ارزیابی کارایی مدل های تجربی بر اساس طبقه بندی ژئومکانیکی (RMR) در پیش بینی مدول دگرشکلی توده سنگ و توسعه مدلی بر اساس سیستم فازی

نوع مقاله: پژوهشی

نویسنده

گروه مهندسی معدن، دانشگاه ولی عصر (عج) رفسنجان

10.29252/anm.8.16.1

چکیده

پیشبینی مدول دگرشکلی تودهسنگ برای تحلیل و طراحی سازههای سنگی از اهمیت خاصی برخوردار است. زیرا مدول دگرشکلی بیانگر رفتار تودهسنگ تحت تاثیر تنشها است. تاکنون مدلهای تجربی و هوش مصنوعی مختلفی برای پیشبینی مدول دگرشکلی توسعه داده شده است. از میان این مدلها مدلی بهینه است که کمترین پارامتر ورودی (زیرا هزینهها و زمان برای تعیین پارامترهای ورودی کاهش پیدا میکند) و قابلیت پیشبینی مدول دگرشکلی در بهترین حالت را داشته باشد. در این مقاله ابتدا کارایی 19 مدل مختلف، که پارامتر ورودی آنها تنها طبقهبندی ژئومکانیکی تودهسنگ (RMR) است؛ ارزیابی و دو مدل که بهترین تقریب را دارند، تعیین میشود. سپس به منظور پیشبینی دقیقتر مدول دگرشکلی، یک مدل بر اساس سیستم فازی توسعه داده شده (پارامتر ورودی آن RMR است) و نتایج با دو مدل انتخاب شده مقایسه گردید. برای این منظور 33 مجموعه داده از ساختگاه سدهای مختلف در کشور ایران جمعآوری شده است. برای ارزیابی مدلها و همچنین اعتبارسنجی مدل توسعه داده شده، از شاخصهای جذر میانگین مربعات خطا (RMSE) ، ضریب تعیین ((R2 و میانگین درصد خطای مطلق (MAPE) استفاده شده است. نتایج نشان میدهد، پیشبینی مدول دگرشکلی با استفاده از مدل فازی توسعه داده شده نسبت به مدلهای تجربی دقیقتر است.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Efficiency evaluation of empirical models on the basis of rock mass rating for prediction of deformation modulus in rock mass and developing a new models according to the fuzzy system

نویسنده [English]

  • Mehdi Mohammadi
Dept. of Mining, Vali-e- Asr University of Rafsanjan
چکیده [English]

Summary
Prediction of deformation modulus for rock mass in analysis and design of rock structures is very important. Because deformation modulus represents behavior of rock mass subjected to stresses. Up to now, various empirical models for prediction of deformation modulus have been developed. In this research, efficiency of different models for prediction of deformation modulus is evaluated. Then, according to the fuzzy model a sufficient model is developed and results are compared with the empirical models. Results indicated the efficiency of the new proposed model in comparison with the available models.
 
Introduction
Determining geo-mechanical parameters for analysis and design of rock structure is inevitable. Deformation modulus of rock mass is one of the most important geo-mechanical parameters. Estimation of deformation modulus for rock mass utilizing empirical models is widely used in civil and mining engineering projects. This is due to its less time requirement , cost and acceptable estimation. But there are differences between results of empirical model and real values of modulus which leads to uncertain decisions for designers in choosing appropriate model. Thus, it is important to investigate and evaluate the experimental models. Fuzzy model is one of the best models that is proposed for determining the deformation modulus in the present study.
 
Methodology and Approaches
For efficiency evaluation of 19 available models and also the proposed model, 33 data sets of different dam’s constructions in Iran is collected. Root Mean Squared Error , Mean Absolute Percentage Error  and index of higher squared correlation coefficient  are applied for evaluation of models’ performance.
 
Results and Conclusions
According to the performance indices, two models are selected from different empirical models that indicated the best predictions. In addition, results showed the efficiency of the proposed fuzzy model for prediction of deformation modulus.

کلیدواژه‌ها [English]

  • Rock Mass Rating
  • Deformation Modulus of Rock Mass
  • Empirical Models
  • Fuzzy Models
[1]           Kayabasi, A., Gokceoglu, C. & Ercanoglu, M., (2003). Estimating the deformation modulus of rock masses: a comparative study. International Journal of Rock Mechanics & Mining Sciences, 40 (1), 55– 63.
[2]           Fattahi, H. (2016). Application of improved support vector regression model for prediction of deformation modulus of a rock mass. Engineering with Computers, DOI 10.1007/s00366-016-0433-6.
[3]           Hoek, E. and Diederichs, M.S. (2006). Empirical estimation of rock mass modulus. International Journal of Rock Mechanics & Mining Sciences, 43, 203– 215.
[4]           Panthee, S., Singh, P. K., Kainthola, A., Das R. & Singh, T.N. (2016). Comparative study of the deformation modulus of rock mass. Bulletin of Engineering Geology and the Environment, doi: 10.1007/s10064-016-0974-3.
[5]           Alemdag, S., Gurocak, Z., Cevik, C., Cabalar, A. F. & Gokceoglu, C., (2016). Modeling deformation modulus of a stratified sedimentary rock mass using neural network, fuzzy inference and genetic programming. Engineering Geology, 203, 70–82.
[6]           Shen, J., Karakus, M., & Xu, C. (2012). A comparative study for empirical equations in estimating deformation modulus of rock masses. Tunnelling and Underground Space Technology, 32, 245–250.
[7]           Hoek, E. and Brown, E.T. (1997). Practical estimates of rock mass strength. International Journal of Rock Mechanics & Mining Sciences, 34 (8), 1165–1186.
[8]           Asrari, A. A., Shahriar, K., & Ataeepour, M. (2015). The performance of ANFIS model for prediction of deformation modulus of rock mass. Arabian Journal of Geosciences. 8, 357–365.
[9]           Sonmez, H., Gokceoglu, C., Nefeslioglu, H.A. & Kayabasi, A. (2006). Estimation of rock modulus: for intact rocks with an artificial neural network and for rock masses with a new empirical equation. International Journal of Rock Mechanics & Mining Sciences, 43, 224–235.
[10]         Aksoy, S.O., Geniş, M., Aldaş, G.U., Özacar, V., Özer, S.C. & Yılmaz, O. (2012) A comparative study of the determination of rock mass deformation modulus by using different empirical approaches. Engineering Geology, 131- 132, 19–28.
[11]         Feng, X. and Jimenez R. (2015). Estimation of deformation modulus of rock masses based on Bayesian model selection and Bayesian updating approach. Engineering Geology. 199, 19–27.
[12]         Diederichs, M.S. and Kaiser, P.K. (1999). Tensile strength and abutment relaxation as failure control mechanisms in underground excavations. International Journal of Rock Mechanics & Mining Sciences, 36, 69–96.
[13]         Gokceoglu, C., Sonmez, H. & Kayabasi, A. (2003). Predicting the Deformation Moduli of Rock Masses. International Journal of Rock Mechanics & Mining Sciences, 40, 701–710.
[14]         Galera, J.M., Alvarez, M. & Bieniawski, Z.T. (2005). Evaluation of the deformation modulus of rock masses: comparison of pressure-meter and dilatometer tests with RMR prediction. The ISP5-PRESSIO, International Symposium, Madrid, Spain.
[15]         Ajalloeian, R., and Mohammadi, M. (2014). Estimation of limestone rock mass deformation modulus using empirical equations. Bulletin of Engineering Geology and the Environment, 73, 541–550.
[16]         Nejati, H.R., Ghazvinian, A.H., Moosavi, S.A. & Sarfarazi, V. (2014). On the use of the RMR system for estimation of rock mass deformation modulus. Bulletin of Engineering Geology and the Environment, 73, 531–540.
[17]         Kavur, B., Cvitanovic, N.S., & Hrzenjak, p. (2015). Comparison between plate jacking and large flat jack test results of rock mass deformation modulus. International Journal of Rock Mechanics & Mining Sciences, 73, 102–114.
[18]         Alemdag, S., Gurocak, Z. & Gokceoglu, C. (2015). A simple regression based approach to estimate deformation modulus of rock masses. Journal of African Earth Sciences, 110, 75–80.
[19]         Allami, M., and Hoseini, M. (2012). Presentation of Empirical Relations for Determining Deformation Modulus in Rock Masses of Southwest of Iran. Iranian Journal of Mining Engineering 7(16):79-87 (In Persian).
[20]         Rezaei, M., Asadizadeh, M., Majdi, A., & Farouq Hossaini, M. (2015). Prediction of representative deformation modulus of longwall panel roof rock strata using Mamdani fuzzy system. International Journal of Mining Science and Technology, http://dx.doi.org/10.1016/j.ijmst.2014.11.007.
[21]         Bashari, A., Beiki, M. & Talebinejad, A. (2011). Estimation of deformation modulus of rock masses by using fuzzy clustering-based modeling. International Journal of Rock Mechanics & Mining Sciences, 48, 1224–1234.
[22]         Mohammadi, H. and Rahmannejad, R. (2010). The estimation of rock mass deformation modulus using regression and artificial neural networks analysis. Arabian Journal for Science and Engineering, 35, 205- 217.
[23]         Asadizadeh M., and Farouq Hossaini M. (2016). Predicting rock mass deformation modulus by artificial
Intelligence approach based on dilatometer tests. Arabian Journal of Geosciences. DOI 10.1007/s12517-015-2189-5.
[24]         Gholamnejad, J., Bahaaddini, H.R. & Rastegar, M. (2013). Prediction of the deformation modulus of rock masses using Artificial Neural Networks and Regression methods, Journal of Mining & Environment, 4(1) 35- 43.
[25]         BJVC (2009a) Bakhtiary Dam and HEPP, Engineering geology and rock mechanics report; Report No 4673/4049 Rev 1.
[26]         IWPCO (Iran water and power resources dev. Co.) (2005) Rock mechanics studies. Final report. Upper Gotvand dam and HPP.
[27]         Moshanir Consultant Engineer, (2002). The Siah Bishe Pumped Storage Project in Iran, Report No. 39.
[28]         Mahab Ghods Consulting Engineers Co. (2009) Rock mechanics report of Khersan II project. Mahab Ghods Consulting Engineers Co., Tehran
[29]         Bashari, A. (2009). Statistical reliability evaluation of existing empirical methods for determination of in situ rock mass deformation modul. M.Sc. thesis (In Persian).
[30]         Zadeh, L.A. (1965). Fuzzy sets. Information and Control, 8, 338–353.
[31]         Lee, W., (1999). Fuzzy systems and fuzzy control. Translated by: Teshneh Lab M., K.N.Toosi University of Technology pub.