پیش‌بینی مقاومت فشاری بتن خودتراکم حاوی فیلر‌های مختلف با کمک شبکه‌های عصبی مصنوعی

نوع مقاله : مقاله پژوهشی

نویسندگان

دانشکده فنی مهندسی، دانشگاه مازندران، بابلسر، ایران

چکیده

بتن‌های خودمتراکم با خواص رئولوژی و مکانیکی مناسب، از بتن‌های جدید محسوب می‌شوند که در اواخر قرن 20 و اوایل قرن 21 مورد توجه محققین و صاحبان صنایع قرار گرفت. دقت در بتن‌‌ریزی، تراکم بتن و همچنین ظاهر بتن به عنوان یک متریال اکسپوز همواره از دغدغه‌های طراحان و مجریان پروژه‌های عمرانی محسوب می‌شود. بتن‌های خودمتراکم با خاصیت تراکم وزنی همواره می‌تواند از گزینه‌های پیش روی طراحان باشد. تنوع در مواد مورد استفاده در بتن‌های خودمتراکم از جمله مواد بازیافتی، با خاصیت پوزولانی و پر کنندگی در جهت رسیدن به اهداف رئولوژی و مکانیکی، از چالش‌هایی است که طراحان با آن روبرو هستند. همچنین دقت در تعیین نسبت‌های اختلاط و نتایج حاصل از آن بسیار زمان بر و پرهزینه می‌باشد. علوم کامپیوتر با بهره‌گیری از محاسبات نرم و شبکه‌های عصبی الهام گرفته از ساختار بیولوژیکی مغز انسان، سعی در افزایش سرعت، دقت و همچنین کاهش هزینه به جهت جلوگیری از آزمایشات مخرب می‌پردازد. در این پژوهش با کمک دو شبکه ANN و LSTM با بهره‌گیری از 320 نمونه بتن خودمتراکم با پراکندگی و جامعیت مصالح رایج مورد استفاده در آن توسط محققیق مختلف، سعی در پیش‌بینی مقاومت فشاری 28 روزه بتن خودتراکم، بررسی عملکرد و افزایش دقت توسط 6 الگوریتم آموزشی مختلف شده است. در مجموع حدودا 200 تکرار آموزش بر روی 320 نمونه بتن خودتراکم با 14 ویژگی انجام شد، که با مقایسه بهترین نتایج حاصل از الگوریتم‌های آموزشی، بهترین عملکرد با ریشه میانگین مربعات خطای 4/97 و ضریب همبستگی 0/9484 در آزمایش، برای شبکه ANN با الگوریتم آموزشی Beyesian Regularization گزارش شد، که نشان دهنده‌ی دقت بالای آن شبکه می‌باشد.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Prediction of compressive strength of self-compacting concrete containing different fillers with the help of Artificial Neural Networks

نویسندگان [English]

  • Seyyed Moein Masrori Saadat
  • Ehsan Jahani
civil engineering department, , university of Mazandaran
چکیده [English]

Self-compacting concretes with suitable rheological and mechanical properties, are among the new concretes that were considered by researchers and industrialists in the late 20th and early 21st centuries. Accuracy in pouring concrete, concrete density and also the appearance of concrete as an exposed material is always a concern of designers and executors of construction projects. Self-compacting concrete with weight compression properties can always be one of the options available to designers. The variety of materials used in self-compacting concrete, including recycled materials, with pozzolanic properties and fillers to achieve rheological and mechanical goals, is one of the challenges that designers face. Also, accurate determination of mixing ratios and their results are very time-consuming and costly. Using soft computing and neural networks inspired by the biological structure of the human brain, computer science seeks to increase speed, accuracy, and cost reduction to prevent malicious experiments. In this study, with the help of ANN and LSTM networks, using 320 samples of self-compacting concrete with dispersion and comprehensiveness of common materials used in it by various researchers, tried to predict the 28-day compressive strength of self-compacting concrete, evaluate performance and increase accuracy by 6 The training algorithm is different. In total, about 200 repetitions of training were performed on 320 samples of self-compacting concrete with 14 characteristics, which by comparing the best results obtained from training algorithms, best performance with root mean square error of 4.97 and correlation coefficient of 0.9484 in the test, for the network. ANN was reported with the Beyesian Regularization training algorithm, which indicates the high accuracy of that network.

کلیدواژه‌ها [English]

  • Self-compacting concrete
  • Compressive strength prediction
  • ANN neural network
  • LSTM neural network
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