ارائه مدلی برای تعیین خرج ویژه بر اساس پارامترهای ژئومکانیکی با استفاده از روش آنالیز مولفه¬های اصلی
چکیده
يکي از مهمترين پارامترهاي فني و اقتصادي در طراحي الگوهاي حفاري و انفجار تونل ها، خرج ويژه است. از این رو پيش بيني و بهينه سازي آن از اهميت بالايي برخوردار است. مقدار خرج ويژه به پارامترهاي متعددي از قبیل شرايط زمين شناسي، خصوصيات مکانيک سنگي و پارامترهای هندسي طراحي بستگي دارد. در اين تحقيق با تکيه بر خواص ژئومکانيکي توده سنگ در عملیات ساخت تونل آبرسان سد سیمره، مدلی مناسب برای تعیین خرج ویژه با استفاده از روش هاي آماري ارائه شده است. در اين راستا به منظور حذف اثر همخطي بين متغيرهاي ورودي در مدلهای پيش بيني، از روش آنالیز PCA استفاده کرده و برای ارزيابي و مقایسه مدلهاي ساخته شده، از پارمترهاي ضريب تعيين مدل (R2) و متوسط مربعات خطا (MSE) بهره گرفته شده است. مقایسه مدلها نشان مي دهد که رفع همخطي بين متغيرهاي ورودي با بکارگیری روش PCA، نتايج پيش بيني بهتري را به همراه داشته است.
مراجع
Persson,A , Holmberg,R. Lee J. 1994. Rock Blasting and Explosives Engineering.
Holmberg, R. 2000. Explosives and Blasting Technique.
Lu,Sh. Zhou,Ch. Jiang,N. Xu,X. 2015.Effect of Excavation Blasting in an Under-Cross Tunnel on Airport Runway. Geotechnical and Geological Engineering. 33. 4. 973-981
Bhalchandra V. Gokhale. 1979. Rotary Drilling and Blasting in Large Surface Mines.
B. Mohanty. 1996. Rock Fragmentation by Blasting.
E. Lopez Jimeno, C. Lopez Jimino, Ayala. 1978. Drilling and Blasting of Rocks.
Dey,K. Sen,Ph. 2003. Concept of Blastability. Indian mining & engineering journal. 42. 24-31
Singh, P, Sinha A. 2012Rock Fragmentation by Blasting.
Rossmanith, H.1993. Rock Fragmentation by Blasting.
Kuznetsov, V.M. 1973. The mean diameter of fragments formed by blasting rock. Soviet mining sci. 9. 2 144-148
Cunningham, C.V.B. 1983. The KUZ-RAM model for prediction of fragmentation from blasting. 1st Int. Symp. On rock fragmentation. 2. 439-453
Cunningham, C.V.B. 1987. Fragmentation estimations and the KUZ-RAM model four year on. 2nd Int. Symp. Rock fragmentation by blasting. Keystone. August 23-26
Rustan, A. 1998. Rock Blasting Terms and Symbols
Pal Roy, P. 2005. Rock Blasting: Effects and Operations.
Jolliffe,I. 1986. Principal Component Analysis.
Markland,j . 1974. The analysis of principal components of orientation data. International Journal of Rock Mechanics and Mining Sciences & Geomechanics Abstracts. 11.5. 157-163
Salimi,A. Rostami,J. Moormann,Ch. Delisio,A. 2016. Application of non-linear regression analysis and artificial intelligence algorithms for performance prediction of hard rock TBM. Tunnelling and Underground Space Technology. 58. 236-246
Salimi,A. /moormann,Ch. Singh,T.N. Jain,P. 2015. TBM Performance Prediction in Rock Tunneling Using Various Artificial Intelligence Algorithms. Regional tunneling conference tunnels and the future.
Saeidi,O. Torabi,R. Ataei,M. Rostami,J. 2014. A stochastic penetration rate model for rotary drilling in surface mines. International Journal of Rock Mechanics & Mining Sciences. 68. 55-65
Cai,W. Dou,L. Si,G. Cao,A. He,J. Liu,S. 2016. A principal component analysis/fuzzy comprehensive evaluation model for coal burst liability assessment. International Journal of Rock Mechanics & Mining Sciences. 81. 62-69.
Mohamed,F. Hafsaoui,A. Talhi,K. Menacer,k. 2015. Study of the powder factor in surface bench blasting. Procedia earth and planetary science. 15. 892-899.
Esmaeili,M. Salimi,A. Drebenstedt,C. Abbaszadeh,M. Aghajani Bazazi,A. 2015. Application of PCA, SVR, and ANFIS for modeling of rock fragmentation. Arab J Geosci. 8.9.6881-6893
Heather,L. Andrew,W. Arthur,M. 2016. Dynamic failure in coal seams: Implications of coal composition for bump susceptibility. International Journal of Mining Science and Technology. 26.1. 3-8
Yun,H. Park,S. Mehdawi,N. Mokhtari,S. Chopra,M. N.Reddi,L. Park,K. 2014. Monitoring for close proximity tunneling effects on an existing tunnel using principal component analysis technique with limited sensor data. Tunnelling and Underground Space Technology. 43. 398-412.
Sayadi,A. Lashgari,A. Paraszczak,J. 2012. Hard-rock LHD cost estimation using single and multiple regressions based on principal component analysis. Tunnelling and Underground Space Technology. 27. 133-141
Lashgari,A. Sayadi, A. 2013. Statistical approach to determination of overhaul and maintenance cost of loading equipment in surface mining. International Journal of Mining Science and Technology. 23. 441-446
Yan-wei,Ch. Yao-cai,W. Tao,L. Zhi-jie,W. 2008Blended coal’s property prediction model based on PCA and SVM. J. Cent. South Univ. Technol. 15. 2. 331-335.
Yan-wei,Ch. Yao-cai,W. Tao,L. Zhi-jie,W. 2008. Fault diagnosis of a mine hoist using PCA and SVM techniques. J China Univ Mining & Technol. 18. 327-331.
Shi-xiong,X. Qiang,N. Yong,Zh. Lei,Zh. 2008. Mine-hoist fault-condition detection based on the wavelet packet transform and kernel PCA. J China Univ Mining & Technol. 18. 567-570.
Lindsay,S, 2002, A tutorial on principal component analysis, February 26, Department science, University of Califonia, San Diego, December 10.
Engelbrecht, A.P., 2007. Computational Intelligence: An Introduction. John Wiley & Sons, New York.
Gujarati,D. 2004. Basic Econometrics
gujarati, D., 2003. Basic Econometrics, fourth ed. McGraw-Hill, New York, NY. Hardle, W., Simar, L., 2003. Applie