پیش‌بینی مقاومت فشاری بتن‌های معمولی، حاوی خاکستر بادی و سرباره بر اساس روش‌‌های نوین و ارائه طرح مخلوط‌های بهینه آن‌ها

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشگاه صنعتی امیرکبیر، تهران، ایران

2 دانشگاه امیرکبیر، تهران، ایران

3 دانشجو، دانشکده مهندسی عمران و محیط زیست دانشگاه علم و صنعت، تهران، ایران

4 دانشگاه صنعتی امیر کبیر

چکیده

در این مطالعه، چهار دسته بتن شامل خاکستر بادی، خاکستر بادی و سرباره، بتن معمولی و بتن حاوی سرباره مورد بررسی قرار گرفته ‌است و با استفاده از دو روش یادگیری ماشین معرفی شده (الگوریتم ژنتیک و رقابت لیگ فوتبال) و چهار روش رگرسیونی، مقاومت فشاری بتن‌های مذکور پیش‌بینی شده ‌است. با استفاده از شاخص‌های آماری دقت هر مدل برآورد شده و با دقت‌ترین مدل برای هر دسته بتن معرفی شده است و از آن برای حل مسئله بهینه‌سازی استفاده شد. روش یادگیری ماشین مبتنی بر رقابت لیگ فوتبال برای هر چهار دسته بتن به جز بتن معمولی از دقت بالاتری برخوردار بود و برای بتن معمولی روش یادگیری ماشین مبتنی بر الگوریتم ژنتیک به عنوان بهترین مدل معرفی گردید. هدف از مسئله ‌بهینه‌سازی کمینه کردن هزینه هر دسته بتن با در نظر گرفتن مقاومت بتن 40 مگاپاسکالی بوده است. بتن حاوی خاکستر ‌بادی، خاکستر بادی و سرباره و همچنین بتن حاوی سرباره نسبت به بتن معمولی به ترتیب 35/2، 29/9 و 23/1 درصد نسبت به بتن معمولی هزینه ساخت را کاهش می‌دهند. تولید سیمان یکی از عوامل آلودگی محیط ‌زیست می‌باشد. بتن حاوی خاکستر ‌بادی، خاکستر بادی و سرباره، بتن حاوی سرباره و بتن معمولی به ترتیب 217/25، 150/47، 102 و 414/64 کیلوگرم بر مترمکعب سیمان در طرح مخلوط بهینه مورد استفاده قرار گرفتند. که بتن شامل سرباره، کمترین مقدار مصرف سیمان برای بتنی با مقاومت 40 مگاپاسکال را در بین 4 دسته بتن دارد و حدود 75/4 درصد نسبت به بتن معمولی مصرف سیمان را کاهش داده ‌است.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Compressive strength prediction of ordinary concrete, fly ash concrete, and slag concrete by novel techniques and presenting their optimal mixtures

نویسندگان [English]

  • Mehrdad Ehsani 1
  • Hamed Naseri 2
  • Ruhollah Saeedi Nezhad 1
  • Mohammadali Etebari Ghasbeh 3
  • Fereidoon Moghadas Nejad 4
1 Amirkabir University of Technology, Tehran, Iran
2 Department of Civil and Environmental Engineering, Amirkabir University of Technology, Tehran, Iran.
3 Graduate student, Department of Civil Engineering, Iran University of Science and Technology
4 Dep. of Civil Engineering, Amirkabir University of Technology
چکیده [English]

In this study, four concrete types, including ordinary Portland cement concrete, fly ash concrete, slag concrete, and slag-fly ash concrete, are taken into account in order to estimate their compressive strength by two novel machine learning methods (genetic algorithm and soccer league competition algorithm), and four types of regressions (linear, 2nd order polynomial, exponential, and logarithmic). Subsequently, the precision of prediction models is compared based on performance indicators, and the most accurate models are applied in the optimization problem modeling. Drawing on results, the most precise model to estimate the compressive strength of ordinary Portland cement concrete is the genetic algorithm, and the soccer league competition is the most accurate model to estimate the strength of other concrete types. Afterward, a model is developed so as to design mixture proportions of 40MPa concretes. Fly ash concrete, slag-fly ash concrete, and slag concrete reduce the unit cost by 35.2%, 29.9%, and 23.1%, respectively, compared with ordinary Portland cement concrete. Fly ash concrete, slag-fly ash concrete, slag concrete, and ordinary Portland cement concrete require 217.25 kg, 150.47 kg, 102 kg, and 414.64 kg cement to be manufactured. Furthermore, slag concrete can reduce the amount of cement in the mixture proportion by 75.4%, and it is the most eco-friendly concrete.

کلیدواژه‌ها [English]

  • Compressive strength prediction
  • Mixture design optimization
  • Machine learning
  • Regression
  • Metaheuristic algorithms

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نشریه مهندسی عمران امیرکبیر
دوره 53، شماره 10
دی 1400
صفحه 4105-4124

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