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پیشبینی شاخص تردی سنگ با استفاده از رگرسیون چند متغیره غیر خطی و درخت رگرسیون CART | ||
| نشریه مهندسی عمران و محیط زیست دانشگاه تبریز | ||
| مقاله 4، دوره 48.3، شماره 92، پاییز 1397، صفحه 33-40 اصل مقاله (1.06 MB) | ||
| نوع مقاله: مقاله کامل پژوهشی | ||
| نویسندگان | ||
مسعود سمائی1؛ مسعود رنجبرنیا  1؛ مسعود زارع نقدهی2
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| 1دانشکده مهندسی عمران، دانشگاه تبریز | ||
| 2دانشکده مهندسی معدن، دانشگاه صنعتی همدان | ||
| چکیده | ||
| شاخص تردی (شکنندگی) سنگ یکی از مهمترین پارامترهای مؤثر بر حفاریهای زیرزمینی به ویژه در حفاری با ماشین (TBM) به حساب میآید که محاسبه دقیق این پارامتر برای طراحیهای ژئوتکنیکی بسیار مهم و کاربردی است. در این مقاله، شاخص تردی سنگ با استفاده از دو روش رگرسیون چند متغیره غیر خطی و همچنین درخت رگرسیون CART بر روی پایگاه داده شامل 48 ردیف دادهای از 30 پروژه تونلسازی مختلف پیشبینی شده است. این پایگاه دادهای بازه قابل قبولی از اعداد را در بر میگیرد که شامل مقاومت فشاری، مشاومت کششی و وزن مخصوص انواع مختلفی از سنگها است. علاوه بر مقاومت تک محوری سنگ، مقاومت کششی برزیلی و وزن مخصوص، جنس سنگ به عنوان پارامتر چهارم در ارائه معادله و توسعه درخت پیشبینی تردی سنگ لحاظ شده است. معادله پیشنهاد شده در این مطالعه دارای ضریب تشخیص 91/0R2= و درخت رگرسیون توسعه داده نیز دارای ضریب تشخیص 94/0 R2=است. با توجه به اعمال جنس سنگ به صورت کد عددی در پیشبینیها مشاهده شد که اعمال این کد نه تنها باعث کاهش دقت در پیشبینیها نمیشود، بلکه باعث افزایش آن و باعث درک بهتری از معادلات و روشهای پیشبینی نیز میشود. | ||
| کلیدواژه‌ها | ||
| شاخص تردی؛ شکنندگی؛ سنگ؛ رگرسیون چند متغیره؛ درخت تصمیم | ||
| عنوان مقاله [English] | ||
| Prediction of the Rock Brittleness Index Using Nonlinear Multivariable Regression and the CART Regression Tree | ||
| نویسندگان [English] | ||
| Masoud Samaie1؛ Masoud Ranjbarnia1؛ Masoud Zare Naghadehi2 | ||
| 1Department of Geotechnical Engineering , Faculty of Civil Engineering, University of Tabriz | ||
| 2Faculty of Mining Engineering, Hamedan University of Technology | ||
| چکیده [English] | ||
| From 1960s several attempts have been made to measuring the rock brittleness index BI. Schwartz (1964) using results of a series of triaxial tests on rock samples, stated that the rock’s behavior from frangibility to ductility happens in 4.3 ratios of principal stresses. Altindag (2002; 2003) introduced a new method for prediction of the BI by the division of the uniaxial compressive strength (UCS) of the rock to Brazilian tensile strength (BTS). In the late 1960s punch penetration test (PPT) introduced by Handewith (1971) to measure some physical properties of rock sample related to hardness and toughness of rock. Yagiz (2006) stated that the PPT’s results for measuring the BI have a very high correlation with TBM penetration rate. Although the PPT has very delightful results, application of this test is very expensive and needs much time as well. Since 2002, researchers have made some efforts to predict the BI results acquired by PPT. Yagiz (2009) using 48 sets of 30 different tunnel rock’s PPT test data, introduced a new linear multivariable equation for prediction of the BI. In that equation, 3 major rock’s physical properties as UCS, BTS and unit weight were used. Also, Yagiz (2010) introduced a new nonlinear multivariable equation and improved the accuracy of prediction from R2=0.88 to R2=0.89. As well Khandewal et al (2016) developed a new equation using Genetic Programming (GP) based on Yagiz (2009) data with R2=0.90, but none of these equations include the type of rock as a major factor that influences the BI. In the present study, the BI will be investigated by considering the rock type (or texture) as an important parameter, and a new nonlinear multivariable equation will be introduced. As well using a classification and regression tree (CART) a new attempt will be made to predict the PPT’s result for the BI. | ||
| کلیدواژه‌ها [English] | ||
| Brittleness index, Rock, Nonlinear multivariable regression, Classification and Regression Tree (CART) | ||
| مراجع | ||
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ltindag R, “The evaluation of rock brittleness concept on rotary blast hole drills”, South African Inst. Min. Metall, 2002, 102 (1), 61-66. Altindag R, “The correlation of specific energy with rock brittleness concept on rock cutting”, South African Institute of Mining and Metallurgy, 2003, 103, 163-171. Blindheim O, Bruland A, “Boreability testing. Norwegian TBM tunneling 30 years of experience with TBMs in Norwegian tunneling”, Norwegian Soil and Rock Engineering Association Publication, 1998, 11, 29-34. Breiman L, Friedman JH, Olshen RA, “Classification and regression trees”, stone, CJ. Chapman & Hall Co. 1968. Gong Q, Zhao J, “Influence of rock brittleness on TBM penetration rate in Singapore granite”, Tunnelling and underground space technology, 2007, 22, 317-324. Handewith H, “predicting economic success of continuous tunelling in hard rock”, Canadian Mining and Metallurgical Bulletin, 2007, 63 (697), 595. Hucka V, Das B, “Brittleness determination of rocks by different methods”, International Journal of Rock Mechanics and Mining Sciences & Geomechanics Abstracts, 1974, 11 (10), 389-392. Kahraman S, “Correlation of TBM and drilling machine performances with rock brittleness”, Engineering Geology, 2002, 65 (4), 269-283. Kaunda R, Asbury B, “Prediction of rock brittleness using nondestructive methods for hard rock tunneling”, Journal of Rock Mechanics and Geotechnical Engineering, 2016, 8 (4), 533-540. Khandelwal M, Shirani Faradonbeh R, Monjezi M, Jahed Armaghani J, Abd Majid M, Yagiz S, “Function development for appraising brittleness of intact rocks using genetic programming and non-linear multiple regression models”, Engineering with Computers, 2017, 33 (1), 13-21. Protodyakonov M, “Mechanical properties and drillability of rocks”, Proceedings of the Fifth Symposium on Rock Mechanics, University of Minnesota, Minneapolis, US, 1967. Ramsay J, “Folding and Fracturing of Rocks”, McGraw-Hill, London, 1967. Schwartz A, “Failure of rock in the triaxial shear test”, 6th US Symposium on Rock Mechanics (USRMS), American Rock Mechanics Association, 1964, 109-135. Szwedzicki T, “Draft ISRM suggested method for determining the indentation hardness index of rock materials”, International Journal of Rock Mechanics and Mining Sciences, 1998, 35 (6), 831-835. Yagiz S, “A model for prediction of tunnel boring machine performance. Substructures and underground space. In Engineering geology for tomorrow’s cities", The 10th International Association of Engineering Geologists Congress, 2006. Yagiz S, “Utilizing rock mass properties for predicting TBM performance in hard rock condition”, Tunnelling and Underground Space Technology, 2008, 23 (3), 326-339. Yagiz S, “Assessment of brittleness using rock strength and density with punch penetration test”, Tunnelling and Underground Space Technology, 2009, 24 (1), 66-74. Yagiz S, Gokceoglu C, “Application of fuzzy inference system and nonlinear regression models for predicting rock brittleness”, Expert Systems with Applications, 2010, 37 (3), 2265-2272. | ||
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