Sustainable strategy of eggshell waste usage in cementitious composites: An integral testing and computational study for compressive behavior in aggressive environment

Wang, Nanlan; Xia, Zhengjun; Amin, Muhammad; Ahmad, Waqas; Khan, Kaffayatullah; Althoey, Fadi; Alabduljabbar, Hisham

doi:10.1016/j.conbuildmat.2023.131536

Cited by 16 publications

(4 citation statements)

References 60 publications

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“…The dataset was split into training (70% of the data) and testing (30% of the data) for the modelling phase. This allocation was taken considering pertinent literature in the field [30][31][32]. All of the algorithms needed to run the models were crafted in the programming language Python, and the software that was employed was the Spyder (version 5.4.3) accessed through the Anaconda Navigator.…”

Section: Data Descriptionmentioning

confidence: 99%

Strength Estimation and Feature Interaction of Carbon Nanotubes-Modified Concrete Using Artificial Intelligence-Based Boosting Ensembles

Zhu,

Wu,

et al. 2024

Buildings

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The standard approach for testing ordinary concrete compressive strength (CS) is to cast samples and test them after different curing times. However, testing adds cost and time to projects, and, therefore, construction sites experience delays. Because carbon nanotubes (CNTs) vary in length, composition, diameter, and dispersion, experiment and formula fitting alone cannot reliably predict the strength of CNTs-based composites. For empirical equations or traditional statistical approaches to properly forecast complex materials’ mechanical characteristics, various significant parameters, databases, and nonlinear relationships between variables must be considered. Machine learning (ML) tools are the most advanced for accurate predictions of material behaviour. This study employed gradient boosting, light gradient boosting machine, and extreme gradient boosting techniques to forecast the CS of CNTs-modified concrete. Also, in order to explore the influence and interaction of various features, an interaction analysis was conducted. In terms of R2, gradient boosting, light gradient boosting machine, and extreme gradient boosting models proved their accuracy. Extreme gradient boosting had the highest R2 of 0.97, followed by light gradient boosting machine and gradient boosting with scores of 0.94 and 0.93, respectively. This type of research may help both academics and industry forecast material properties and influential elements, thereby reducing lab test requirements.

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Section: Data Descriptionmentioning

confidence: 99%

Strength Estimation and Feature Interaction of Carbon Nanotubes-Modified Concrete Using Artificial Intelligence-Based Boosting Ensembles

Zhu,

Wu,

et al. 2024

Buildings

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“…While prior research in the field of ML has primarily focused on evaluating the performance of traditional cementitious materials [40,41], there is limited literature that specifically addresses the prediction of properties in cementitious composites modified with ESP and RGP. Wang et al [42] developed ML models to forecast the CS loss after an acid-attack test on ESP-based mortar. Similarly, Alfaiad et al [43] claimed to address the CS loss in an acidic environment after performing lab tests on RGPbased mortar.…”

Section: Figurementioning

confidence: 99%

“…Furthermore, a deliberate attempt was undertaken to choose publications with comparable components. The relevant data required for determining the proportion loss in the CS of ESP and RGP-based reformed mortar has been acquired from the existing literature sources [42,43]. Cement, glass, and eggshell types were the same throughout the database.…”

Section: Datamentioning

confidence: 99%

“…The CS data, after an acid attack, rely on eight input parameters: cement, s ter, silica fume, superplasticizer, glass powder, eggshell powder, and 90 days of and RGP are often observed ingredients frequently included in the database s Furthermore, a deliberate attempt was undertaken to choose publications with rable components. The relevant data required for determining the proportion lo CS of ESP and RGP-based reformed mortar has been acquired from the existing l sources [42,43]. Cement, glass, and eggshell types were the same throughout t base.…”

Section: Datamentioning

confidence: 99%

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Strength Reduction Due to Acid Attack in Cement Mortar Containing Waste Eggshell and Glass: A Machine Learning-Based Modeling Study

Zhu,

Wu,

et al. 2024

Buildings

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The present study utilized machine learning (ML) techniques to investigate the effects of eggshell powder (ESP) and recycled glass powder (RGP) on cement composites subjected to an acidic setting. A dataset acquired from the published literature was employed to develop machine learning-based predictive models for the cement mortar’s compressive strength (CS) decrease. Artificial neural network (ANN), K-nearest neighbor (KNN), and linear regression (LR) were chosen for modeling. Also, RreliefF analysis was performed to study the relevance of variables. A total of 234 data points were utilized to train/test ML algorithms. Cement, sand, water, silica fume, superplasticizer, glass powder, eggshell powder, and 90 days of CS were considered as input variables. The outcomes of the research showed that the employed models could be applied to evaluate the reduction percentage of CS in cement composites, including ESP and RGP, after being exposed to acid. Based on the R2 values (0.87 for the ANN, 0.81 for the KNN, and 0.78 for LR), as well as the assessment of variation between test values and anticipated outcomes and errors (1.32% for ANN, 1.57% for KNN, and 1.69% for LR), it was determined that the accuracy of the ANN model was superior to the KNN and LR. The sieve diagram exhibited a correlation amongst the model predicted and target results. The outcomes of the RreliefF analysis suggested that ESP and RGP significantly influenced the CS loss of samples with RreliefF scores of 0.26 and 0.21, respectively. Based on the outcomes of the research, the ANN approach was determined suitable for predicting the CS loss of mortar subjected to acidic environments, thereby eliminating lab testing trails.

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A comparative study of prediction models for alkali-activated materials to promote quick and economical adaptability in the building sector

Ul Arifeen,

Nasir Amin,

Ahmad

et al. 2023

Construction and Building Materials

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Sustainable strategy of eggshell waste usage in cementitious composites: An integral testing and computational study for compressive behavior in aggressive environment

Cited by 16 publications

References 60 publications

Strength Estimation and Feature Interaction of Carbon Nanotubes-Modified Concrete Using Artificial Intelligence-Based Boosting Ensembles

Strength Estimation and Feature Interaction of Carbon Nanotubes-Modified Concrete Using Artificial Intelligence-Based Boosting Ensembles

Strength Reduction Due to Acid Attack in Cement Mortar Containing Waste Eggshell and Glass: A Machine Learning-Based Modeling Study

A comparative study of prediction models for alkali-activated materials to promote quick and economical adaptability in the building sector

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