52 results about "Rock failure" patented technology

The invention relates to the technical field of computers, and discloses a rock creep model construction method and device and a rock failure mode determination method. The method comprises the steps of obtaining a data set; samples in the data set comprise input data and output data, the input data comprise creep load holding time, sample rock stress data, initial macroscopic damage data, initial microscopic damage data, long-term strength and a creep starting threshold value, and the output data is a strain true value; fitting the initial rock creep model by using the data set to obtain a final rock creep model; wherein the initial rock creep model comprises at least one of a damaged elastomer strain item, a damaged viscoelastomer strain item and a damaged viscoplastic body strain item; each strain item comprises a macro-micro nonlinear coupling damage item, and the macro-micro nonlinear coupling damage item is generated based on the initial micro damage data and the initial macro damage data. The model constructed by the method can truly reflect the nonlinear mechanical response of the rock mass under the combined action of the multi-scale initial defects.

The invention provides a fractured rock damage prediction method and system, and belongs to the field of rock mechanics and engineering monitoring. The problem that a prediction result obtained by an existing fractured rock damage prediction method is not accurate is solved. According to the technical scheme, the method comprises the following steps: synchronously obtaining a DIC image sequence and an acoustic emission signal in a loading process of a to-be-measured fractured rock, carrying out data processing to generate a gray variance evolution curve and an acoustic emission b value evolution curve, and extracting a gray variance surge point on the gray variance evolution curve and a b value surge point on the acoustic emission b value evolution curve; performing time sequence comparison on the gray variance sharp increase point and the b value sudden drop point on a time axis, and when the gray variance sharp increase point and the b value sudden drop point meet a preset consistency condition on the time axis, judging the corresponding gray variance sharp increase point or b value sudden drop point as a damage critical point of the fractured rock; the method is applied to rock failure prediction.

The invention relates to a rock mechanics testing method and system based on artificial intelligence, and the method comprises the steps: carrying out the three-dimensional scanning of a rock sample, and obtaining three-dimensional model data; sending into a convolutional neural network to analyze the surface characteristics of the rock sample, and obtaining the initial state parameters of the rock sample; according to the initial state parameters, a rock mechanics test loading scheme is formulated through an adaptive optimization algorithm, and a test execution instruction is obtained; applying a preset load to the rock sample according to the test execution instruction, and synchronously acquiring mechanical response data and a deformation image sequence; according to the deformation image sequence, constructing a full displacement field and a strain field of the rock sample, and obtaining global deformation characteristics; and based on the global deformation characteristics and the mechanical response data, identifying damage behavior parameters of the rock sample through a time sequence characteristic extraction algorithm, and obtaining a damage type. Intellectualization, data closed loop and self-evolution of the whole rock mechanics test process are achieved, and the test precision and the reliability of rock damage prediction are improved.

This invention belongs to the fields of rock mechanics, signal processing, and deep learning. It provides an intelligent method, device, equipment, and medium for identifying rock fracture types. The method includes: acquiring acoustic emission signal data, loading stress-strain data, and rock image / video of the rock sample; extracting multimodal features from the acoustic emission signal data and loading stress-strain data; performing principal component analysis on the multimodal features to obtain a dimensionality-reduced feature matrix; labeling training data using a rock failure event time-series correlation method to obtain crack type labels; training and detecting using a gated recurrent unit network based on the dimensionality-reduced feature matrix and crack type labels to obtain a shear crack probability sequence, and then calculating the tension-shear ratio within a time step; and generating time stamps in the rock image / video based on the tension-shear ratio. This invention achieves accurate and rapid identification of rock fracture types and real-time monitoring of the dynamic evolution of fractures.

The application discloses a rock residual strength determination method based on an energy drop ratio parameter and belongs to the technical field of geological engineering. According to a rock stress-strain curve obtained through a rock triaxial test, the energy of each component in a failure process is calculated according to the rock stress-strain curve characteristics, and based on the energy evolution law in the rock failure process, an energy drop ratio parameter is proposed to quantitatively represent the strength drop. A residual strength determination method considering the whole rock failure process is proposed through the energy drop ratio parameter. The application comprehensively considers the influence of the pre-peak stage and the post-peak stage on the residual strength, proposes a rock residual strength determination method suitable for different post-peak drop trends, and calculates the stability of the broken rock mass through the residual strength, which has an important guiding role for the excavation, design and stability evaluation of the rock mass engineering.

The invention discloses a three-dimensional failure criterion construction method considering bedding and three-dimensional stress and intermediate principal stress effect degradation, and the method comprises the steps: S1, obtaining rock strength data through true triaxial static tests under different bedding dip angles, direction angles and intermediate principal stress; s2, constructing a transverse isotropic strength characteristic function based on the spatial relationship between the bedding structure and the loading direction; s3, introducing an intermediate principal stress degradation parameter, and establishing a rock failure criterion considering three-dimensional stress and intermediate principal stress effect degradation; s4, constructing a three-dimensional failure criterion considering bedding space and three-dimensional stress and intermediate principal stress effect degradation; s5, determining model parameters by fitting static test data, and drawing a strength evolution curve; and S6, based on the three-dimensional failure criterion parameters, drawing a stratified rock strength prediction curve. According to the method, quantitative characterization of true three-dimensional stress, middle principal stress degradation and bedding three-dimensional space effect coupling of the strength of the layered rock is achieved for the first time.

The invention discloses a test method and system of an indoor model for ultra-large-span cavern excavation support and a medium, and relates to the technical field of geotechnical engineering physical model tests. Through the counter-force frame and the three-dimensional gradient active loading system, a real non-uniform three-dimensional crustal stress field is precisely restored. By means of a numerical control mechanical arm carrying a head-replaceable end effector, a finish-milling cutting head and a negative pressure suction system, extra disturbance of excavation to surrounding rock is reduced. Excavation and support triggering conditions are preset to control actuator tool switching, and excavation-support time sequence linkage is achieved. Through cooperative acquisition and analysis of a pre-embedded sensor, monitoring equipment around a model box and a multi-source data acquisition cabinet, full-dimensional data is obtained, and the finally constructed four-dimensional surrounding rock failure evolution comprehensive map completely presents the whole process from microscopic crack initiation to macroscopic instability. And a high-fidelity test basis is provided for risk pre-judgment and design optimization of a super-large-span cavern project.

The application relates to the technical field of geotechnical engineering, and in particular to a rock failure precursor identification method and device, a storage medium and computer equipment. The method comprises the following steps: acquiring a multimodal acoustic signal of a rock under the superposition of different types of loads; dividing the multimodal acoustic signal according to a preset time window to obtain a multimodal acoustic signal segment; performing time-frequency analysis on the multimodal acoustic signal segment to construct a time-frequency matrix of the multimodal acoustic signal segment; performing singular value decomposition on the time-frequency matrix to obtain a singular value sequence of the time-frequency matrix; calculating the information entropy of the singular value sequence to obtain the time-frequency singular entropy of the multimodal acoustic signal segment corresponding to the singular value sequence; constructing a time-frequency singular entropy sequence of the multimodal acoustic signal according to the time-frequency singular entropy of the multimodal acoustic signal segment; and identifying a rock failure precursor based on the time-frequency singular entropy sequence of the multimodal acoustic signal. The application avoids false positives and false negatives in rock failure precursor identification, and makes early warning more reliable.

The application discloses a kind of shallow-buried tunnel surrounding rock failure simulation device, belong to tunnel construction technical field, including transparent and top opening test box, detachably connected with tunnel model between the front and rear sidewall of the test box, surrounding rock for covering tunnel model is arranged in the test box, base is fixed in the tunnel model, a plurality of support rods connected with the inner wall of tunnel model are arranged on the base, monitoring instrument is arranged at the end of the support rod, vibration generator is further arranged in the tunnel model;Building model is arranged above the surrounding rock, and pile foundation model is arranged in the surrounding rock in the lower end of building model;Sensor for detecting the settlement of building model is arranged on the test box;The purpose of the application is to solve the problem that the existing shallow-buried tunnel surrounding rock failure simulation device does not consider the influence of tunnel surrounding rock failure on building (structure).

The invention relates to a rock failure stage identification method based on multi-channel acoustic emission feature fusion. The method comprises the following steps: collecting acoustic emission signals of a plurality of acoustic emission sensors arranged on a rock to be detected; counting the acoustic emission signals according to time windows with preset duration, and extracting acoustic emission characteristic parameters corresponding to each time window; and inputting the acoustic emission characteristic parameters corresponding to the acoustic emission sensors in the same time window into the trained rock failure stage identification model to obtain the failure stage of the rock to be detected in the current time window. Real-time and high-precision identification of a rock damage stage is realized through multi-channel acoustic emission signal fusion and a deep learning model; multiple sensors are used for collecting signals, the model automatically extracts high-dimensional features and enhances key information by means of an attention mechanism, and the recognition accuracy and robustness are remarkably improved; compared with a traditional method, the method can output judgment in real time, and provides effective support for engineering safety monitoring and early warning.

The application provides a coal rock damage real-time prediction method and device based on acoustic emission signal analysis, which comprises the following steps: in the case of performing coal rock damage test, obtaining statistical information of multiple acoustic emission signals; the statistical information comprises waveform data and the number of acoustic emission events; determining the fracture type of each acoustic emission signal according to the waveform data, and determining the b value and b absolute change rate of the value of each acoustic emission signal according to the waveform data and the number of acoustic emission events; determining the shear fracture proportion corresponding to the multiple acoustic emission signals according to the fracture type, and analyzing the statistical feature evolution information of the waveform data to obtain a target statistical feature; determining the warning threshold according to the b value, b absolute change rate of the value, the shear fracture proportion and the target statistical feature, and performing coal rock damage real-time prediction through the warning threshold. The method and device provided by the application can realize warning only by using the signals before the current time, and improve the accuracy and real-time performance of coal rock damage real-time prediction.

The invention discloses an experimental device, a prediction device and a method for rock water inrush damage based on combination of acoustic emission and resistivity, belongs to the field of rock damage monitoring, and aims to improve the accuracy of water inrush damage monitoring. The device is characterized in that the device comprises a shell, a stress loading plate, an electrode plate, a pressurization system, a hydraulic fracturing system, a resistivity monitoring system, an acoustic emission monitoring system and a processing system, the processing system builds the relation between fracturing pressure and the resistivity of a rock sample in the time dimension according to the fracturing pressure and the resistivity of the rock sample, and the resistivity of the rock sample is measured according to the relation. Determining the variation trend of the resistivity of the rock sample in the water inrush damage process caused by the fracturing pressure according to the relationship, obtaining the variation trend of the resistivity of the rock sample in the time range from the time when the fracturing pressure is applied to the rock sample to the time when the fracturing fluid overflows from the rock sample, and calculating the position information of the water inrush point according to the acoustic emission signal.

The invention discloses a quantitative characterization method for evolution characteristics of a micro-pore structure in a rock failure process, and belongs to the technical field of rock pore structure research. On the basis that an existing characterization method cannot accurately describe pore evolution in rock and a damage process under a loaded condition, a three-dimensional model of a rock sample is constructed by applying a CT image interactive threshold segmentation and three-dimensional model reconstruction method on the basis of a uniaxial compression in-situ CT real-time scanning test of mudstone and sandstone; according to the method, a three-dimensional pore model and a pore network bat model of a digital rock core are established, a plurality of parameters such as a porosity evolution coefficient, a porosity overall evolution coefficient, a pore fractal dimension, a throat fractal dimension, a pore network structure coefficient and a damage evolution factor are defined, and the evolution characteristics and the distribution form of a pore structure in the rock damage process are quantitatively represented. The fine identification and visual quantitative characterization of the rock internal pore evolution in the uniaxial compression process are realized, and a new method is provided for quantitatively evaluating the damage evolution of the rock.

The invention relates to a method for simulating mining-induced fault surrounding rock fracture and water inrush evolution, which comprises the following steps of: establishing a water-conducting fault surrounding rock fracture numerical model based on an FDEM according to a simulation early-stage preparation condition; setting a coal seam mining scheme; analyzing fracture evolution of the surrounding rock of the fault by simulating a coal mining process on the basis of the numerical model of fracture of the surrounding rock of the water-conducting fault; a dominant water inrush channel is selected, and a CFD-based water diversion fault surrounding rock fracture water inrush seepage numerical model is established; and analyzing seepage element evolution based on the water diversion fault surrounding rock fracture water inrush seepage numerical simulation result. By constructing the simulation method for inducing fracture and water inrush evolution of the fault surrounding rock by mining, the coal mining process can be comprehensively and accurately simulated, fracture and water inrush evolution of the fault surrounding rock can be analyzed, and a powerful means is provided for research on fault water inrush.

This application provides a method for finely characterizing the fractured area of ​​the floor strata in a mining face, relating to the field of coal seam floor characterization technology. The method includes: collecting the original microseismic event signals and environmental correlation parameters corresponding to each microseismic event in the fractured area of ​​the mining face floor strata; performing feature extraction to obtain a microseismic event feature matrix corresponding to each microseismic event; inputting a microseismic event classification model to obtain the rupture correlation confidence score of the microseismic event; if the source type of any microseismic event is rupture type, and the corresponding rupture correlation confidence score is greater than a preset rupture correlation confidence score threshold, then the fractured area of ​​the mining face floor strata is characterized based on this microseismic event. This application achieves fine characterization of the fractured area of ​​the mining face floor strata, providing a reliable basis for engineering safety control.

This invention provides a method and system for predicting pile end resistance based on a mudstone damage constitutive model, relating to the field of geotechnical engineering. Addressing the difficulty in quantitatively analyzing and calculating the bearing capacity of dynamically driven piles in mudstone foundations and the damage characteristics of the surrounding mudstone, this invention establishes a statistical damage constitutive model for mudstone based on the stress state-determined damage variable D and its association with rock failure criteria. Model parameters are determined according to triaxial test results, and the relationship between the critical damage variable and confining pressure is established. The damage characteristics of the mudstone surrounding the dynamically driven pile are analyzed, thereby calculating the pile end resistance and meeting the need for quantitative analysis of pile end damage characteristics.

The invention discloses a surrounding rock failure mode recognition method and system and a computer storage medium, and relates to the technical field of model driving information processing and digital twinning, and the method comprises the steps: constructing and dynamically updating a digital twinning model of tunnel surrounding rock through fusing LiDAR point cloud data and GPR geological radar data; performing simulation prediction in combination with real-time construction parameters, and analyzing a surrounding rock failure trend; dividing a key area and a non-key area by using a digital twin model identification result, and setting a differential sampling frequency and a monitoring strategy according to regional importance; when the model predicts that the damage mode tends to a dangerous state, a construction parameter adjusting scheme is automatically output, differential monitoring and risk early warning can be achieved while the safety of the scheme can be simulated and evaluated in combination with the construction parameters, the dangerous state damage mode can be effectively prevented from occurring in the construction process, and tunnel construction safety and efficiency are improved.

The invention discloses a three-dimensional discrete fracture network modeling and simulation method based on fracture statistical characteristics, and relates to the technical field of water conservancy and hydropower engineering rock soil. According to the method, geometric and distribution characteristics of rock mass fractures are collected on site, and a random statistical model containing parameter relevance is constructed; generating a three-dimensional DFN model based on a random statistical model, and performing intersection processing, invalid fracture elimination and boundary correction optimization; the three-dimensional DFN model is embedded into a numerical platform, a fracture-rock system is constructed, Mohr-Coulomb constitutive parameters are given, crustal stress inversion, excavation supporting and operation load simulation are completed in sequence, and mechanical response data are obtained; and finally analyzing surrounding rock failure characteristics and optimizing a support scheme. The problem that a traditional model is difficult to represent a discontinuous structure and local instability is solved, the prediction precision is improved, the method is adaptive to various complex fractured rock mass projects, and reliable support is provided for stability evaluation and support design.

This invention discloses a method for determining the rock failure surface during drilling, comprising the following steps: 1) simulating and determining the drilling force model, and determining the relationship between the components in the model; 2) calculating the maximum shear stress at which the rock structure undergoes plastic failure; 3) determining the shape of the failure surface and the maximum shear stress τ in the shear zone determined in step 2) according to the maximum shear stress criterion. max This is related to predicting the failure surface; 4) Based on the maximum shear stress criterion, the iterative method is used to solve the force acting on the cutting surface, and the function of the failure surface is determined by inputting the basic drilling parameters, quantitatively presenting the failure surface morphology. The method of this invention introduces the Michel solution of elastoplastic mechanics to solve the stress state of the rock shear zone during drilling, and derives a specific response function of rock properties to drilling parameters based on the stress characteristics of the drill bit; referring to the maximum shear stress criterion, the shear stress of the unit body is compared with the shear strength of the rock to determine whether the rock is broken.

The invention discloses a coal mine roof rock loaded fracture discharge spark testing device and an ignition energy evaluation method. The coal mine roof rock loaded fracture discharge spark testing device comprises a closed explosion reaction box, a mechanical loading system driven by a hydraulic cylinder, a replaceable compression / shearing mold, a silver pole piece, a high-speed camera, a photoelectric detector, a gas environment control assembly, a data acquisition and processing unit and the like. The underground gas concentration environment is simulated through the gas environment control assembly, rock failure mode simulation is achieved through the mechanical loading system, and the multi-source assembly synchronously collects parameters such as the electric charge quantity, the voltage and the spark form. According to the evaluation method, a direct method and an indirect method are fused, effective ignition energy is output after data verification, and the risk is judged by comparing the effective ignition energy with an explosion threshold. The device can simulate the whole process of discharging electric sparks in a gas-containing environment and perform quantitative evaluation, realizes accurate quantification of ignition energy, provides data support for explosion risk evaluation of roof rocks with different lithology, assists coal mine gas explosion early warning, and ensures safe production.

The invention relates to a round roadway surrounding rock plastic zone development form determination method, and belongs to the field of roadway surrounding rock stability numerical analysis. Defining a constitutive model; defining material parameters; defining initial conditions; defining a gravitational acceleration and large deformation calculation mode; automatically calculating to balance; the empty model is used for simulating circular roadway excavation; the method comprises the following steps: defining an FISH function volumeplasma, and calling the FISH function by utilizing a historical command; automatically calculating to balance again; the method comprises the following steps of: defining a FISH (fluorescence in situ hybridization) function (fluorescence in situ hybridization) function (fluorescence in situ hybridization); the method comprises the following steps of: defining a fluorescence in situ hybridization (FISH) function (depthmax), and executing the FISH function; the method comprises the following steps of: defining a fluorescence in situ hybridization (FISH) function (depthaveage), and executing the FISH function; the method comprises the following steps of: defining a fluorescence in situ hybridization (FISH) function volumeplasma sub and executing the FISH function; the method can accurately quantify information such as the maximum development depth, the average development depth, the volume evolution process and the type volume ratio of the circular roadway surrounding rock plastic zone of the circular roadway surrounding rock plastic zone, and is of great significance to reveal a circular roadway surrounding rock failure mechanism.

The invention provides a coal rock damage charge and stress correlation quantification and optimal time window determination method, and relates to the technical field of coal rock monitoring. The method comprises the following steps: firstly, acquiring stress and charge signals in a coal rock failure process, then decomposing the stress signals into a series of intrinsic mode functions and a residual component by adopting an empirical mode decomposition method, and determining spectrum characteristics corresponding to the intrinsic mode functions; removing low-frequency trend terms according to spectrum distribution conditions, and reconstructing residual intrinsic mode functions to obtain detrended stress signals; calculating the wavelet coherence coefficient of the detrended stress signal and charge signal by using a wavelet coherence analysis method, and outputting a wavelet coherence map; and finally, determining an optimal observation time scale according to the relevance between the stress and the charge signal after trend removal, and analyzing a collaborative change rule of the stress and the charge signal in the optimal observation time scale. According to the method, the quantification of the correlation between the charge and the stress in the coal rock destruction process and the determination of the optimal observation time window can be realized.

The invention discloses a rock fracture classification method based on a time-frequency double-branch fusion convolutional neural network model, and belongs to the field of rock mechanics and geotechnical engineering. The method comprises the following steps: firstly, collecting an acoustic emission signal in a rock damage process, synchronously generating a time domain oscillogram and a frequency domain spectrogram for the same acoustic emission event, and constructing bimodal input data; secondly, constructing a double-branch convolutional neural network architecture, and respectively inputting the time domain graph and the spectrogram into two independent convolutional neural network branches to extract features; and then, splicing and fusing the feature vectors output by the two branches to form a composite feature with time-frequency domain information. And finally, sending the fusion features into a classifier to realize the discrimination of the type of the rock tension and shear fracture. According to the method, the time-frequency complementarity of the acoustic emission signals is fully utilized, high-precision and high-robustness fracture classification can be realized only through single-sensor data, and an effective intelligent tool is provided for accurate analysis of a rock fracture mechanism and engineering safety early warning.

The invention discloses a method and a system for jointly representing rock progressive failure based on stress and sound wave change, and relates to the technical field of rock mass mechanics, and the method comprises the following steps: applying incremental constant clamping pressure to rock test blocks of the same type to obtain response data of the rock test blocks, and carrying out a yield test to obtain rock peak strength; the strain and the sound wave response are used as indication response data, and the initial training weight is determined and dynamically corrected by analyzing the correlation between the indication response and the damage response in each damage stage. Establishing a multi-layer perceptron model, taking the preprocessed indication response data as input and the damage response data as a label for training, obtaining a damage response data predicted value by inputting the indication response data of the rock to be analyzed, and further calculating an internal damage coefficient and an external indication coefficient so as to comprehensively analyze the progressive damage degree of the rock. And the prediction deviation possibly caused by the static weight in the traditional method is reduced.

This invention discloses a method and system for extracting precursor features of coal and rock failure under load based on differential networks, comprising: monitoring acoustic emission (AE) signals and electromagnetic radiation (EMR) signals during the coal and rock failure process, wherein AE and EMR sensors are arranged at different spatial positions on the sample; setting a sliding step size s and a time window length l, and processing the AE signal time series A t ={A i t‑l+1 ,...,A i t‑2 A i t‑1 A i t} and EMR signal time series E t ={E j t‑l+1 ,...,E j t‑2 E j t‑1 E j t} as sample data at time t; construct reference samples and perturbation samples, calculate the Pearson correlation coefficient between every two channels of each, establish the Pearson correlation coefficient matrix, and construct the correlation network sequence N with edge assignment. t Compare the differences in correlation networks at adjacent time points to construct difference network sequences (DNs) at different time points. t ; Calculate DN t The average value I(t) and its cumulative value I of the edge weights a (t) serves as a precursor characteristic, I(t) and I a The period of rapid increase in (t) is the critical point for coal and rock failure under load. This invention is of great significance for early warning and prevention of coal and rock dynamic disasters.

The application discloses a rock creep constitutive model construction and application method, device, equipment and medium, relates to the rock mechanics numerical simulation technical field, and the method comprises the steps of taking the CVISC model as a basic framework; driving the creep damage coefficient and the viscosity coefficient to evolve in time through the rock failure time and the damage process correlation coefficient, constructing a creep damage mechanism, and introducing the creep damage mechanism into the basic framework; controlling the rock elastic modulus and the viscosity coefficient reduction driving creep strain increment increase through the water content, constructing a water damage unit, and introducing the water damage unit into the basic framework; determining the water influence range according to the geometric characteristics of the rock internal defects and generating a non-uniform water content field, coupling the non-uniform water content field to the water damage unit as an input parameter; and numerically simulating the nonlinear creep damage evolution process of the rock. The application can improve the prediction ability of the long-term deformation and damage process of the rock.

The application discloses a deep well rock mass instability damage time accurate prediction and early warning system and relates to the technical field of deep well rock mass damage early warning. The application discloses a deep well rock mass instability damage time accurate prediction and early warning system and relates to the technical field of deep well rock mass damage early warning. The application discloses a deep well rock mass instability damage time accurate prediction and early warning system and relates to the technical field of deep well rock mass damage early warning. The application discloses a deep well rock mass instability damage time accurate prediction and early warning system and relates to the technical field of deep well rock mass damage early warning. The application discloses a deep well rock mass instability damage time accurate prediction and early warning system and relates to the technical field of deep well rock mass damage early warning. The application discloses a deep well rock mass instability damage time accurate prediction and early warning system and relates to the technical field of deep well rock mass damage early warning. The application discloses a deep well rock mass instability damage time accurate prediction and early warning system and relates to the technical field of deep well rock mass damage early warning. The application discloses a deep well rock mass instability damage time accurate prediction and early warning system and relates to the technical field of deep well rock mass damage early warning. The application discloses a deep well rock mass instability damage time accurate prediction and early warning system and relates to the technical field of deep well rock mass damage early warning. The application discloses a deep well rock mass instability damage time accurate prediction and early warning system and relates to the technical field of deep well rock mass damage early warning. The application discloses a deep well rock mass instability damage time accurate prediction and early warning system and relates to the technical field of deep well rock mass damage early warning. The application discloses a deep well rock mass instability damage time accurate prediction and early warning system and relates to the technical field of deep well rock mass damage early warning. The application discloses a deep well rock mass instability damage time accurate prediction and early warning system and relates to the technical field of deep well rock mass damage early warning. The application discloses a deep well rock mass instability damage time accurate prediction and early warning system and relates to the technical field of deep well rock mass damage early warning. The application discloses a deep well rock mass instability damage time accurate prediction and early warning system and relates to the technical field of deep well rock mass damage early warning. The application disc

This application relates to the field of computer technology and discloses a method, apparatus, and method for determining rock failure modes in constructing a rock creep model. The method includes acquiring a dataset; the dataset includes input and output data, where the input data includes creep holding time, stress data of the sample rock, initial macroscopic damage data, initial microscopic damage data, long-term strength, and creep initiation threshold; the output data is the true strain value. The initial rock creep model is fitted using the dataset to obtain the final rock creep model. The initial rock creep model includes at least one of the following: a damaged elastic body strain term, a damaged viscoelastic body strain term, and a damaged viscoplastic body strain term. Each strain term includes a macroscopic-microscopic nonlinear coupled damage term, which is generated based on the initial microscopic damage data and the initial macroscopic damage data. The model constructed in this application can realistically reflect the nonlinear mechanical response of rock mass under the combined action of multiple-scale initial defects.

The invention relates to the technical field of geotechnical engineering, in particular to a rock damage precursor identification method and device, a storage medium and computer equipment. The method comprises the following steps: acquiring a multi-mode acoustic signal of a rock under the superposition action of different types of loads; dividing the multi-modal acoustic signal according to a preset time window to obtain multi-modal acoustic signal segments; performing time-frequency analysis on the multi-modal acoustic signal segments to construct a time-frequency matrix of the multi-modal acoustic signal segments; performing singular value decomposition on the time-frequency matrix to obtain a singular value sequence of the time-frequency matrix; the information entropy of the singular value sequence is calculated, and the time-frequency singular entropy of the multi-mode acoustic signal segment corresponding to the singular value sequence is obtained; constructing a time-frequency singular entropy sequence of the multi-modal acoustic signal according to the time-frequency singular entropy of the multi-modal acoustic signal segment; and based on the time-frequency singular entropy sequence of the multi-modal acoustic signal, identifying the rock damage precursor. According to the invention, false alarm and missing alarm of rock damage precursor identification are avoided, so that early warning is more reliable.

The present application relates to coal rock instability analysis and early warning field, especially to a kind of coal mine working face dynamic disaster multi-parameter intelligent analysis and early warning method and system.The method comprises the following steps: based on five-parameter perception array, parameter collection is carried out to coal mine working face mining process, and multi-source monitoring data stream is generated;Multi-source monitoring data stream is carried out self-adapting wavelet threshold hierarchical denoising and multi-index parameter calculation, and nine index parameters are obtained;Based on nine index parameters, multi-parameter judgment matrix sequence is constructed;Based on multi-parameter judgment matrix sequence, stability state discrimination is carried out, and the stability state discrimination result under different parameters is obtained;According to the stability state discrimination result, dynamic state evaluation is carried out, and dynamic evaluation state sequence is generated.The present application realizes early accurate identification and hierarchical early warning of coal rock failure instability process, so as to improve the real-time performance and reliability of mine safety monitoring in engineering application, and reduce disaster risk.