2714 results about "Wall rock" patented technology

The invention discloses a tunnel multi-source fusion dynamic twin surrounding rock intelligent prediction and control method and system, and the method comprises the steps: collecting multi-source heterogeneous data in real time through intelligent drilling equipment, a distributed optical fiber sensing network and a mobile scanning device which are disposed on a tunnel construction surface; inputting the multi-source heterogeneous data into a space-time diagram convolutional network, constructing a space-time correlation model of geological features and construction disturbance parameters, and outputting a coupling feature matrix; based on the coupling characteristic matrix, simulating the dynamic response of the surrounding rock-support system through a streaming voxelization engine and a multi-physics coupling engine, and generating a risk evolution three-dimensional thermodynamic diagram; and inputting the risk evolution three-dimensional thermodynamic diagram into the deep reinforcement learning model, and dynamically optimizing to generate a construction parameter regulation and control instruction meeting a safety constraint condition. According to the technology, a full-link channel of'data perception-mechanical analysis-decision execution 'is opened in a closed-loop mode, and therefore the double risks of'data distortion misjudgment' and'response lag out-of-control 'of a traditional method are avoided.

The invention relates to the technical field of tunnel engineering, in particular to a tunnel surrounding rock grading method and system, comprising intelligent sensing and data acquisition, multi-source data fusion and modeling, hybrid model dynamic grading, real-time decision and support optimization, online learning and dynamic feedback, and risk early warning and emergency response. Compared with the prior art that a geological data acquisition mode combining manual drilling coring and low-resolution geophysical prospecting is adopted, efficiency is low, subjective errors are large, and a complex geological structure is difficult to cover, unmanned aerial vehicle LiDAR scanning, intelligent rock core image analysis and a high-density IoT sensor network work cooperatively, and the working efficiency is greatly improved. Real-time dynamic acquisition of full-section geological information is achieved, manual intervention errors are eliminated in combination with a multi-source data fusion algorithm, the automation level and three-dimensional space representation precision of data acquisition are remarkably improved, and a high-resolution holographic data base is provided for surrounding rock classification.

The invention discloses a loess tunnel surrounding rock deformation monitoring method, and relates to the technical field of civil engineering and geological engineering, and the method comprises the steps: integrating a spiral winding type optical fiber sensor, a double-cavity humidity compensation vibrating wire sensor and a microseismic array, and capturing surrounding rock strain, vibration and geological activities; the vibrating wire sensor suppresses humidity interference through a silicone oil damping medium and self-adaptive excitation frequency, and the optical fiber sensor is fixed through a pre-embedded silica gel sleeve to adapt to surrounding rock deformation; edge computing nodes are deployed on the inner wall of the tunnel, an FPGA chip and a lightweight GRU model are integrated, optical fiber strain, a micro-seismic energy spectrum and laser point cloud displacement field data are fused in real time, the strain gradient is analyzed, and a crack propagation probability cloud picture is generated; redundant optical fiber link switching is combined with a six-degree-of-freedom mechanical arm to realize breakpoint self-repairing, and a self-cleaning air curtain is integrated to inhibit dust adhesion; and constructing a geological parameter library based on a BIM-GIS fusion platform, driving a finite element-discrete element coupling model to dynamically update boundary conditions, and predicting surrounding rock deformation and collapse risks.

The invention provides an underground cavern surrounding rock parameter collaborative sensing method and system based on multi-source data fusion, and the method comprises the steps: firstly deploying a sensor group comprising an optical fiber strain sensor, a micro-seismic monitoring array, a three-dimensional laser scanner and an acoustic emission sensor, the optical fiber strain sensor is arranged along the axis of a cavern, the micro-seismic monitoring array is deployed according to a triangular network, and the three-dimensional laser scanner is arranged along the axis of the cavern; performing time synchronization on the sensor group through a synchronization pulse signal, establishing a local coordinate system at a central point of a cavern entrance, uniformly collecting data, generating a time-aligned data stream and a space-unified data set, and triggering associated sensors to enter a hierarchical response collection mode according to a strain rate and a vibration signal; and generating a multi-modal monitoring data set, finally carrying out dynamic weight distribution and conflict resolution processing on the multi-modal monitoring data set, extracting surrounding rock deformation characteristics and fracture event density, and outputting a surrounding rock stability evaluation result, thereby realizing underground cavern surrounding rock parameter collaborative perception and stability evaluation.

The invention discloses a real-time surrounding rock deformation monitoring and data acquisition method and system, which is applied to long-distance weak surrounding rock tunnel construction, and comprises the following steps: determining the dynamic change trend of underground water seepage rate and ground stress distribution gradient by adopting a time sequence analysis method; based on the trend, carrying out risk partitioning on the tunnel construction section by adopting a K-means clustering algorithm, determining a deformation sensitive area, and optimizing the spatial distribution of the monitoring points according to the deformation sensitive area; monitoring data are acquired in real time, and when the data fluctuation period exceeds a threshold value, the data acquisition frequency of the corresponding monitoring point is automatically improved; processing high-frequency acquired data by adopting a long-short-term memory network to obtain a real-time surrounding rock deformation prediction result; the prediction result and the multi-source real-time geological parameters are fused, a Bayesian updating method is adopted for processing, a quantitative surrounding rock stability evaluation result is obtained, closed-loop self-adaptive optimization of a monitoring scheme and accurate risk prediction are achieved, and the safety early warning capacity of tunnel construction and the utilization efficiency of monitoring resources are remarkably improved.

The invention discloses a tunnel blasting quality evaluation and optimization method based on multi-source data fusion, and belongs to the field of tunnel blasting quality evaluation, and the method comprises the steps: collecting and preprocessing multi-source data of a tunnel blasting region; based on the preprocessed multi-source data, performing blasting quality evaluation according to local back break, a blasting contour line, average linear back break and point cloud extraction to obtain a blasting quality evaluation result; according to the blasting quality evaluation result, the blasting quality is graded, and a comprehensive blasting quality score is calculated and graded; and establishing a database containing geological parameters, surrounding rock response parameters and blasting process parameters, training through a convolutional neural network model to generate a blasting parameter optimization scheme, and dynamically adjusting blasting parameters of the next cycle according to the comprehensive blasting quality score. According to the method, the blasting parameter optimization and the quality evaluation process are closely combined to form a closed-loop system, the specific situation in the construction can be reflected in real time, and the accuracy of the blasting effect is ensured.

The invention relates to the technical field of geophysical exploration, and particularly discloses a tunnel surrounding rock stability automatic monitoring and early warning method and system. The method comprises the steps that surrounding rock microseismic signals, convergence displacement and osmotic pressure data are collected through a space-time synchronization array to generate a multi-source fusion data set; based on a dynamic constitutive model separation frequency dispersion effect, extracting correlation characteristics of a microseismic event rate and a displacement rate; generating an instability energy density index; dynamically switching impact or creep damage criteria according to the deformation behavior of the surrounding rock; inputting the indexes into corresponding criteria to generate a damage state result; and if the result exceeds the threshold value, verifying the spatial focusing property of the micro-seismic event cluster, and outputting an early warning instruction when the focusing degree exceeds the critical value. Through multi-source fusion and a dynamic criterion mechanism, the accuracy of tunnel surrounding rock instability early warning is remarkably improved.

A stability assessment method of roadway surrounding rock includes: obtaining actual stratum rock parameters; establishing a two-dimensional geological model through numerical simulation based on a drill core columnar diagram and the actual stratum rock parameters; changing influencing factors, and recording amounts and acceleration values of deformation of roadway sidewalls, and whether failure occurs to obtain dynamic response characteristics of roadway surrounding rock, combining the changed influencing factors and the dynamic response characteristics as labels to obtain a dataset, and obtaining multiple datasets including the dataset; dividing the multiple datasets into a training set and a validation set, inputting the training set into a PSO-BP neural network and a GA-SVM deep learning model to obtain a preliminary stability assessment model, and adjusting and validating the preliminary stability assessment model by the validation set to obtain an optimized stability assessment model; and using the optimized stability assessment model to assess roadway stability.

The invention relates to the technical field of grouting simulation tests, in particular to a sandy gravel stratum catastrophe grouting prevention and control virtual-real blending collaborative test method and system, and the method comprises the steps: carrying out a grouting test on a sandy gravel stratum simulation model, and obtaining grouting parameters and monitoring parameters in the grouting process; based on the resistivity and the radar data, further obtaining a slurry diffusion path and a diffusion range of the area with the higher surrounding rock grade; based on the resistivity and the optical fiber sensing data, the slurry diffusion path and the diffusion range of the area with the low surrounding rock grade are further obtained; inputting the initial grouting parameters into the trained neural network model to obtain simulation data; carrying out preprocessing and alignment operation on the previous data, and carrying out fusion analysis; and according to the fusion analysis result, grouting equipment parameters and a grouting scheme are pre-controlled and adjusted. And real-time regulation and control of a grouting scheme and equipment are realized.

The invention relates to the technical field of rock mechanics, and discloses a rock three-dimensional reconstruction method and system based on multi-source information fusion, and the method comprises the steps: obtaining and preprocessing data, carrying out the spatial feature learning of a fusion feature vector through a 3D-CNN network, and constructing a three-dimensional voxel model of rock microscopic damage; converting the fused image data into a point cloud model of the underground cavern surrounding rock structure by adopting a three-dimensional reconstruction algorithm based on point cloud, and constructing a digital twin framework of the underground cavern surrounding rock structure based on an implicit surface reconstruction algorithm; feature parameters output by the three-dimensional voxel model and the digital twinning framework are used as input, and the optimal supporting opportunity and supporting parameters are output through an LSTM-CNN fusion model; in the underground engineering construction process, surrounding rock deformation data are collected in real time, and a supporting scheme is adjusted in real time through a depth deterministic strategy gradient algorithm; according to the method, the scientificity and timeliness of support design under complex geological conditions can be improved.

The invention discloses a tunnel blasting simulation method and system for a karst area, and relates to the field of tunnel blasting simulation, and the method comprises the steps: building a three-dimensional geologic model of the karst area, and generating an initial ground stress distribution rule through numerical simulation; generating refined initial crustal stress field data; calculating stress wave intensity and time sequence data generated by explosion; acquiring stress wave propagation data; calculating the surrounding rock damage degree and the crack initial position and direction; simulating the surrounding rock crack propagation process, and calculating the penetration probability and the propagation speed of the crack near the karst cave in combination with the karst cave characteristic parameters; analyzing the surrounding rock stability and damage evolution trend of the karst cave area; and optimizing explosive source item parameters and stress wave propagation boundary conditions, and generating a karst area tunnel blasting simulation result. According to the method, the influence of the karst cave on stress wave propagation is accurately simulated, powerful support is provided for optimizing a blasting scheme and improving construction safety and efficiency, and the method has important guiding significance on tunnel engineering under complex geological conditions.

The invention discloses a tunnel back break intelligent finishing and surrounding rock stability dynamic evaluation method and system, relates to the field of tunnel construction, and provides a tunneling-oriented back break intelligent finishing and surrounding rock stability dynamic evaluation method which integrates point cloud sensing, geological modeling, finite element analysis and deep learning prediction. And a closed-loop control process from identification, control, modeling, prediction and feedback is constructed. An over-excavation and under-excavation area is identified through a high-precision point cloud and a neural network, a trimming instruction is generated, a three-dimensional model and a support time sequence model are dynamically constructed in combination with geologic features, a variable parameter reduction method is introduced to realize more real stability analysis, and future displacement and a safety coefficient of surrounding rock are predicted by using deep learning. The method can improve the intelligent level of tunneling control and construction safety, and has remarkable engineering application value.

The invention provides a soft rock tunnel surrounding rock parameter dynamic identification method and system based on data driving, and relates to the technical field of underground tunnel mechanical parameter dynamic identification, and the method comprises the steps: obtaining multi-element tunnel surrounding rock parameters, and building a joint probability distribution model of the multi-element surrounding rock parameters based on a Copula theory; performing Monte Carlo simulation, and generating a high-dimensional parameter sample library meeting physical constraints in the parameter constraint space based on the joint probability distribution model; establishing a tunnel three-dimensional numerical model and performing automatic numerical simulation to generate multivariate response data; constructing a Kriging agent model of a Gaussian kernel function based on multivariate response data training, establishing a nonlinear mapping relation between parameter input and deformation output, constructing an inversion objective function by taking the minimum root-mean-square error of multi-measurement-point displacement as an objective, and performing inversion solution by using an adaptive particle swarm optimization algorithm to obtain inversion identification parameters, and a dynamic feedback mechanism is constructed to realize adaptive tracking of the time-varying characteristics of the surrounding rock parameters.

The invention provides a tunnel disaster source multi-attribute joint inversion method and system based on physical property constraint, and relates to the technical field of tunnel engineering and joint inversion, and the method comprises the steps: constructing a tunnel face three-dimensional geologic model, applying controlled disturbance to surrounding rock physical property parameters, analyzing the change characteristics of earthquake and electromagnetic observation response, and obtaining a tunnel face three-dimensional geologic model; establishing a quantitative mapping relation between the surrounding rock physical property parameter disturbance quantity and the multi-field response signal variable quantity; constructing a multi-dimensional sensitivity evaluation index, and generating a joint sensitivity result; the surrounding rock units are identified and divided, differentiated partition rock physical relationship constraints are constructed, and a physical multi-attribute inversion target of joint inversion is determined based on the partition rock physical relationship constraints; and on the basis of a physical multi-attribute inversion target, a multi-source joint inversion target function is constructed in combination with the joint sensitivity result, joint solving and iterative updating are carried out, and a tunnel face advanced inversion result is output. According to the invention, calamity source fine identification and risk quantitative evaluation in front of the tunnel face during the tunnel construction period are realized.

An intelligent design method for a support scheme of a rock burst section in a TBM tunnel, relating to the field of tunnel engineering. The intelligent design method for the support scheme of the rock burst section in the TBM tunnel comprises: determining a surrounding rock class on the basis of TBM mainframe operation data, surrounding rock feedback evaluation, and a tunnel engineering geological survey report, and using a rock burst intensity assessment method to predict a rock burst grade; and on the basis of the surrounding rock class and the rock burst grade, using a computer deep learning technology to determine an initial surrounding rock support method. The method effectively avoids the impact of subjective factors, actively utilizes TBM mainframe operation data and surrounding rock feedback evaluation, utilizes big data processing and an artificial intelligence technology to propose an intelligently designed support scheme for a rock burst section in a TBM tunnel, and can provide an efficient support scheme for field construction workers for a rock burst section in a tunnel.

The invention relates to the field of tunnel engineering geology and support design, and discloses a weak surrounding rock tunnel deformation risk discrimination method based on shear expansion-shear constitutive, which comprises the following steps: constructing a nonlinear coupling model between a shear expansion angle and shear stress, normal stress and joint parameters, and obtaining the shear expansion angle; establishing a volumetric strain rate discrimination formula; inverting an initial crustal stress tensor field; reconstructing an irregular tunnel boundary; constructing a risk level discrimination model, and outputting a risk level; supporting schemes such as supporting rigidity, anchor rod parameters and spraying layer thickness are matched according to the risk grades; establishing a model to predict a risk trend; a support adjustment suggestion is generated; collecting monitoring data to dynamically correct model parameters; and all the modules are integrated in a deployment system. According to the method, the coupling relation between the shear expansion angle and the shear strength is introduced, the coupling type constitutive discrimination model is established, the risk grading system and the support correction strategy associated with the support response are constructed, and active early warning of the high-risk section and dynamic adjustment of the support rigidity are achieved.

The invention relates to the technical field of underground engineering and geotechnical mechanics, and provides a marine facies soft soil tunnel construction disturbance induced surrounding rock energy dissipation grading evaluation method which comprises the following steps: step 1, establishing a marine facies soft soil energy dissipation constitutive model based on thermodynamics; 2, determining mechanical parameters of the constitutive model; 3, establishing a tunnel construction mechanical model, embedding the constitutive model in the step 1 and the mechanical parameters in the step 2 into the tunnel construction mechanical model, and outputting an energy subitem output quantity in real time; step 4, establishing a damage unit identification system; 5, arranging a monitoring system to obtain monitoring data and feeding the monitoring data back to the numerical model for verification; and step 6, performing graded early warning according to the closeness degree of the structural unit to the instability failure energy threshold value. According to the scheme, a surrounding rock damage grading system is constructed through an energetic perspective, rapid risk identification is realized in combination with multi-source monitoring data, and a new theoretical and engineering means is provided for safe construction of the marine soft soil tunnel.

The invention discloses a rock burst global monitoring and dynamic and static far and near field cooperative early warning method and system, and the early warning method comprises the steps: determining a stope space key monitoring region and a stress wave type based on a dynamic load stress wave far field generation and propagation mechanism, delineating a working face surrounding rock high-stress region, and analyzing the structural characteristics of a rock stratum. Arranging a micro-seismic sensor in a far-field key area, and obtaining parameters such as seismic source fracture size, energy and magnitude through waveform inversion; an acoustoelectric system is arranged in a near-field high-stress area, coal rock fracture characteristics are dynamically monitored, and the static load energy storage state of surrounding rock is quantitatively analyzed. And in combination with a stress wave propagation attenuation coefficient, a far and near field fusion early warning criterion under the action of dynamic and static energy superposition is established, and advanced accurate early warning of the rock burst is realized through collaborative analysis of multi-source monitoring data. The disaster-pregnant space global monitoring technology fusing earthquake, sound and electricity is provided, the chain evolution disaster-causing process of dynamic load generation, cross-space propagation and working face surrounding rock damage is considered, and the accuracy of rock burst early warning can be remarkably improved.

The invention relates to the technical field of layered surrounding rock inversion analysis, and discloses a layered surrounding rock three-dimensional crustal stress field inversion intelligent analysis system and method, and the system comprises a dimensional geological value module, an intelligent inversion analysis module and an inversion result verification module. The method comprises the steps of collecting geological data in target layered surrounding rock; carrying out normalization processing on the geological data, and carrying out sensitivity analysis; constructing a three-dimensional geologic model, and performing numerical simulation; a crustal stress field inversion intelligent analysis model is generated based on the three-dimensional geologic model; training the crustal stress field inversion intelligent analysis model by using the training sample; inputting the real-time geological data into the crustal stress field inversion intelligent analysis model to obtain a crustal stress inversion analysis result; carrying out visual output on the ground stress analysis result; and adjusting the crustal stress field inversion intelligent analysis model according to the actually measured data. Scientific ground stress field analysis basis can be provided for complex projects such as tunnel engineering, underground engineering and hydropower stations.

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 deep water-rich weak-impact coal seam surrounding rock control method based on rapid tunneling, and relates to the technical field of surrounding rock control. The method comprises the steps that S1, a high-risk area is identified through address detection, a waterproof curtain is constructed, and the tunneling disturbance risk is reduced; s2, rapid tunneling and instant supporting are conducted, the surrounding rock exposure time is shortened, tunneling and supporting are completed synchronously, and deformation development is restrained; s3, sensing a surrounding rock-support response in real time, optimizing parameters and early warning risks, and realizing dynamic monitoring and feedback regulation and control; s4, later-stage continuous reinforcement maintenance is conducted, so that the roadway adapts to the rheological characteristics of deep surrounding rock, and the service cycle of the roadway is prolonged; according to the method, a high-risk area is identified through various detection technologies, and instant supporting is achieved through cutting process optimization and digging and supporting collaborative operation; real-time monitoring and dynamic feedback are achieved, and automatic optimization of supporting parameters is achieved; and later reinforcement and periodic maintenance are adopted to ensure that the surrounding rock supporting system can adapt to the rheological characteristics of deep surrounding rock for a long time.

The invention relates to a fractured rock stratum water-rich tunnel excavation water inrush early warning method and system. The method comprises the steps that real-time monitoring data in the tunnel excavation process are obtained; based on the real-time monitoring data, predicting by using the water flow inrush prediction model, and generating a water flow inrush amount prediction result; analyzing crack propagation, deformation and instability conditions of the surrounding rock of the tunnel under different excavation parameters through the rock mass damage evaluation model in combination with the real-time monitoring data, and generating a rock mass damage evaluation result; generating a construction optimization scheme based on the water flow inflow prediction result and the rock mass damage evaluation result; comparing the real-time monitoring data with a preset safety threshold value, and judging whether a water inrush risk or a surrounding rock instability risk exists or not; if the real-time monitoring data exceed the preset safety threshold value, a safety early warning signal is triggered, and emergency measures are taken immediately. The method has the effect of improving the safety in tunnel construction.

The invention relates to the technical field of construction surrounding rock stability evaluation, discloses a tunnel surrounding rock stability evaluation method and system based on multi-physics field parameter inversion, and aims to solve the problems that an existing method is insufficient in data collaboration, high in parameter inversion multiplicity and poor in surrounding rock stability evaluation accuracy and timeliness. According to the scheme, the method mainly comprises the steps that an acousto-optic electromagnetic vibration drill multi-mode sensing array is arranged, and time-space synchronization is implemented; establishing a mutual interference entropy spectral density model to realize multi-physics field collaborative excitation and acquisition; a unified feature vector is obtained through data correction, feature extraction and weighted fusion; a joint inversion objective function embedded with rock physical constraints is constructed, a three-dimensional physical property parameter field is obtained through inversion, and a dynamic permeability field is calculated in combination with acoustic emission energy; and finally, dynamically updating the model by utilizing ensemble Kalman filtering, and obtaining a final risk probability based on updated parameters and seepage-uncertainty coupling correction. According to the method, the accuracy, the real-time performance and the reliability of the stability evaluation of the surrounding rock of the deep-buried tunnel are improved.

The invention discloses a spatial registration method for tunnel multi-source heterogeneous data, and the method comprises the steps: constructing a lightweight digital twinborn model based on a design parameter library, a construction log library and a real-time monitoring library; mapping the multi-source sensor data in the real-time monitoring library to a digital twin model space node through a space-time synchronization algorithm; probability distribution model-based abnormal value elimination is performed on the geological exploration data, the while-drilling parameters and the geological sketch, and a discrete smooth interpolation algorithm is adopted to generate a surrounding rock mechanical parameter three-dimensional distribution field; the surrounding rock mechanical parameter three-dimensional distribution field serves as input, and front surrounding rock classification probability distribution is dynamically predicted through a machine learning model optimized through transfer learning; based on the three-dimensional point cloud data of the tunnel face in the digital twin model, extracting geometric parameters of the structural plane and constructing a fracture network model constrained by a normal vector; according to the method, the surrounding rock risk location is accurately positioned based on double-threshold triggering, and multi-scale model seamless fusion is realized through gradient constraint topology fusion.

The invention belongs to the technical field of tunnel and underground engineering intelligent construction and geotechnical engineering informatization, and discloses a high-energy geological environment surrounding rock classification and decision-making method based on digital twinning and multi-source feedback. The problems caused by difficulty in realizing surrounding rock state dynamic sensing, multi-source data fusion classification and construction decision closed-loop linkage in the prior art in a high-energy geological environment are solved. The method comprises the following steps: firstly, constructing a tunnel three-dimensional geology-structure digital twinborn body based on initial survey data; in the construction process, multi-source data such as geology, construction disturbance and surrounding rock response are collected in real time through the Internet of Things technology and mapped to the digital twinborn body, and virtual-real synchronous updating is achieved. And then, constructing a deep learning-parameter inversion hybrid model on the basis of the multi-source fusion data, outputting a dynamic surrounding rock classification index DRCI and key mechanical parameters, inputting the DRCI and the key mechanical parameters into a multi-objective optimization module, and giving a self-adaptive drilling and blasting scheme. And finally, reversely correcting the model through a construction feedback result to realize closed-loop self-learning.

The invention provides a large chamber surrounding rock stress characteristic dynamic prediction method and device, and relates to the technical field of underground engineering surrounding rock stability prediction. The method comprises the following steps: establishing a loss function of a dynamic multi-physics field coupled neural network framework by combining a data fitting item residual item, a time sequence continuity regularization item and a large chamber surrounding rock mixed physical constraint loss function constructed based on a layered time sequence parameter, a multi-source physics field parameter and a geological parameter; layered time sequence characteristics of layered excavation of the surrounding rock of the large chamber are extracted, and sampling data are dynamically determined; then, the weights of different physical fields in the large-chamber surrounding rock mixed physical constraint loss function are optimized; and according to the optimized loss function, obtaining a trained dynamic multi-physics field coupled neural network framework, and then generating a dynamic prediction result of the stress characteristics of the surrounding rock of the large chamber. According to the method, the problems of multi-field coupling equation embedding, time sequence dynamic feature capturing and high-risk region data optimization can be effectively solved.

The invention belongs to the field of surrounding rock rockburst early warning, and provides a rockburst early warning method and system based on micro-seismic energy and support stress coupling monitoring, which are used for carrying out deep gold mine roadway surrounding rock micro-seismic monitoring and anchor rod support stress monitoring. Establishing a micro-seismic energy data set, and carrying out statistical analysis to obtain a micro-seismic energy early warning index; the anchor rod supporting stress is analyzed, and then an anchor rod stress early warning index is determined; and carrying out information fusion on the micro-seismic energy early-warning index and the anchor rod stress early-warning index, establishing a combined early-warning method based on micro-seismic energy and support stress, and carrying out evaluation and early-warning on the rock burst grade of the surrounding rock. Rockburst occurrence risks can be effectively reduced, and safety of constructors and mechanical equipment is guaranteed.

The invention discloses a roadway surrounding rock deformation monitoring method based on an optical fiber sensor, and the method comprises the steps: laying on the surface of roadway surrounding rock through employing a distributed optical fiber sensing technology, and obtaining original optical signal data; processing backscattered light data in the original optical signal data through a Brillouin optical time domain analysis algorithm, extracting frequency deviation of an optical signal, and obtaining strain distribution data; according to the strain distribution data, in combination with a pre-established surrounding rock mechanical model, performing initial inversion calculation by adopting a finite element method to obtain initial three-dimensional deformation distribution; on the basis of the initial three-dimensional deformation distribution, updating dynamic boundary conditions of the surrounding rock mechanical model by acquiring real-time geological parameter data to obtain an optimized mechanical model; carrying out inversion calculation on the strain distribution data again by adopting an optimized mechanical model to generate final three-dimensional deformation distribution; and based on the final three-dimensional deformation distribution, performing comparison judgment through a preset deformation threshold, determining the stability state of the surrounding rock, and generating an early warning signal.

The invention provides a tunnel surrounding rock grouting control method and system, and relates to the technical field of tunnel engineering.The tunnel surrounding rock grouting control method comprises the steps that grouting parameters are obtained, and the grouting parameters comprise grouting pressure and slurry viscosity; performing multi-dimensional real-time monitoring on the surrounding rock fracture, and constructing a dynamic three-dimensional geologic model through the multi-dimensional real-time monitored data; performing grout diffusion path prediction based on the three-dimensional geologic model, and generating an initial grouting scheme through the predicted grout diffusion path; the fractal dimension is optimized according to a genetic algorithm, the grouting parameters are dynamically corrected through the optimized fractal dimension, and a grout diffusion result is obtained; establishing a multi-field coupling equation based on the slurry diffusion result, and optimizing the initial grouting scheme through the multi-field coupling equation to obtain an optimal grouting scheme; and according to the optimal grouting scheme, power pressure compensation is conducted, and grouting control is conducted. The problem that slurry diffusion is difficult to accurately control is solved.

The invention belongs to the technical field of gas storages, and particularly relates to an experimental method and device for evaluating fault stability under injection-production alternating stress of a gas storage. The method comprises the following steps: acquiring reservoir information of a to-be-detected gas storage; making a three-dimensional model according to the reservoir information, and configuring model parameters according to formation water and natural gas initially injected into the three-dimensional model; an injection-production pipe is inserted into the three-dimensional model, the multi-cycle gas injection-production process is simulated, and fault changes in the three-dimensional model are monitored; and optimizing model parameters of the three-dimensional model according to the fault change condition, and determining optimal model parameters. A fault with any dip angle can be simulated, the experimental model comprises a whole geologic body simulating a gas storage, surrounding rocks including a cover layer and a bottom supporting layer and wrapping the periphery of a reservoir body, the reservoir is disconnected by the fault, the contact relation of the two reservoirs can be designed according to the actual condition of the stratum, and an injection-production well is designed in the model. The injection-production well simulates high-speed injection-production of the gas storage.