidence: 99%

Basalt Fibers in Modified Whisker Reinforced Cementitious Composites

Khan

Cao

et al. 2022

Period. Polytech. Civil Eng.

Self Cite

View full text Add to dashboard Cite

The calcium carbonate whisker (CW) and basalt fiber are gaining popularity due to its enhanced mechanical properties in composites. Also, the short and long fibers provide bridging role and resistance against cracking from micro- to macro-scale, respectively. The usage of long and short hybrid basalt fiber along with addition of CW in cement-based composites is still a research gap. In this work, experimental behavior of CW basalt hybrid fiber reinforced mortar is considered with various content and length (3 mm, 6 mm, 12 mm, and 20 mm) of hybrid basalt fibers. In addition to this, synergy performance index is determined to quantitatively evaluate the positive interaction of hybrid basalt fiber in cementitious materials. The strengthening effect of whiskers and basalt fibers are also studied using scanning electron microscopy. The CW with various basalt fiber contents having different length of hybrid basalt fiber is used. It was found that the four various length of hybrid basalt fiber together with CW in cement mortar exhibited enhanced compressive, flexural, and split tensile strength than that of pure mortar and single length basalt fiber reinforced cementitious mortar. The results of synergy performance index showed similar trend with the experimental results. The strengthening effect caused by step by step crack arresting mechanism was also observed in cementitious material.

show abstract

“…The higher potassium oxide concentration of H.CWA aided the strength growth [ 49 ] and self-activation of the geopolymer with no need for an activator [ 50 ]. Additionally, the use of secondary cementitious materials and fibers (both natural and artificial) in geopolymer concrete has shown effective performance [ 51 , 52 , 53 , 54 , 55 , 56 ]. However, Figure 1 shows a schematic illustration of the GP concrete manufacturing process.…”

Section: Introductionmentioning

confidence: 99%

Application of Machine Learning Approaches to Predict the Strength Property of Geopolymer Concrete

Cao

Fang

et al. 2022

Materials

View full text Add to dashboard Cite

Geopolymer concrete (GPC) based on fly ash (FA) is being studied as a possible alternative solution with a lower environmental impact than Portland cement mixtures. However, the accuracy of the strength prediction still needs to be improved. This study was based on the investigation of various types of machine learning (ML) approaches to predict the compressive strength (C-S) of GPC. The support vector machine (SVM), multilayer perceptron (MLP), and XGBoost (XGB) techniques have been employed to check the difference between the experimental and predicted results of the C-S for the GPC. The coefficient of determination (R2) was used to measure how accurate the results were, which usually ranged from 0 to 1. The results show that the XGB was a more accurate model, indicating an R2 value of 0.98, as opposed to SVM (0.91) and MLP (0.88). The statistical checks and k-fold cross-validation (CV) also confirm the high precision level of the XGB model. The lesser values of the errors for the XGB approach, such as mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE), were noted as 1.49 MPa, 3.16 MPa, and 1.78 MPa, respectively. These lesser values of the errors also indicate the high precision of the XGB model. Moreover, the sensitivity analysis was also conducted to evaluate the parameter’s contribution towards the anticipation of C-S of GPC. The use of ML techniques for the prediction of material properties will not only reduce the effort of experimental work in the laboratory but also minimize the cast and time for the researchers.

show abstract

Effectiveness of Calcium Carbonate Whisker in Cementitious Composites

Cited by 29 publications

References 23 publications

Investigating the Ultrasonic Pulse Velocity of Concrete Containing Waste Marble Dust and Its Estimation Using Artificial Intelligence

Investigating the Ultrasonic Pulse Velocity of Concrete Containing Waste Marble Dust and Its Estimation Using Artificial Intelligence

Basalt Fibers in Modified Whisker Reinforced Cementitious Composites

Application of Machine Learning Approaches to Predict the Strength Property of Geopolymer Concrete

Contact Info

Product

Resources

About