A scientometric review of waste material utilization in concrete for sustainable construction

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

doi:10.1016/j.cscm.2021.e00683

Cited by 80 publications

(57 citation statements)

References 178 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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“…LoI: loss on Ignition; I.R. : Insoluble Residue, (1) Soluble silica (SiO 2 S ) and alumina (Al 2 O 3 S ), (2) Reactive silica (SiO 2 R ).…”

Section: Methodsmentioning

confidence: 99%

“…Portland cement concrete is the construction material most widely used the world over. Its large output and consumption are primarily attributable to the ready availability of the raw materials nearly anywhere on the planet, the ease and diversity of its preparation [1] and its high performance in terms of both mechanical strength and durability [2,3]. Global production of concrete presently comes to over 3 Gt year −1 .…”

Section: Introductionmentioning

confidence: 99%

“…The excess effective dose does not include the absorbed dose due to the background radiation from natural sources, defined to be 50 nGy h −1 [9,10]. ACI is determined for the end building material, not its individual components, from Equation (1) 232 Th and 40 K activity concentration in Bq kg −1 in the building material. The 226 Ra, 232 Th and 40 K activity concentrations in building materials, with or without industrial waste/by-products, are normally determined with gamma-ray spectrometry on hydrated or anhydrous powder samples.…”

Section: Introductionmentioning

confidence: 99%

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Hybrid Cements: Mechanical Properties, Microstructure and Radiological Behavior

et al. 2022

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The use of more eco-efficient cements in concretes is one of the keys to ensuring construction industry sustainability. Such eco-efficient binders often contain large but variable proportions of industrial waste or by-products in their composition, many of which may be naturally occurring radioactive materials (NORMs). This study explored the application of a new gamma spectrometric method for measuring radionuclide activity in hybrid alkali-activated cements from solid 5 cm cubic specimens rather than powder samples. The research involved assessing the effect of significant variables such as the nature of the alkaline activator, reaction time and curing conditions to relate the microstructures identified to the radiological behavior observed. The findings showed that varying the inputs generated pastes with similar reaction products (C-S-H, C-A-S-H and (N,C)-A-S-H) but different microstructures. The new gamma spectrometric method for measuring radioactivity in solid 5 cm cubic specimens in alkaline pastes was found to be valid. The variables involved in hybrid cement activation were shown to have no impact on specimen radioactive content. The powder samples, however, emanated 222Rn (a descendent of 226Ra), possibly due to the deformation taking place in fly ash structure during alkaline activation. Further research would be required to explain that finding.

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“…There are threats to the environment caused by the process of cement production, including its high energy demand and the emission of numerous gases [6][7][8]. In order to overcome these threats, one option is to utilize additional materials that have binding properties, such as supplementary cementitious materials (SCMs), including silica fume, fly ash, blast furnace slag (BFS), etc., in place of cement, either during cement production or while manufacturing concrete [9][10][11]. Utilizing SCMs in concrete provides numerous advantages, including greater ultimate strength, increased durability, economic benefits, the prevention of surface cracking, and increased sustainability [12].…”

Section: Introductionmentioning

confidence: 99%

Computation of High-Performance Concrete Compressive Strength Using Standalone and Ensembled Machine Learning Techniques

Ahmad

et al. 2021

Materials

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The current trend in modern research revolves around novel techniques that can predict the characteristics of materials without consuming time, effort, and experimental costs. The adaptation of machine learning techniques to compute the various properties of materials is gaining more attention. This study aims to use both standalone and ensemble machine learning techniques to forecast the 28-day compressive strength of high-performance concrete. One standalone technique (support vector regression (SVR)) and two ensemble techniques (AdaBoost and random forest) were applied for this purpose. To validate the performance of each technique, coefficient of determination (R2), statistical, and k-fold cross-validation checks were used. Additionally, the contribution of input parameters towards the prediction of results was determined by applying sensitivity analysis. It was proven that all the techniques employed showed improved performance in predicting the outcomes. The random forest model was the most accurate, with an R2 value of 0.93, compared to the support vector regression and AdaBoost models, with R2 values of 0.83 and 0.90, respectively. In addition, statistical and k-fold cross-validation checks validated the random forest model as the best performer based on lower error values. However, the prediction performance of the support vector regression and AdaBoost models was also within an acceptable range. This shows that novel machine learning techniques can be used to predict the mechanical properties of high-performance concrete.

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A scientometric review of waste material utilization in concrete for sustainable construction

Cited by 80 publications

References 178 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

Hybrid Cements: Mechanical Properties, Microstructure and Radiological Behavior

Computation of High-Performance Concrete Compressive Strength Using Standalone and Ensembled Machine Learning Techniques

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