مهندسی سازه و ساخت

مهندسی سازه و ساخت

رویکرد نوین در پیش‌بینی مقاومت بتن خود متراکم حاوی سنگدانه‌های بازیافتی در دماهای بالا: کاربرد برنامه نویسی بیان ژنی

نوع مقاله : علمی - پژوهشی

نویسندگان
1 استادیار، گروه مهندسی عمران، دانشگاه فردوسی مشهد، مشهد، ایران
2 دانشجوی کارشناسی ارشد، گروه مهندسی عمران، دانشگاه فردوسی مشهد، مشهد، ایران
چکیده
افزایش ضایعات ساختمانی به دلیل رشد سریع ساخت‌وساز و بازسازی زیرساخت‌ها، همراه با ضرورت دستیابی به توسعه پایدار، استفاده از سنگدانه‌های بازیافتی (RA) در بتن را به یک اولویت زیست‌محیطی تبدیل کرده است. در این راستا، پژوهش حاضر به ارائه مدلی برای پیش‌بینی مقاومت فشاری بتن خودمتراکم (SCC) حاوی RA در دماهای بالا با استفاده از برنامه‌نویسی بیان ژنی (GEP) می‌پردازد. یک پایگاه داده جامع شامل 62 نمونه آزمایشگاهی از 11 طرح اختلاط منحصر‌به‌فرد از مطالعات معتبر برای این تحلیل گردآوری شد. متغیرهای ورودی شامل نسبت آب به سیمان، مقدار سنگدانه درشت بازیافتی، شرایط دمایی، و مقاومت فشاری در دمای اتاق انتخاب شدند. برای پیش‌بینی مقاومت فشاری SCC بر اساس این متغیرها، چهار مدل عددی GEP توسعه داده شد. از میان این مدل‌ها، مدل سوم (GEP3) با ضریب همبستگی𝑅2 = 0.9474 عملکرد بهتری نشان داد و دقت بالای آن در پیش‌بینی رفتار بتن تأیید شد. تحلیل نتایج نشان داد که SCC حاوی RA در دماهای بالا نه‌تنها خواص مکانیکی بهتری ارائه می‌دهد، بلکه می‌تواند به‌عنوان یک رویکرد پایدار برای کاهش مصرف منابع طبیعی و کاهش اثرات زیست‌محیطی مطرح شود. این مطالعه تأثیر مثبت استفاده از RA در بهبود خواص مکانیکی بتن و نقش کلیدی آن در کاهش ضایعات ساختمانی و افزایش بهره‌وری منابع را برجسته می‌کند.
کلیدواژه‌ها

موضوعات


عنوان مقاله English

A novel approach to predict the compressive strength of recycled aggregate self-compacting concrete at high temperatures: The application of Gene expression programming

نویسندگان English

Amirreza Masoodi 1
Morteza Ghodratnama 2
1 Assistant Professor, Department of Civil Engineering, Ferdowsi University of Mashhad, Iran
2 M.Sc. Student, Department of Civil Engineering, Ferdowsi University of Mashhad, Mashhad, Iran
چکیده English

The accumulation of construction waste and the urgent need for sustainable development have highlighted the importance of using recycled aggregates (RA) in concrete production. Self-Compacting Concrete (SCC), known for its superior flowability and reduced construction costs, offers significant potential for integrating RA into structural applications. This study aims to develop a reliable predictive model for the compressive strength of RA-SCC under high-temperature conditions using Gene Expression Programming (GEP), an advanced machine learning technique. A comprehensive database of 62 laboratory-tested samples, derived from 11 unique mix designs documented in reputable studies, was assembled for this purpose. The model’s input variables included the water-to-cement (W/C) ratio, recycled coarse aggregate (RCA) content, temperature conditions, and compressive strength at room temperature. Four numerical models based on GEP were developed to predict the compressive strength of RA-SCC under thermal stress. Among these, the third model (GEP3) demonstrated the highest accuracy, achieving a correlation coefficient (𝑅2 = 0.9474). This result validates the model’s ability to capture the intricate relationships between mix design parameters and thermal conditions affecting RA-SCC performance. The findings reveal that RA-SCC exhibits enhanced mechanical properties at elevated temperatures, demonstrating the feasibility of incorporating RA into advanced concrete technologies. Moreover, the study underscores the dual benefits of RA in SCC: reducing dependence on natural aggregates and increasing environmental resilience under extreme conditions. This work offers a robust computational framework for optimizing RA-SCC, marking progress toward innovative and eco-friendly construction materials and practices.

کلیدواژه‌ها English

Self-compacting concrete (SCC)
Gene expression programming
sustainable development
recycled aggregates
high temperatures
compressive strength
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