1989 results about "Wall rock" patented technology

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 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.

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 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 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 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.

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 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 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 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 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 discloses a numerical simulation-based roadway surrounding rock deformation monitoring method and device. The method comprises the following steps of: constructing a three-dimensional geologic model of a roadway area; based on the three-dimensional geologic model, establishing a numerical model of heat conduction, stress action and seepage multi-physics field coupling, and based on elastic-plastic characteristics of rocks in the roadway area, simulating simulation data of surrounding rocks in the roadway area under the multi-physics field action by the numerical model; acquiring real-time monitoring data of various types of sensors arranged in a risk monitoring area of surrounding rock deformation corresponding to the simulation data; denoising, filtering and abnormal value processing are carried out on the real-time monitoring data, and then multi-source data fusion is carried out to calibrate the numerical model; and predicting the deformation trend of the surrounding rock based on the calibrated target numerical model and the real-time monitoring data. Therefore, by means of geological modeling, numerical model simulation, sensor arrangement optimization, numerical model optimization and surrounding rock deformation trend prediction, roadway surrounding rock monitoring precision is improved, and safe operation of a roadway is guaranteed.

The invention relates to the technical field of mine safety, and provides a cross-layer roadway multi-rock-stratum rigidity matching collaborative segmented supporting method which comprises the steps that plastic zone ranges of different rock stratum areas in a cross-layer roadway are determined, and rock stratum joint fissure zones and weak intercalated layers are identified; monitoring the elastic zone rigidity and the plastic zone weakening rate of the surrounding rock, constructing a surrounding rock mechanical model, obtaining the supporting structure rigidity for inhibiting the expansion of the plastic zone through the surrounding rock mechanical model, and constructing a supporting structure according to the supporting rigidity; and the reinforcing material is sprayed to the surface of the surrounding rock through high-pressure jet flow to form a protective layer. Through the cooperative control thought of weak layer control, rigidity adjustment and resistance transfer, breakthrough transformation of crossing roadway support from passive defense to active control is achieved, according to a scientific surrounding rock mechanical model, the rigidity of the support structure can effectively inhibit expansion of a plastic zone, the stability of a system is maintained, and the stability of the system is improved. And finally, an ultrahigh strength performance and gradient spraying layer structure technology is adopted, so that the compression strength and the shear strength of the surrounding rock are remarkably improved.

The invention provides a fractured rock mass surrounding rock grouting quality evaluation method, and relates to the technical field of geotechnical engineering.The method comprises the following steps that permeability test data and acoustic velocity test data of fracture connectivity and mechanical states of a target surrounding rock area are obtained before grouting, and a characterization parameter data set of fractures before grouting is formed; synchronously acquiring three-dimensional point cloud data to calculate the volume of a fracture cavity; in the grouting process, electromagnetic field intensity data is collected to calculate the grouting filling rate, resistivity time sequence data is collected to calculate the cementation uniformity index, strain time sequence data is collected to calculate the strain gradient, and a grouting integrity index is formed through multi-feature fusion; after grouting, permeability and sound wave velocity data are obtained again, and the fracture characterization performance improvement rate is calculated; acquiring penetration test data to calculate a stone body strength parameter; and performing multivariate fusion on the grouting integrity index, the fracture characterization performance improvement rate and the stone body strength parameter to calculate a comprehensive quality score, and comparing the comprehensive quality score with a preset threshold value to perform grading evaluation.

The invention discloses a spherical crown dome horizontal circumference drilling and blasting parameter model optimization construction method, and the method comprises the steps: collecting geological, crustal stress, hydrological and structural plane data, building a three-dimensional geological information model, carrying out the working condition partitioning based on a surrounding rock quality evaluation index, and constructing a parameter probability distribution model and an uncertainty boundary of each partition. Therefore, precise space description and parameter variability quantification of the spherical crown dome construction area are realized. The method comprises the following steps: establishing a three-dimensional numerical model of a cavern group, integrating blasting power, seepage and stress multi-physical field effects by adopting a finite element-discrete element coupling method, introducing a surrounding rock deterioration constitutive model considering cumulative damage and unloading aging, calibrating key parameters through an indoor test and field trial explosion, and establishing a three-dimensional model of the cavern group. And the numerical model can truly reflect the mechanical property degradation track of the surrounding rock under repeated blasting disturbance.

The invention relates to the technical field of deformation monitoring, in particular to a mountain tunnel surrounding rock deformation monitoring method and system based on distributed optical fiber sensing. The method comprises the following steps that Brillouin frequency shift original data and center wavelength original data are collected, tunnel construction progress data and surrounding rock geological survey data are obtained at the same time, format standardization and time sequence alignment processing is carried out, and preprocessed optical fiber sensing basic data, construction time sequence data and geological attribute data are generated; surrounding rock deformation characteristic parameters are extracted based on the optical fiber sensing basic data, and surrounding rock deformation multi-dimensional characteristic vectors are constructed; performing deformation mode density clustering and surrounding rock deformation dynamic prediction based on the surrounding rock deformation multi-dimensional feature vector to generate a surrounding rock deformation dynamic prediction value; and carrying out comparison and iterative correction on the dynamic prediction values of the surrounding rock deformation, and carrying out regionalized polymerization to generate a mountain tunnel surrounding rock deformation spatial and temporal distribution visualization result. According to the invention, full-time-space accurate monitoring and early warning of tunnel surrounding rock deformation can be realized.

The invention relates to the technical field of roadway surrounding rock danger identification, and discloses a roadway surrounding rock danger identification model construction method, which comprises the steps of collecting multi-modal data, and generating preprocessed data through synchronous calibration and denoising; extracting a seismic wave frequency domain and image texture features, and generating a multi-modal feature matrix; in combination with a geological prior clustering mining abnormal mode, generating a labeled sample data set; generating a danger identification model based on a transfer learning and feature fusion training network; and the edge deployment model performs real-time reasoning, and generates an early warning result through an adaptive algorithm. According to the method, the frequency domain features of the seismic fluctuation signals and the depth texture features of the surrounding rock images are fused, the multi-modal feature matrix is constructed, abnormal mode mining is carried out in combination with geological prior knowledge, and early weak abnormal signals such as hidden fault slippage or asymmetric microfracture extension which are difficult to find by a single monitoring means can be effectively recognized.

The invention discloses a deep underground engineering large deformation real-time monitoring and control method based on deep learning. The method comprises the steps of deployment of a monitoring system, preprocessing of monitoring data, construction of a neural network model, training of the neural network model, output of a prediction result, generation of a control instruction and quantitative evaluation of a control effect. According to synchronously acquired multi-source information such as surrounding rock strain, temperature, seepage pressure and micro-seismic disturbance energy, a deep learning model based on physical mechanism constraint is introduced, so that the reliability and generalization ability of a prediction result are remarkably improved; intelligent identification of surrounding rock stability assessment and failure modes is realized, a control instruction is automatically generated, and an implementation effect is quantitatively assessed.

The invention provides a coal seam surrounding rock stability monitoring method based on micro-seismic monitoring, which relates to the technical field of coal seam surrounding rock stability monitoring and comprises four steps of micro-seismic sensor network arrangement, real-time data acquisition and processing, surrounding rock stability evaluation model construction and real-time early warning and feedback of coal seam surrounding rock stability. According to the invention, a roof-side wall-support body cooperative monitoring architecture is adopted to cooperate with the micro-seismic sensor, and three-dimensional sensing network deployment is established, so that the micro-fracture activity in the surrounding rock of the whole stope can be continuously monitored in real time in a three-dimensional space full-coverage manner, the limitation of traditional point-type monitoring is overcome, and the dynamic evolution law of the micro-seismic activity is analyzed, so that the micro-fracture activity in the surrounding rock of the whole stope can be monitored in a real-time manner. According to the method, before macroscopic physical damage occurs, an area with abnormal stress concentration and accelerated damage accumulation can be identified, advanced early warning is realized, and precious time is gained for active prevention and control.

The invention relates to the technical field of construction optimization, in particular to a deep underground engineering construction optimization method and system based on an intelligent support system. According to the technical scheme, stress field, seepage field and deformation field data of surrounding rock are collected in real time; constructing a digital twinborn model, and dynamically updating model parameters; adopting a multi-objective genetic algorithm to optimize support parameters; and executing adaptive control. According to the method, the stress field, seepage field and deformation field data of the surrounding rock are collected in real time through the multi-physics field sensing network, the state change of the surrounding rock can be timely and accurately monitored, meanwhile, the model parameters are dynamically updated so that the model can more accurately reflect the actual situation of the surrounding rock, and the real-time performance of the surrounding rock is improved. And through jet control and anchor rod compensation in self-adaptive control, support parameters can be adjusted in real time according to the actual condition of the surrounding rock, the reliability of a support structure is improved, the stability of the surrounding rock is enhanced, and the safety risk in the construction process is reduced, so that the construction safety is improved.

The invention provides a tunnel surrounding rock dynamic grading and blasting parameter optimization method and system, and relates to the technical field of data processing.The method comprises the steps that geological parameters of a current tunnel face are collected in real time through a multi-source sensing system deployed on the tunnel face; based on the acquired geological parameters, identifying a plurality of characteristic sampling points with geological representativeness in a tunnel face spatial domain; four non-coplanar feature sampling points are selected to construct an initial tetrahedron, the point, farthest from the surface of the current convex hull, in the remaining points is included in sequence through an iterative extension method, the surface of the convex hull is recalculated till all the feature sampling points are enveloped, and a convex polyhedron evaluation area boundary is formed. According to the invention, data full-process connection and function collaboration can be realized.

The invention discloses a tunnel surrounding rock deformation monitoring method based on a time sequence neural network, and the method comprises the steps: obtaining mountain tunnel surrounding rock deformation monitoring data, carrying out the preprocessing of the data through a time sequence preprocessing method, and obtaining a preprocessed time sequence data set; for the preprocessed time series data set, performing multi-scale decomposition on the data by adopting a wavelet transform method to obtain a decomposed multi-scale feature set; obtaining a plurality of feature matrixes according to the decomposed multi-scale feature set; carrying out matrix splicing by adopting a feature fusion method to generate a comprehensive feature matrix; according to the comprehensive characteristic matrix, time sequence modeling is carried out through a long and short term memory neural network model, the model is trained to monitor the surrounding rock deformation trend of the future time step, and a monitored deformation trend sequence is obtained. According to the method, through multi-scale feature extraction and fusion, the accuracy and reliability of surrounding rock deformation trend monitoring are improved by utilizing time sequence characteristics of monitoring data and combining external influence factors.

The invention discloses an underground space surrounding rock excavation damage area testing method, which belongs to the technical field of rock mass damage testing, and comprises the following steps: sampling rocks with different depths, carrying out ultrasonic testing, and determining rock sample attenuation characteristics and wave velocity; determining a comprehensive damage index range of the rock samples with different damage grades; then, sensors are arranged in the excavation process to collect ultrasonic and acoustic emission data; then, an initial calculation model is constructed, and a surrounding rock three-dimensional wave velocity model is obtained through inversion of collected data; and finally, determining a damage grade according to the comprehensive damage index, and marking in the model to realize damage area testing. According to the method, the mapping relation between the damage degree and the wave velocity change is established, the comprehensive damage index range threshold value of different subareas is provided, quantitative and gradable classification is achieved, the three-dimensional wave velocity model is inversed based on test and monitoring data, and three-dimensional presentation and grading labeling of the damage area are achieved.

The invention belongs to the field of soil rock engineering, and provides a three-surface integrated geologic structure modeling method for a deep and large foundation pit in a river-adjacent region, which comprises the following steps: acquiring physical and mechanical parameters and hydrogeological information of the deep and large foundation pit in the river-adjacent region; dividing spatial distribution of different soil layers and rock stratums; describing the influence of water level time variation on the pore water pressure and subsurface erosion of the surrounding rock of the foundation pit through an unstable seepage equation; dividing a finite element or finite difference grid, and applying a dynamic water level and ground load boundary condition; gradually unloading and analyzing the evolution of an underground water flow field and a soil-rock deformation field in each excavation step, and enabling the water surface-ground-soil-rock surface coupling effect to penetrate through the whole excavation process; and numbering the support members, sequentially simulating local failures of the support members, recording the maximum horizontal displacement, the maximum vertical displacement or the change curve of the safety coefficient along with the number of the failure members after each failure of the foundation pit, quantifying the redundancy of the support system, and identifying key members. According to the scheme, the accuracy of the model is improved.

The invention relates to the technical field of tunnel construction safety monitoring, and discloses a tunnel construction safety evaluation method and system based on multi-dimensional data fusion analysis, and the method comprises the steps: constructing a digital twinborn model, and building an initial twinborn body comprising a BIM model and a three-dimensional geologic model; performing mechanical parameter inversion and dynamic updating on the three-dimensional geologic model through real-time multi-dimensional monitoring data to obtain a dynamic twinborn body; performing fusion analysis on the monitoring data by adopting a fusion algorithm, and outputting a safety risk coupling evaluation result; based on an evaluation result, combining with surrounding rock mechanical response of dynamic twinborn simulation, and adopting fuzzy comprehensive evaluation to output a safety level; and performing risk traceability and positioning main risk factors through a Bayesian network based on the security level, generating a customized support decision through case reasoning, and performing visual display and early warning in a digital twinborn model. The system corresponds to the method. According to the invention, dynamic accurate evaluation and intelligent decision support of tunnel construction safety are realized.

The invention discloses a complex geological weak surrounding rock tunnel micro-disturbance green blasting construction method which comprises the following steps: acquiring multi-source data through a sensor and geological survey equipment, and extracting a standardized feature set; and constructing a blasting parameter and surrounding rock response prediction model by using a random forest algorithm, inputting explosive load and hole net parameters, and outputting predicted surrounding rock deformation and vibration values. And if the vibration value exceeds the environment-friendly threshold value, adjusting the parameter by adopting a gradient descent algorithm until the requirement is met. And in combination with real-time monitoring data, the surrounding rock response is dynamically updated by using a Kalman filtering algorithm. If the overexcavation probability exceeds the threshold value, the parameters are further optimized through a genetic algorithm. And finally, optimizing resource allocation through a linear programming algorithm, and determining a final blasting scheme. Intelligent regulation and control of blasting parameters of the weak surrounding rock tunnel are achieved, vibration and deformation are effectively controlled, the overexcavation risk is reduced, the construction efficiency and quality are improved, and a new technical scheme is provided for similar projects.

The invention relates to the technical field of geomechanical simulation of tunnel engineering, in particular to a tunnel surrounding rock water immersion weakening fluid-solid coupling simulation method and system.The method comprises the steps that mechanical parameters and seepage parameters of typical tunnel surrounding rock materials are obtained, and then an evolution equation of water immersion equivalent strain along with time is constructed; constructing a water immersion equivalent strain-seepage parameter dynamic correlation model based on the evolution equation and the seepage parameters; a seepage-stress coupling elastic-plastic damage constitutive model is constructed on the basis of a water immersion equivalent strain dynamic evolution model, and coupling circulation of a seepage-stress-damage field under the influence of water immersion degradation is represented on the basis of the seepage-stress coupling elastic-plastic damage constitutive model. The technical blank that bidirectional strong coupling of a seepage field and a mechanical field under water immersion response cannot be realized in the prior art is filled, and a high-precision simulation technical scheme is provided for stability analysis of tunnel surrounding rock in a water-rich environment.

A rock mass mechanical parameter prediction method based on an underground powerhouse of a pumped storage power station relates to the technical field of mechanical parameter prediction, and comprises the following steps: identifying an embedding position and distribution characteristics of a sulfur-containing shale interlayer by constructing a three-dimensional geologic structure model and lithologic information of a construction area, and establishing a structural domain partition parameter prediction area system; constructing a nonlinear regression function model taking infrared spectrum characteristic factors and environment control parameters as input and taking elasticity modulus and the like as output by combining infrared spectrum characteristic data and a historical evolution track; the model is embedded into a regional system for dynamic assignment, and a parameter space-time change atlas is constructed; the laser point cloud data and the micro-seismic monitoring data are fused to invert the actual response of the surrounding rock; when the monitoring value deviates from the prediction map, triggering parameter degradation function re-calibration to complete parameter dynamic updating and prediction closed loop; according to the method, the accuracy of rock mass mechanical parameter prediction of the pumped storage power station underground powerhouse can be improved.