ارائه مدل های بهینه شده هوش مصنوعی جهت بررسی رفتار مقاومتی بتن پایدار حاوی ریزدانه های بازیافتی پلی اتیلن ترفتالات

نوع مقاله : علمی - پژوهشی

نویسندگان

1 دانشجوی دکتری مهندسی و مدیریت ساخت، گروه مهندسی عمران، واحد رودهن، دانشگاه آزاد اسلامی، رودهن، ایران

2 استادیار گروه مهندسی عمران، واحد رودهن، دانشگاه آزاد اسلامی، رودهن، ایران

3 استادیار گروه عمران، دانشگاه آزاد اسلامی، واحد روهن

چکیده

یکی از موارد مصرف مجدد ضایعات بدون داشتن اثرات مخرب زیست محیطی، استفاده در صنعت بتن می باشد .از این رو، بررسی اثرگزاری مولفه‌های طرح اختلاط در مطالعات آزمایشگاهی و ارائه مدل‌های محاسباتی جهت ارزیابی خواص مکانیکی بتن رو به گسترش می‌باشد. از جمله تحقیقات به روز در این مورد، توسعه مدل‌های رگرسیونی محاسباتی جهت ارزیابی خواص بتن های سازگار با محیط زیست حاوی ریزدانه‌های پلی اتیلن ترفتالات (پت) بازیافت شده با استفاده از روش‌های هوش مصنوعی می‌باشد. مدل‌های توسعه داده شده می‌تواند به عنوان جایگزین فرایند آزمایشگاهی در ارائه پیش طرح اختلاط‌ها و صرفه جویی‌های اقتصادی و زیست محیطی شود. در این تحقیق، روش-های هوش مصنوعی مارس و ماشین یادگیری سریع با الگوریتم ازدحام ذرات تجمیع شده تا مدل‌هایی با دقت بالا و جامع برای تخمین خواص بتن ارائه شود. مدل‌های مبتنی بر روابط محاسباتی جهت تخمین مقادیر خواص بتن سازگار با محیط زیست حاوی پت با استفاده از مدل های هوشمند، توسعه یافته و کیفیت مدل‌ها در جهت تخمین مشخصه-های بتن و بررسی مولفه‌های طرح اختلاط این بتن بررسی شد. نتایج مدل‌های هوشمند نشان داد، استفاده از الگوریتم‌ در روند بهینه یابی ضرایب و وزن‌های روش‌های مورد استفاده، عملکرد مد‌ل‌های محاسباتی را با دقت قابل توجهی مواجه کرده است. پیش‌بینی رفتار مقاومتی مدل‌ها در مدل تلفیقی مارس بهینه شده (% 5/3 RSE=836/4 RMSE=،902/0R=) در مقایسه با دیگر مدل‌ها در‌ این مطالعه دقت قابل توجهی را بیان نموده است. همچنین جهت بررسی مولفه‌های اثرگزار در مقادیر خواص مقاومتی تحلیل حساسیت انجام و نتایج نشان داد ریزدانه با درصد 30/20 % بیشترین اثرگزاری را در بررسی مولفه‌های طرح اختلاط دارا بوده است. در نهایت عدم قطعیت مدل‌ها با استفاده از شبیه سازی مونت کارلو نشان داد مدل تلفیقی مارس با درصد عدم قطعیت 42/14 کمترین میزان عدم قطعیت را در بین مدل‌های توسعه داده شده کسب نموده است.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Presenting an Optimized Artificial Intelligent Models to Investigate the Strength Behavior of Sustainable Concrete Containing Recycled Polyethylene Terephthalate-based Fine Aggregate

نویسندگان [English]

  • Mahdi Mirzagoltabar Roshan 1
  • Mohammadhadi AlizadeElizei 2
  • Reza Esmaeilabadi 3
1 PhD candidate, Department of civil engineering, Roudehen Branch, Islamic Azad University, Roudehen, Iran
2 Department of civil engineering, Roudehen Branch, Islamic Azad University, Roudehen, Iran
3 Assistant professor , Department of Civil Engineering, Roudehen Branch,Tehran, Iran,
چکیده [English]

In this research, optimized intelligent models were developed to design optimal sustainable concrete containing recycled Polyethylene Terephthalate (PET). For this aim, evolutionary Artificial Intelligence (AI) approach was implemented based on the integration of the Multivariate Adaptive Regression Splines (MARS) and Extreme Learning Machine (ELM) integrated with particle swarm optimization algorithm to investigate the strength behavior of sustainable concrete containing recycled polyethylene terephthalate-based fine aggregate. The experimental database consisting 273 records comprising mixture components at different ages are collected from published papers and optimal variables are identified using principal component analysis. The capability and efficiency of proposed model are validated through standalone MARS and ELM. Performance metrics indicated that proposed evolutionary formula-based models (MARS-PSO and ELM-PSO with the ((R= 0.902, RMSE=4.836 MPa and RSE=3.5) and (R= 0.900, RMSE=4.881 MPa and RSE=2.24), respectively) outperformed than other standalone AI models for CS prediction. Uncertainty analysis of the standalone and hybridized models is also applied using Monte-carlo simulation to prove that the hybridized multiscale model has less uncertainty in the prediction of the compressive strength compared to those benchmark models. The findings of the present paper presented the superiority of the model’s development in constructing reasonable and robustness evolutionary Model for formulation of CS of eco-friendly concrete containing recycled PET.

کلیدواژه‌ها [English]

  • Sustainable concrete Polyethylene terephthalate (PET)
  • Artificial intelligence
  • MARS
  • Extreme learning machine
  • Monte-Carlo simulation

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مهندسی سازه و ساخت
دوره 9، شماره 2 - شماره پیاپی 55
اردیبهشت 1401
صفحه 172-197

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  • تاریخ دریافت: 30 فروردین 1400
  • تاریخ بازنگری: 19 مرداد 1400
  • تاریخ پذیرش: 02 آبان 1400

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