ارائه‌ الگویی برای بهبود بهره‌وری ماشین‌آلات خاکبرداری در پروژه‌های زیرساختی با استفاده از الگوریتم‌های یادگیری ماشین (نمونه موردی: خط لوله گاز)

نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه فناوری معماری (مدیریت پروژه و ساخت)، دانشکده معماری و شهرسازی، دانشگاه هنر ایران، تهران، ایران

2 دانشیار، گروه فناوری معماری (مدیریت پروژه و ساخت)، دانشکده معماری و شهرسازی، دانشگاه هنر ایران، تهران، ایران

3 استادیار، دانشکده معماری، دانشگاه تهران، تهران، ایران

چکیده

با توجه به جایگاه برجسته کشور ایران در دنیا از منظر منابع انرژی، مخصوصاً حوزه گازی، اجرای پروژه­های زیرساختی انرژی، به‌ویژه خط لوله گاز، ضروری می‌نماید. با این وجود، یکی از چالش­های اساسی در این نوع از پروژه‌ها، موضوع عدم بهره­وری مناسب منابع (ماشین‌آلات و...) است. از این‌رو، هدف اصلی پژوهش حاضر عبارت است از بهبود بهره‌وری ماشین‌آلات خاکبرداری پروژه‌های احداث خط لوله گاز، با کمک الگوریتم‌‌های یادگیری ماشین. در این پژوهش، با بهره‌گیری از مطالعات کتابخانه‌ای، اسنادی (گزارش‌های روزانه هفت پروژه‌ خطوط انتقال گاز)، قضاوت خبرگان، روش متن‌کاوی (و نرم‏افزار رپیدماینر)، معیارهای مؤثر بر تعیین بهره‌وری ماشین‌آلات خاکبرداری در پروژه‌های احداث خط لوله گاز، شناسایی و نهایی شدند. به‌طور خلاصه، نتیجه اصلی پژوهش کنونی اشاره دارد که پیش‌بینی حجم خاکبرداری، از طریق الگوریتم پیش‌بینانه (به‌عنوان مبنای بهینه‌سازی بهره‌وری ماشین‌آلات خاکبرداری) و نیز الگوریتم دسته‌بندی و با استفاده از مدل یادگیری عمیق (به‌عنوان مدل منتخب و دارای بهترین عملکرد در پیش‌بینی حجم خاکبرداری)، قابل اجرا است. در واقع، یافته‌های پژوهش فعلی در راستای پیش‌بینی حجم خاکبرداری، پیش از شروع پروژه و تهیه برنامه زمان­بندی کلی که در نهایت موجب بهبود بهره‌وری ماشین‌آلات خاکبرداری در پروژه‌های خط لوله گاز می‌شود، قابل استفاده است.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Providing a Pattern to Improve the Productivity of Earthmoving Machinery in Infrastructure Projects Using Machine Learning Algorithms (Case Study: Gas Pipeline)

نویسندگان [English]

  • Rojin Mohaghegh 1
  • Behnod Barmayehvar 2
  • Hossein Toosi 3
1 Department of Architectural Technology (Project and Construction Management), Faculty of Architecture and Urban Planning, Iran University of Art, Tehran, Iran
2 Associate Professor, Department of Architectural Technology (Project and Construction Management), Faculty of Architecture and Urban Planning, Iran University of Art, Tehran, Iran
3 Assistant Professor, Faculty of Architecture, University of Tehran, Tehran, Iran
چکیده [English]

The objective of this study is to compare the effectiveness of industrial wastes including cement kiln dust (CKD), fly ash (FA), and ground granulated blast furnace slag (GGBFS) for stabilization of clay soils. To chemically stabilize the soil, optimal amounts of CKD (10%-20%), class C FA (20%-25%) and GGBFS (20%-30%) have been suggested. Considering the soil pH value with different percentages of additives, the amount of each additive was considered the same (20%) for better comparison. Standard compaction and California Bearing Ratio (CBR) tests were conducted on the mixtures. To investigate the microstructural effect of additives, the samples were subjected to scanning electrocleen microscopy (SEM) and X-ray diffraction (XRD) analysis. Results showed that CKD and FA decreased maximum dry density and increased optimum moisture content. Meanwhile, GGBFS decreased optimum moisture content of the samples and increased maximum dry density. The CBR in soil stabilized with CKD, FA, and GGBFS was 21.7, 13.3, and 15.7 times that of pure soil, respectively. According to the results of the SEM and XRD analysis, the increase in strength in the stabilized soil is caused by pozzolanic reactions and creation of cementation products, and as a result, binding of soil particles and stabilizers and filling of the pores. The higher the amount of free lime in the stabilizer, the greater the increase in soil strength. In practical projects, factors such as delay time (the time between the first contact of the additive and water and the final compaction of the mixture) and moisture content that affect the strength parameters should be considered. Also, environmental issues, such as potential of these additives to enter groundwater, are important.

کلیدواژه‌ها [English]

  • Productivity of Earthmoving Machinery
  • Machine Learning Algorithm
  • Energy Infrastructure Projects
  • Gas Pipeline Construction Project

مراجع

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سابقه مقاله

  • تاریخ دریافت: 27 تیر 1403
  • تاریخ بازنگری: 05 شهریور 1403
  • تاریخ پذیرش: 07 شهریور 1403

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