In the underground environment with large buried depth and high ground stress, man-made disturbance is very easy to cause the rapid expansion of primary fractures in the rock, and then developed into the macrodynamic performance of rock. Based on the propagation law of elastic waves in discontinuous media, the application of acoustic emission detection technology can quickly determine whether there are primary fractures in the rock and predict its approximate location. In this work, CT scanning experiments of intact sandstone specimens and fractured sandstone specimens were performed. The gray value distribution of intact sandstone specimens and fractured sandstone specimens was studied. The sandstone specimens were divided into 4 zones (I~IV) from top to bottom. The height of each zone is from 0 mm to 25 mm, and the upper end face of each zone is the starting face. Acoustic emission experiments of intact sandstone and fractured sandstone are carried out based on the equilateral triangle sensor array. The dispersion of AE wave velocity and amplitude in intact sandstone specimens and fractured sandstone specimens is studied. The results show that the crack evolution law of sandstone specimens before and after preloading is closely related to the density distribution. The regular trend is from low density to high density. And the decay law of AE eigenvalue before and after preloading of sandstone specimen is consistent with the change trend of gray value. This shows that it is feasible to explore the spatial location of primary fractures and the degree of development of primary fractures in the rock through the equilateral triangle sensor array. In the actual project, it can provide some guidance and suggestions for related projects.
Accurate prediction and reasonable warning for dam displacement are important contents of dam safety monitoring. However, it is difficult to identify abnormal displacement based on deterministic point prediction results. In response, this paper proposes a model that integrates several strategies to achieve high-precision point prediction and interval prediction of dam displacement. Specifically, the interval prediction of dam displacement is realized in three stages. In the first stage, a displacement prediction model based on Extreme gradient boosting (XGBoost) is constructed. In the second stage, the prediction error sequence of XGBoost model is generated by the residual estimation method proposed in this paper, and the residual prediction model based on artificial neural network (ANN) is constructed through the maximum likelihood estimation method. In the third stage, the interval estimation of the noise sequence composed of the training error of the ANN model is carried out. Finally, the results obtained above are combined to realize the interval prediction of the dam displacement. The performance of the proposed model is verified by the monitoring data of an actual concrete dam. The results show that the hybrid model can not only achieve better point prediction accuracy than the single model, but also provide high quality interval prediction results.
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