A Probabilistic Model to Determine Main Caving Span by Evaluating Cavability of Immediate Roof Strata in Longwall Mining
- PDF / 2,287,299 Bytes
- 17 Pages / 547.087 x 737.008 pts Page_size
- 49 Downloads / 207 Views
(0123456789().,-volV) ( 01234567 89().,-volV)
ORIGINAL PAPER
A Probabilistic Model to Determine Main Caving Span by Evaluating Cavability of Immediate Roof Strata in Longwall Mining Sadjad Mohammadi Naj Aziz
. Mohammad Ataei
. Reza Kakaie . Ali Mirzaghorbanali .
Received: 23 May 2020 / Accepted: 23 October 2020 Springer Nature Switzerland AG 2020
Abstract Caving process is a complex dynamic phenomenon influences safety and productivity of coal longwall mining. It improves safety due to reduction of load on support, face convergence and abutment stresses. Proper caving with respect to the quality and time of occurrence ensures continuity of operation and subsequently, the productivity of coal extraction. Therefore, a reliable prediction of strata behaviour and its caving potential are imperative in design of longwall projects. This paper presents a hybrid probabilistically qualitative–quantitative model to evaluate cavability of immediate roof and to estimate
S. Mohammadi (&) M. Ataei R. Kakaie School of Mining, Petroleum and Geophysics, Shahrood University of Technology, Shahrood, Iran e-mail: [email protected] M. Ataei e-mail: [email protected] R. Kakaie e-mail: [email protected] A. Mirzaghorbanali School of Civil Engineering and Surveying, University of Southern Queensland, West St, Darling Heights, Toowoomba, QLD 4350, Australia e-mail: [email protected] N. Aziz School of Civil, Mining and Environmental Engineering, University of Wollongong, Wollongong, NSW 2500, Australia e-mail: [email protected]
main caving span in longwall mining by combining empirical model and numerical solution. For this purpose, numerical simulation was incorporated to Roof Strata Cavability index (RSCi) as summation of ratings for nine significant parameters. Distinct element code was used to simulate numerically main caving span corresponding to various RSCi classes probabilistically. The newly proposed model was verified against actual field data collected from different longwall panels around the world. The results of proposed model agreed well with those of collected data. Keywords Longwall mining Immediate roof Cavability Main caving span Empirical model Numerical simulation List of Symbols C Cohesion E Young’s modulus E(X) Mean value of data distribution F(x) Cumulative distribution function (CDF) f(x) Probability density function (PDF) L Rock Quality Index (RQI) K Number of subsets K1 In situ strength coefficient K2 Creep coefficient K3 In situ water content coefficient rci Intact compressive strength Kn Normal stiffness Ks Shear stiffness
123
Geotech Geol Eng
n Sm t tb tm c ce m q r rc rh rt u w RSCi SVR COA
A constant value depending upon RQD Main caving span Thickness of roof layer Bed thickness Thickness of main roof Average unit weight of the bed Effective unit weight of rock Poisson’s ratio Density Standard deviation Intact compressive strength Average in situ horizontal stress Tensile strength Angle of internal friction Angle of dilatancy Roof Strata Cavability in
Data Loading...
