Prediction of Fresh and Hardened State Properties of UHPC: Comparative Study of Statistical Mixture Design and an Artificial Neural Network Model

Ghafari, Ehsan; Bandarabadi, Mojtaba; Costa, Hugo; Júlio, Eduardo

doi:10.1061/(asce)mt.1943-5533.0001270

Cited by 93 publications

(37 citation statements)

References 16 publications

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“…The artificial neural network (ANN) is a powerful method used in modelling complex systems. The ANN is used for modelling the properties of the normal concrete [38][39][40][41][42][43][44][45][46]. Thus, the ANN can be used for modelling the fly ash-based geopolymer mortar to understand the effect of the fly ash characteristics on the selection of the alkaline activator component.…”

Section: Introductionmentioning

confidence: 99%

Effects of fly ash characteristics and alkaline activator components on compressive strength of fly ash-based geopolymer mortar

Hadi

Al-azzawi

2018

Construction and Building Materials

130

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The compressive strength of fly ash-based geopolymer mortar (FBGM) made from five different sources of fly ash was examined. The weight ratio of alkaline activator to fly ash content (AL/FA), the weight ratio of sodium silicate to sodium hydroxide (Na 2 SiO 3 /NaOH) and concentration of the NaOH were considered the main parameters. A total of 180 FBGM mixes were prepared and tested for compressive strength. The results indicated that the optimum AL/FA ratio for activating fly ashes was in the range of 0.5 and 0.6. The compressive strength of the FBGM that were mixed with different fly ashes exhibited different response to the increase of the Na 2 SiO 3 /NaOH ratio and NaOH concentration. Fly ashes with a high content of fine particles and amorphous components such as SiO 2 and Al 2 O 3 activated at a low dosage of Na 2 SiO 3 /NaOH ratio and NaOH. While the activation of fly ashes with a low content of fine particles and amorphous components required a high dosage of Na 2 SiO 3 /NaOH ratio and NaOH. An artificial neural network was developed and the analytical results showed high performance in predicting the compressive strengths of the FBGM.

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Section: Introductionmentioning

confidence: 99%

Effects of fly ash characteristics and alkaline activator components on compressive strength of fly ash-based geopolymer mortar

Hadi

Al-azzawi

2018

Construction and Building Materials

130

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“…5,6 The associated computational methods such as molecular modeling and quantum-mechanical atomistic simulations are well-established 7,8 and have recently been expanded to include artificial intelligence (AI) and machine learning (ML). 3,4,[9][10][11][12][13][14][15][16] In the last decade, AI an ML have acquired a pervasive presence in all branches of science, going from natural languages to fluid dynamics. 17,18 With a steady pace, new achievements are being reported in the field of materials research.…”

Section: Introductionmentioning

confidence: 99%

“…17,18 With a steady pace, new achievements are being reported in the field of materials research. 3,9,[19][20][21][22][23][24][25] In general, these achievements are rooted in the access to suitable large datasets, both theoretically and experimentally. [14][15][16][26][27][28] However, even though such big datasets (and access to them) are becoming common place, 3,27,[29][30][31][32] they do not represent the datasets most materials researchers work with on a day-to-day basis.…”

Section: Introductionmentioning

confidence: 99%

Small data materials design with machine learning: When the average model knows best

Vanpoucke

Knippenberg²,

Hermans³

et al. 2020

Journal of Applied Physics

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People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website.• The final author version and the galley proof are versions of the publication after peer review.• The final published version features the final layout of the paper including the volume, issue and page numbers. Link to publication General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.• Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal.If the publication is distributed under the terms of Article 25fa of the Dutch Copyright Act, indicated by the "Taverne" license above, please follow below link for the End User Agreement:

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“…The developed model could predict the optimum quantity of admixtures which have strong influence on the rheological properties of the cement paste. Modeling of the fresh stage properties of concrete using ANN and other soft computing methods are also reported in other studies (Yeh 2007(Yeh , 2008a(Yeh , 2008bGhafari et al 2015).…”

Section: Background Literaturementioning

confidence: 99%

Modeling the Fresh and Hardened Stage Properties of Self-Compacting Concrete using Random Kitchen Sink Algorithm

Sathyan

Anand

Prakash

et al. 2018

Int J Concr Struct Mater

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High performance concrete especially self compacting concrete (SCC) has got wide popularity in construction industry because of its ability to flow through congested reinforcement without segregation and bleeding. Even though European Federation of National Associations Representing for Concrete (EFNARC) guidelines are available for the mix design of SCC, large number of trials are required for obtaining an SCC mix with the desired engineering properties. The material and time requirement is more to conduct such large number of trials. The main objective of the study presented in this paper is to demonstrate use of regularized least square algorithm (RLS) along with random kitchen sink algorithm (RKS) to effectively predict the fresh and hardened stage properties of SCC. The database for testing and training the algorithm was prepared by conducting tests on 40 SCC mixes. Parametric variation in the SCC mixes were the quantities of fine and coarse aggregates, superplasticizer dosage, its family and water content. Out of 40 test results, 32 results were used for training and 8 set results were used for testing the algorithm. Modelling of both fresh state properties viz., flowing ability (Slump Flow), passing ability (J Ring), segregation resistance (V funnel at 5 min) as well as hardened stage property (compressive strength) of the SCC mix was carried out using RLS and RKS algorithm. Accuracy of the model was checked by comparing the predicted and measured values. The model could accurately predict the properties of the SCC within the experimental domain.

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Prediction of Fresh and Hardened State Properties of UHPC: Comparative Study of Statistical Mixture Design and an Artificial Neural Network Model

Cited by 93 publications

References 16 publications

Effects of fly ash characteristics and alkaline activator components on compressive strength of fly ash-based geopolymer mortar

Effects of fly ash characteristics and alkaline activator components on compressive strength of fly ash-based geopolymer mortar

Small data materials design with machine learning: When the average model knows best

Modeling the Fresh and Hardened Stage Properties of Self-Compacting Concrete using Random Kitchen Sink Algorithm

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