پیش‌بینی مقاومت فشاری ملات استاندارد ماسه سیمان با استفاده از شبکه عصبی مصنوعی و در نظر گرفتن اثر نرمی سیمان

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری سازه، گروه مهندسی عمران، دانشکده مهندسی، دانشگاه فردوسی مشهد

2 استادیار گروه مهندسی عمران، دانشکده مهندسی، دانشگاه فردوسی مشهد

3 کارشناسی ارشد مدیریت ساخت، کارخانه سیمان زاوه تربت

چکیده

نقش نرمی سیمان در روند هیدراتاسیون و رشد مقاومت فشاری در سنین پایین مواد پایه سیمانی غیرقابل انکار است؛ بر این اساس می‌طلبد که اثر آن در مدل‌های پیش‌بینی مورد بررسی قرار گیرد. از این رو، در این پژوهش یک بررسی گسترده شامل 640 ترکیب سیمان (1920 آزمونه ملات سیمان) از محصولات کارخانه سیمان با درصدهای مختلف مواد خام ورودی به کوره سیمان انجام شد. مواد خام اولیه شامل اکسیدهای سیلیس، آلومینیوم، آهن، کلسیم، منیزیم، گوگرد، پتاسیم و سدیم برای پیش‌بینی مقاومت فشاری 7 روزه ملات استاندارد سیمان با استفاده از روش شبکه عصبی مصنوعی (ANN) در نظر گرفته شد. نرمی پودر سیمان مورد استفاده در این نمونه‌ها به عنوان یک عامل اثرگذار نیز مورد بررسی قرار گرفت. به این منظور، دو مدل در دو حالت با و بدون در نظر گرفتن نرمی سیمان در پارامترهای ورودی به کار گرفته شد. پس از بررسی نتایج به دست آمده می‌توان مشاهده کرد که مدلANN با در نظر گرفتن نرمی سیمان عملکرد مناسب‌تری نسبت به مدل دیگر دارد. یافته‌های این پژوهش برای پیش‌بینی مقاومت فشاری سیمان تولیدی در کارخانه‌ها می‌تواند هزینه‌های آزمایشگاهی مربوطه را به شدت کاهش دهد.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Prediction of Standard Sand cement Mortar Compressive Strength Using Artificial Neural Network and Considering the Effect of Cement Fineness

نویسندگان [English]

  • Sahar Mahdinia 1
  • Mohammadreza Tavakkolizadeh 2
  • Mahdi Ahmadi Jalayer 3
1 PhD Student, Structural Engineering, Department of Civil Engineering, Ferdowsi University of Mashhad
2 Assistant Professor, Department of Civil Engineering, Ferdowsi University of Mashhad
3 MSc in Construction Management, Zaveh Torbat Cement Factory
چکیده [English]

The role of cement fineness in the process of hydration and development of compressive strength in the early ages of cement-based materials is irrefutable and it requires that its effect be investigated by predicting models. Therefore, an extensive study including 640 cement composition (1920 cement mortar specimens) from a cement factory with different percentages of raw materials feeding to the cement kiln including SiO2, Al2O3, Fe2O3, CaO, MgO, SO3, K2O, and Na2O were used to predict the 7-day compressive strength of cement mortar by artificial neural network (ANN). To investigate the effect of cement fineness, two models have been developed in two states of with and without fineness effect. Results confirmed the significant role of cement fineness as an input parameter in the performance of predicting model. The findings of this research can be used in cement production facilities in order to reduce the laboratory costs.

کلیدواژه‌ها [English]

  • Compressive Strength
  • Cement Mortar
  • Artificial Neural Network
  • Cement Fineness
  • Raw Materials

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  • تاریخ دریافت: 15 آذر 1401
  • تاریخ بازنگری: 05 دی 1401
  • تاریخ پذیرش: 01 بهمن 1401

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