Sustainable use of waste eggshells in cementitious materials: An experimental and modeling-based study

Alsharari, Fahad; Khan, Kaffayatullah; Amin, Muhammad; Ahmad, Waqas; Khan, Usama; Mutnbak, Mohammed; Houda, Moustafa; Yosri, Ahmed M.

doi:10.1016/j.cscm.2022.e01620

Cited by 17 publications

(13 citation statements)

References 74 publications

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“…Therefore, the error dispersal demonstrated that the BR approach is more exact than the SVM that was employed. By employing SVM to fine-tune the bagging algorithm over 20 separate submodels, the BR approach is more exact than SVM since it is an induvial algorithm [ 77 ].…”

Section: Results and Analysismentioning

confidence: 99%

“…Nevertheless, the SVM model's prediction was also accurate and in agreement with the experimental data. It has been established via previous research that the BR approach is more accurate compared to the individual ML methods in predicting the strength characteristics of various materials [77][78][79][80]. For example, Alsharari et al [77] forecasted the compressive strength of cement mortar using BR and SVM ML techniques.…”

Section: Plos Onementioning

confidence: 99%

“…It has been established via previous research that the BR approach is more accurate compared to the individual ML methods in predicting the strength characteristics of various materials [77][78][79][80]. For example, Alsharari et al [77] forecasted the compressive strength of cement mortar using BR and SVM ML techniques. According to reports, the performance of BR methods was found to be superior to the SVM in estimating the compressive strength of cement mortar.…”

Section: Plos Onementioning

confidence: 99%

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Experimental and machine learning approaches to investigate the effect of waste glass powder on the flexural strength of cement mortar

et al. 2023

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Using solid waste in building materials is an efficient approach to achieving sustainability goals. Also, the application of modern methods like artificial intelligence is gaining attention. In this regard, the flexural strength (FS) of cementitious composites (CCs) incorporating waste glass powder (WGP) was evaluated via both experimental and machine learning (ML) methods. WGP was utilized to partially substitute cement and fine aggregate separately at replacement levels of 0%, 2.5%, 5%, 7.5%, 10%, 12.5%, and 15%. At first, the FS of WGP-based CCs was determined experimentally. The generated data, which included six inputs, was then used to run ML techniques to forecast the FS. For FS estimation, two ML approaches were used, including a support vector machine and a bagging regressor. The effectiveness of ML models was assessed by the coefficient of determination (R2), k-fold techniques, statistical tests, and examining the variation amongst experimental and forecasted FS. The use of WGP improved the FS of CCs, as determined by the experimental results. The highest FS was obtained when 10% and 15% WGP was utilized as a cement and fine aggregate replacement, respectively. The modeling approaches’ results revealed that the support vector machine method had a fair level of accuracy, but the bagging regressor method had a greater level of accuracy in estimating the FS. Using ML strategies will benefit the building industry by expediting cost-effective and rapid solutions for analyzing material characteristics.

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Section: Results and Analysismentioning

confidence: 99%

Section: Plos Onementioning

confidence: 99%

Section: Plos Onementioning

confidence: 99%

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Experimental and machine learning approaches to investigate the effect of waste glass powder on the flexural strength of cement mortar

et al. 2023

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“…The R 2 value measures the degree of agreement between actual and expected results. The R 2 number indicates the discordance between the predicted and real data [ 20 ]. Closer to 1 indicates less divergence, whereas closer to 0 indicates greater divergence.…”

Section: Methodsmentioning

confidence: 99%

“…Cement manufacturing requires considerable energy and produces significant carbon dioxide emissions, which contribute to global warming [ 20 ]. Cement manufacturers may cut costs and carbon dioxide emissions by recycling and reusing waste [ 21 – 24 ].…”

Section: Introductionmentioning

confidence: 99%

Evaluating the compressive strength of glass powder-based cement mortar subjected to the acidic environment using testing and modeling approaches

et al. 2023

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This study conducted experimental and machine learning (ML) modeling approaches to investigate the impact of using recycled glass powder in cement mortar in an acidic environment. Mortar samples were prepared by partially replacing cement and sand with glass powder at various percentages (from 0% to 15%, in 2.5% increments), which were immersed in a 5% sulphuric acid solution. Compressive strength (CS) tests were conducted before and after the acid attack for each mix. To create ML-based prediction models, such as bagging regressor and random forest, for the CS prediction following the acid attack, the dataset produced through testing methods was utilized. The test results indicated that the CS loss of the cement mortar might be reduced by utilizing glass powder. For maximum resistance to acidic conditions, the optimum proportion of glass powder was noted to be 10% as cement, which restricted the CS loss to 5.54%, and 15% as a sand replacement, which restricted the CS loss to 4.48%, compared to the same mix poured in plain water. The built ML models also agreed well with the test findings and could be utilized to calculate the CS of cementitious composites incorporating glass powder after the acid attack. On the basis of the R2 value (random forest: 0.97 and bagging regressor: 0.96), the variance between tests and forecasted results, and errors assessment, it was found that the performance of both the bagging regressor and random forest models was similarly accurate.

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A comprehensive characterization of chicken eggshell as a novel bioflocculant for biofloc harvesting in aquaculture wastewater

Jusoh,

Juahir,

Nasir

et al. 2024

Int. J. Environ. Sci. Technol.

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Sustainable use of waste eggshells in cementitious materials: An experimental and modeling-based study

Cited by 17 publications

References 74 publications

Experimental and machine learning approaches to investigate the effect of waste glass powder on the flexural strength of cement mortar

Experimental and machine learning approaches to investigate the effect of waste glass powder on the flexural strength of cement mortar

Evaluating the compressive strength of glass powder-based cement mortar subjected to the acidic environment using testing and modeling approaches

A comprehensive characterization of chicken eggshell as a novel bioflocculant for biofloc harvesting in aquaculture wastewater

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