Sustainable approach of using sugarcane bagasse ash in cement-based composites: A systematic review

Ahmad, Waqas; Ahmad, Ayaz; Ostrowski, Krzysztof; Aslam, Fahid; Joyklad, Panuwat; Zajdel, Paulina

doi:10.1016/j.cscm.2021.e00698

Cited by 55 publications

(41 citation statements)

References 83 publications

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“…The concrete and aggregates production leads to the emission of carbon dioxide, CO 2 gas, dust, and other harmful gases, which ultimately results in environmental pollution [10][11][12]. The demand for waste concrete is also increasing because of natural disasters such as earthquakes around the world, leading to serious environmental problems [13][14][15][16]. RCA concrete is considered as one of the potential solutions to reduce the utilization rate of the resources produced naturally and uses the waste concrete appearing from natural disasters, also from the demolition of construction [17,18].…”

Section: Introductionmentioning

confidence: 99%

Predicting the Mechanical Properties of RCA-Based Concrete Using Supervised Machine Learning Algorithms

Shang

Ahmad

et al. 2022

Materials

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Environment-friendly concrete is gaining popularity these days because it consumes less energy and causes less damage to the environment. Rapid increases in the population and demand for construction throughout the world lead to a significant deterioration or reduction in natural resources. Meanwhile, construction waste continues to grow at a high rate as older buildings are destroyed and demolished. As a result, the use of recycled materials may contribute to improving the quality of life and preventing environmental damage. Additionally, the application of recycled coarse aggregate (RCA) in concrete is essential for minimizing environmental issues. The compressive strength (CS) and splitting tensile strength (STS) of concrete containing RCA are predicted in this article using decision tree (DT) and AdaBoost machine learning (ML) techniques. A total of 344 data points with nine input variables (water, cement, fine aggregate, natural coarse aggregate, RCA, superplasticizers, water absorption of RCA and maximum size of RCA, density of RCA) were used to run the models. The data was validated using k-fold cross-validation and the coefficient correlation coefficient (R2), mean square error (MSE), mean absolute error (MAE), and root mean square error values (RMSE). However, the model’s performance was assessed using statistical checks. Additionally, sensitivity analysis was used to determine the impact of each variable on the forecasting of mechanical properties.

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Section: Introductionmentioning

confidence: 99%

Predicting the Mechanical Properties of RCA-Based Concrete Using Supervised Machine Learning Algorithms

Shang

Ahmad

et al. 2022

Materials

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“…SCMs hydrate cement hydraulically or pozzolanically in pore solution [ 27 , 28 , 29 ]. Thus, utilizing industrial solid waste in construction as SCMs is an effective approach for eco-friendly construction [ 30 ]; it could reduce cement demand, reduce CO 2 emission, and solve waste management problems. From the various kinds of industrial byproducts that can be used as SCMs, the most common is FA.…”

Section: Introductionmentioning

confidence: 99%

Fly Ash Application as Supplementary Cementitious Material: A Review

Zhou

Ahmad

et al. 2022

Materials

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This study aimed to expand the knowledge on the application of the most common industrial byproduct, i.e., fly ash, as a supplementary cementitious material. The characteristics of cement-based composites containing fly ash as supplementary cementitious material were discussed. This research evaluated the mechanical, durability, and microstructural properties of FA-based concrete. Additionally, the various factors affecting the aforementioned properties are discussed, as well as the limitations associated with the use of FA in concrete. The addition of fly ash as supplementary cementitious material has a favorable impact on the material characteristics along with the environmental benefits; however, there is an optimum level of its inclusion (up to 20%) beyond which FA has a deleterious influence on the composite’s performance. The evaluation of the literature identified potential solutions to the constraints and directed future research toward the application of FA in higher amounts. The delayed early strength development is one of the key downsides of FA use in cementitious composites. This can be overcome by chemical activation (alkali/sulphate) and the addition of nanomaterials, allowing for high-volume use of FA. By utilizing FA as an SCM, sustainable development may promote by lowering CO2 emissions, conserving natural resources, managing waste effectively, reducing environmental pollution, and low hydration heat.

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“…Construction is a vital component of any economy [ 1 ]. The building sector generates large amounts of waste and emits considerable amounts of greenhouse gases (GHGs) into the environment [ 2 , 3 ]. Cement-based materials such as concrete are the primary building materials utilized in the construction industry worldwide [ 4 , 5 , 6 , 7 ].…”

Section: Introductionmentioning

confidence: 99%

Compressive Strength Estimation of Geopolymer Composites through Novel Computational Approaches

et al. 2022

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The application of artificial intelligence approaches like machine learning (ML) to forecast material properties is an effective strategy to reduce multiple trials during experimentation. This study performed ML modeling on 481 mixes of geopolymer concrete with nine input variables, including curing time, curing temperature, specimen age, alkali/fly ash ratio, Na2SiO3/NaOH ratio, NaOH molarity, aggregate volume, superplasticizer, and water, with CS as the output variable. Four types of ML models were employed to anticipate the compressive strength of geopolymer concrete, and their performance was compared to find out the most accurate ML model. Two individual ML techniques, support vector machine and multi-layer perceptron neural network, and two ensembled ML methods, AdaBoost regressor and random forest, were employed to achieve the study’s aims. The performance of all models was confirmed using statistical analysis, k-fold evaluation, and correlation coefficient (R2). Moreover, the divergence of the estimated outcomes from those of the experimental results was noted to check the accuracy of the models. It was discovered that ensembled ML models estimated the compressive strength of the geopolymer concrete with higher precision than individual ML models, with random forest having the highest accuracy. Using these computational strategies will accelerate the application of construction materials by decreasing the experimental efforts.

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Sustainable approach of using sugarcane bagasse ash in cement-based composites: A systematic review

Cited by 55 publications

References 83 publications

Predicting the Mechanical Properties of RCA-Based Concrete Using Supervised Machine Learning Algorithms

Predicting the Mechanical Properties of RCA-Based Concrete Using Supervised Machine Learning Algorithms

Fly Ash Application as Supplementary Cementitious Material: A Review

Compressive Strength Estimation of Geopolymer Composites through Novel Computational Approaches

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