• Structural Engineering and Mechanics
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• v.67 no.2
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• pp.105-113
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• 2018

To improve the prediction accuracy of the strong-unloading rock slope performance and obtain the range of variation in the slope displacement, a new displacement time-series prediction model is proposed, called the fuzzy information granulation (FIG)-genetic algorithm (GA)-back propagation neural network (BPNN) model. Initially, a displacement time series is selected as the training samples of the prediction model on the basis of an analysis of the causes of the change in the slope behavior. Then, FIG is executed to partition the series and obtain the characteristic parameters of every partition. Furthermore, the later characteristic parameters are predicted by inputting the earlier characteristic parameters into the GA-BPNN model, where a GA is used to optimize the initial weights and thresholds of the BPNN; in the process, the numbers of input layer nodes, hidden layer nodes, and output layer nodes are determined by a trial method. Finally, the prediction model is evaluated by comparing the measured and predicted values. The model is applied to predict the displacement time series of a strong-unloading rock slope in a hydropower station. The engineering case shows that the FIG-GA-BPNN model can obtain more accurate predicted results and has high engineering application value.

• Journal of the Korean Geotechnical Society
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• v.27 no.6
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• pp.17-26
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• 2011

Ground anchor method is one of the most popular reinforcing technology in Korea. For the sound monitoring of slope reinforced by permanent anchor for a long period, monitoring the tension force of ground anchor is very important. However, special technology except conventional load cell has not been developed for this purpose. In this paper, a new method is described to replace the conventional strain gauge and V.W. type load cell which has been commonly used as a prestress force monitoring tool for a short-term and long-term. Four 11.5 m long strain detectable tension type anchors were made using FBG sensor embedded tendon since FBG sensor is smaller than strain gauge type load cell and does not have noise from electromagnetic wave. Each two set strain detectable tension type anchors were installed into the different ground conditions, i.e., soft rock and weathered granite soil. Prestress force of ground anchor was monitored during the loading-unloading step from in-situ pullout test using proposed FBG sensor embedded in the tendon and the conventional load cell Test results show that the prestress force monitored from FBG sensor may well be used practically, for it almost matches with that measured from expensive load cell.