ارائه مدلی جهت پیش‌بینی مقاومت بتن خودتراکم به کمک شبکه‌های عصبی مصنوعی

نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه مهندسی عمران، دانشکده فنی و مهندسی، دانشگاه لرستان، خرم آباد، ایران.

2 دانشگاه آزاد اسلامی واحد علوم و تحقیقات، تهران، ایران.

10.22091/cer.2024.11015.1564

چکیده

بتن خودتراکم یک عرصه پویا در زمینه ساخت‌و‌ساز در دنیا می‌باشد. این بتن، شامل بازه گسترده‌ای از طرح‌های مخلوط است که خواص بتن تازه و سخت شده لازم برای کاربری‌های خاص دارا می‌باشند. اگرچه مقاومت، همچنان معیار اصلی موفقیت این بتن می‌باشد؛ اما ویژگی‌های بتن تازه آن بسیار گسترده‌تر از بتن معمولی و متراکم شده توسط لرزاننده‌ها است. این خواص مطلوب باید در زمان، محل و بتن‌ریزی حفظ شوند. بتن خودتراکم در مواردی که شبکه‌بندی آرماتورها فشرده است، گزینه‌ای مطلوب می‌باشد. همچنین عدم نیاز به لرزاننده، آلودگی صوتی محیط را به نحو قابل‌ملاحظه‌ای کاهش می‌دهد. علی‌رغم ویژگی‌های مطلوب، طرح مخلوط و اجرای این نوع بتن به عوامل متعددی از قبیل دانه‌بندی مصالح سنگی، نوع مواد افزودنی و همچنین پرکننده‌های مورد استفاده بستگی دارد. در نظر گرفتن هریک از معیارهای فوق، کیفیت بتن سخت شده و کارپذیری بتن تازه را تحت تأثیر قرار می‌دهد. این پژوهش به دلیل نیاز به بهبود دقت و کارایی در طراحی مخلوط بتن و کاهش زمان و هزینه‌های آزمایش‌های فیزیکی انجام شده است. در این مقاله، مقاومت بتن خودتراکم به کمک داده‌های آزمایشگاهی و به‌کارگیری شبکه‌های عصبی مصنوعی پیش‌بینی گردیده است. نتایج نشان‌دهنده دقت بالای تخمین‌های انجام شده به کمک محاسبات نرم می‌باشد. 

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Proposing a Model for Compressive Strength Prediction of Self Compacting Concrete Using ANN

نویسندگان [English]

  • Mohammad Reza Torabi 1
  • Mojtaba Hosseini 1
  • Ramin Hajimohammadrezaee 2
1 Department of Civil Engineering, Faculty of Engineering, Lorestan University, Khorramabad, Iran.
2 Science and Research Branch, Islamic Azad University, Tehran, Iran.
چکیده [English]

Self-compacting concrete (SCC) is a dynamic field in construction worldwide. This type of concrete encompasses a wide range of mix designs that possess the necessary fresh and hardened concrete properties for specific applications. Although strength remains the primary criterion for the success of SCC, its fresh concrete properties are significantly broader than those of conventional vibrated concrete. These desirable properties must be maintained during placement and at the site. SCC is a preferred option in cases where reinforcement bars are densely arranged. Moreover, the absence of the need for vibrators significantly reduces environmental noise pollution. Despite its favorable features, the mix design and execution of SCC depend on various factors, such as the gradation of aggregates, the type of additives, and the fillers used. Considering each of these criteria influences the quality of hardened concrete and the workability of fresh concrete. This research has been conducted due to the need for improving accuracy and efficiency in SCC mix design and reducing the time and cost of physical testing. In this paper, the strength of SCC has been predicted using laboratory data and the application of artificial neural networks. The results indicate a high level of accuracy in the estimates made through soft computing techniques.

کلیدواژه‌ها [English]

  • Self compacting concrete
  • Artificial neural network
  • Concrete mix design
  • Concrete compressive strength
  • Sensitivity Analysis
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