بررسی آزمایشگاهی و مدل سازی پیش‌بینی مقاومت فشاری بتن ژئوپلیمری پوزولانی بوسیله برنامه نویسی بیان ژن

نوع مقاله: مقاله پژوهشی

نویسندگان

1 دانشجوی کارشناسی ارشد، گروه مهندسی عمران، دانشکده فنی و مهندسی، دانشگاه مازندران، بابلسر، ایران

2 دانشیار، گروه مهندسی عمران، دانشکده فنی و مهندسی، دانشگاه مازندران، بابلسر، ایران

3 دانشجوی کارشناسی، گروه مهندسی عمران، دانشکده فنی و مهندسی، دانشگاه مازندران، بابلسر، ایران

10.30478/jcsm.2020.214158.1141

چکیده

با توجه به اثرات مخرب زیست محیطی تولید سیمان، استفاده از بتن ژئوپلیمری می‌تواند به عنوان یک رویکرد سازگار با محیط زیست در ساخت بتن در نظر گرفته شود. هدف این تحقیق بررسی تاثیر جایگزینی بخشی از سرباره به عنوان پایه بتن ژئوپلیمری (جایگزینی 5، 10، 15، 20، 25 و 30%) با زئولیت طبیعی و دوده سیلیس و همچنین تغییرات غلظت هیدروکسید سدیم (4، 6 و 8 مولار) همراه با محلول سدیم سیلیکات به عنوان فعال کننده بتن ژئوپلیمری بر مقاومت فشاری می‌باشد. نتایج نشان می‌دهد که افزایش غلظت هیدروکسید سدیم باعث کاهش مقاومت فشاری بتن و در مقابل، افزودن زئولیت طبیعی و دوده سیلیس باعث افزایش آن می‌شود. علاوه بر این، از برنامه نویسی بیان ژن برای ساخت مدل‌های ریاضی برای پیش بینی مقاومت فشاری بتن ژئوپلیمری بر پایه سرباره، استفاده شده است. با استفاده از نتایج آزمایشگاهی، یک پایگاه داده گسترده و قابل اعتماد از مقاومت فشاری بتن ژئوپلیمری بر پایه سرباره به دست آمد. این پایگاه داده شامل نتایج مقاومت فشاری 351 نمونه ساخته شده از 117 طرح اختلاط است. پنج پارامتر تأثیرگذار از قبیل، سن نمونه‌ها، غلظت محلول هیدروکسید سدیم، میزان زئولیت طبیعی، دوده سیلیس و سرباره، به عنوان پارامترهای ورودی برای مدل سازی در نظر گرفته شدند. نتایج نشان داد که مدل‌های ارائه شده دقیق هستند و قابلیت پیش بینی بالایی دارند. یافته‌های این پژوهش می‌تواند به بهبود استفاده مجدد از سرباره برای ساخت بتن ژئوپلیمری کمک کند.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Experimental investigation and predictive modeling of compressive strength of pozzolanic geopolymer concrete using gene expression programming

نویسندگان [English]

  • Amir Ali Shahmansouri 1
  • Habib Akbarzadeh Bengar 2
  • Saeed Ghanbari 3
1 Graduate Student, Department of Civil Engineering, Faculty of Engineering and Technology, University of Mazandaran, Babolsar, Iran
2 Associate Professor, Department of Civil Engineering, Faculty of Engineering and Technology, University of Mazandaran, Babolsar, Iran
3 Undergraduate Student, Department of Civil Engineering, Faculty of Engineering and Technology, University of Mazandaran, Babolsar, Iran
چکیده [English]

With regard to the adverse environmental impacts of cement production, the use of geopolymer concrete (GPC) can be considered as a more environmentally friendly approach for concreting. This study deals with an experimental investigation on the effects of partial replacement of the GGBS (replaced with 5, 10, 15, 20, 25, and 30%) used in GPC with natural zeolite (NZ) and silica fume (SF) simultaneously with different concentration (4, 6 and 8 M) of sodium hydroxide (NaOH) together with sodium silicate (water glass) solution on the compressive strength. Results indicate that increasing concentration of NaOH yields decreases the compressive strength of the concrete. In contrast, adding NZ and SF into concrete results in increasing the compressive strength. In addition, gene expression programming (GEP) was employed to develop mathematical models for predicting the compressive strength of GPC based on GGBS. Using the experimental results, an extensive and reliable database of compressive strength of GGBS-based GPC was obtained. The database comprises the compressive strength results of 351 specimens produced from 117 different mixtures. The five most influential parameters i.e., age of specimens, NaOH solution concentration, NZ, SF and GGBS content of GPC, were considered as the input parameters for modeling. The results reflected that the proposed models are accurate and possess a high prediction capability. The findings of this study can enhance the re-use of GGBS for the development of GPC leading to environmental protection and monetary benefits.

کلیدواژه‌ها [English]

  • Geopolymer concrete
  • Pozzolan
  • Compressive strength
  • strength prediction
  • Gene expression programming
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