Sugarcane bagasse ash as supplementary cementitious material in concrete – a review

Thomas, Blessen Skariah; Yang, Jian; Bahurudeen, A.; Abdalla, Jamal A.; Hawileh, Rami A.; Hamada, Hussein M.; Nazar, Sohaib; Jittin, V.; Ashish, Deepankar Kumar

doi:10.1016/j.mtsust.2021.100086

Cited by 73 publications

(32 citation statements)

References 95 publications

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“…Concrete consists of four main elements: water, fine and coarse aggregates, and cement as a binder [29][30][31]. Some supplementary elements such as fly ash, silica fume, or chemical combinations have been utilized to enhance the performance and strength of concrete [32][33][34][35][36]. The literature indicates that researchers are concentrating increasingly on these supplementary materials, since they are often waste materials created as a result of industrial, agricultural, and municipal processes [37][38][39][40][41].…”

Section: Literature Reviewmentioning

confidence: 99%

A Systematic Review of the Research Development on the Application of Machine Learning for Concrete

Khan

Ahmad²,

Amin³

et al. 2022

Materials

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Research on the applications of new techniques such as machine learning is advancing rapidly. Machine learning methods are being employed to predict the characteristics of various kinds of concrete such as conventional concrete, recycled aggregate concrete, geopolymer concrete, fiber-reinforced concrete, etc. In this study, a scientometric-based review on machine learning applications for concrete was performed in order to evaluate the crucial characteristics of the literature. Typical review studies are limited in their capacity to link divergent portions of the literature systematically and precisely. Knowledge mapping, co-citation, and co-occurrence are among the most challenging aspects of innovative studies. The Scopus database was chosen for searching for and retrieving the data required to achieve the study’s aims. During the data analysis, the relevant sources of publications, relevant keywords, productive writers based on publications and citations, top articles based on citations received, and regions actively engaged in research into machine learning applications for concrete were identified. The citation, bibliographic, abstract, keyword, funding, and other data from 1367 relevant documents were retrieved and analyzed using the VOSviewer software tool. The application of machine learning in the construction sector will be advantageous in terms of economy, time-saving, and reduced requirement for effort. This study can aid researchers in building joint endeavors and exchanging innovative ideas and methods, due to the statistical and graphical portrayal of participating authors and countries.

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Section: Literature Reviewmentioning

confidence: 99%

A Systematic Review of the Research Development on the Application of Machine Learning for Concrete

Khan

Ahmad²,

Amin³

et al. 2022

Materials

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“…Concrete's durability is improved by adding the proper quantity of steel fiber (0-1.5%) [1]. Different types of fibers, i.e., both natural and artificial, are utilized for improving the mechanical characteristics and crack resistance behavior of concrete and cementitious materials [2][3][4][5][6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Compressive Strength of Steel Fiber-Reinforced Concrete Employing Supervised Machine Learning Techniques

Zhang

Kamiński

et al. 2022

Materials

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Recently, research has centered on developing new approaches, such as supervised machine learning techniques, that can compute the mechanical characteristics of materials without investing much effort, time, or money in experimentation. To predict the 28-day compressive strength of steel fiber–reinforced concrete (SFRC), machine learning techniques, i.e., individual and ensemble models, were considered. For this study, two ensemble approaches (SVR AdaBoost and SVR bagging) and one individual technique (support vector regression (SVR)) were used. Coefficient of determination (R2), statistical assessment, and k-fold cross validation were carried out to scrutinize the efficiency of each approach used. In addition, a sensitivity technique was used to assess the influence of parameters on the prediction results. It was discovered that all of the approaches used performed better in terms of forecasting the outcomes. The SVR AdaBoost method was the most precise, with R2 = 0.96, as opposed to SVR bagging and support vector regression, which had R2 values of 0.87 and 0.81, respectively. Furthermore, based on the lowered error values (MAE = 4.4 MPa, RMSE = 8 MPa), statistical and k-fold cross validation tests verified the optimum performance of SVR AdaBoost. The forecast performance of the SVR bagging models, on the other hand, was equally satisfactory. In order to predict the mechanical characteristics of other construction materials, these ensemble machine learning approaches can be applied.

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“…Nonetheless, due to the widespread use of concrete in modern civilization, concrete production accounts for a significant portion of global CO 2 emissions through construction [32]. Aiming toward sustainable development, the usage of environment-friendly by-products is considered an effective strategy toward reducing CO 2 emissions [31,[33][34][35]. Marble dust (MD), having abundant availability in Turkey, China, Iran, Italy, and India, is also an alternative which can be used as a replacement for cement in the production of concrete.…”

Section: Introductionmentioning

confidence: 99%

Exploring the Use of Waste Marble Powder in Concrete and Predicting Its Strength with Different Advanced Algorithms

et al. 2022

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Recently, the high demand for marble stones has progressed in the construction industry, ultimately resulting in waste marble production. Thus, environmental degradation is unavoidable because of waste generated from quarry drilling, cutting, and blasting methods. Marble waste is produced in an enormous amount in the form of odd blocks and unwanted rock fragments. Absence of a systematic way to dispose of these marble waste massive mounds results in environmental pollution and landfills. To reduce this risk, an effort has been made for the incorporation of waste marble powder into concrete for sustainable construction. Different proportions of marble powder are considered as a partial substitute in concrete. A total of 40 mixes are prepared. The effectiveness of marble in concrete is assessed by comparing the compressive strength with the plain mix. Supervised machine learning algorithms, bagging (Bg), random forest (RF), AdaBoost (AdB), and decision tree (DT) are used in this study to forecast the compressive strength of waste marble powder concrete. The models’ performance is evaluated using correlation coefficient (R2), root mean square error, and mean absolute error and mean square error. The achieved performance is then validated by using the k-fold cross-validation technique. The RF model, having an R2 value of 0.97, has more accurate prediction results than Bg, AdB, and DT models. The higher R2 values and lesser error (RMSE, MAE, and MSE) values are the indicators for better performance of RF model among all individual and ensemble models. The implementation of machine learning techniques for predicting the mechanical properties of concrete would be a practical addition to the civil engineering domain by saving effort, resources, and time.

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Sugarcane bagasse ash as supplementary cementitious material in concrete – a review

Cited by 73 publications

References 95 publications

A Systematic Review of the Research Development on the Application of Machine Learning for Concrete

A Systematic Review of the Research Development on the Application of Machine Learning for Concrete

Compressive Strength of Steel Fiber-Reinforced Concrete Employing Supervised Machine Learning Techniques

Exploring the Use of Waste Marble Powder in Concrete and Predicting Its Strength with Different Advanced Algorithms

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