بررسی عددی - آزمایشگاهی اثر مقاومت فشاری سیمان بر ویژگی مکانیکی بتن به کمک الگوهای مبتنی بر هوش مصنوعی

نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه آموزشی مهندسی عمران، دانشکده مهندسی، دانشگاه فردوسی مشهد

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

3 کارخانه سیمان زاوه تربت

10.22124/jcr.2024.26549.1646

چکیده

نقش مقاومت فشاری ملات ماسه سیمانی استاندارد در مقاومت فشاری بتن غیر قابل انکار است. از همین رو در این پژوهش، روش شبکه عصبی مصنوعی (ANN) و ژنتیک بیان مسئله (GEP) به عنوان فرآیندهای فراتکاملی جهت پیش‌بینی مقاومت فشاری بتن بر پایه ی مقاومت فشاری ملات سیمان متناظر به کار می رود. برای رسیدن به این هدف، تعداد 286 طرح اختلاط ملات ماسه سیمان دارای نسبت‌های یکسان مواد خام نخستین ورودی به کوره سیمان و در دسته‌بندی سیمان تیپ 2 (مقاومت 32/5 مگاپاسکال) مورد مطالعه قرار گرفت. نتایج آزمایش‌های مقاومت فشاری ملات‌های ماسه سیمان استاندارد (3 آزمونه) و بتن ساخته شده با طرح اختلاط یکسان (3 آزمونه) در سن 28 روز در دسترس قرار گرفتند. بر پایه ی همین نتایج الگوی گسترش یافته می تواند به پیش‌بینی مقاومت فشاری بتن بر پایه ی مقاومت فشاری ملات متناظر با تمرکز بر نقش نرمی سیمان با دقت و شاخص عملکرد بالا بپردازد.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Numerical and Experimental Investigation of the Influence of Cement Compressive Strength on the Mechanical Properties of Concrete Using Artificial Intelligence-Based Models

نویسندگان [English]

  • Sahar Mahdinia 1
  • Mohammad Reza Tavakkolizadeh 2
  • Amir R. Masoodi 2
  • Roushan Montazerian 3
1 Department of Civil Engineering, Faculty of Engineering, Ferdowsi University of Mashhad
2 Department of Civil Engineering, Faculty of Engineering, Ferdowsi University of Mashhad
3 Zaveh Torbat Cement Company
چکیده [English]

The role of compressive strength of standard sand-cement mortar in the undeniable strength of concrete is crucial. Additionally, the Blaine of cement is one of the key factors in the compressive strength of cement mortar and concrete. In this research, the Artificial Neural Network (ANN) and Gene Expression Programming (GEP) methods are employed as comprehensive processes to predict the compressive strength of concrete based on the compressive strength of corresponding cement mortar. To achieve this goal, 286 mixed designs of sand-cement mortar with consistent ratios of raw materials were introduced into the cement kiln, focusing on Type 2 cement (32.5 MPa strength). The results of compressive strength tests on standard sand-cement mortar samples (3 specimens) and concrete produced with consistent mix designs (3 specimens) at the age of 28 days were obtained. Based on these results, the developed model can accurately and effectively predict the compressive strength of concrete based on the compressive strength of corresponding mortar, emphasizing the role of cement Blaine.

کلیدواژه‌ها [English]

  • Standard sand-cement mortar
  • concrete compressive strength
  • cement Blaine
  • Artificial Neural Network (ANN)
  • Genetic Expression Programming (GEP)
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