Heterogeneous information phase space reconstruction and stability prediction of filling body–surrounding rock combination

Traditional research believes that the filling body can effectively control stress concentration while ignoring the problems of unknown stability and the complex and changeable stress distribution of the filling body–surrounding rock combination under high-stress conditions.Current monitoring data processing methods cannot fully consider the complexity of monitoring objects,the diversity of monitoring methods,and the dynamics of monitoring data.To solve this problem,this paper proposes a phase space reconstruction and stability prediction method to process heterogeneous information of backfill–surrounding rock combinations.The three-dimensional monitoring system of a large-area filling body–surrounding rock combination in Longshou Mine was constructed by using drilling stress,multipoint displacement meter,and inclinometer.Varied information,such as the stress and displacement of the filling body–surrounding rock combination,was continuously obtained.Combined with the average mutual information method and the false nearest neighbor point method,the phase space of the heterogeneous information of the filling body–surrounding rock combination was then constructed.In this paper,the distance between the phase point and its nearest point was used as the index evaluation distance to evaluate the stability of the filling body–surrounding rock combination.The evaluated distances(ED)revealed a high sensitivity to the stability of the filling body–surrounding rock combination.The new method was then applied to calculate the time series of historically ED for 12 measuring points located at Longshou Mine.The moments of mutation in these time series were at least 3 months ahead of the roadway return dates.In the ED prediction experiments,the autoregressive integrated moving average model showed a higher prediction accuracy than the deep learning models(long short-term memory and Transformer).Furthermore,the root-mean-square error distribution of the prediction results peaked at 0.26,thus outperforming the no-prediction method in 70%of the cases.

Compression failure conditions of concrete-granite combined body with different roughness interface

Bedrock and concrete lining are typical composite structures in the engineering field and the stability of the geological body and engineering body is directly connected to the mechanical properties of the composite body.Under this background,the study provides the transverse isotropic equivalent model of concrete-granite double-layer composite based on the notion of strain energy equivalence.Assuming that the strength failure of concrete and granite meets the Mohr-Coulomb criterion,then the strength failure model of the combined body considering the joint roughness coefficient(JRC)is derived,and the influences of JRC,the height ratio of concrete to granite,and confining pressure on the strength failure characteristics of the combined body are emphatically analyzed.Finally,the model applicability is illustrated by the uniaxial and triaxial compression tests on concrete monomer,granite monomer and concretegranite composite samples(CGCSs)with different JRCs.The results revealed that the compressive strength of the composite is closer to the concrete with lower strength in the combined body under different confining pressures.Adding interface roughness causes to raise the compressive strength of the composite due to interfacial adhesion between concrete and granite,and a slowing growth trend is observed in compressive strength as roughness.The model can provide a certain reference for the stability design and evaluation of engineering rock mass.

A novel binary effective medium model to describe the prepeak stressstrain relationship of combined bodies of rock-like material and rock

Combined bodies of rock-like material and rock are widely encountered in geotechnical engineering,such as tunnels and mines.The existing theoretical models describing the stress-strain relationship of a combined body lack a binary feature.Based on effective medium theory,this paper presents the governing equation of the“elastic modulus”for combined and single bodies under triaxial compressive tests.A binary effective medium model is then established.Based on the compressive experiment of concretegranite combined bodies,the feasibility of determining the stress threshold based on crack axial strain is discussed,and the model is verified.The model is further extended to coal-rock combined bodies of more diverse types,and the variation laws of the compressive mechanical parameters are then discussed.The results show that the fitting accuracy of the model with the experimental curves of the concretegranite combined bodies and various types of coal-rock combined bodies are over 95%.The crack axial strain method can replace the crack volumetric strain method,which clarifies the physical meanings of the model parameters.The variation laws of matrix parameters and crack parameters are discussed in depth and are expected to be more widely used in geotechnical engineering.

Individual automatic detection and identification of big cats with the combination of different body parts

The development of facial recognition technology has become an increasingly powerful tool in wild animal indi-vidual recognition.In this paper,we develop an automatic detection and recognition method with the combinations of body features of big cats based on the deep convolutional neural network(CNN).We collected dataset including 12244 images from 47 individual Amur tigers(Panthera tigris altaica)at the Siberian Tiger Park by mobile phones and digital camera and 1940 images and videos of 12 individual wild Amur leopard(Panthera pardus orientalis)by infrared cameras.First,the single shot multibox detector algorithm is used to perform the automatic detection process of feature regions in each image.For the different feature regions of the image,like face stripe or spots,CNNs and multi-layer perceptron models were applied to automatically identify tiger and leopard individuals,in-dependently.Our results show that the identification accuracy of Amur tiger can reach up to 93.27%for face front,93.33%for right body stripe,and 93.46%for left body stripe.Furthermore,the combination of right face,left body stripe,and right body stripe achieves the highest accuracy rate,up to 95.55%.Consequently,the combination of different body parts can improve the individual identification accuracy.However,it is not the higher the number of body parts,the higher the accuracy rate.The combination model with 3 body parts has the highest accuracy.The identification accuracy of Amur leopard can reach up to 86.90%for face front,89.13%for left body spots,and 88.33%for right body spots.The accuracy of different body parts combination is lower than the independent part.For wild Amur leopard,the combination of face with body spot part is not helpful for the improvement of identification accuracy.The most effective identification part is still the independent left or right body spot part.It can be applied in long-term monitoring of big cats,including big data analysis for animal behavior,and be helpful for the individual identification of other wildlife species.