5735 results about "Rock body" patented technology

The application provides a method for evaluating stability of a tunnel surrounding rock considering creep characteristics of a structural plane, which includes the following steps: S1, discretizing a tunnel surrounding rock region, and constructing a numerical model of a tunnel-surrounding rock structure; S2, initializing the numerical model; S3, presetting a creep time, and obtaining a normal force of a structural plane node based on an initialized numerical model; S4, based on the normal force, obtaining an unbalanced force of a rock mass region element; and S5, judging the unbalanced force of the rock mass region element, and completing a stability evaluation of the tunnel surrounding rock.

The invention discloses a mining rock mass structure degradation monitoring method and system based on deep learning, particularly relates to the technical field of mine safety monitoring, and is used for solving the problems of monitoring lag and misjudgment caused by the fact that an existing static model cannot automatically adapt to rock mass damage dynamic evolution. The method comprises the following steps: constructing a dynamic damage incremental data set by collecting multi-source heterogeneous monitoring data under mining disturbance in real time, extracting newly-added damage features, and calculating a distribution offset degree between the newly-added damage features and historical features; when the deviation degree exceeds the limit, verifying and screening a damage feature subset according with a mechanical law based on a rock constitutive equation, and blocking non-physical noise interference; a geological structure evolution mode is matched through damage path dependence modeling, and a potential damage path thermodynamic diagram is generated in combination with a space gradient; performing cross-stage association on the thermodynamic diagram and a historical feature library by adopting a knowledge distillation mechanism to generate a dynamic weight matrix fused with a mining time sequence; and finally, updating the damage assessment model through a parameter reweighting mechanism to realize adaptive assessment of the rock mass degradation risk.

The invention provides a road slope data monitoring method under a complex geological condition, and relates to the technical field of road slope monitoring, and the method comprises the steps: carrying out the temperature compensation of a change rate, analyzing the thermal expansion characteristic of a material, calculating the deformation caused by the temperature change, correcting the monitored side length, and generating a temperature compensation side length; according to the temperature compensation side length and the surrounding rock rheological equation, correcting the two adjacent side lengths through the strain difference of the adjacent sides so as to obtain the final side length; combining the final side length with the reference interior angle to generate a topological structure; based on the topological structure, constructing a three-dimensional deformation index matrix containing a diagonal displacement difference, an adjacent side curvature ratio and an interior angle variation coefficient; and inputting the index matrix, the support structure strain space-time matrix and the rock mass fragmentation tensor into a space-time attention graph neural network together, and outputting a deformation evolution trend including a diagonal displacement difference, an adjacent side curvature ratio and an interior angle variation coefficient. According to the invention, high-precision real-time monitoring can be realized.

The invention discloses a collapse geological disaster vulnerability model and risk assessment method and system, and relates to the technical field of geological disaster risk assessment, and the method comprises the steps: collecting and preprocessing target rock mass information; collecting monitoring data and long-term rainfall data of the three-axis acceleration sensor; the acceleration fluctuation characteristics are analyzed, and the risk of rock mass instability is recognized in combination with rainfall data; the instability risk of the rock mass is quantified based on a Monte Carlo method; predicting a motion path and a stacking range of the collapsed rockfall by utilizing numerical simulation, and evaluating threats of the collapsed rockfall to surrounding electric power facilities; and performing level-to-level management on the surrounding area of the transformer substation through the risk level model. Real-time monitoring, probability calculation and numerical simulation technologies are adopted, and the problem that in the prior art, power facilities lack dynamic monitoring, accurate evaluation and graded protection is solved. According to the overall scheme, real-time and automatic monitoring and risk grading of collapse geological disasters are achieved, and the safety and reliability of electric power facilities under the disaster condition are effectively improved.

A real-time quantitative characterization method, equipment and a medium for rock mass evolution are disclosed. The method includes: the multiphase field detection and monitoring data is fused; a deep learning model driven by physical principles is established based on the physical principles of fluid density; the deep learning model is trained using real-time fused data as input and corresponding evolution distribution images as output; a trained deep learning model is used to obtain an evolution distribution image based on multi-phase field detection and monitoring data of different time periods and types; a mathematical model is used to quantitatively characterize of physical and mechanical parameters in the whole process of progressive failure of the dynamic evolution of the rock mass based on the macroscopic mechanical parameters and evolution distribution images synchronized with multiphase field detection and monitoring data.

The invention discloses a grouting method and system for enhancing strip mine slope stability, and belongs to the technical field of slope protection. The grouting method comprises the steps that early-stage data are pre-collected, and an analysis model is established; grouting is designed according to a model analysis result; a pressure-resistant grouting pipe is installed and connected with a grouting pump; grouting parameters are adjusted in real time according to feedback of the sensor; after grouting, all data in the grouting holes are analyzed; outputting a slurry permeation three-dimensional cloud picture, a stress change curve and a stability evaluation index; and storing all monitoring data and model parameters. A cyclic self-adaptive system is formed, it is ensured that the grouting process is efficient and accurate, the overall stability is improved for heterogeneous rock mass, dependence on a static geological report is reduced, and therefore the problem that grouting parameters depend on engineering experience or conservative of the static report is avoided.

The invention belongs to the technical field of three-dimensional geologic modeling, and particularly relates to a three-dimensional geologic model construction method based on multi-source data fusion, and the method comprises the steps: 1, extracting drilling data, and constructing a drilling database; 2, constructing a topographic and geomorphic surface model, and completing the fusion of topographic and geomorphic data and drilling data; 3, coupling information of strata, rock mass, fracture, ore body and the like, and constructing a geologic body comprehensive model; 4, extracting geophysical and geochemical data, and constructing a geophysical model and a geochemical model; and 5, coupling the borehole database, the landform surface model, the geologic body comprehensive model and the geophysical model, and constructing a multi-source data fused three-dimensional geologic model. The method improves the accuracy and precision of the three-dimensional geologic model, improves the data processing and analysis speed, improves the data analysis capability and depth, provides technical support for uranium resource potential analysis and prediction exploration, and guides uranium exploration.

The invention relates to the technical field of reservoir bedrock anti-seepage grouting, and discloses a reservoir bedrock anti-seepage grouting method which comprises the following steps: acquiring fracture data through geological exploration, and constructing a reservoir bedrock fracture distribution model; simulating a flow path and permeability characteristics of the slurry in the fracture based on the fracture model; the grouting pressure and flow are determined through an optimization algorithm, and a grouting scheme is formulated; screening grouting materials according to fracture characteristics, and analyzing stress-strain and permeability of the grouting materials; a construction process is formulated, the grouting state is monitored in real time, and parameters are dynamically adjusted; and after grouting, carrying out permeation and pressure test to evaluate whether the curing effect reaches the standard or not. Based on the three-dimensional fracture structure modeling and flow field simulation technology, high-precision recognition and dynamic evolution tracking of the fracture permeation path in the complex rock mass of the reservoir bank are achieved, so that grouting hole layout is more accurate, the slurry flow direction is more controllable, and the method is different from a point-selecting grouting mode of a traditional empirical method.

The invention relates to the technical field of geological survey and measurement, in particular to a rock mass anomaly detection method based on vibration and visual data multi-modal fusion, which comprises the following steps: firstly, respectively acquiring vibration data and image data, and then performing data feature extraction and fusion through a deep learning model; specifically, a combined model of a long short-term memory network LSTM and a convolutional neural network CNN is used to extract time sequence features and frequency information of vibration data, a Swinin-T model is used to extract spatial features of image data, and then a shared Transform encoder and a cross attention mechanism are used to realize multi-modal data alignment and fusion. And inputting the fused features into an anomaly detection module for training and tuning, and finally deploying the trained model to edge equipment for anomaly detection of the rock mass. According to the invention, through multi-modal data fusion, abnormity can be accurately identified in various complex environments, so that the probability of false alarm and missing alarm is reduced, and meanwhile, by combining with an edge computing architecture, field deployment and real-time monitoring are facilitated, and the method is suitable for various application scenes.

The invention provides a tunnel rock mass dynamic disaster monitoring system based on digital twinning, and relates to the technical field of tunnel rock mass dynamic disaster monitoring based on digital twinning. By integrating a multi-mode sensing network, the system comprises a self-adaptive infrared structured light scanner, a distributed micro-seismic sensing array and a fiber grating strain sensor; comprehensive and real-time monitoring of the rock mass state is realized; meanwhile, an intelligent analysis algorithm based on deep learning is introduced, abnormal conditions are automatically recognized, a rockburst risk comprehensive index is generated, in addition, a physical entity and a virtual model are combined through a dynamic digital twinborn modeling technology, and visual analysis of rock mass state changes is achieved; and finally, an edge-cloud cooperative communication technology is adopted to ensure low delay and reliability of data transmission and ensure that the monitoring system can quickly respond and timely take emergency measures.

The invention discloses a tunnel grouting diffusion form prediction system and method. The system comprises a tunnel simulation device used for preparing a tunnel model with cracks to simulate a tunnel rock mass; the grouting simulation system is used for conducting grouting on the tunnel simulation device by adjusting grout diffusion influence factors, and the grout diffusion state under the influence of multiple factors is obtained. The grouting diffusion form quantification system is used for cutting the grouted tunnel simulation device to obtain a plurality of slices at depth positions, and performing grid division on the surface of the slice at each depth position to obtain a grout diffusion form quantification data set; and the grouting diffusion form prediction system is used for constructing a multi-modal space-time fusion depth prediction model and inputting the grout diffusion form quantitative data set into the multi-modal space-time fusion depth prediction model to predict the diffusion form of the future grout. According to the method, the prediction model is established, and the grout diffusion form quantitative data set is used as the space-time sequence data set, so that the space-time prediction of the grouting diffusion form is realized.

The invention provides a deep geologic body occurrence characteristic quantitative representation modeling method and system, and the model focuses on the multi-physics field occurrence environment of rock mass material composition, structural characteristics, stress distribution and the like under the continuous mining condition of a deep underground metal mine. An information expression framework capable of crossing a'microcosmic-experimental-engineering 'multi-level spatial scale is constructed, microcosmic rock core mineral components and crack information, laboratory mechanical response parameters and structural surface shear characteristics and macroscopic fault system and stress disturbance data reflected by microseismic events are introduced, a heterogeneous data fusion method is comprehensively applied, and a microcosmic-experimental-engineering multi-level spatial scale is constructed. A three-dimensional tensor model with dynamic evolution capability and physical interpretation capability is established, high-precision, dynamic and multi-scale comprehensive modeling of a complex deep geologic body occurrence environment is realized, and the model has the characteristics of clear information hierarchy, high fusion precision, strong physical interpretability and the like. And dynamic perception and scientific research and judgment of geologic mass mechanics and structure states can be realized in a complex deep environment.

The invention belongs to the field of tunnel surrounding rock investigation, and relates to a tunnel face image and tunnel face front rock mass information are obtained by taking surrounding rock in front of a tunnel face in a tunnel construction process as a research object and adopting a mobile photographing device and a geological radar advanced geological forecast technology. And a deep learning method is adopted to perform feature extraction and recognition on the tunnel face image and the geological radar map, so that the tunnel surrounding rock dynamic grading method based on the geological radar and transfer learning is established, and rapid and accurate surrounding rock dynamic grading on the tunnel face and the surrounding rock in front in the tunnel construction excavation process is realized.

The invention discloses a digital core-based reef limestone voxel dynamic merging modeling method and system, and the method comprises the steps: carrying out the CT scanning of reef limestone, and obtaining a rock mass section gray image; carrying out image processing on the rock mass section grayscale image to obtain a binary image; establishing a three-dimensional digital core model of a rock microstructure according to the binarized image; calculating a representative volume unit size according to the three-dimensional digital core model; performing dynamic combination on the spatially continuous minimum homoplastid in the three-dimensional digital core model to obtain a voxel combination result; and modeling according to the representative body unit size and the voxel merging result to obtain a reef limestone three-dimensional pore and matrix numerical model. According to the embodiment of the invention, the number of units and nodes of the model can be reduced in the modeling process, the established finite element model not only can truly reflect the pore structure characteristics of the reef limestone, but also can remarkably improve the calculation efficiency and the modeling precision, and can be widely applied to the technical field of computers.

The invention provides a rock mass structural plane intelligent identification method based on three-dimensional point clouds, and belongs to the technical field of rock mass structural plane intelligent identification, and the method comprises the steps: selecting a target collection point, constructing an initial point cloud set, calculating an initial normal vector, setting a spherical accumulator, and rotating the point clouds in unstructured point clouds formed by a rock mass structural plane; obtaining an accurate normal vector; according to an improved region growing algorithm, obtaining a better number of structural surfaces, and obtaining a single structural surface of a plurality of rock mass point clouds; and traversing a single structural surface of each rock mass point cloud, replacing a distance function with an angle error quadratic sum according to an improved K-means + + clustering method, determining an optimal clustering number, and completing the intelligent identification method for the rock mass structural surface. According to the method, the high precision of the normal vector at the edge junction of the structural plane is realized under the normal vector smoothing phenomenon, the high standard of K-means clustering iteration and the high accuracy of the optimal cluster number of selected clustering are realized, and the problems of density anisotropy and the marginalization effect of the point cloud structural plane are solved.

The invention relates to the technical field of mine safety monitoring, and discloses a roadway surrounding rock stability evaluation method, which comprises the following steps: step 1, collecting roadway surrounding rock geological data, step 2, constructing a three-dimensional geomechanical model, step 3, inverting mechanical parameters, step 4, calculating a stability coefficient, and step 5, outputting an evaluation result. According to the method, rock mass types, structural plane occurrence, crustal stress vectors and permeability parameters are integrally collected through geological radar scanning, drilling coring and crustal stress measuring devices, the problem of model distortion caused by single data in a traditional method is solved, deformation of a stress concentration area is accurately captured by adopting a non-uniform grid division strategy, inversion mechanical parameters are combined, and the method is suitable for large-scale popularization and application. The network is trained by using in-situ direct shear and triaxial test data, the inversion efficiency is improved compared with manual trial and error, the whole surrounding rock fracture process is simulated based on a finite element-discrete element coupling algorithm, plastic zone distribution and a comprehensive index S are output, and automatic judgment of the stability level is realized.

The invention discloses a geological disaster monitoring system based on multi-modal data, and relates to the technical field of geological disaster monitoring, and the system comprises a crack analysis module which carries out the time-space correlation mining of the crack propagation rate of a monitoring region, and analyzes the nonlinear evolution characteristics and spatial differentiation rules of rock mass fracture; and the critical identification module is used for performing wavelet packet energy spectrum analysis on the inclination angle change rate of the geologic body, identifying a critical turning point of rigidity attenuation of the geologic structure in combination with a preset algorithm, judging whether the overall stability enters an instability acceleration stage or not, and performing multi-parameter collaborative detection, crack evolution cross validation and dynamic trend stability verification to obtain the stability of the geologic structure. The accuracy and reliability of geological structure rigidity attenuation critical turning point recognition are remarkably improved, a more accurate rigidity attenuation stage judgment basis is provided for an early warning module, and the capturing capacity of a multi-modal data geological disaster monitoring system for structure instability precursor is enhanced.

The invention provides a rock mass real structural surface direct shear value test simulation method, and belongs to the technical field of rock mechanics. Preparing an artificial fracture on the standard rock sample through Brazilian splitting; scanning the surface of the artificial fracture by adopting a three-dimensional scanning technology to obtain point cloud data of a real structural surface of the rock mass; establishing a direct shear test geometric numerical model of the real rock mass structural surface based on the point cloud data; according to the geometric numerical model of the direct shear test of the real rock mass structural plane, establishing a control equation set considering the wear of the real rock mass structural plane; according to the control equation set, numerical simulation of the direct shear test of the real rock mass structural plane considering abrasion is achieved. The method disclosed by the invention lays a solid foundation for accurately knowing and scientifically mastering the shearing mechanical response and wear evolution law of the real structural surface of the rock mass, and improves the accuracy.

Disclosed in the present invention are a method and system for simulation based on identification of a rock mass structural plane and prediction of a mechanics parameter. The method comprises: obtaining rock mass multi-scale structural plane data and a rock mass physical mechanics parameter at a tunnel construction site; according to the rock mass multi-scale structural plane data, constructing a tunnel construction numerical calculation model; inputting the rock mass physical mechanics parameter into the tunnel construction numerical calculation model, and performing tunnel excavation numerical simulation; during simulation, obtaining a real-time tunneling parameter of construction machinery; according to the real-time tunneling parameter of the construction machinery and a correlation model between tunneling parameters and rock mass physical mechanics parameters, obtaining a predicted physical mechanics parameter of a rock mass in front of a tunneling point; and updating the tunnel construction numerical calculation model by means of the predicted physical mechanics parameter of the rock mass in front of the tunneling point. The accurate simulation of excavation of a tunnel is implemented.

The invention relates to the technical field of geological exploration and three-dimensional modeling, in particular to a mining unmanned aerial vehicle high-precision three-dimensional geological modeling system which comprises a multispectral imaging acquisition module, a point cloud-image high-precision registration module, a geological feature fusion extraction module, a three-dimensional geological model construction module and a geological attribute visualization output module. Wherein the multispectral imaging acquisition module is used for acquiring registration image data and original point cloud data; the point cloud-image high-precision registration module is used for carrying out consistency matching and outputting a registration point cloud-image data set; the geological feature fusion extraction module is used for identifying geological boundaries and outputting geological feature vector data; and the three-dimensional geologic model construction module is used for generating a three-dimensional grid model with attribute topology. According to the method, through accurate three-dimensional modeling and lithology visualization, geological features and lithology information are efficiently combined, and reliable data support is provided for rock mass stability analysis.

The invention relates to the technical field of route planning, in particular to an underground cavern inspection method and system. The method comprises the following steps: acquiring geological survey data, micro-seismic monitoring data and historical deformation monitoring data; according to the geological survey data and the micro-seismic monitoring data, rock mass viscoelasticity local modeling is carried out, and a rock mass viscoelasticity model is obtained; performing deformation trend prediction according to the rock mass viscoelastic model and the historical deformation monitoring data to obtain deformation trend data; performing deformation disturbance resonance analysis according to the deformation trend data and the micro-seismic monitoring data to obtain deformation disturbance resonance data; performing risk area grading on the deformation disturbance resonance data to obtain risk area grading data; and generating a routing inspection graph according to the risk area grading data to obtain routing inspection graph data so as to carry out underground cavern routing inspection auxiliary operation. The method accurately identifies the potential risk area, dynamically plans the inspection task, and improves the inspection efficiency and decision timeliness.

The invention relates to the field of safety risk early warning, in particular to a BIM (Building Information Modeling)-based open-air goaf charging safety risk early warning method and a BIM-based open-air goaf charging safety risk early warning system. A three-dimensional BIM geologic model containing rock mass hardness data, joint density and fracture distribution data is constructed through unmanned aerial vehicle scanning and rock core sampling; determining a minimum effective explosive loading amount meeting a preset crushing lumpiness mean value in the three-dimensional BIM geological model, performing dynamic simulation through the three-dimensional BIM geological model to obtain a simulation result, and outputting a final explosive loading amount when the simulation result meets a preset safety constraint condition; and generating charge parameters according to the final charge amount, binding the charge parameters with space coordinates of the three-dimensional BIM geologic model, and outputting an early warning area through a visual interface. By determining the optimal explosive loading amount and optimizing the blast hole layout and the detonating sequence, the explosive loading safety risk is effectively reduced while the blasting effect is guaranteed.

The invention provides a rock mass trace automatic extraction method and system based on point cloud, and belongs to the technical field of geotechnical engineering / data processing. The method comprises the following steps: acquiring regular body point cloud data and rock mass point cloud data, taking the regular body point cloud data as a source domain based on an induced transfer learning thought, training a universal trace feature point extraction model by using an artificial neural network (ANN) technology, and then transferring the trained trace feature point extraction model to a target domain, namely actual rock mass point cloud. And carrying out coarse extraction on the rock mass point cloud trace feature points, and then sequentially carrying out fine extraction, feature point connection and trace combination processing on the rock mass trace feature points to obtain a final rock mass trace. According to the method, for the problem that threshold selection of different rock mass data densities is difficult, differences of different rock mass point clouds are considered, a trace feature point extraction method combining a multi-scale surface change index and machine learning is provided, threshold setting in the point clouds is not needed, and trace extraction precision and efficiency are improved.

The invention relates to the technical field of fracture network fracture water channel identification, and discloses an identification method for analyzing a dominant flow channel in a three-dimensional fracture network based on a topological network. According to the method, probability distribution parameters such as geometric occurrence, gap width and density of a rock mass multi-scale fracture system in a target area are obtained through field surveying and mapping and three-dimensional scanning, and a spatial topological structure of the three-dimensional fracture system is reconstructed by adopting Monte Carlo method simulation and discrete fracture network modeling technology; secondly, abstracting the fracture network into a three-dimensional topological graph model based on a graph theory principle, defining fracture center points and cross points as graph nodes, and converting fracture sections into weighted edges; the method breaks through the continuous medium hypothesis limitation of traditional seepage analysis, and has important engineering application value in the fields of deep geological energy storage reservoir seepage risk assessment, shale gas fracture network optimization design, rock slope stability analysis and the like; compared with a traditional numerical simulation method, the method has the technical advantages of being high in calculation efficiency, good in prediction precision, high in multi-scale applicability and the like.

The invention belongs to the technical field of tunnel safety monitoring, and particularly relates to a rock stratum stress-deformation coupled tunnel safety real-time dynamic modeling method, which comprises the following steps of: embedding a rock mass mechanical relationship into a neural network model as a hard constraint, and establishing explicit mapping of monitoring data and a physical field, a model parameter field is dynamically optimized by adopting ensemble Kalman filtering real-time general field detection data, and a data driving and physical mechanism collaborative deduction mechanism is designed, so that long-term prediction error accumulation is effectively inhibited; a stress field, a deformation field and other physical quantities deduced and output by the model are directly utilized to calculate disaster risk indexes with clear mechanical significance, and a physically interpretable early warning decision is realized by fusing fuzzy reasoning and multi-source risks; the technical problems of physical misalignment, weak long-term generalization and poor early warning interpretability of a data-driven model are solved.

The invention relates to the technical field of metal mine ultra-deep vertical shaft construction, in particular to an advanced pressure relief construction method for a metal mine ultra-deep vertical shaft, aims to solve the problems that the construction safety of a deep shaft is difficult to guarantee and the construction difficulty is high, and is used for a large-diameter vertical shaft with the depth exceeding 1500 m. The lithology and physical mechanics parameters of shaft surrounding rock are obtained by adopting methods such as shaft rock core logging, surrounding rock joint fissure investigation and rock mechanics tests; then mechanical properties of the shaft surrounding rock are analyzed, and shaft surrounding rock excavation disturbance stress field distribution characteristics are obtained; the supporting opportunity and the supporting thickness of the well wall concrete lining are determined, high stress accumulated in a rock mass is released through advanced pressure relief blasting, so that the stress state of the shaft surrounding rock of the non-lining section is actively regulated and controlled, the interaction between the shaft surrounding rock and the subsequent well wall concrete lining is weakened, and the construction difficulty is effectively reduced; various means such as shaft stratum transparency analysis and ground pressure disaster prevention and control early warning are adopted, and safety and high efficiency of digging and supporting construction of the ultra-deep shaft are guaranteed.

The invention provides a rock mass high-temperature deformation simulation method and system based on deep learning near-field dynamics, and relates to the field of rock mass thermal-mechanical coupling analysis and research, and the method comprises the steps: obtaining the parameter information of a to-be-simulated target rock mass, including the geometric shape of the rock mass and the physical attribute of the rock mass; dispersing the target rock mass into a plurality of material points according to the parameter information, and constructing a thermal-mechanical coupling model of the target rock mass; based on the initialized thermal-mechanical coupling model, time step iterative simulation of a solid mechanical field is conducted, and finally the crack propagation process and temperature distribution of the target rock mass are obtained; according to the method, a solid field near-field dynamics theory based on strain energy density is introduced, a fluid field near-field dynamics equation is combined, temperature distribution in a rock medium is predicted through a physical information neural network, a deep learning near-field dynamics framework of rock mass high-temperature deformation damage is formed, and therefore rock mass high-temperature deformation is efficiently and accurately simulated.

The invention provides a multi-physics field cooperative regulation and control method in coal mine rock burst prevention and control, and relates to the technical field of coal mine rock burst prevention and control, and the method comprises the steps: synchronously collecting stress field, fracture field and vibration wave field data in real time through a distributed optical fiber sensor network and a micro-seismic monitoring system, and constructing a dynamically updated three-dimensional geomechanical model; based on an adaptive Kalman filtering algorithm, noise reduction multi-source data are fused, a dynamic stress concentration factor and an energy accumulation critical index are extracted, and a rock burst risk probability model is established; a multi-objective optimization algorithm is adopted to generate graded regulation and control instructions of grouting reinforcement, mining speed adjustment and pressure relief drilling, and the graded regulation and control instructions are executed in real time; and iteratively optimizing the model weight by using a transfer learning algorithm in combination with a historical case library to form a closed-loop feedback adaptive prevention and control system. According to the method, the analysis precision of rock mass fracture evolution under complex geological conditions is improved through multi-physics field collaborative perception and dynamic modeling, and risk quantification and real-time regulation and control are realized through multi-algorithm coupling analysis.

The invention discloses a rock mass structural plane shear degradation constitutive model under true three-dimensional stress and a numeralization method, and the method comprises the following steps: carrying out a rock mass structural plane pre-peak and post-peak cyclic loading and unloading true triaxial shear test, and obtaining the strength, deformation and damage characteristics of the structural plane; based on a test result, establishing a three-dimensional structural surface shear strength criterion considering the lateral stress effect, and proposing a calculation method of shear modulus, cohesive force, friction angle and dilatancy angle in the shear fracture process of the structural surface; constructing a three-dimensional shear degradation constitutive model considering the lateral stress and shear parameter degradation effect and a continuous and discontinuous numeralization method of the three-dimensional shear degradation constitutive model; the three-dimensional shear degradation constitutive model is verified through numerical simulation of a true triaxial shear indoor test; and carrying out excavation response analysis on the complex stratum tunnel by adopting the three-dimensional shear degradation constitutive model. The invention provides a rock mass structural plane shear degradation constitutive model and a numerical method under true three-dimensional stress, and a theoretical basis is provided for rock mass structural plane underground engineering catastrophe prediction.

The invention provides an intelligent optimization method and system for tunnel smooth blasting blast holes in layered rock mass, and belongs to the technical field of tunnel engineering and blasting. The method comprises the steps of collecting stratified rock geological data, evaluating and extracting features, constructing a multi-objective optimization function, solving by an intelligent optimization algorithm, performing construction feedback and dynamic optimization and the like. The method comprises the following steps of: acquiring data by using various geological exploration equipment, performing evaluation processing on the data by using a support vector machine model, constructing an optimization function considering multiple indexes, solving an optimal blast hole parameter combination by combining a genetic algorithm and a particle swarm optimization algorithm, and finally feeding back a blasting effect to dynamically adjust an optimization scheme. According to the intelligent optimization method and system for the smooth blasting blast holes of the tunnel in the layered rock mass, the blasting effect can be improved, over-break and under-break and concrete consumption can be reduced, the construction cost can be reduced, the construction safety and efficiency can be enhanced, and remarkable economic and social benefits can be achieved for layered rock mass tunnel engineering construction.