پیش‌بینی عملکرد ماشین حفار بازویی در حفر تونل با استفاده از الگوریتم کرم شب‌تاب و الگوریتم مبتنی بر آموزش و یادگیری- مطالعه موردی

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشیار؛ دانشکده‌ی مهندسی علوم زمین، دانشگاه صنعتی اراک

2 دانشجو؛ دانشکده‌ی مهندسی علوم زمین، دانشگاه صنعتی اراک

10.22044/tuse.2021.11010.1423

چکیده

ماشین‌ حفار بازویی از آن دسته از ماشین‌هایی می‌باشند که قابلیت حفاری بالایی در سنگ‌هایی با مقاومت کم تا متوسط را دارا می‌باشند. از این رو به طور گسترده در حفریات زیرزمینی مورد استفاده قرار می‌گیرند. تخمین عملکرد ماشین حفار بازویی یکی از موضوعات اصلی و مهم در تخمین تقریبی زمان اتمام پروژه و همچنین هزینه‌های پروژه‌ به حساب می‌آید. به همین منظور هدف از نگارش این مقاله پیشنهاد مدل‌های پیش‌بینی هوشمند برای تخمین عملکرد ماشین حفار بازویی بوسیله‌ی دو روش هوشمند الگوریتم کرم شب‌تاب (FA) و الگوریتم مبتنی بر آموزش و یادگیری (TLBO) و با استفاده از یک پایگاه داده (یک مطالعه موردی) است. در این مدل‌ها از مقادیر واجهشی چکش اشمیت و شاخص کیفیت توده‌سنگ (RQD) به عنوان پارامترهای ورودی و از نرخ برش ماشین حفار بازویی به عنوان پارامتر خروجی استفاده شده است. در پایان برای ارزیابی دقت مدل‌ها و مدلسازی از شاخص‌های ضریب همبستگی مربع (R2)، شمول واریانس (VAF)، جذر میانگین خطای مربع (RMSE) و میانگین خطای مربع (MSE) استفاده شده است. با توجه به نتایج بدست آمده در این مقاله و همچنین اعتبارسنجی مدل ایجاد شده، مقادیر پیش‌بینی عملکرد ماشین حفار بازویی توسط الگوریتم‌های بهینه‌سازی مبتنی بر آموزش و یادگیری و کرم شب‌تاب با مقادیر واقعی بسیار نزدیک بوده و از خطای کمی برخوردار است. بنابراین از مدل ایجاد شده می‌توان برای عملکرد ماشین حفار بازویی در شرایط زمین‌شناسی مشابه دیگر استفاده کرد.

کلیدواژه‌ها


عنوان مقاله [English]

Predicting the performance of roadheader in tunnel excavation using teaching learning based optimization algorithm and firefly algorithm-A case study

نویسندگان [English]

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

Roadheader machine is one of those machines that have high drilling capability in rocks with low to medium strength. Hence they are widely used in underground excavations. Estimating the performance of roadheader machine is one of the main and important issues in estimating the approximate project completion time as well as project costs. Therefore, the purpose of this paper is to propose intelligent forecasting models for estimating the performance of roadheader machine by two intelligent methods (the firefly algorithm (FA) and the Teaching-learning based optimization algorithm (TLBO)) and using a database (a case study). Is. In these models, the Schmidt hammer rebound values and the rock quality degree (RQD) are used as input parameters and the cutting rate of the roadheader is used as the output parameter. Finally, to evaluate the accuracy of the models and modeling, the indices of square correlation coefficient (R2), variance account for (VAF), root mean square error (RMSE) and mean square error (MSE) have been used. The results indicated that the two models have strong potentials to estimate roadheader performance with high degrees of accuracy and robustness.

کلیدواژه‌ها [English]

  • Roadheader
  • TLBO algorithm
  • Firefly algorithm
  • Schmidt hammer rebound values
  • Tunnel excavation
Abdolreza, Y.-C., & Siamak, H. Y. (2013). A new model to predict roadheader performance using rock mass properties. Journal of Coal Science and Engineering (China), 19(1), 51-56.
Averin, E., Zhabin, A., Polyakov, A., Linnik, Y., & Linnik, V. (2019). Preliminary Assessment of Roadheaders Efficiency Based on Empirical Methods and Index of Equivalent Rock Strength. Mining of Mineral Deposits. 2019. Т. 13. № 3. С. 113, 118.
Bilgin, N., Dincer, T., Copur, H., & Erdogan, M. (2004). Some geological and geotechnical factors affecting the performance of a roadheader in an inclined tunnel. Tunnelling and Underground Space Technology, 19(6), 629-636.
Bilgin, N., Seyrek, T., Erding, E., & Shahriar, K. (1990). Roadheaders plean valuable tips for Istanbul metro. Tunnels & tunnelling, 22(10), 29-32.
Bilgin, N., Seyrek, T., & Shahriar, K. (1988). Roadheader performance in Istanbul. Golden Horn clean-up contributes valuable data. Tunnels & tunnelling, 20(6), 41-44.
Copur, H., Ozdemir, L., & Rostami, J. (1998). Roadheader applications in mining and tunneling industries. PREPRINTS-SOCIETY OF MINING ENGINEERS OF AIME.
Douglas, W. (1985). ROADHEADERS OPEN NEW HORIZONS AT SAN-MANUEL. E&MJ-ENGINEERING AND MINING JOURNAL, 186(8), 22-25.
Ebrahimabadi, A., Azimipour, M., & Bahreini, A. (2015). Prediction of roadheaders' performance using artificial neural network approaches (MLP and KOSFM). Journal of Rock Mechanics and Geotechnical Engineering, 7(5), 573-583.
Ebrahimabadi, A., Goshtasbi, K., Shahriar, K., & Cheraghi Seifabad, M. (2011). A model to predict the performance of roadheaders based on the Rock Mass Brittleness Index. Journal of the Southern African institute of Mining and Metallurgy, 111(5), 355-364.
Ebrahimabadi, A., Goshtasbi, K., Shahriar, K., & Seifabad, M. C. (2012). A universal model to predict roadheaders’ cutting performance. Archives of Mining Sciences, 57.
Fattahi, H. (2020). A New Method for Forecasting Uniaxial Compressive Strength of Weak Rocks. Journal of Mining and Environment, 11(2), 505-515.
Fattahi, H., & Babanouri, N. (2017). Predicting tensile strength of rocks from physical properties based on support vector regression optimized by cultural algorithm. Journal of Mining and Environment, 8(3), 467-474.
Fattahi, H., & Bazdar, H. (2017). Applying improved artificial neural network models to evaluate drilling rate index. Tunnelling and Underground Space Technology, 70, 114-124.
Fattahi, H., & Moradi, A. (2017). Risk Assessment and Estimation of TBM Penetration Rate Using RES-Based Model. Geotechnical and Geological Engineering, 35(1), 365–376.
Goktan, R., & Gunes, N. (2005). A comparative study of Schmidt hammer testing procedures with reference to rock cutting machine performance prediction. International journal of rock mechanics and mining sciences (1997), 42(3), 466-472.
Hucka, V. (1965). A rapid method of determining the strength of rocks in situ. International Journal of Rock Mechanics and Mining Sciences & Geomechanics Abstracts,
Kahraman, S., Aloglu, A. S., Aydin, B., & Saygin, E. (2019). The needle penetration index to estimate the performance of an axial type roadheader used in a coal mine. Geomechanics and Geophysics for Geo-Energy and Geo-Resources, 5(1), 37-45.
Karimpouli, S., & Fattahi, H. (2018). Estimation of P-and S-wave impedances using Bayesian inversion and adaptive neuro-fuzzy inference system from a carbonate reservoir in Iran. Neural Computing and Applications, 29(11), 1059-1072.
Ocak, I., & Bilgin, N. (2010). Comparative studies on the performance of a roadheader, impact hammer and drilling and blasting method in the excavation of metro station tunnels in Istanbul. Tunnelling and Underground Space Technology, 25(2), 181-187.
Ozfirat, K. M., Malli, T., Ozfirat, P. M., & Kahraman, B. (2017). The performance prediction of roadheaders with response surface analysis for underground metal mine. Kuwait Journal of Science, 44(2).
Özşen, H., Dursun, A. E., & Aras, A. (2021). Estimation of Specific Energy and Evaluation of Roadheader Performance Using Rock Properties and Bond Work Index. Mining, Metallurgy & Exploration, 1-10.
Poole, R., & Farmer, I. (1980). Consistency and repeatability of Schmidt hammer rebound data during field testing. International Journal of Rock Mechanics and Mining Science, 17(3).
Rao, R. V., Savsani, V. J., & Vakharia, D. (2011). Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-Aided Design, 43(3), 303-315.
Rao, R. V., Savsani, V. J., & Vakharia, D. (2012). Teaching–learning-based optimization: an optimization method for continuous non-linear large scale problems. Information sciences, 183(1), 1-15.
Sandbak, L. A. (1985). Roadheader drift excavation and geomechanical rock classification at San Manuel, Arizona. Proceedings of the Rapid Excavation and Tunnelling Conference, New York,
Shahriar, K. (1988). Rock cuttability and geotechnical factors affecting the penetration rates of roadheaders PhD thesis, Istanbul Technical University].
Su, O., & Akkaş, M. (2020). Assessment of pick wear based on the field performance of two transverse type roadheaders: a case study from Amasra coalfield. Bulletin of Engineering Geology and the Environment, 79(5), 2499-2512.
Yang, X.-S. (2008). Nature-inspired metaheuristic algorithms. Luniver press.
Yang, X.-S. (2013). Multiobjective firefly algorithm for continuous optimization. Engineering with Computers, 29(2), 175-184.
Zhang, D., Liu, S., & Jia, J. (2021). Influence of motion parameters on cutting performance of boom-type roadheader during the swing cutting. Arabian Journal for Science and Engineering, 46(5), 4387-4397.