Application of Artificial Neural Networks in Estimation of Drilling Rate Index Using Data of Rock Brittleness and Mechanical Properties
- Adel Asadi (Islamic Azad University) | Amin Abbasi (Islamic Azad University) | Amirhossein Bagheri (University of Tehran)
- Document ID
- International Society for Rock Mechanics and Rock Engineering
- 3rd Nordic Rock Mechanics Symposium - NRMS 2017, 11-12 October, Helsinki, Finland
- Publication Date
- Document Type
- Conference Paper
- 2017. Finnish Association of Civil Engineers RIL. Permission to distribute - International Society for Rock Mechanics and Rock Engineering
- Drilling Rate Index Prediction, Rock Properties, Brittleness, Artificial Neural Networks
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One of the necessities in drilling operations is the ability to predict the performance of rock drills. To explain the effects of various parameters on the drilling rate (drilling velocity) and the drilling tool wear, the term drillability is being used. In this research, drillability is defined as a penetration rate. The correlation between drilling rate index (DRI) and some rock properties is inspected in this survey in order to examine the influences of properties of strength indexes and brittleness of rocks on drillability. To achieve this, uniaxial compressive strength (UCS) and Brazilian tensile strength (BTS) values of different rock samples were used as geomechanical properties data. Then, the brittleness of rocks which use the uniaxial compressive strength and tensile strength of rocks were determined from calculations. Afterwards, artificial neural networks (ANN) as an artificial intelligence technique was employed in order to relate datasets of UCS, BTS and brittleness as input data to the DRI as the target. The suggested correlation between DRI and both mechanical rock properties and brittleness concepts were analyzed, and acceptable correlations between drillability of rocks and the input parameters was achieved. It is concluded that by the use of data of uniaxial compressive strength, Brazilian tensile strength and rock brittleness, ANNs can evaluate drilling rate index accurately.
Nowadays, Tunnel excavation utilizing mechanical excavation techniques such as tunnel boring machines (TBM’s) and roadheaders is growingly becoming common. Choosing the machinery and hardware must be under consideration of physical, mechanical and petrographic properties of rock, otherwise it can result in considerable detriments. Hence, earlier than tunnelling operations, it is vital to investigate rock properties (Yarali and Soyer, 2011).
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