YaBeSH Engineering and Technology Library

    • Journals
    • PaperQuest
    • YSE Standards
    • YaBeSH
    • Login
    View Item 
    •   YE&T Library
    • ASCE
    • Journal of Materials in Civil Engineering
    • View Item
    •   YE&T Library
    • ASCE
    • Journal of Materials in Civil Engineering
    • View Item
    • All Fields
    • Source Title
    • Year
    • Publisher
    • Title
    • Subject
    • Author
    • DOI
    • ISBN
    Advanced Search
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Archive

    Compressive Strength of Sandy Soils Stabilized with Alkali-Activated Volcanic Ash and Slag

    Source: Journal of Materials in Civil Engineering:;2021:;Volume ( 033 ):;issue: 011::page 04021295-1
    Author:
    Nader Shariatmadari
    ,
    Hadi Hasanzadehshooiili
    ,
    Pooria Ghadir
    ,
    Fatemeh Saeidi
    ,
    Farshad Moharami
    DOI: 10.1061/(ASCE)MT.1943-5533.0003845
    Publisher: ASCE
    Abstract: In recent years, compared with the traditional portland cement, environmentally friendly geopolymers have gained more attention as construction materials. This paper considered volcanic ash (VA) and ground granulated blast furnace slag (GGBFS) in different percentages (0%, 3%, 7%, and 10%) as a replacement for the conventionally used portland cement to stabilize sandy soils. NaOH and Na2SiO3 in different concentrations (4, 8, and 12  M) and alkali to binder ratios (1, 1.5, 2, and 3) were used as alkali activator solutions to build new geopolymers. Samples were cured at both ambient and oven temperatures and for 1, 7, and 28 days. Unconfined compressive strength (UCS) of samples then was evaluated. Two predictive approaches, artificial neural network (ANN) modeling and the evolutionary polynomial regression technique (EPR), were applied to model UCS of geopolymerized sand samples. Regarding the high value of the coefficient of determination of the proposed ANN, 97%, and acceptable prediction errors, RMS error of 0.0439 and MAE of 0.0336, an 8-5-10-1 ANN was introduced as a more accurate tool for the prediction of UCS. Next, three-dimensional parametrical studies investigated the effects of simultaneous changes in alkali solution, binder, and curing condition parameters on UCS values of geopolymerized samples. Sensitivity analysis based on the cosine amplitude method introduced the Si/Al ratio as the parameter most affecting and VA content as the parameter least affecting the compressive strength of samples. Results were analyzed further using pH and electrical conductivity tests and interpreted based on microstructural investigations using scanning electron microscopy (SEM) images and X-ray diffraction analysis.
    • Download: (3.999Mb)
    • Show Full MetaData Hide Full MetaData
    • Get RIS
    • Item Order
    • Go To Publisher
    • Statistics

      Compressive Strength of Sandy Soils Stabilized with Alkali-Activated Volcanic Ash and Slag

    URI
    https://yetl.yabesh.ir/yetl1/handle/yetl/4272510
    Collections
    • Journal of Materials in Civil Engineering

    Show full item record

    contributor authorNader Shariatmadari
    contributor authorHadi Hasanzadehshooiili
    contributor authorPooria Ghadir
    contributor authorFatemeh Saeidi
    contributor authorFarshad Moharami
    date accessioned2022-02-01T22:02:50Z
    date available2022-02-01T22:02:50Z
    date issued11/1/2021
    identifier other%28ASCE%29MT.1943-5533.0003845.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4272510
    description abstractIn recent years, compared with the traditional portland cement, environmentally friendly geopolymers have gained more attention as construction materials. This paper considered volcanic ash (VA) and ground granulated blast furnace slag (GGBFS) in different percentages (0%, 3%, 7%, and 10%) as a replacement for the conventionally used portland cement to stabilize sandy soils. NaOH and Na2SiO3 in different concentrations (4, 8, and 12  M) and alkali to binder ratios (1, 1.5, 2, and 3) were used as alkali activator solutions to build new geopolymers. Samples were cured at both ambient and oven temperatures and for 1, 7, and 28 days. Unconfined compressive strength (UCS) of samples then was evaluated. Two predictive approaches, artificial neural network (ANN) modeling and the evolutionary polynomial regression technique (EPR), were applied to model UCS of geopolymerized sand samples. Regarding the high value of the coefficient of determination of the proposed ANN, 97%, and acceptable prediction errors, RMS error of 0.0439 and MAE of 0.0336, an 8-5-10-1 ANN was introduced as a more accurate tool for the prediction of UCS. Next, three-dimensional parametrical studies investigated the effects of simultaneous changes in alkali solution, binder, and curing condition parameters on UCS values of geopolymerized samples. Sensitivity analysis based on the cosine amplitude method introduced the Si/Al ratio as the parameter most affecting and VA content as the parameter least affecting the compressive strength of samples. Results were analyzed further using pH and electrical conductivity tests and interpreted based on microstructural investigations using scanning electron microscopy (SEM) images and X-ray diffraction analysis.
    publisherASCE
    titleCompressive Strength of Sandy Soils Stabilized with Alkali-Activated Volcanic Ash and Slag
    typeJournal Paper
    journal volume33
    journal issue11
    journal titleJournal of Materials in Civil Engineering
    identifier doi10.1061/(ASCE)MT.1943-5533.0003845
    journal fristpage04021295-1
    journal lastpage04021295-20
    page20
    treeJournal of Materials in Civil Engineering:;2021:;Volume ( 033 ):;issue: 011
    contenttypeFulltext
    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
    yabeshDSpacePersian
     
    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
    yabeshDSpacePersian