Mix design determination procedure for geopolymer concrete based on target strength method

Rathnayaka, Madushan; Karunasingha, Dulakshi; Gunasekara, Chamila; Law, David W.; Wijesundara, Kushan; Lokuge, Weena

doi:10.1007/s43452-024-01002-8

Artykuł - szczegóły

Tytuł artykułu

Mix design determination procedure for geopolymer concrete based on target strength method

Autorzy

Rathnayaka Madushan , Karunasingha Dulakshi , Gunasekara Chamila , Law David W. , Wijesundara Kushan , Lokuge Weena

Wybrane pełne teksty z tego czasopisma

http://www.springer.com/journal/43452

Identyfikatory

DOI

10.1007/s43452-024-01002-8

Warianty tytułu

Języki publikacji

Abstrakty

This study presents the development and validation of a mix design determination procedure for geopolymer concrete to achieve the desired compressive strength. The procedure integrates artificial neural network (ANN) model developed based on a comprehensive data base from literature, data clustering, and parameter optimization techniques to enhance accuracy and reliability. Experimental validation is undertaken to demonstrate the mix design determination procedure’s capability to accurately predict mix designs for geopolymer concrete based on the target compressive strength, validating its efficacy for mix proportion determination. The integration of chemical oxide content in fly ash, curing time, curing temperature, and activator properties results in a 15.9% improvement in prediction accuracy for the training dataset and a 68.3% enhancement for the testing dataset, compared to the base ANN model that includes only the weight of fly ash and activator properties. Employing data clustering techniques enables the identification of prior estimates for the mix design parameters related to specific fly ash types and target compressive strength, streamlining the mix design process by analyzing pertinent data subsets. Parameter optimization ensures refined mix proportions, achieving the desired target strength economically while minimizing material waste and cost. The development of a user interface facilitates easy manipulation of mix designs, catering to users of varying expertise levels. Additional options for deeper insights into geopolymer concrete characteristics can be integrated into the mix design determination procedure. To assess the mix design determination procedure's ability to generalize effectively, a variety of fly ash samples with distinct chemical compositions were utilized, differing from those already present in the database. This approach allows for a thorough evaluation of the mix design determination procedure's performance when presented with fly ash compositions it has not encountered before. By doing so, this provides insights into the adaptability of the mix design determination procedure beyond the limitations of the training and testing datasets.

Słowa kluczowe

geopolymer concrete class F fly ash mix design machine learning compressive strength data clustering

beton geopolimerowy popiół lotny wytrzymałość na ściskanie

Wydawca

Springer
Wrocław University of Science and Technology

Czasopismo

Archives of Civil and Mechanical Engineering

Rocznik

2024

Tom

Vol. 24, no. 3

Strony

art. no. e192, 2024

Opis fizyczny

Bibliogr. 58 poz., rys., tab., wykr.

Twórcy

autor

Rathnayaka Madushan

School of Engineering, RMIT University, Melbourne, Australia

autor

Karunasingha Dulakshi

Department of Engineering Mathematics, University of Peradeniya, Peradeniya, Sri Lanka

autor

Gunasekara Chamila

chamila.gunasekara@rmit.edu.au

School of Engineering, RMIT University, Melbourne, Australia

https://orcid.org/0000-0003-2013-8720

autor

Law David W.

School of Engineering, RMIT University, Melbourne, Australia

autor

Wijesundara Kushan

Faculty of Engineering, University of Peradeniya, Peradeniya, Sri Lanka

autor

Lokuge Weena

School of Engineering, University of Southern Queensland, Toowoomba, Australia

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025)

Typ dokumentu

Bibliografia

Identyfikator YADDA

bwmeta1.element.baztech-50f39513-a11f-4d2b-8f85-3cd1d75c693f