Support Vector Machines for the Estimation of Specific Charge in Tunnel Blasting

Mine tunnels, short transportation tunnels, and hydro-power plan underground spaces excavations are carried out based on Drilling and Blasting (D&B) method. Determination of specific charge in tunnel D&B, according to the involved parameters, is very significant to present an appropriate D&B design. Suitable explosive charge selection and distribution lead to reduced undesirable effects of D&B such as inappropriate pull rate, over-break, under-break, unauthorized ground vibration, air blast, and fly rock. So far, different models are presented to estimate specific charge in tunnel blasting. In this study, 332 data sets, including geomechanical characteristics, D&B, and specific charge are gathered from 33 tunnels. The data are related to three dams and hydropower plans in Iran (Gotvand, Masjed-Solayman, and Siah-Bishe). Specific charge is modeled in inclined hole cut drilling pattern. In this regard, Support Vector Machine (SVM) algorithm based on polynomial Kernel function is used as a tool for modeling. Rock Quality Designation (RQD) index, Uniaxial Compressive Strength (UCS), tunnel cross-section area, maximum depth of blast hole, and blast hole coupling ratio are considered as independent input variables and the specific charge is considered as a dependent output variable. The modeling results confirm the acceptable performance of SVM in specific charge estimation with minimum error

ISTRAŽIVANJE UTJECAJA TEKUĆINA ZA HLAĐENJE/PODMAZIVANJE NA VELIČINU STRUJE REZNIH STROJEVA S DISKOM ZA TVRDE STIJENE

One of the most crucial steps in producing dimension rocks is the rock cutting process, which incurs a high cost. The amperage draw of rock cutting machines is a major cost factor of this production process. Determining the effect of factors, such as the machine’s operating configurations, mechanical and physical characteristics of the rock, and type of cooling/lubricant fluid, on the cutting machine’s performance can significantly reduce operational costs. This study evaluates the electrical current consumption of a disc cutting machine during the cutting of hard rocks for producing dimension rocks under different operating conditions and using different fluids for cooling/lubrication. For this purpose, a number of cutting tests were performed under different operating conditions (cutting depths of 0.5, 0.7, 1, and 1.3 cm and feed rates of 45, 60, 75, and 90 cm/min) with five cooling/lubrication fluids, including tap water, soap water with a ratio of 1:40 and 1:20, and a commercial cutting power (Abtarash) with a ratio of 30:10 and 15:10. After examining the relationship between operating parameters and the amperage draw of the cutting machine in the presence of five fluids, several linear and nonlinear multivariate statistical models were developed to predict the amperage draw of the cutting machine. The developed models were evaluated using the t-test and F-test statistical methods. The results showed that using the developed models, the amperage draw of the cutting machine can be accurately predicted from the properties of the cooling/lubrication fluid, including viscosity and pH.Jedan od najvažnijih koraka u obradi arhitektonsko-građevnoga kamena jest proces rezanja, koji uzrokuje visoku cijenu proizvodnje. Veličina električne struje kod strojeva za rezanje glavni je faktor troškova ovoga proizvodnog procesa. Određivanje radnih čimbenika, kao što su radne konfiguracije stroja, mehaničke i fizičke karakteristike stijene te vrsta tekućine za hlađenje/podmazivanje, na performanse stroja za rezanje može znatno smanjiti operativne troškove. Ovo istraživanje procijenilo je potrošnju električne struje reznoga stroja s diskom tijekom rezanja tvrdih stijena pri obradi arhitektonsko-građevnoga kamena u različitim radnim uvjetima i pri korištenju različitih tekućina za hlađenje/podmazivanje. Proveden je niz ispitivanja rezanja u različitim radnim uvjetima (dubine rezanja od 0,5, 0,7, 1 i 1,3 cm te brzine rezanje od 45, 60, 75 i 90 cm/min) s pet tekućina za hlađenje/podmazivanje, uključujući vodu iz slavine, sapunicu omjera 1 : 40 i 1 : 20 te komercijalni prah za rezanje (Abtarash) u omjeru 30 : 10 i 15 : 10. Nakon ispitivanja odnosa između radnih parametara i veličine struje reznoga stroja uz upotrebu pet tekućina razvijeno je nekoliko linearnih i nelinearnih multivarijantnih statističkih modela kako bi se predvidjela veličina struje reznoga stroja. Razvijeni modeli procijenjeni su statističkim metodama t-testa i F-testa. Rezultati su pokazali kako se pomoću razvijenih modela može točno procijeniti veličina struje stroja za rezanje iz svojstava tekućine za hlađenje/podmazivanje, uključujući viskoznost i PH

Application of response surface method to the prediction of TBM penetration rate

Performance prediction of the tunnel boring machine (TBM) is one of the crucial issues for estimating excavation costs and construction time of tunnel projects. TBM performance highly depends on an achieved penetration rate. The aim of this study is to develop TBM penetration rate prediction models using Response surface method (RSM) and then to compare the results obtained from various meta-heuristics optimization techniques including Differential Evolution (DE), Hybrid Harmony Search (HS-BFGS) and Grey Wolf Optimizer (GWO). To achieve this aim, the database uniaxial compressive strength (UCS), intact rock brittleness (BI), the angle between plane of weakness and TBM driven direction and distance between planes of weakness are assembled by collecting data from Queens water tunnel project. According to the results, it can be said that the proposed model is a useful and reliable means to predict TBM penetration rate provided that a suitable dataset exists. From the prediction results the squared correlation coefficient (R2) between the observed and predicted values of the proposed model was obtained 0.939, which shows a high conformity between predicted and actual penetration rate. The performance of different predictor models controlled by Mean Absolute Percentage Error (MAPE), Route Mean Square Error (RMSE), Variance Absolute Relative Error (VARE), Variance Account for (VAF) and Correlation Coefficient (CC). Response surface method based model with higher VAF and CC as well as lower MAPE, RMSE, VARE will show better performance

TOČNA PROCJENA BRZINE BUŠENJA STIJENE BUŠAĆIM DLIJETOM POMOĆU NADZIRANIH TEHNIKA STROJNOGA UČENJA KOJE SE TEMELJE NA LABORATORIJSKI ODREĐENIM PODATCIMA

Knowing the rate of penetration of a drill bit in rocks is among the most important parameters in their behaviour measurement. However, the direct measurement of ROP in rocks is a high-cost and time-intensive process. Therefore, obtaining the ROP parameter through a method other than direct measurement can be very useful and effective. Predictive machine learning methods are among the strong and precise techniques for the indirect measurement of ROP. To this end, 492 samples were tested under different UCS, µ, WOB, and ω conditions to obtain the corresponding ROP. To achieve an accurate model, three methods of linear regression analysis, lasso regression, and ridge regression were compared in terms of prediction accuracy. These models were compared through performance criteria of the prediction process and error-based charts. The performance criteria were measured using three measures: mean absolute percentage error, D-squared pinball score, and mean Poisson deviance error. For the MAPE index, the Lasso and Ridge models performed the best with values of 0.2557. Concerning the D2PS index, the linear regression model and Ridge performed better with values of 0.4083 and 0.4025, respectively. Finally, for the MPDE index, the Ridge model provided a more accurate performance with a value of 0.0105. For a better comparison, an objective function was created and calculated by combining these three indicators. The results showed the best rank for the Ridge model with an estimated value of 659.475. Finally, it was concluded that the Ridge model is a reliable and accurate model for predicting the ROP.Poznavanje brzine bušenja (ROP eng. rate of penetration) dlijeta jedan je od najvažnijih parametara u njihovu vrednovanju. Međutim, izravno mjerenje ROP-a u stijenama skup je i vremenski zahtjevan postupak. Zbog toga može biti vrlo korisno i učinkovito određivanje ROP parametra metodom koja nije izravno mjerenje. Prediktivne metode strojnoga učenja moćne su i precizne tehnike za neizravno mjerenje ROP-a. Ispitana su 492 uzorka pod različitim uvjetima jednoosne tlačne čvrstoće, viskoznosti, mase dlijeta i brzine rotacije dlijeta kako bi se odredila odgovarajuća brzina bušenja. Točniji model za procjenu nastojao se pronaći usporedbom triju metoda: linearna regresija, LASSO regresija i hrbatna regresija. Njihovi modeli uspoređeni su pomoću kriterija izvedbe procesa procjene i grafikona temeljenih na greškama. U kriteriju izvedbe uspoređene su tri mjere uspješnosti: srednja apsolutna postotna pogreška, D2PS indeks i MPDE indeks. Prema srednjoj apsolutnoj postotnoj pogrešci najbolje su se pokazali modeli LASSO i hrbatne regresije s vrijednostima od 0,2557. Prema D2PS indeksu, modeli linearne regresije i hrbatne regresije pokazali su bolje rezultate s vrijednostima od 0,4083 odnosno 0,4025. Prema MPDE indeksu model hrbatne regresije pružio je točniju procjenu s vrijednosti od 0,0105. Radi još bolje usporedbe kreirana je objektivna funkcija koja je izračunana kombinacijom prije spomenutih triju pokazatelja. Rezultati su pokazali najbolji rang za model hrbatne regresije s procijenjenom vrijednošću od 659,475. Zaključeno je da je model hrbatne regresije pouzdan i točan za procjenjivanje brzine bušenja

The station of modeling the mine resources in economical geology investigations and determination of mineral deposits genes & reserves

In recent days, computer is becoming one of the most essential instruments in advanced countries for the researchers and the domain of its application is going to be increased every day. Using the 3D modeling of the earth, its mine resources and the brilliant details which are given by the models, the researching and exploring groups will find out the inconspicuous and attractive aspects of the genetic structure and the geological history of these resources. In this paper which is a result of the researches done as the case study on a group of aragonite deposits in West Azarbaijan province, modeling of under study mineral deposits and the genetic approaches obtained from the models lead into explore and discover some other resources of the same minerals which is widely accepted recently in the market of decorative rocks in Iran. In modeling procedure of these resources which is a product of the geysers, the profile of these lime generating springs and their directional order on some specific hidden fracture is determined and approximate location of the new resources for the next explorations is assigned. At the moment, these assigned locations as new resources are being explored and even exploited