کلاس‌بندی سنگ‏های ساختمانی از دیدگاه قابلیت برش با استفاده از روش خوشه‌بندی فازی

نوع مقاله : یادداشت فنی

نویسندگان

1 دانشکده مهندسی معدن و مواد دانشگاه صنعتی ارومیه

2 دپارتمان مهندسی عمران، دانشگاه کالابریا، رنده، ایتالیا

3 دانشکده مهندسی معدن، نفت و ژئوفیزیک، دانشگاه صنعتی شاهرود

10.29252/anm.2019.1629

چکیده

پیش‌بینی قابلیت برش سنگ به عنوان یکی از فاکتورهای موثر در تخمین هزینه‏ها و پیش‌بینی میزان تولید یک کارخانه فرآوری سنگ از اهمیت بالایی برخوردار است. بنابراین شناخت کامل سنگ‏های ساختمانی و ارزیابی توان اجرایی دستگاه‏های برش در کارخانه‏های فرآوری، طراحان و برنامه‏ریزان تولید را به سمت بهبود سرعت فرآوری و افزایش تولید سوق می‏دهد. از این رو، به کارگیری روش‏های نو و کاربردی برای دست‏یابی به این اهداف لازم و ضروری است. در این تحقیق سعی شده است پس از تعیین مهم‌ترین پارامترهای فیزیکی و مکانیکی موثر در فرایند برش، قابلیت برش‌پذیری نمونه سنگ‏های ساختمانی با استفاده از روش خوشه‌بندی فازی (Fuzzy C-means) مورد ارزیابی و کلاس‌بندی قرار ‏گیرد. بدین ترتیب 12 نمونه سنگ ساختمانی مشتمل بر دو گروه از سنگ‏های ساختمانی سخت و نرم مورد آزمایش و ارزیابی قرار گرفت. نمونه‌ها در مدل‏های 3، 4 و 5 کلاسه مورد ارزیابی و کلاس‌بندی قرار گرفت، سپس نتایج با شدت جریان مصرفی دستگاه برش مورد اعتبارسنجی قرار گرفت. نتایج حاصل از بررسی‏ها پس از اعتبارسنجی با آزمایش‏های دقیق، نشان داد که روش خوشه‌بندی فازی می‏تواند به عنوان یکی از روش‏های نو و کاربردی برای طبقه‌بندی و بررسی قابلیت برش نمونه سنگ‏های ساختمانی با توجه به معیارهای تاثیرگذار نظیر مقاومت فشاری تک محوری، سختی موهس، سایندگی شیمازک و مدول الاستیسیته مورد استفاده قرار گیرد.

کلیدواژه‌ها


عنوان مقاله [English]

Clustering of Ornamental Stones Based Upon the Sawability by Using the Fuzzy Clustering Technique

نویسندگان [English]

  • reza mikaeil 1
  • sina shaffiee haghshenas 2
  • Mohammad Ataei 3
1 Dept. of Mining Engineering, Faculty of Environment, Urmia University of Technology, Urmia, Iran
2 Dept. of Civil Engineering, University of Calabria, Via Bucci, 87036 Rende (CS), Italy
3 Dept. of Mining, Petroleum & Geophysics, Shahrood University of Technology, Shahrood, Iran
چکیده [English]

Summary
The complete knowledge of the cutting process and the performance of the sawing machine can help increase the efficiency and quality of the manufactured product.  During the field studies, 12 rock samples from two kinds of ornamental stones were collected and some major mechanical properties including uniaxial compressive strength (UCS), Mohs hardness (Mh), Schimazek’s F-abrasiveness factors (SF-a), and young modulus (YM) were measured. Also, Fuzzy C-means has been used as a soft computing technique in this study. All different studied rock samples are classified into 3, 4, and 5 classes. Then, the results of classification were compared with consumed energy values. Finally, it can be concluded that FCM could be a simple but efficient tool in the evaluation of the sawability of ornamental stone as one of the most important factors to estimation and prediction of production cost and productivity of processing plants.
 
Introduction
The rock sawability and prediction of consumed energy is one of the most important factors in the estimation and prediction of production cost and productivity of processing plants. In this study, for laboratory tests, some rock blocks were collected from the studied quarries. Then, by using the fuzzy C-means clustering approached and considering laboratory results of rock samples, all of the rocks have been classified in 3, 4, and 5 separate clusters. The results clearly showed that the FCM algorithm was used as a reliable and efficient tool for classifying the ornamental stone.
 
Methodology and Approaches
Nowadays, with the increasing growth of uncertain problems, the use of soft computing techniques with high ability in solving these kinds of problems has increased significantly. Assessment of the sawability of ornamental stone is the most important factor in the identification and prediction of production cost and productivity of processing plants. Given the unreliability of all experimental laboratory, one of the most important tasks in the sawability of ornamental stone’ classification is using a method with the highest possible accuracy. According to the importance of the issue, in this study, the Fuzzy C-means algorithm is used for the classification. Four important physical and mechanical characteristics of 12 rock samples are considered for the classification of sawability of ornamental stone from two kinds of ornamental stone, including hard and soft rock groups using Fuzzy C-means Optimization, including uniaxial compressive strength, Mohs hardness, Schmiazek F-abrasivity factor, and Young's modulus. Finally, in this study, the results of the classification are compared with consumed energy.
 
Results and Conclusions
In this study employing Fuzzy C-means as soft computing technique and some major mechanical properties such as uniaxial compressive strength, Mohs hardness, Schmiazek F-abrasivity factor, and Young's modulus to evaluate the sawability of ornamental stone, 3 to 5 classes are considered. Generally, according to this research, the following remarks can be concluded:
1- The 12 rock samples from two kinds of ornamental stones, including hard and soft rock groups, are evaluated and tested.
2- A comparison was made between 3 different models (3 classes from 3 to 5 classes) with consumed energy of the sawing machine. The results show that all 12 rock samples are classified as very suitable.
3- In comparison to all classes, it can be concluded that FCM is a reliable technique for clustering the sawability of ornamental stone with highly acceptable degrees of robustness.

کلیدواژه‌ها [English]

  • Ornamental stone
  • Sawability
  • Mechanical properties
  • Fuzzy clustering

تا کنون مطالعات نسبتاً خوبی در مقیاس‏‏های مختلف صنعتی و آزمایشگاهی در زمینه قابلیت برش سنگ‏ها انجام شده است. در این میان برخی از محققین به بررسی خصوصیات سنگ و برخی هم به بررسی مشخصات طرح برش و برخی دیگر نیز به بررسی پارامترهای عملیاتی برش پرداخته‏اند. از میان این عوامل، مشخصات سنگ از جمله مهم‌ترین پارامترهایی بوده است که در بیش از نیمی از مطالعات مورد بررسی قرار گرفته است. مطالعات انجام شده در زمینه قابلیت برش سنگ‏ها را می‏توان در دو بخش ارائه سیستم‏های طبقه‏بندی سنگ‏ها و روابط تجربی برای ارزیابی عملکرد برش، تقسیم‏بندی کرد. جدول 1 مطالعات صورت گرفته توسط محققین در بخش سیستم‏های طبقه‌بندی سنگ‏های ساختمانی را نشان می‏دهد.

[1]           Birle, J. D., and Ratterman, E. (1986), An approximate ranking of the sawability of hard building stones based on laboratory tests. Dimensional Stone Magazine, 3: pp. 3-29.
[2]           Kahraman, S., and Ulker, U. (2005), A quality classification of building stones from P-wave velocity and its application to stone cutting with gang saw, In 4th Congress of the Balkan Geophysical Society.
[3]           Mikaeil, R., Yousefi, R., Ataei, M., Farani, R. A. (2011), Development of a new classification system for assessing of carbonate rock sawability. Archives of Mining Sciences, 56(1): pp. 59-70.
[4]           Mikaeil, R., Yousefi, R., Ataei, M. (2011), Sawability ranking of carbonate rock using fuzzy analytical hierarchy process and TOPSIS approaches. Scientia Iranica, 18(5): pp. 1106-1115.
[5]           Mikaeil, R., Ataei, M., Yousefi, R. (2011), Evaluating the Power Consumption in Carbonate Rock Sawing Process by Using FDAHP and TOPSIS Techniques. INTECH Open Access Publisher.
[6]           Mikaeil, R., Ozcelik, Y., Yousefi, R., Ataei, M., Hosseini, S. M. (2013), Ranking the sawability of ornamental stone using Fuzzy Delphi and multi-criteria decision-making techniques. International Journal of Rock Mechanics and Mining Sciences, 58: pp. 118-126.
[7]           Tutmez, B., Kahraman, S., Gunaydin, O. (2007), Multifactorial fuzzy approach to the sawability classification of building stones. Construction and Building Materials, 21(8): pp. 1672-1679.
[8]           Wei, X., Wang, C. Y., and Zhou, Z. H. (2003): Study on the fuzzy ranking of granite sawability. Journal of materials processing technology, 139(1): pp. 277-280.
[9]           Mikaeil, R., Haghshenas, S. S., Haghshenas, S. S., and Ataei, M. (2018), Performance prediction of circular saw machine using imperialist competitive algorithm and fuzzy clustering technique. Neural Computing and Applications, 29(6): pp. 283-292.
[10]         Mikaeil, R., Ozcelik, Y., Ataei, M., and Shaffiee Haghshenas, S. (2019), Application of harmony search algorithm to evaluate performance of diamond wire saw. Journal of Mining and Environment, 10(1): pp. 27-36.
[11]         Samani, H. Y., and Bafghi, A. R. Y. (2012), Prediction of the sawing quality of Marmarit stones using the capability of artificial neural network. International Journal for Numerical and Analytical Methods in Geomechanics, 36(7): pp. 881-891.
[12]         Mikaeil, R., Shaffiee Haghshenas, S., Ozcelik, Y., and Shaffiee Haghshenas, S. (2017), Development of Intelligent Systems to Predict Diamond Wire Saw Performance. Soft Computing in Civil Engineering, 1(2): pp. 52-69.
[13]         Mikaeil, R., Haghshenas, S. S., Shirvand, Y., Hasanluy, M. V., and Roshanaei, V. (2016), Risk Assessment of Geological Hazards in a Tunneling Project Using Harmony Search Algorithm (Case Study: Ardabil-Mianeh Railway Tunnel). Civil Engineering Journal, 2(10): pp. 546-554.
[14]         Salemi, A., Mikaeil, R., and Haghshenas, S. S. (2018), Integration of finite difference method and genetic algorithm to seismic analysis of circular shallow tunnels (Case study: Tabriz urban railway tunnels). KSCE Journal of Civil Engineering, 22(5): pp. 1978-1990.
[15]         Mikaeil, R., Haghshenas, S. S., and Hoseinie, S. H. (2018), Rock penetrability classification using artificial bee colony (ABC) algorithm and self-organizing map. Geotechnical and Geological Engineering, 36(2): pp. 1309-1318.
[16]         Aryafar, A., Mikaeil, R., Shafiee Haghshenas, S., and Shafiei Haghshenas, S. (2018), Utilization of soft computing for evaluating the performance of stone sawing machines, Iranian Quarries. International Journal of Mining and Geo-Engineering, 52(1): pp. 31-36.
[17]         Aryafar, A., Mikaeil, R., Haghshenas, S. S., and Haghshenas, S. S. (2018). Application of metaheuristic algorithms to optimal clustering of sawing machine vibration. Measurement, 124: pp. 20-31.
[18]         Tumac, D. (2016), Artificial neural network application to predict the sawability performance of large diameter circular saws. Measurement, 80: pp. 12-20.
[19]         Mahdevari, S., Shahriar, K., Sharifzadeh, M., and Tannant, D. D. (2017), Stability prediction of gate roadways in longwall mining using artificial neural networks. Neural Computing and Applications, 28(11): pp. 3537-3555.
[20]         Haghshenas, S. S., Neshaei, M. A. L., Pourkazem, P., and Haghshenas, S. S. (2016), The Risk Assessment of Dam Construction Projects Using Fuzzy TOPSIS (Case Study: Alavian Earth Dam). Civil Engineering Journal, 2(4): pp. 158-167.
[21]         Mokhtarian Asl, M., and Sattarvand, J. (2016), An imperialist competitive algorithm for solving the production scheduling problem in open pit mine. Int. Journal of Mining & Geo-Engineering, 50(1): pp. 131-143.
[22]         Haghshenas, S. S., Haghshenas, S. S., Mikaeil, R., Ardalan, T., Sedaghati, Z., and Kazemzadeh Heris. P., (2017), Selection of an Appropriate Tunnel Boring Machine Using TOPSIS-FDAHP Method (Case Study: Line 7 of Tehran Subway, East-West Section). (22.10): pp. 4047-4062. EJGE.
[23]         Faradonbeh, R. S., and Taheri, A. (2019), Long-term prediction of rockburst hazard in deep underground openings using three robust data mining techniques. Engineering with Computers, 35(2): pp. 659-675.
[24]         Mohammadi, J., Ataei, M., Kakaie, R., Mikaeil, R., and Shaffiee Haghshenas, S. (2019), Performance evaluation of chain saw machines for dimensional stones using feasibility of neural network models. Journal of Mining and Environment, 10(4): pp. 1105-1119.
[25]         Mohammadi, J., Ataei, M., Kakaei, R. K., Mikaeil, R., and Haghshenas, S. S. (2018), Prediction of the Production Rate of Chain Saw Machine using the Multilayer Perceptron (MLP) Neural Network. Civil Engineering Journal, 4(7): pp. 1575-1583.
[26]         Hollingsworth, K., Rouse, K., Cho, J., Harris, A., Sartipi, M., Sozer, S., and Enevoldson, B. (2018), Energy Anomaly Detection with Forecasting and Deep Learning. In 2018 IEEE International Conference on Big Data (Big Data) : pp. 4921-4925.
[27]         Mokhtarian Asl, M., and Sattarvand, J. (2018), Integration of commodity price uncertainty in long-term open pit mine production planning by using an imperialist competitive algorithm. Journal of the Southern African Institute of Mining and Metallurgy, 118(2): pp. 165-172.
[28]         Haghshenas, S. S., Mikaeil, R., Haghshenas, S. S., Naghadehi, M. Z., and Moghadam, P. S. (2017), Fuzzy and classical MCDM techniques to rank the slope stabilization methods in a rock-fill reservoir dam. Civil Engineering Journal, 3(6): pp. 382-394.
[29]         Fahmi, H., and Abdinia, A. D. (2006), Application of Fuzzy Clustering in Continuous Classification: A Case study. Iran-Water Resources Research, 2(1).
[30]         Yager, R. R., and Filev, D. P. (1994), Essentials of fuzzy modeling and control. New York.
[31]         Chiu, S. L. (1994), Fuzzy model identification based on cluster estimation. Journal of Intelligent & Fuzzy Systems, 2(3): pp. 267-278.
[32]         Bezdek, J. C., Ehrlich, R., and Full, W. (1984), FCM: The fuzzy c-means clustering algorithm. Computers & Geosciences, 10(2-3): pp. 191-203.
[33]         Gustafson, D. E., and Kessel, W. C. (1979), Fuzzy clustering with a fuzzy covariance matrix. In 1978 IEEE conference on decision and control including the 17th symposium on adaptive processes: pp. 761-766.
[34]         Rad, M. Y., Haghshenas, S. S., and Haghshenas, S. S. (2014), Mechanostratigraphy of cretaceous rocks by fuzzy logic in East Arak, Iran. In The 4th International Workshop on Computer Science and Engineering-Summer, WCSE.
[35]         Haghshenas, S. S., Haghshenas, S. S., Barmal, M., and Farzan, N. (2016), Utilization of Soft Computing for Risk Assessment of a Tunneling Project Using Geological Units. Civil Engineering Journal, 2(7): pp. 358-364.
[36]         Mikaeil, R., Dormishi, A., Sadegheslam, G., and Shaffiee Haghshenas, S. (2017); An Investigation of the Effect of Freezing on Strength and Durability of Dimension Stones Using Fuzzy Clustering Technique and Statistical Analysis. Journal of Analytical and Numerical Methods in Mining Engineering.‎ 6(Special Issue): pp.1-10.
[37]         Haghshenas, S. S., Haghshenas, S. S., Mikaeil, R., Sirati Moghadam, P., and Haghshenas, A. S. (2017), A new model for evaluating the geological risk based on geomechanical properties—case study: the second part of emamzade hashem tunnel. Electron J Geotech Eng, 22(01): pp. 309-320.
[38]         Mikaeil, R., Haghshenas, S. S., Ozcelik, Y., and Gharehgheshlagh, H. H. (2018), Performance evaluation of adaptive neuro-fuzzy inference system and group method of data handling-type neural network for estimating wear rate of diamond wire saw. Geotechnical and Geological Engineering, 36(6): pp. 3779-3791.
[39]         Aryafar, A., Mikaeil, R., Doulati Ardejani, F., Shaffiee Haghshenas, S., and Jafarpour, A. (2019), Application of non-linear regression and soft computing techniques for modeling process of pollutant adsorption from industrial wastewaters. Journal of Mining and Environment, 10(2): pp. 327-337.
[40]         Dormishi, A. R., Ataei, M., Khaloo Kakaie, R., Mikaeil, R., and Shaffiee Haghshenas, S. (2019), Performance evaluation of gang saw using hybrid ANFIS-DE and hybrid ANFIS-PSO algorithms. Journal of Mining and Environment, 10(2): pp. 543-557.
[41]         Bezdek James, C. (1981), Pattern Recognition with Fuzzy Function Algorithms.
[42]         Koorehpzan Dezfuli, A., (2008), principles of theory of fuzzy sets and its applications in modeling water engineering problems", Jihad University, Amir Kabir branch, Tehran : pp.103-136.( in Persian)
[43]         Brown, E. T. (1981), ISRM suggested methods. Rock characterization testing and monitoring. London: Royal School of Mines.