ارائه یک متدولوژی جدید در تخمین فشار سینه‌کار ماشین TBM-EPB: مطالعه موردی

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشیار؛ دانشکده‌ی مهندسی علوم زمین، دانشگاه صنعتی اراک

2 دانشجوی کارشناسی ارشد؛ دانشکده‌ی مهندسی علوم زمین، دانشگاه صنعتی اراک.

3 دانشجوی کارشناسی؛ دانشکده‌ی مهندسی علوم زمین، دانشگاه صنعتی اراک

10.22044/tuse.2022.11569.1444

چکیده

با گسترش روزافزون محیط‌های شهری، ایجاد و توسعه سیستم‌های حمل‌ونقل درون شهری به منظور کاهش ترافیک، آلودگی‌ها و کاهش هزینه‌های ناشی از عبور و مرور درون شهری امری ضروری است. با توجه به اینکه بخش مهمی از هزینه ساخت مترو مربوط به حفاری و نگهداری تونل‌ها می‌شود. بنابراین یکی از مهم‌ترین تصمیم‌ها در بحث ساخت تونل‌های مترو روش حفاری در محیط‌های آبرفتی و ریزشی می‌باشد. حفاری تونل توسط ماشین TBM-EPB در مقایسه با سایر روش‌های حفاری در خاک‌های نرم و مناطق ریزشی یک روش سریع، پرقدرت و همراه با نگهداری است. یکی از عوامل بسیار مهم در جلوگیری از ریزش سینه‌کار در حین حفاری در زمین‌ها‌ی نرم و آبرفتی برآورد فشار سینه‌کار بهینه ماشین حفاری در هر مرحله حفاری (کیلومتراژهای مختلف) می‌باشد. زیرا کم و یا زیاد بودن فشار سینه‌کار ماشین حفاری منجر به افزایش هزینه‌ها، خسارت‌های جانی، سختی زیاد و همچنین منجر به وقفه در اتمام پروژه می‌شود. در این مقاله به دلیل عدم قطعیت در پارامترهای ژئوتکنیکی و حساسیت تونل‌های شهری، مسئله از دیدگاه احتمالاتی مورد مطالعه قرار گرفته است. به همین منظور،  ابتدا برای 50 حالت مختلف مدل‌سازی عددی خط 2 مترو تبریز با استفاده از نرم‌افزار PLAXIS3D2020 صورت گرفته و در ادامه از روش شبیه‌سازی مونت‌کارلو برای تولید اعداد تصادفی و اختصاص توزیع‌های احتمالاتی مناسب استفاده شده است. سپس با استفاده از الگوریتم فراابتکاری گرگ خاکستری (GWO) فشار سینه‌کار ماشینTBM-EPB  با کمک رابطه پیش‌بینی بدست آمده، تخمین زده شده است. در نهایت به منظور ارزیابی و صحت‌سنجی رابطه بدست آمده از شاخص‌های آماری ضریب همبستگی مربع (R2)، شمول واریانس (VAF)، میانگین درصد خطای مطلق (MAPE)، جذر میانگین خطای مربع (RMSE) و میانگین خطای مربع (MSE) استفاده شده است. با توجه به اعتبارسنجی مدل، رابطه ایجاد شده توسط الگوریتم گرگ خاکستری به واقعیت مسئله بسیار نزدیک بوده و از آن می‌توان برای ادامه مسیر در مناطق مشابه دیگر استفاده کرد.

کلیدواژه‌ها


عنوان مقاله [English]

Presenting a new methodology in estimating TBM-EPB machine face pressure: A case study

نویسندگان [English]

  • H. Fattahi 1
  • H. Ghaedi 2
  • F. Malekmahmodi 3
1 Faculty of Earth Sciences Engineering, Arak University of Technology, Arak, Iran
2 .Faculty of Earth Sciences Engineering, Arak University of Technology, Arak, Iran
3 .Faculty of Earth Sciences Engineering, Arak University of Technology,
چکیده [English]

With the increasing expansion of urban environments, the creation and development of intra-city transportation systems in order to reduce traffic, pollution and reduce the costs of intra-city traffic is essential. Considering that an important part of the construction cost of the metro is related to the excavation and maintenance of tunnels. Therefore, one of the most important decisions in the construction of subway tunnels is the excavation method in alluvial and fall environments. Tunnel excavation by TBM-EPB machine is a fast, powerful and maintenance method compared to other excavation methods in soft soils and fall areas. One of the most important factors in preventing the face pressure from falling during excavation in soft and alluvial fields is estimating the optimal face pressure of the excavation machine in each excavation stage (different kilometers). Because the high or low face pressure of the excavation machine leads to increased costs, loss of life, high hardness and also leads to delays in the completion of the project. In this paper, due to the uncertainty in geotechnical parameters and the sensitivity of urban tunnels, the issue has been studied from a probabilistic perspective. For this purpose, first for 50 different numerical modeling modes of Tabriz Metro Line 2 using PLAXIS3D2020 software and then Monte Carlo simulation method has been used to generate random numbers and assign appropriate probabilistic distributions. Then, using the Gray Wolf meta-heuristic algorithm (GWO), the face pressure of the TBM-EPB machine was estimated using the prediction relation. Finally, in order to evaluate and validate the relationship, the statistical indicators of square correlation coefficient (R2), variance inclusion (VAF), mean absolute error percentage (MAPE), root mean square error (RMSE) and mean square error (MSE) were used. Is. According to the model validation, the relationship created by the gray wolf algorithm is very close to the reality of the problem and it can be used to continue the route in other similar areas.

کلیدواژه‌ها [English]

  • TBM-EPB machine face pressure
  • Gray Wolf algorithm
  • PLAXIS3D2020 software
  • Monte Carlo Simulation
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