فصلنامه علمی کارافن

فصلنامه علمی کارافن

مدل های هوشمند برای پیش بینی مقاومت فشاری بتن سبز ساخته‌شده با ریزدانه و درشت‌دانه های ضایعات سرباره مس

نوع مقاله : مقاله پژوهشی (نظری)

نویسندگان
1 دانشکده مهندسی عمران، دانشگاه صنعتی سیرجان، سیرجان، ایران.
2 گروه مهندسی عمران، دانشکده مهندسی، دانشگاه بزرگمهر قائنات، قائن، ایران.
3 گروه مهندسی عمران، دانشگاه ملی مهارت، تهران، ایران.
چکیده
در سال­های اخیر استفاده از ضایعات سرباره مس به‌عنوان جایگزین مصالح سنگدانه در بتن به دلیل صرفه اقتصادی و کمک به محیط‌زیست رواج یافته است. از طرفی، زمان­بر بودن و هزینه­ زیاد مطالعات آزمایشگاهی سبب تمایل به استفاده از مدل­های مبتنی بر هوش مصنوعی برای پیش­بینی خواص انواع مختلف بتن شده است. با این وجود، تاکنون رابطه­ای برای تخمین مقاومت فشاری بتن­های حاوی سرباره مس به‌عنوان جایگزین ریزدانه­ها و درشت‌دانه­های طبیعی ارائه نشده است. در این مطالعه، با جمع­آوری بانک داده قدرتمند شامل 458 نمونه از مطالعات آزمایشگاهی معتبر، یک رابطه مؤثر برای تخمین مقاومت فشاری این نوع بتن ارائه شده است. مجموعه داده‌های جمع­آوری‌شده شامل متغیرهای ورودی مختلفی ازجمله نسبت آّب به مواد پودری، مقدار مواد پودری، مقدار ریزدانه­ها، مقدار درشت‌دانه‌ها، درصد سرباره مس و سن عمل‌آوری بتن می­باشد. بدین منظور، از دو مدل هوشمند مبتنی بر برنامه­ریزی ژنتیک[1] (GP) و سیستم استنتاج منطق فازی- عصبی [2](ANFIS) استفاده شده است. همچنین، از الگوریتم ازدحام ذرات (PSO) برای تنظیم پارامترها و بهینه‌سازی مدل ANFIS بهره گرفته شد. نتایج در حالت کلی بیان­گر قابلیت تعمیم­پذیری و دقت بیشتر مدل ANFIS (94/0R2 =) نسبت به مدل GP می­باشد. مدل ترکیبی ANFIS-PSO با تنظیم بهینه پارامترها، بهترین جواب (96/0R2 =) را در مقایسه با سایر مدل­ها ارائه کرد. با تحلیل حساسیت متغیرهای ورودی مشخص گردید که سن عمل‌آوری و مواد پودری به­ترتیب بیشترین تأثیر مثبت را دارند و نسبت آب به مواد پودری نیز بیشترین تأثیر منفی بر مقاومت فشاری بتن سبز حاوی سرباره مس را دارد؛ افزایش حجم ریزدانه­ ها نیز باعث افت شدید مقاومت فشاری این نوع بتن­های می­شود. رابطه پیشنهادی بر مبنای مدل GP، تخمین مقاومت فشاری و انجام مطالعات پارامتری تکمیلی را بدون نیاز به انجام محاسبات پیچیده و هزینه اضافی امکان‌پذیر می­سازد.
کلیدواژه‌ها
موضوعات

عنوان مقاله English

Intelligent Models for Predicting the Compressive Strength of Green Concrete Made with Fine and Coarse Grains of Waste Copper Slag

نویسندگان English

Yaser Moodi 1
Naser Safaeian Hamzehkolaei 2
Iman Afshoon 3
1 Department of Civil Engineering, Sirjan University of Technology, Sirjan, Iran.
2 Department of Civil Engineering, Bozorgmehr University of Qaenat, Qaen, Iran.
3 Department of Civil Engineering, National University of Skills (NUS), Tehran, Iran.
چکیده English

In recent years, using copper slag waste as a substitute for aggregate materials in concrete has become popular due to economic savings and environmental benefits. However, the time-consuming and costly nature of the experimental studies has prompted the use of artificial intelligence-based models to predict concrete properties. Thus far, no method has been proposed to estimate the compressive strength of concretes with copper slag as a substitute for natural aggregates. This study presents an effective relationship for estimating the compressive strength of such concrete based on a robust database of 458 samples from valid laboratory studies. The dataset included variables such as the ratio of water-to-powdered materials, amount of powdered materials, amount of fine and coarse aggregates, copper slag percentage, and concrete curing age. Two intelligent models, Genetic Programming (GP) and Adaptive Neuro-Fuzzy Inference System (ANFIS), were used. The Particle Swarm Optimization (PSO) algorithm was employed to tune parameters and optimize the ANFIS model. Results showed that the ANFIS model (R2=0.94) outperformed the GP model in generalization capability and accuracy. The hybrid ANFIS-PSO model with optimal parameter tuning achieved the best performance (R2=0.96) compared to other models. Through sensitivity analysis of the input variables, it was determined that curing age and powdered materials had the highest positive effects, respectively, while the ratio of water-to-powdered material had the most negative effect on the compressive strength of green concrete containing copper slag. An increase in the volume of fine aggregates also led to a significant decrease in the compressive strength of this type of concrete. The proposed GP-based predictive model enables the estimation of compressive strength and the conduct of supplementary parametric studies without the need for complex calculations and additional costs.

کلیدواژه‌ها English

Green Concrete
Copper Slag
Concrete Aggregates Compressive Strength Prediction Models
Genetic Programming
ANFIS
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دوره 21، شماره 3
فنی و مهندسی
پاییز 1403
صفحه 367-394

  • تاریخ دریافت 25 بهمن 1402
  • تاریخ بازنگری 08 خرداد 1403
  • تاریخ پذیرش 21 شهریور 1403