Development of environment-friendly and ductile recycled aggregate concrete through synergetic use of hybrid fibers

Ali, Babar

doi:10.1007/s11356-022-18627-y

Cited by 21 publications

(8 citation statements)

References 49 publications

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“…The incorporation of steel fibers in concrete improves the mechanical characteristics, i.e., compressive strength, flexural strength, and tensile strength, making the concrete more tough and resistible to cracks, as reported in previous works of literature [ 1 , 2 , 3 , 4 , 5 , 6 ]. Steel fiber-reinforced concrete significantly increased flexural strength compared to regular concrete [ 7 ].…”

Section: Introductionsupporting

confidence: 63%

Flexural Strength Prediction of Steel Fiber-Reinforced Concrete Using Artificial Intelligence

Zheng

Sufian

et al. 2022

Materials

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Research has focused on creating new methodologies such as supervised machine learning algorithms that can easily calculate the mechanical properties of fiber-reinforced concrete. This research aims to forecast the flexural strength (FS) of steel fiber-reinforced concrete (SFRC) using computational approaches essential for quick and cost-effective analysis. For this purpose, the SFRC flexural data were collected from literature reviews to create a database. Three ensembled models, i.e., Gradient Boosting (GB), Random Forest (RF), and Extreme Gradient Boosting (XGB) of machine learning techniques, were considered to predict the 28-day flexural strength of steel fiber-reinforced concrete. The efficiency of each method was assessed using the coefficient of determination (R2), statistical evaluation, and k-fold cross-validation. A sensitivity approach was also used to analyze the impact of factors on predicting results. The analysis showed that the GB and RF models performed well, and the XGB approach was in the acceptable range. Gradient Boosting showed the highest precision with an R2 of 0.96, compared to Random Forest (RF) and Extreme Gradient Boosting (XGB), which had R2 values of 0.94 and 0.86, respectively. Moreover, statistical and k-fold cross-validation studies confirmed that Gradient Boosting was the best performer, followed by Random Forest (RF), based on reduced error levels. The Extreme Gradient Boosting model performance was satisfactory. These ensemble machine learning algorithms can benefit the construction sector by providing fast and better analysis of material properties, especially for fiber-reinforced concrete.

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Section: Introductionsupporting

confidence: 63%

Flexural Strength Prediction of Steel Fiber-Reinforced Concrete Using Artificial Intelligence

Zheng

Sufian

et al. 2022

Materials

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“…Hence, the reported enhancements in the flexural strength of most of the plant-fiber-reinforced cementitious composites may significantly contribute to the structural performance of pavements [277]. However, the hybrid effect of plant fibers with natural mineral fibers will be an interesting area to explore for civil engineering applications, because previous studies [278][279][280][281][282][283][284][285][286] have reported the enhanced mechanical properties of concrete with the use of natural mineral fibers.…”

Section: Flexural Strengthmentioning

confidence: 99%

A Comprehensive Review of Types, Properties, Treatment Methods and Application of Plant Fibers in Construction and Building Materials

et al. 2022

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Sustainable development involves the usage of alternative sustainable materials in order to sustain the excessive depletion of natural resources. Plant fibers, as a “green” material, are progressively gaining the attention of various researchers in the field of construction for their potential use in composites for stepping towards sustainable development. This study aims to provide a scientometric review of the summarized background of plant fibers and their applications as construction and building materials. Studies from the past two decades are summarized. Quantitative assessment of research progress is made by using connections and maps between bibliometric data that are compiled for the analysis of plant fibers using Scopus. Data refinement techniques are also used. Plant fibers are potentially used to enhance the mechanical properties of a composite. It is revealed from the literature that plant-fiber-reinforced composites have comparable properties in comparison to composites reinforced with artificial/steel fibers for civil engineering applications, such as construction materials, bridge piers, canal linings, soil reinforcement, pavements, acoustic treatment, insulation materials, etc. However, the biodegradable nature of plant fibers is still a hindrance to their application as a structural material. For this purpose, different surface and chemical treatment methods have been proposed in past studies to improve their durability. It can be surmised from the gathered data that the compressive and flexural strengths of plant-fiber-reinforced cementitious composites are increased by up to 43% and 67%, respectively, with respect to a reference composite. In the literature, alkaline treatment has been reported as an effective and economical method for treating plant fibers. Environmental degradation due to excessive consumption of natural resources and fossil fuels for the construction industry, along with the burning of waste plant fibers, can be reduced by incorporating said fibers in cementitious composites to reduce landfill pollution and, ultimately, achieve sustainable development.

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“…Industrial by-products and waste fibre have already been used to partially replace cement and other concrete ingredients in previous studies [ 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 ]. Natural resources can be conserved, and the negative environmental impact of the construction industry can be reduced by using RA as coarse aggregates in new concrete mixes [ 19 , 20 , 21 , 22 , 23 ]. However, due to variation in the properties of RAs, the mechanical properties of concrete with RA also vary compared to that of conventional natural aggregate concrete (CNAC).…”

Section: Introductionmentioning

confidence: 99%

Use of Artificial Intelligence for Predicting Parameters of Sustainable Concrete and Raw Ingredient Effects and Interactions

et al. 2022

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Incorporating waste material, such as recycled coarse aggregate concrete (RCAC), into construction material can reduce environmental pollution. It is also well-known that the inferior properties of recycled aggregates (RAs), when incorporated into concrete, can impact its mechanical properties, and it is necessary to evaluate the optimal performance. Accordingly, artificial intelligence has been used recently to evaluate the performance of concrete compressive behaviour for different types of construction material. Therefore, supervised machine learning techniques, i.e., DT-XG Boost, DT-Gradient Boosting, SVM-Bagging, and SVM-Adaboost, are executed in the current study to predict RCAC’s compressive strength. Additionally, SHapley Additive exPlanations (SHAP) analysis shows the influence of input parameters on the compressive strength of RCAC and the interactions between them. The correlation coefficient (R2), root mean square error (RMSE), and mean absolute error (MAE) are used to assess the model’s performance. Subsequently, the k-fold cross-validation method is executed to validate the model’s performance. The R2 value of 0.98 from DT-Gradient Boosting supersedes those of the other methods, i.e., DT- XG Boost, SVM-Bagging, and SVM-Adaboost. The DT-Gradient Boosting model, with a higher R2 value and lower error (i.e., MAE, RMSE) values, had a better performance than the other ensemble techniques. The application of machine learning techniques for the prediction of concrete properties would consume fewer resources and take less time and effort for scholars in the respective engineering field. The forecasting of the proposed DT-Gradient Boosting models is in close agreement with the actual experimental results, as indicated by the assessment output showing the improved estimation of RCAC’s compressive strength.

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Development of environment-friendly and ductile recycled aggregate concrete through synergetic use of hybrid fibers

Cited by 21 publications

References 49 publications

Flexural Strength Prediction of Steel Fiber-Reinforced Concrete Using Artificial Intelligence

Flexural Strength Prediction of Steel Fiber-Reinforced Concrete Using Artificial Intelligence

A Comprehensive Review of Types, Properties, Treatment Methods and Application of Plant Fibers in Construction and Building Materials

Use of Artificial Intelligence for Predicting Parameters of Sustainable Concrete and Raw Ingredient Effects and Interactions

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