2775 results about "Tunnel engineering" patented technology

The invention provides a prediction method and system for a rock stratum fracture disaster in tunnel engineering, and belongs to the field of tunnel engineering. Comprising the steps of multi-modal monitoring data acquisition and synchronization, data preprocessing and feature calculation, multi-modal information entropy evolution analysis, catastrophe point detection and precursor identification, risk assessment and early warning output and the like. Multiple types of sensors are arranged at key positions of a tunnel, an edge computing gateway is accessed through an industrial-grade field bus, and space-time reference unification of multi-source heterogeneous data is achieved; carrying out preprocessing and feature calculation on the collected original data; performing sliding window probability distribution modeling on each modal feature, calculating an information theory index, and analyzing non-linear relevance among the multi-modal features; judging whether a catastrophe precursor event exists or not by adopting multiple methods; and finally, based on a catastrophe point detection result, performing risk assessment on the tunnel region by using a multi-dimensional risk quantification model, and performing early warning output on high-risk and emergency states.

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 relates to the technical field of tunnel engineering construction, and discloses an intelligent material transportation and safety monitoring system and method for shield construction, and the system comprises a visual perception unit, a sensor network module, an AI analysis center module, a safety decision module and a human-computer interaction interface. According to the invention, data acquisition is carried out through the visual perception unit and the sensor network module, multi-target detection operation is carried out on image frames through the AI analysis center module after target identification and track prediction, target types, space coordinates, contour boundaries and confidence coefficients are identified and extracted, and safety judgment and early warning output are carried out. According to the invention, by integrating the multi-view camera equipment and the UWB, GNSS and other sensors and adopting a deep learning target detection algorithm, high-precision identification and continuous tracking can be carried out on construction site personnel, equipment, segments and other key objects, and accurate input is provided for subsequent risk analysis.

The invention discloses a modeling method based on shield tunneling data feature analysis and parameter relevance, and relates to the field of tunnel engineering data processing. The method comprises the steps that shield tunneling time sequence parameters are obtained, and a non-uniform time sequence is resampled into a space-aligned standardized footage domain sequence through state cleaning and coordinate domain transformation; by means of mixed variable rejection and lagging correlation analysis, environment common cause interference is stripped, physical response delay among parameters is recognized, and a time-delay directed correlation graph model is constructed; and inputting the footage domain sequence and the graph model into a graph neural network, performing feature learning by using a time delay compensation aggregation mechanism, and outputting a key parameter influence degree set with symbols based on a prediction gradient. According to the method, the problem of data space-time dislocation caused by propelling speed fluctuation and the problem of parameter relevance misjudgment caused by physical response lag are solved, and accurate identification and explanation of shield tunneling key parameters are achieved.

The invention relates to the technical field of tunnel engineering intelligent construction, and discloses a hard rock TBM shield tunneling machine auxiliary tunneling parameter dynamic adaptation regulation and control system, which comprises a high-precision multi-source sensing network, a high-precision multi-source sensing network, a high-precision multi-source sensing network, a high-precision multi-source sensing network, a high-precision multi-source sensing network, a high-precision multi-source sensing network and a high-precision multi-source sensing network, the TBM-geological environment digital twin predicts the tunneling short-term trend based on the high-fidelity physical simulation and data assimilation technology; and the multi-modal deep learning collaborative decision-making module deeply fuses real-time and prediction data and generates an optimal parameter solution set through a network trade-off tunneling multi-conflict target based on Pareto optimization. And the system executes a decision and forms closed-loop feedback through a parameter dynamic adaptation and adaptive learning module, and continuously optimizes a self model and a knowledge base. According to the method, passive response of TBM tunneling is converted into active pre-judgment, the decision accuracy, the construction safety and the comprehensive tunneling efficiency under the complex working condition are remarkably improved, and the method has the sustainable evolution capacity.

The invention relates to the technical field of tunnel engineering, and discloses a tunnel multi-field coupling nonlinear deformation analysis method and system.The method comprises the steps that geological environment information of a tunnel area is collected, a multi-source physical field boundary condition model is built based on collected data, and a heat-seepage-stress-time four-field coupling control model is built based on the collected data; specifying a nonlinear response model for the geological medium to truly reflect the stress-strain behavior of the geotechnical material; solving a coupling equation set, and optimizing the four-field coupling control model; introducing measured data to correct the model; the system comprises a geological data acquisition module, a boundary condition modeling module, a coupling model establishment module, a numerical solution module, a measured data correction module and a visual output and control interface module. According to the method, high-fidelity prediction and evolution analysis of the deformation behavior of the tunnel under the complex geological condition are realized based on comprehensive acquisition of the geological environment, multi-field boundary modeling, control equation construction and numerical calculation.

An asymmetric high stress tunnel rock burst micro-seismic sensor arrangement method, relating to the technical field of tunnel engineering. On the basis of tunnel surrounding rock geological data, three-dimensional numerical simulation is carried out for asymmetric high stress tunnel stability on the basis of asymmetric in-situ rock geo-stress distribution and tunnel section geometric characteristics, and asymmetric high stress tunnel energy is calculated; the location of a rock burst risk area is determined; micro-seismic sensors are arranged in front of and behind a stress concentration area; the area at a high risk and nearby areas thereof are always incorporated in two micro-seismic sensor arrays arranged in front of and behind a tunnel face. The micro-seismic sensors work cooperatively and are staggered in space, the mounting and arrangement of the micro-seismic sensors changes forwards and backwards along with the stress concentration area, and the micro-seismic sensors always keep a certain distance range with the stress area. The tunnel stress concentration area and more nearby effective micro-crack source signals can be captured by the micro-seismic sensors, thereby improving the precision of locating a micro-seismic source and laying a foundation for accurate prediction and forecast of deep-buried hard rock tunnel disasters.

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 provides a tunnel crack identification method and system based on multi-source image processing, and relates to the technical field of tunnel engineering, and the method comprises the steps: obtaining multi-source data of a horizontal rock stratum tunnel; performing spatial registration on the multi-source data to generate a multi-modal image under the same reference system; extracting multi-modal features according to the multi-modal image, and constructing a multi-source feature map under the same space grid; performing crack initial detection on the multi-source feature map in combination with multi-scale filtering and structure tensor analysis to obtain a crack candidate region mask; accurate crack identification is carried out through the crack candidate area mask, a crack identification result is obtained by combining bedding direction constraint and a microconditional random field, and the crack identification result comprises a crack segmentation map and a crack category. The method solves the problem that existing crack identification does not consider the bedding characteristics of the horizontal rock stratum tunnel.

The invention discloses a multi-source collaborative advanced geological exploration method based on TBM construction, and belongs to the technical field of tunnel engineering geological exploration. A three-dimensional geological model is constructed through multi-scale collaborative operation of directional long exploration drilling, branch short exploration drilling and in-hole transient electromagnetic detection; the directional long exploration adopts a secondary drilling structure and a screw motor directional drilling technology to realize accurate control of a deep stratum, the branch short exploration realizes dynamic and rapid verification through a TBM integrated drilling machine, meanwhile, in-hole transient electromagnetic exploration adopts three-component measurement and an intelligent inversion algorithm to fill the blank of geological information between drill holes, and by means of mutual data correction of the three methods, the detection accuracy is improved. A three-in-one detection system of drilling, geophysical prospecting and intelligent analysis is formed, and then accurate advanced prediction is achieved, so that the problems that a traditional single detection technology is low in precision and insufficient in coverage range are effectively solved, the water inrush risk is remarkably reduced, construction safety is guaranteed, and the method is effectively suitable for deep tunnel TBM safety construction.

The invention relates to the technical field of tunnel engineering, and discloses an intelligent regulation and control shield segment crack minimally invasive repair method, which comprises the following operation steps: intelligently identifying a crack, comprehensively scanning a shield segment by using high-definition camera equipment, obtaining a high-definition image of the surface of the segment, and obtaining a high-definition image of the surface of the segment by applying an image processing technology and a deep learning algorithm. The image is analyzed and processed, key parameters such as the position, the width, the length and the depth of the crack are automatically identified, a crack three-dimensional model is constructed, and accurate data support is provided for subsequent minimally invasive repair; and making a minimally invasive repair scheme, and combining factors such as a segment material and a stress condition according to a crack identification result. According to the intelligent regulation and control shield segment crack minimally invasive repair method, accurate recognition, minimally invasive treatment and effect evaluation of shield segment cracks are achieved, and the purposes of improving the repair efficiency, reducing the construction risk and ensuring the safety and stability of a tunnel structure are achieved.

The invention discloses a tunnel state monitoring method based on structure and appearance data, and relates to the technical field of tunnel engineering health monitoring. By integrating the structure and the appearance data, the accuracy of tunnel health assessment and the timeliness of early warning are remarkably improved. According to the method, through deep mining of data features, construction of a comprehensive feature matrix and application of a multi-modal data fusion technology, the problem of information isolation is effectively solved, evaluation comprehensiveness is enhanced, a dynamic health evaluation model is combined with historical and real-time data, tunnel state changes are accurately predicted through time sequence analysis, and the evaluation accuracy is improved. In addition, through a risk linkage network and optimized state prediction, accuracy of risk management and data driving of maintenance decision are realized, and powerful technical support is provided for safe operation and management of the tunnel.

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 provides a tunnel excavation ground surface settlement prediction method and system based on a machine learning hybrid model, and relates to the technical field of tunnel engineering and machine learning crossing, and the method comprises the steps: obtaining the multi-source heterogeneous information of a target tunnel, and constructing a ground surface settlement data set; a Transform-BiLSTM hybrid model is constructed, the robustness of the algorithm in a noise environment is enhanced based on a VMD (variational mode decomposition) algorithm, hyper-parameters are adaptively adjusted and optimized by using a PSO (particle swarm optimization) algorithm based on a ground surface settlement data set, the model prediction precision is maximized, and a ground surface settlement prediction model is obtained; and analyzing decision logic of the ground surface settlement prediction model through the SHAP value, and outputting interpretable engineering guidance suggestions. By constructing a machine learning hybrid model, high-precision and real-time prediction of ground surface settlement in the whole process of tunnel excavation is realized. The precision and generalization ability of the model are improved, the characterization ability of complex spatial-temporal characteristics is enhanced, and overfitting is avoided; and the interpretability is optimized, and the influence of key parameters on a prediction result is quantified, so that construction parameter adjustment is guided.

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 particularly relates to a laser point cloud over-break and under-break volume calculation method for tunnel engineering, and relates to the technical field of tunnel engineering construction quality detection. Modeling a tunnel design contour; matching and aligning the point cloud with the design model; carrying out over-break and under-break area division and volume calculation; and result visualization and report output. According to the method, point cloud preprocessing parameter optimization and high-precision registration are achieved through multi-dimensional quantitative analysis, and the reliability of an over-break and under-break calculation benchmark is remarkably improved; in a point cloud denoising link, a voxel evaluation coefficient is introduced to ensure that the filtered point cloud not only keeps key details such as tunnel vault radian and side wall broken lines, but also can simplify the data volume; in a model registration stage, outlier interference is eliminated and a strict iteration stop condition is set, so that a reliable reference is provided for back break deviation calculation.

The invention relates to the technical field of civil engineering, and particularly discloses a tunnel early-high-strength rock anchoring and shotcreting synergistic effect bearing arch active support design method which comprises the following steps: determining a rock anchor bearing arch acting load and establishing a rock anchor bearing arch calculation model; calculating to obtain the most unfavorable section displacement and stress of the rock anchor bearing arch; verifying whether the displacement of the rock anchor bearing arch meets the requirement; a safety evaluation method of the pre-stressed anchor rods is provided, and the safety of the pre-stressed anchor rods is evaluated according to a rock anchor bearing arch calculation result; establishing a sprayed concrete calculation model; calculating to obtain the most unfavorable section displacement and stress of the sprayed concrete; a sprayed concrete safety and durability evaluation method is provided; active support parameters are determined according to the calculation result of the rock anchor bearing arch and the calculation result of the sprayed concrete. By means of the method, the safety and durability of rock-anchor-shotcrete active supporting under different supporting parameters can be simply, rapidly and accurately calculated, reference is provided for tunnel engineering design adopting active supporting, and good engineering value is achieved.

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.

The invention discloses a shield muck volume estimation method and system based on image processing, and particularly relates to the technical field of tunnel engineering monitoring, and the method comprises the steps: obtaining muck RGB and depth images through an image collection device, generating high-precision three-dimensional point cloud data through the combination of laser scanning and a multispectral technology, and calculating the muck volume through a slicing method. And fusing the mass flow and water content data of the belt weigher, dynamically generating a calibration factor, and correcting a volume estimation result in real time. The system comprises an image acquisition module, a point cloud processing module, a volume calculation module and a dynamic calibration module, has the characteristics of automation, high precision, real-time feedback and the like, effectively solves the problems of large error and slow response of a traditional manual metering and single weighing mode, and is suitable for continuous and accurate monitoring of the volume of muck in the shield construction process.

The invention discloses crack-chloride ion double-triggered subsea tunnel corrosion-resistant concrete and a preparation method thereof, and relates to the field of subsea tunnel engineering materials. The concrete is prepared from the following components in parts by weight: 400 to 500 parts of Portland cement, 50 to 80 parts of fly ash, 60 to 100 parts of superfine mineral powder, 1000 to 1100 parts of coarse aggregate, 700 to 800 parts of fine aggregate, 140 to 160 parts of water, 10 to 20 parts of self-repairing microcapsules and 1 to 5 parts of a water reducing agent, the self-repairing microcapsule comprises a capsule wall material, a self-repairing material and a layered dihydroxyl composite metal oxide. A crack-chloride ion double-response system is constructed, and the limitation of traditional single trigger repair is broken through. When a crack with a certain width appears in the concrete, the capsule wall of the self-repairing microcapsule is broken to release the repairing component, so that effective filling of the crack is realized; and meanwhile, the layered double-metal hydroxide can directionally adsorb chloride ions to form a stable compound, so that the migration rate of the chloride ions is remarkably reduced.

The invention discloses a tunnel-landslide mass stability evaluation method based on space-time multi-scale coupling, and belongs to the field of tunnel-landslide masses, and the method comprises the following steps: S1, extracting a feature parameter set and an abnormal event mark set; s2, constructing a three-dimensional geological-mechanical model; s3, space-time multi-scale modeling of multi-physics field coupling is carried out; s4, outputting a stability prediction level by using the constructed ISSA-LSTM-GARCH combined prediction model; s5, generating an early warning in combination with an association rule engine; and S6, knowledge updating and systematic evolution of closed-loop optimization are carried out. According to the tunnel-landslide mass stability evaluation method based on space-time multi-scale coupling, multi-source monitoring data and a multi-physical field coupling model are fused, landslide mass stability under the influence of tunnel engineering is accurately and comprehensively analyzed through high-precision positioning and parameter inversion optimization, and powerful support is provided for engineering decision making.

The invention relates to a rock shear failure whole process prediction method based on acoustic emission monitoring, which comprises the following steps: firstly, collecting a rock sample, processing the rock sample into a plurality of rock test pieces, manufacturing an acoustic emission mold, secondly, installing an acoustic emission test platform, carrying out multiple acoustic emission tests of the rock shear failure whole process by using the acoustic emission test platform, and recording data of the multiple acoustic emission tests, the method comprises the following steps: selecting and calculating calculation parameters of a rock shear failure whole-process prediction model based on experimental data, finally constructing the rock shear failure whole-process prediction model based on an XGBoost algorithm, inputting the calculation parameters of the rock shear failure whole-process prediction method into the model, and outputting a rock shear failure prediction result. According to the method, the stability of the rock mass can be continuously monitored in real time, different stages in the direct shear process of the rock are predicted in a classified mode, and the safety and reliability of rock mass engineering such as tunnel engineering can be improved.

The invention relates to the technical field of tunnel engineering monitoring, in particular to a tunnel abnormal data early warning method based on an LSTM model. Comprising the following steps: S1, data preprocessing; s2, building a model, wherein the structure of the model comprises two layers of LSTM networks and one layer of full connection layer; s3, model hyper-parameters are selected; s4, network training: performing parameter iteration training through optimization calculation and loss calculation; s5, establishing a dynamic prediction model: continuously inputting measured data, and comparing the measured data with a mean square error standard value to judge whether parameters are retrained or not; and S6, early warning judgment: calculating a difference value between a predicted value and a measured value of the LSTM model, comparing the difference value with a preset threshold value, and carrying out graded early warning. Compared with the prior art, the method has the advantages that the prediction curve is more stable, the calculation standard deviation is larger, the prediction confidence interval range is wider, the change of the data trend can be predicted in advance, the change of the data trend can be identified in advance, and the early warning advancement is better.

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 relates to the technical field of tunnel engineering safety monitoring, in particular to an autonomous controllable three-dimensional tunnel gushing analysis method and system. The method comprises the following steps: collecting multi-source heterogeneous geological, hydrological and construction data, fusing to obtain multi-source heterogeneous data, and constructing a dynamically updated three-dimensional geological-construction coupling digital twinborn model after alignment coding; carrying out multi-scale physical-AI hybrid modeling, correcting real-time data, carrying out water gushing evolution rolling prediction, and outputting prediction data; a three-dimensional visual platform is constructed, and multi-level linkage early warning is started based on a risk threshold value; the system comprises a data fusion module, a water gushing evolution rolling prediction module and a multi-stage linkage early warning module. By fusing geological data, hydrological monitoring data and construction parameters and combining three-dimensional visual rendering and deep learning fluid simulation technologies, the requirements for rapid early warning and timely decision making of water burst disasters in the tunnel construction process are met, and intelligent support is provided for engineering disaster prevention decision making.

The invention belongs to the field of tunnel engineering, and particularly discloses a tunnel surrounding rock multi-mode intelligent fine grading method and system, and the method comprises the steps: 1, constructing a sample library which comprises parameters while drilling of a tunnel face, a digital image and a corresponding tunnel face surrounding rock grade; 2, constructing a while-drilling parameter refined grading characteristic system; 3, constructing a surrounding rock intelligent grading model based on multi-modal data fusion; 4, dividing the rock mass in front of the tunnel face into a plurality of three-dimensional space unit bodies according to the tunnel contour and blast hole distribution characteristics; and 5, based on the surrounding rock intelligent grading model, finely and intelligently judging the grade of the tunnel face surrounding rock. According to the method, the intelligent surrounding rock grading model based on the multi-modal data fusion theory is constructed, the refined intelligent judgment of the tunnel face surrounding rock grade is realized, the precision and robustness of the refined intelligent judgment of the surrounding rock grade of the model are improved, the surrounding rock grading level in the construction stage is improved, more comprehensive reference is provided for a subsequent construction scheme, and the construction efficiency is improved. And tunnel intelligent construction is guided.

The invention discloses a cold region tunnel freeze injury diagnosis, risk grading and control system and method, and relates to the technical field of tunnel engineering. The system comprises a basic data layer for storing disease, index and measure databases; the core analysis layer is used for diagnosing a frost heaving mechanism, identifying single-factor, double-factor and three-factor frost heaving types, calculating freezing depth and frost heaving force and analyzing sensitivity; the risk level evaluation layer is used for dividing a disease zone, a frost heaving level, a freezing injury risk and a freezing injury risk response level; and the intelligent decision-making layer constructs an intelligent comprehensive decision-making analysis platform to realize risk-measure accurate matching and closed-loop management and control. The method comprises the four steps of data acquisition and storage, frost heaving mechanism diagnosis and quantification, risk grade evaluation and intelligent decision measure matching, through multi-source data fusion, multi-factor coupling analysis and zoning and grading prevention and control, the whole-process accurate management and control of the cold region tunnel frost damage is realized, and the frost damage treatment efficiency and the tunnel operation safety are improved.

The invention relates to the technical field of tunnel engineering monitoring, in particular to a tunnel abnormity early warning and trend prediction method and system. Comprising the following steps: S1, data collection and arrangement; s2, tunnel health monitoring data anomaly recognition: monitoring a long-term change trend in a sequence by taking an LSTM method as a main method, and giving an early warning signal before an anomaly occurs; an ARIMA method is used as a supplement, a relatively strict threshold value judgment method is set, a sequence short-term change trend is monitored, and an early warning signal is timely identified and sent out, so that application and implementation of an overall data anomaly identification algorithm are realized; s3, decomposing tunnel health monitoring data elements, and constructing a prediction model; and S4, performing spatial-temporal change feature analysis on the decomposed elements. Compared with the prior art, the method is more suitable for a real-time anomaly cleaning algorithm of tunnel health monitoring data, a system constructed based on the whole method achieves dynamic graph drawing of measured values and model predicted values through input tunnel monitoring point historical numerical values and a model of a built-in algorithm, dynamic early warning of the tunnel health monitoring data is more visually reflected, and the method is more suitable for real-time anomaly cleaning of tunnel health monitoring data. And necessary services are provided for a tunnel intelligent operation and maintenance management platform.