Flowability and compressive strength test on self compacting mortar using graphene oxide

Pesaralanka, Vyshnavi; Khed, Veerendrakumar C.

doi:10.1016/j.matpr.2020.05.067

Cited by 7 publications

(2 citation statements)

References 17 publications

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“…The technological, economic, and environmental benefits of SCM have been widely demonstrated [ 5 , 6 , 7 , 8 , 9 , 10 ]. The advantageous properties of SCM are its high fluidity [ 11 ] and simple placement in densely reinforced structures [ 12 ], which can minimize the requirement for skilled labor and the concrete casting period. As the old reinforced concrete structure deteriorates over time, it becomes necessary to repair the portions that suffer major damage.…”

Section: Introductionmentioning

confidence: 99%

Hybrid BO-XGBoost and BO-RF Models for the Strength Prediction of Self-Compacting Mortars with Parametric Analysis

Ahmed,

Song,

Zhang

et al. 2023

Materials

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Self-compacting mortar (SCM) has superior workability and long-term durable performance compared to traditional mortar. The strength of SCM, including both its compressive and flexural strengths, is a crucial property that is determined by appropriate curing conditions and mix design parameters. In the context of materials science, predicting the strength of SCM is challenging because of multiple influencing factors. This study employed machine learning techniques to establish SCM strength prediction models. Based on ten different input parameters, the strength of SCM specimens were predicted using two different types of hybrid machine learning (HML) models, namely Extreme Gradient Boosting (XGBoost) and the Random Forest (RF) algorithm. HML models were trained and tested by experimental data from 320 test specimens. In addition, the Bayesian optimization method was utilized to fine tune the hyperparameters of the employed algorithms, and cross-validation was employed to partition the database into multiple folds for a more thorough exploration of the hyperparameter space while providing a more accurate assessment of the model’s predictive power. The results show that both HML models can successfully predict the SCM strength values with high accuracy, and the Bo-XGB model demonstrated higher accuracy (R2 = 0.96 for training and R2 = 0.91 for testing phases) for predicting flexural strength with low error. In terms of compressive strength prediction, the employed BO-RF model performed very well, with R2 = 0.96 for train and R2 = 0.88 testing stages with minor errors. Moreover, the SHAP algorithm, permutation importance and leave-one-out importance score were used for sensitivity analysis to explain the prediction process and interpret the governing input variable parameters of the proposed HML models. Finally, the outcomes of this study might be applied to guide the future mix design of SCM specimens.

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Section: Introductionmentioning

confidence: 99%

Hybrid BO-XGBoost and BO-RF Models for the Strength Prediction of Self-Compacting Mortars with Parametric Analysis

Ahmed,

Song,

Zhang

et al. 2023

Materials

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“…The high percentage of GO demanded the higher percentage of polycarboxylate superplasticizer to balance the self-compacting nature. However, the adverse effects of polycarboxylate superplasticizer over dosage in the form of bleeding and segregation were observed during fresh properties [ 27 ]. There is no doubt that good flowability is the precondition of concrete agitation and pump transport, which is a key performance for cement-based materials in engineering applications.…”

Section: Introductionmentioning

confidence: 99%

Comparison Study on the Adsorption Behavior of Chemically Functionalized Graphene Oxide and Graphene Oxide on Cement

Wang

Yao

2020

Materials

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Chemical functionalization of graphene oxide (GO) is one kind of advanced strategy to eliminate the negative effects on the flowability of cement with GO. The adsorption behavior of admixture on cement plays a vital role in the flowability of cement-based materials. Herein, the comparison study on the adsorption behavior (including adsorption amount, adsorption kinetics, adsorption isotherms and adsorption layer thickness) of three kinds of chemically functionalized graphene oxides (CFGOs) with different polyether amine branched-chain lengths and GO on cement is reported. The results of CFGOs and GO adsorption data on cement particles were all best fitted with the pseudo-second-order kinetic model, and also conformed to the Freundlich isothermal model, indicating that the adsorption of CFGOs and GO on cement both were multilayer type and took place in a heterogeneous manner. The adsorption of CFGOs and GO on cement was not just physical adsorption, but also engaged chemical adsorption. In contrast to GO, the adsorption behavior of CFGOs on cement represented a lesser adsorption amount, weaker adsorption capacity and thinner adsorption layer thickness. Moreover, the longer the branched-chain length of CFGOs, the greater the decreasing degrees of adsorption amount, adsorption capacity and adsorption layer thickness. Due to the consumption of the carboxyl group (-COOH) by chemical functionalization, the anchoring effect of CFGOs was weaker than GO, and the steric hindrance effect generated from branched-chains which weakened the van der Waals forces among CFGOs layers. Moreover, the steric hindrance effect strengthened with the increasing branched-chain length, thus preventing the cement particles from aggregation, which resulted in satisfactory flowability of CFGOs with incorporation of cement rather than GO.

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Sustainable Development of Mortar Using Fly-Ash Waste with Incorporation of Carbon Nanomaterials

Maurya,

Bhatrola,

Kothiyal

2024

Lecture Notes in Civil Engineering

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Flowability and compressive strength test on self compacting mortar using graphene oxide

Cited by 7 publications

References 17 publications

Hybrid BO-XGBoost and BO-RF Models for the Strength Prediction of Self-Compacting Mortars with Parametric Analysis

Hybrid BO-XGBoost and BO-RF Models for the Strength Prediction of Self-Compacting Mortars with Parametric Analysis

Comparison Study on the Adsorption Behavior of Chemically Functionalized Graphene Oxide and Graphene Oxide on Cement

Sustainable Development of Mortar Using Fly-Ash Waste with Incorporation of Carbon Nanomaterials

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