采礦外文翻譯---采礦工業(yè)中實(shí)用的神經(jīng)網(wǎng)絡(luò)應(yīng)用程序-采礦工程-展示頁
2025-01-31 01:34本頁面
【正文】 y Barton, Lien and Lunde, from the Norwegian Geotechnical Institute, the Q factor is based on six factors, which are: ? RQD rock quality designation ? Jn joint set number ? Jr joint roughness number ? Ja joint alteration number ? Jw joint water reduction factor ? SRF stress reduction factor. The actual Q formula is Q= RQD/Jn Jr/Ja Jw/SRF. The Jw/SRF factor was assumed to be for this study because dry conditions are assumed. Stress is factored through modelling and strain measurements. The Q factor ranges on a logarithmic scale ranging from to 1,000 where is extremely poor rock and 1,000 is virtually perfect rock. Span [5] the meaning of span refers to the width of an underground opening in plan view. Span can be determined through the largest diameter of a circle within an underground excavation. SRF’ [2] refers to the adjusting of RMR values relative to stress ratios and previous history of ground conditions. It does not refer directly to SRF used in the calculation of Q. Stress criteria is based upon the ratio of induced stress over unconfined pressive strength (UCS) of the rock. . Output Factors Burst refers to a stope in which a rockburst has occurred. A rockburst is an instantaneous rock failure in or about an excavated area characterized/acpanied by a shock or tremor in the surrounding rock. PUNRF refers to potentially unstable ground with respect to a roof fall. A stope is considered potentially unstable if any of the following conditions occur [2]: ? The opening may exhibit strong discontinuities having orientations that form potential wedges in the back. ? Extra ground support may have been installed to prevent a potential fall of ground. ? Instrumentation installed in the stope has recorded continuing movement of the stope back. ? There may be an increased frequency of ground working or scaling. PUNGW refers to a stope considered potentially unstable due to the likelihood of a ground wedge failure. This is a subset of PUNRF collected separately to identify areas where jointing may result in wedge failures. Cave refers to when uncontrolled ground failures result in caving. 3. NEURAL NETWORK ANALYSIS The above inputs and outputs were run on a neural work to see if a neural work could predict results from the input data and also to see which inputs had the greatest effect on output prediction. A two layer work consisting of 13 nodes was run for 10105 cycles reaching a percent error. Seventy three observations were used to train the work. The remaining 15 observations were used to test the work’s predicting ability. The results of the neural work show that the work correctly predicted all outputs from the training. The reason that this is not surprising is that the work used these 73 observations for prediction training. However, the neural work also predicted burst conditions on the test data which was new data for the neural work. The work appears to have trouble distinguishing between PUNGW and PUNRF but predicted burst conditions on every occasion. The fact that burst conditions were predicted on each occasion was promising with respect to the possibility that neural works may be a useful tool to predict rockbursts. It appears from this database, that SRF has the most significant effect on predicting rockbursts. The bias node, Q, and adjusted RMR are also significant while RMR, span, and depth appear to have a lesser effect. It is not surprising that SRF has the most significance as it is a factor given to rock according to its previous burst
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