Abrasion resistance behaviour of fly ash based geopolymer using nanoindentation and artificial neural network

Lau, Chee Keong; Lee, Hyuk; Vimonsatit, Vanissorn; Huen, Wai Yeong; Chindaprasirt, Prinya

doi:10.1016/j.conbuildmat.2019.04.021

Cited by 31 publications

(10 citation statements)

References 49 publications

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“…Moreover, concrete and mortar resistance in terms of wear is dependent upon material, load, strength, and hardness of surface as presented in Figure 1 . The concrete abrasion resistance is directly related to curing time, compressive strength, and material properties irrespective of cement replacement by filler materials [ 19 , 20 ]. To achieve satisfying abrasion resistance, emphasis and attention are given on sample preparation.…”

Section: Introductionmentioning

confidence: 99%

Simulation of Depth of Wear of Eco-Friendly Concrete Using Machine Learning Based Computational Approaches

et al. 2021

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To avoid time-consuming, costly, and laborious experimental tests that require skilled personnel, an effort has been made to formulate the depth of wear of fly-ash concrete using a comparative study of machine learning techniques, namely random forest regression (RFR) and gene expression programming (GEP). A widespread database comprising 216 experimental records was constructed from available research. The database includes depth of wear as a response parameter and nine different explanatory variables, i.e., cement content, fly ash, water content, fine and coarse aggregate, plasticizer, air-entraining agent, age of concrete, and time of testing. The performance of the models was judged via statistical metrics. The GEP model gives better performance with R2 and ρ equals 0.9667 and 0.0501 respectively and meet with the external validation criterion suggested in the previous literature. The k-fold cross-validation also verifies the accurateness of the model by evaluating R2, RSE, MAE, and RMSE. The sensitivity analysis of GEP equation indicated that the time of testing is the influential parameter. The results of this research can help the designers, practitioners, and researchers to quickly estimate the depth of wear of fly-ash concrete thus shortening its ecological susceptibilities that push to sustainable and faster construction from the viewpoint of environmentally friendly waste management.

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Section: Introductionmentioning

confidence: 99%

Simulation of Depth of Wear of Eco-Friendly Concrete Using Machine Learning Based Computational Approaches

et al. 2021

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“…Abrasion wearing of concrete surface occurred due to mechanical scraping, wearing, or skidding of objects on the concrete surface, and it is one of the key considerations of concrete durability [ 72 ]. The compressive strength of geopolymer concrete, the toughness of aggregate, and the hardness of geopolymer binder are the contributing factors to the abrasion resistance of geopolymer concrete.…”

Section: Abrasion Resistance Of Geopolymer Concretementioning

confidence: 99%

Durability Performance of Geopolymer Concrete: A Review

Wong

2022

Polymers

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Geopolymer concrete is produced from the geopolymerization process, in which molecules known as oligomers integrate to form geopolymer networks with covalent bonding. Its production expends less thermal energy and results in a smaller carbon footprint compared to Ordinary Portland Cement (OPC) concrete. It requires only an alkaline activator to catalyze its aluminosilicate sources such as metakaolin and fly ash, to yield geopolymer binder for the geopolymerization to take place. Because of its eco-friendly technology and practical application, current research interest is mainly concentrated on the endurance of geopolymer concrete to resist heat and chemical aggressions. As such, it is pertinent for this review article to provide critical insight into the recent progress in research on the durability of geopolymer concrete. One significant outcome of the review is that the admixture of geopolymer concrete could be blended with additives such as micro-silica and fibers such as polypropylene fibers, to enhance its durability. The review on the durability aspects of geopolymer concrete showed that it had high compressive strength at an optimal elevated temperature, low to medium chloride ion penetrability, and high resistance to acid attack and abrasion. This makes geopolymer concrete a viable candidate to replace OPC concrete in the construction industry.

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“…Recent research on the single-layer artificial neural network has focused on the neural network modeling of geopolymer concrete [ 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 ], while fewer studies have addressed neural network modeling for geopolymer pastes. An additional problem involves the generalizability of the model.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Compressive Strength of Fly Ash-Slag Based Geopolymer Paste Based on Multi-Optimized Artificial Neural Network

et al. 2023

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The fly ash-slag geopolymer is regarded as one of the new green cementitious materials that can replace cement, but it is difficult to predict its mechanical properties by conventional methods. Therefore, in the present study, the back propagation (BP) artificial neural network technique is used to predict the compressive strength of the fly ash-slag geopolymer. In this paper, data from the published literature were collected as the training set and the experimental results from laboratory experiments were used as the test set. Eight input parameters were determined, as follows: the percentage of fly ash, the percentage of slag, the water–cement ratio, the curing age, the modulus of alkali activator, the mass ratio of NaOH to Na2SiO3 and the moles of Na2O and SiO2 in the alkali activator. Three multilayer artificial neural network models were constructed using the Levenberg–Marquardt (LM), Bayesian regularization (BR) and scaled conjugate gradient (SCG) algorithms to compare the prediction accuracy of the compressive strength of the fly ash-slag geopolymer paste at different ages (3, 7, and 28 d). It was concluded that the training set error of the BR–BP neural network was the smallest. Ultimately, the hyperparameter optimization of the BR–BP neural network was carried out to compare the training set and the test set errors before and after the optimization, and the results show that the BR–BP neural network model with hyperparameter optimization had the highest prediction accuracy.

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Abrasion resistance behaviour of fly ash based geopolymer using nanoindentation and artificial neural network

Cited by 31 publications

References 49 publications

Simulation of Depth of Wear of Eco-Friendly Concrete Using Machine Learning Based Computational Approaches

Simulation of Depth of Wear of Eco-Friendly Concrete Using Machine Learning Based Computational Approaches

Durability Performance of Geopolymer Concrete: A Review

Prediction of Compressive Strength of Fly Ash-Slag Based Geopolymer Paste Based on Multi-Optimized Artificial Neural Network

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