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Undoubtedly, Roadheaders are one of the most versatile excavation machine types operated in soft and medium strength rock formations’ tunneling and mining. An essential aspect of a successful roadheader application is definitely the performance prediction which is basically concerned with machine selection, production rate and also bit consumption. Evolving a new roadheaders’ performance prediction model in various operational conditions and also different material is the primary intention of this research. Investigation on previous works revealed that three main features have great influences on the bit wear of a roadheader. Brittleness which can be utilized as a cuttability factor in mechanical excavation perspective is actually one of some parameters which is absolutely in relation with breakage properties. In addition to the rock brittleness, rock quality designation (RQD) and instantaneous cutting rate are employed as input parameters for the prediction of pick (bit) consumption rate (PCR). For the purpose of this paper, using previously published field datasets, a new prediction model using the application of artificial neural networks as an artificial intelligence technique is developed, trained and tested to estimate PCR based on data of brittleness, RQD and instantaneous cutter rate. Results demonstrated that PCR is highly correlated to the input parameters, and the ANN model could produce acceptable predictions.
In recent years, mining business has been under the influences of global trends, environmental limitations, and variant market requirements to be more and more productive and profitable. Utilizing mechanical miners like roadheaders, continuous miners, impact hammers and tunnel boring machines for ore extraction and excavation of development drivages, increases profitability. The mentioned miners result in continuous operations and consequently, the mechanization of mines with mechanical miners is presumed to make mining projects more productive, more competitive, and less costly. As a result, ordinary drill and blast technique could be avoided. Roadheaders which are applicable in tunnelling, mine development, and mine production of rock types of soft to medium strength, are very adaptable excavation facilities. The efficiency of roadheader application is rudimentary related to machine selection, production rate and bit consumption (Ebrahimabadi et al., 2011).
Macias, Francisco Javier (SINTEF Building and Infrastructure - Rock and Soil Mechanics Group) | Dahl, Filip (SINTEF Building and Infrastructure - Rock and Soil Mechanics Group) | Bruland, Amund (NTNU Department of Civil and Transport Engineering) | Käsling, Heiko (TUM Chair of Engineering Geology) | Thuro, Kurosch (TUM Chair of Engineering Geology)
Drillability is an important parameter in order to assess the influence that intact rock properties have on performance prediction and cost evaluations in connection with drill-and-blast tunnelling, TBM tunnelling, excavations by roadheaders and hydraulic impact hammers and also rock quarrying. Especially in hard rock conditions, drillability will be of great importance for selection of excavation method and a successful project execution. Unanticipated situations and/or inappropriate assessments can result in considerable delays and great risk of cost overruns. Reliable predictions are therefore required; prediction of net penetration rate and tool wear, time consumption and excavation costs, including risk and assessing risk linked to variation in rock mass boreability, establishing and managing contract price regulation. Several methodologies are available to assess drillability (i.e. rock strength, rock surface hardness, rock brittleness, rock abrasivity or rock petrography). This paper includes a review of the state-of-the-art and discussion of relevant parameters that involves drillability assessments in hard rock conditions.
Rock properties have a large impact in connection with excavation and tunnelling by use of drill-and-blast, TBMs, roadheaders, hydraulic impact hammers and also for rock quarrying, especially in hard rock conditions. The term drillability is commonly used to describe the ability of the rock to be drilled or bored and it will be of great importance on performance predictions, cost evaluations and selection of excavation method. Unanticipated situations and/or inappropriate assessments can result in considerable delays and great risk of cost overruns. Reliable predictions are therefore required for; prediction of net penetration rate and tool wear, time consumption and excavation costs, including risk and assessing risk linked to variation in rock mass boreability, establishing and managing contract price regulation. Several methodologies are available to assess drillability (i.e. rock strength, rock surface hardness, rock brittleness, rock abrasivity or rock petrography). This paper includes a brief review of the state-of-the-art and discussion of relevant parameters that involves drillability assessments in hard rock conditions.
ABSTRACT: Cerchar abrasion index (CAI) is commonly used to represent rock abrasion for estimation of bit life and wear in various mining and tunneling applications. The test is simple and fast, but there have been some discrepancies in the test results which are related to the type of equipment, condition of the rock surface, operator skills, testing procedures, and measuring the wear flat. This paper focuses on the estimation of CAI and investigates the impact of various parameters on that. Results of a limited Cerchar tests on a set of rock samples from different laboratories are analyzed to correlate rock properties data to CIA value, which every value indicate an abrasiveness classification. As a result of a literature review, it is concluded that the abrasiveness of a rock sample based on the CAI value is strongly correlated with uniaxial compressive strength (UCS) and brittleness of rock samples. Rock brittleness is a function of UCS and Brazilian tensile strength (BTS). Thus, collected data of these parameters were hired to develop and train artificial neural networks (ANN) as an artificial intelligence (AI) method for estimation of drilling tool wear using data of rock strength and brittleness as inputs. It is pursued by the application of pattern recognition which is achieved by ANNs.
In this research, artificial neural networks (ANN) are applied to predict drilling bit wear using collected and calculated data of rock abrasiveness, including uniaxial compressive strength, Brazilian tensile strength and rock brittleness.
This purpose is pursued by performance of pattern recognition as it is one of the major applications of ANNs. The final proposed neural networks produces the abrasiveness classification by receiving data of UCS, BTS and brittleness.
1.1. Wear and Rock Abrasiveness The wear is in many ways similar to the effect of harder minerals on softer ones and is easily represented by the scratch that the hard objects engrave in soft minerals. Plinninger et al., 2002, depicted that “Abrasive wear” is the predominant wear process in most rock types.
ABSTRACT: Rock is deformed and a failure zone based upon the ductility and brittleness of rocks occurs around the bit while drilling a blasthole. In order to better understand how the formation characteristics affect the drilling performance and the reason of the rock failure, a number of laboratory tests were conducted in this study. In this context, the effect of elastic and strength properties of rocks on the drilling properties was examined and also the influence of the brittleness and drillability on the mechanical properties was analyzed. The results have shown that the drilling rate index (DRI) agreed well with uniaxial compressive strength. This confirmed that the results of mechanical properties based on UCS have a high level of reliability while evaluating the drillability. Nevertheless, the results of DRI did not represent any correlation versus Young’s modulus during blasthole drilling in rotary-percussive method although rotary drilling method indicated good relationships. Therefore, it is concluded that rock drilling properties do not account for a systematic relation to rock deformation in rotary-percussive drilling for medium-hard strength rocks while it does in rotary drilling method.
Opening tunnels and roadways are important underground operations in mining and tunneling, because different types of tunnels such as subways, highways, sewage, are opened to supply the needs of human beings and also for different goals. They can be driven by various machines such as tunnel boring machines, impact hammers, roadheaders or other mechanical excavators. However, if the formation is very hard, drilling blasting method is widely applied for the excavation of the tunnels. The choice of method depends basically on the site geology, tunnel length and the cross-section area of the tunnel. The feasibility analyzes also help to select appropriate method or machine type. Because, excavation costs account for almost half of operating cost of a tunnel.
Penetration rates are major concern of the tunneling activities. In drilling and blasting method, jumbo drills are essential since they are able to drill longer and larger blastholes by utilizing the hydraulic or pneumatic rock drills mounted on the machine. Higher penetration rates can be achieved by optimizing the operational parameters of the drills. Besides, longer holes can be drilled at a short time by increasing the thrust on the drill whereas harder rocks can be drilled shortly by increasing the impact on the rock drill. Based on the penetration and advance rates, completion period of a tunnel is also predicted.
Direct determination of the deformation modulus of rock mass needs sophisticated testing equipment, timeconsuming processes and experienced technical staff. However, this modulus has a crucial importance for all rock engineering project to be constructed on or in a rock mass. For this reason, indirect determination of deformation modulus of rock mass has been attractive subject for rock engineers and engineering geologists. For this reason, during the last two decades, several empirical equations based on statistical analysis and several other soft computing algorithms for indirect determination of deformation modulus of rock masses have been proposed. In the present study, a critical review on these approaches is performed and a summary is given. For the purpose of the study, an extensive literature survey is carried out and the approaches suggested are discussed.
Depending on the increase in World population, new human needs such as high buildings, transportation and energy also increase. As a result of these needs, new infrastructures such as roads, railroads, tunnels, viaducts, dams, ports etch have been constructed and will be constructed. During the project and construction stages of these infrastructures, the deformation modulus of rock mass is necessary. However, field tests to determine this parameter directly are time consuming, expensive and the reliability of the results of these tests is sometimes questionable (Hoek and Diederichs, 2006). For this reason, indirect determination methods can be preferred if the other rock mass and intact rock properties are known well. Considering this reason, during the last two decades, several researchers have suggested empirical equations based on statistical analysis and soft computing algorithms for indirect determination of deformation modulus of rock masses. The main purpose of the present study discusses the approaches suggested for estimation of deformation modulus of rock masses.
The static modulus of deformation is among the parameters that best represent the mechanical behaviour of a rock and of a rock mass, in particular when it comes to underground excavations. The deformation modulus is, therefore, a cornerstone of many geomechanical analyses (Palmstorm and Singh, 2001). The deformation modulus is the most representative parameter describing the pre-failure mechanical behavior of any engineering material (Jiang et al., 2009). However, deformation modulus of a rock mass is affected by various rock mass and intact rock properties as well as environmental conditions.