2202 results about "Tunnel construction" patented technology

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 belongs to the technical field of tunnel construction geological disaster prevention and control, and discloses a multi-source data fused refined treatment decision-making method for a complex stratum disaster source, which comprises the following steps: collecting and fusing multi-source geological data, and constructing a three-dimensional geological model; generating a disaster source risk dynamic assessment and treatment scheme; based on a fluid-solid coupling similarity theory, verifying the preliminary treatment scheme by adopting a physical model test, and determining an optimal treatment scheme; the optimal treatment scheme is executed, and the treatment process is dynamically regulated and controlled; after treatment, the treatment effect is evaluated through posterior data, and the effect data is fed back to the three-dimensional geologic model and the knowledge base, so that the dynamic updating of the model and the self-learning of the decision-making system are realized. By the adoption of the treatment decision method, the problems that a traditional method depends on experience, information is one-sided, and treatment is extensive are solved, advanced accurate forecasting and refined and personalized treatment of complex stratum disaster sources are achieved, and the safety and efficiency of tunnel construction are remarkably improved.

The invention discloses a tunnel ventilation control system and method based on artificial intelligence, and relates to the technical field of ventilation control, and the method comprises the steps: in a tunnel design stage, building an unsteady-state computational fluid dynamics model based on tunnel three-dimensional linear parameters, calculating turbulence structures under different traffic conditions through a large eddy simulation method, and calculating an unsteady-state computational fluid dynamics model; determining an optimal space configuration scheme of the fan group according to the distribution of the velocity field and the pressure field; in the tunnel construction stage, multiple types of sensor arrays are arranged along the vault and the side wall of a tunnel in a layered mode; in the tunnel operation stage, real-time vehicle tracks and speed distribution information of a traffic monitoring system are obtained; establishing a ventilation demand dynamic prediction model based on space-time correlation analysis, constructing a fan cooperative control model considering airflow organization optimization, and solving an optimal operation strategy by adopting a multi-target adaptive weight distribution algorithm; when a fire characteristic signal is monitored, the multiple sets of fans are coordinated to form a relay type smoke exhaust airflow organization.

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 provides a concrete super-early-strength quick-hardening material, and relates to the field of building materials. The high-strength concrete comprises the following components in percentage by mass: 40%-50% of calcium aluminate, 30%-40% of gypsum, 8%-15% of high aluminum, 1%-3% of an additive, 3%-12% of an exciting agent and 1%-5% of an auxiliary material. According to the super-early-strength quick-hardening material provided by the invention, hour-level strength can be quickly and stably improved, and the strength increasing curve of each age is smooth and stable, so that the requirements of scenes such as road repair and tunnel construction on early strength can be met; it can be guaranteed that the 28-day compressive strength of the material breaks through 70 MPa, the later strength is kept stable, and the long-term safety of an engineering structure is guaranteed; the anti-seepage, anti-freezing and anti-erosion capabilities are realized in a complex environment, the service life of a project can be effectively prolonged, and the maintenance cost is reduced. In addition, the invention further provides a preparation method of the concrete super-early-strength and quick-hardening material, and the preparation method is used for preparing the super-early-strength and quick-hardening material provided by the invention.

The invention provides a tunnel deformation early warning method and system based on digital twinborn and data monitoring, and belongs to the technical field of computer data simulation.The method comprises the steps that firstly, entity rock stratum dynamic monitoring data, real-time construction disturbance data and initial digital twinborn model configuration parameters of a tunnel construction area are obtained; and then virtual-real dynamic coupling and feedback iteration processing is carried out on the virtual-real dynamic coupling model to generate an initial virtual-real coupling digital twinborn model, the initial virtual-real coupling digital twinborn model is iteratively optimized based on real-time construction disturbance data to obtain a final virtual-real coupling digital twinborn model, and deduction of the rock stratum deformation linkage effect is carried out according to the final virtual-real coupling digital twinborn model. The method comprises the following steps: generating a rock stratum deformation linkage path set, performing risk linkage mode identification on the rock stratum deformation linkage path set, determining a tunnel rock stratum large deformation risk linkage mode, and finally generating construction early warning information including a risk linkage diffusion area, a risk linkage evolution period and an emergency response priority according to the risk linkage mode, and transmitting the construction early warning information to a tunnel construction management system. And the tunnel construction safety is effectively guaranteed.

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 relates to an intelligent soft rock tunnel surrounding rock deformation automatic monitoring and early warning system, and belongs to the field of tunnel construction safety monitoring. According to the system, multi-source data of surrounding rocks are synchronously acquired through a micron-sized laser displacement sensor, a fiber grating strain sensor, a pore water pressure sensor and a three-dimensional micro-seismic sensor array which are distributed; the edge calculation node fuses the data in real time and calculates the displacement rate, the strain gradient and the osmotic pressure coupling coefficient, and local early warning is triggered in combination with a dynamic threshold value; the cloud early warning platform predicts a deformation trend based on an LSTM-Transform fusion model, generates a multi-stage early warning signal through a risk grading engine, and is linked with the supporting equipment to regulate and control pressure; and meanwhile, a self-calibration device, an explosion-proof shell and a piezoelectric-thermal energy self-powered system are integrated. According to the method, closed-loop control from millimeter-level deformation sensing to active supporting can be achieved, and tunnel construction safety is remarkably improved.

The invention discloses an existing subway structure stress deformation assessment method and system under urban tunnel proximity construction, and the method comprises the steps: collecting stratum physical and mechanical parameters, existing subway structure design parameters and tunnel construction key parameters of an urban tunnel proximity construction influence region, and carrying out the integration to form a three-dimensional basic database; the method comprises the following steps: arranging equipment at key monitoring points to collect and preprocess real-time data, establishing a three-dimensional geology-structure numerical model based on a database, constructing an inversion algorithm by taking the preprocessed data as a target function, dynamically inverting real-time stratum parameters, and updating the database. The method comprises the following steps: constructing a stress deformation prediction model by combining real-time stratum parameters, inputting parameters to solve stress and deformation values of structures in different construction stages, setting an early warning threshold according to safety levels and specifications, comparing and outputting early warning levels, calculating model errors regularly, and supplementing sample retraining to realize dynamic iterative optimization if the early warning levels exceed the range. And safety and stability of the existing subway structure during construction are effectively guaranteed.

The invention relates to a tunnel construction progress model generation method based on artificial intelligence. The method comprises the following steps: acquiring multi-source heterogeneous data such as tunnel field equipment operation and geological change, and performing multi-modal fusion to form a unified data set; based on this, a particle filter algorithm is used to correct equipment positioning deviation, and a real-time position optimization result is generated. Comparing the time and space with the progress plan, and identifying and quantifying deviation points through a critical path method and a deviation analysis algorithm to form deviation data. And constructing a construction progress model by combining construction basic parameters and adopting a genetic algorithm, generating an adjustment scheme by taking a construction period and resources as targets, and obtaining an optimal scheme through compliance screening and fitness calculation iterative optimization. By the adoption of the method, efficient integration of heterogeneous data can be achieved, the equipment positioning precision is improved to the centimeter level, multi-dimensional quantification of progress deviation is achieved, progress deviation recognition and resource allocation efficiency is improved, and a technical scheme is provided for tunnel intelligent construction.

The invention relates to the field of tunnel engineering, in particular to a tunnel multi-physics field coupling simulation and support design method considering construction disturbance, which comprises the following steps of: modeling a disturbance source, collecting disturbance factor information introduced in a tunnel construction process or an external environment, establishing a disturbance source model to represent the spatial position, intensity and evolution characteristics of a disturbance source; constructing a three-dimensional disturbance propagation function; coupling the three-dimensional disturbance propagation function with a multi-physical field model, and introducing disturbance as a source item into a tunnel heat-water-force coupling model; according to the construction method customization mechanism, modeling parameters and solving strategies are adjusted according to the characteristics of different tunnel construction methods, and corresponding disturbance source model parameters, boundary conditions and constitutive relation adjustment coefficients are set according to the different construction methods. The problems that disturbance modeling is rough, a disturbance propagation mechanism is idealized, the disturbance propagation mechanism is disjointed with a multi-physical field model, and a construction method is not included in a disturbance response system are solved.

The invention relates to the technical field of tunnel construction, and discloses a coal mine goaf karst tunnel construction method which comprises the following steps: S1, performing three-dimensional scanning on the axis and the periphery of a tunnel by adopting a high-frequency geological radar in combination with aerial survey of an unmanned aerial vehicle, and accurately positioning the boundary of a goaf, the development form of a karst cave and filler distribution; and S2, integrating a seismic wave reflection method, drilling and coring data and a geological sketch result, constructing a three-dimensional geological model, and dynamically predicting a potential water inrush and mud inrush risk area. According to the method, a high-frequency geological radar is combined with aerial survey of an unmanned aerial vehicle, three-dimensional scanning is conducted on the axis and the periphery of a tunnel, geological radar data, a point cloud model and tunnel design BIM are integrated, the boundary of a goaf (the error is smaller than or equal to 0.3 m) and the karst cave form are marked, and the collapse risk level is predicted based on rock mass mechanical parameters (compressive strength and fracture density) and hydrological data (seepage paths). Therefore, the method has the advantages of advanced detection, multi-source data fusion and the like, and the resolution of geological forecast is improved.

The invention provides a time-space evolution data processing method and system applied to tunnel deformation analysis, and the method comprises the steps: obtaining a time sequence monitoring sequence of different monitoring dimensions in a tunnel construction process, constructing a time-space evolution correlation model of tunnel deformation based on the time sequence monitoring sequence, and analyzing a dynamic coupling relation between structure response data and geological environment action data, generating a space-time correlation feature matrix, generating a feature evolution sequence of tunnel deformation based on the space-time correlation feature matrix, and performing nonlinear mutation feature extraction on the feature evolution sequence to obtain a mutation feature index set; according to the sudden change characteristic index set and a preset accumulated deformation rate threshold value, performing dynamic division of tunnel deformation safety levels, generating a safety level division result, and based on the safety level division result and the spatial distribution data of the tunnel construction section, executing spatial positioning and boundary contour extraction of the potential risk damage area. And obtaining a risk damage area distribution map. According to the invention, the tunnel construction safety risk can be accurately evaluated and effectively controlled.

The invention provides a shield tunneling parameter intelligent optimization method and system based on an AI big language model, and relates to the technical field of tunnel construction optimization. Comprising the following steps: training an open source large model to obtain a preliminary shield tunnel construction industry large model; an upstream task and a downstream task which are relatively independent are set, and the upstream task comprises a tunneling speed prediction module, a tool wear prediction module, a ground surface settlement prediction module and a tunneling attitude prediction module; a downstream task combines an RAG technology and an NSGA-III multi-objective optimization framework, four prediction modules and an energy consumption calculation formula serve as objective functions, the value range of constraint conditions is determined, a tunneling parameter optimization module is established, a professional shield tunnel construction industry large model is formed, and scientific and intelligent decision making of tunneling parameters is achieved. According to the method, information in multiple aspects can be comprehensively considered during decision making, the reasoning process is output, and scientificity, comprehensiveness and reliability of decision making are ensured.

The invention relates to the technical field of tunnel and underground engineering construction, and discloses an intelligent closed-loop control system and method for a tunnel construction section contour, and the method comprises the steps: collecting geological-geometric data through the fusion of a three-dimensional laser scanner and a geological radar before blasting; dynamically generating a theoretical blasting contour line and adaptive blasting parameters through a convolutional neural network; iMU positioning and a PID controller are combined to realize millimeter-level accurate control of the drill boom; generating a three-dimensional deviation field by adopting an iterative point cloud registration algorithm after blasting; when the deviation exceeds a threshold value, triggering incremental learning to optimize model parameters; and finally, closed-loop iteration is formed. Through multi-source data fusion, contour and parameter intelligent generation, precise execution control, three-dimensional deviation field feedback and an incremental learning mechanism, a complete intelligent closed-loop control system is constructed, the limitation of traditional blasting design depending on experience is broken through, and the precision, efficiency and economic benefits of tunnel construction are remarkably improved.

The invention provides a tunnel three-dimensional geologic body modeling method and system based on in-hole camera shooting and while-drilling lithology perception, and relates to the technical field of tunnel and underground engineering and intelligent geological information processing.The method comprises the steps that firstly, a drill hole wall optical image sequence and while-drilling engineering parameters are synchronously collected and preprocessed respectively; then establishing a depth-time mapping relation to realize accurate alignment of the multi-source data in a depth domain; then fracture intelligent identification and parameter extraction are realized through an improved U-Net network, and lithology classification is completed by using an XGBoost model; feature level and decision level fusion is carried out on the fracture features and the lithologic features, and rock mass quality grades are generated; and finally, constructing an integrated geologic model fusing lithologic distribution and a three-dimensional fracture network by adopting an implicit modeling and discrete fracture network technology. According to the method, the problem of splitting of in-hole visual information and while-drilling physical information is solved, automation and precision of geological recognition are improved, and reliable geological information support is provided for tunnel construction safety and support design.

The invention discloses a tunnel construction method for a large deformation section of soft rock, and relates to the technical field of tunnel construction for the large deformation section of the soft rock, and the tunnel construction method for the large deformation section of the soft rock comprises the following steps: drilling shallow large-aperture stress release holes and deep small-aperture stress release holes to form a layered release system; differential pressure grouting is conducted around the stress release holes, and a deep reinforcing ring and a shallow coupling ring are formed; a steel frame is erected, and a three-stage stretching locking bottom supporting assembly is installed to form a closed supporting ring; the bottoms of the adjacent steel frames are crossly connected through prestressed pull rods to form an integral rigid grid; a secondary lining is constructed after the longitudinal attenuation conduction period is judged according to the surrounding rock deformation spatial-temporal characteristics; inverted arch first and then arch wall segmented pouring is adopted, and a delayed expanding agent is doped into arch wall concrete. The method can effectively control surrounding rock deformation in the tunnel construction process of the large deformation section of the soft rock, improves the overall rigidity and stability of a supporting system, ensures that the lining is tightly attached to the surrounding rock, and reduces the construction risk.

The invention relates to a complex soft rock double-layer primary supporting super-large section tunnel and a construction method. The complex soft rock double-layer primary supporting super-large section tunnel comprises a long pipe shed cover arch supporting system, a deformation control strong supporting structure, an anti-disengaging early-warning lining pouring trolley, a temporary inverted arch deformation control structure and a buffering yielding supporting system. The method has the beneficial effects that the curved surface formwork can be rapidly installed on the upper portion of the guide pipe and the lower portion of the steel arch frame, the installation efficiency is high, stability is good, and the construction period is shortened; a long pipe shed and a small guide pipe penetrate through the deformation control strong supporting structure and are driven into a tunnel, grouting is conducted, the overall stability of the supporting structure is enhanced, and the complex soft rock geology is reinforced; concrete pouring is monitored in real time, and the compactness of vault concrete is guaranteed; section steel is clamped in the inverted arch, reinforcing connecting ribs are welded between the section steel in a penetrating mode, surrounding rock collapse is prevented, and tunnel construction safety is guaranteed. And a buffer yielding support device is arranged between the interface and the double-layer arch frame, so that the pressure at the interface of the layered rock and soil stratum is in smooth transition, and the overall balance is improved.

The invention belongs to the technical field of tunnel construction, and discloses an upper-soft lower-hard composite stratum shield hob impact load determination method and system.The method comprises the steps that rock is divided into a plurality of concentric circle areas through a cavity expansion model, the radius of each area is determined, and the relation between the damage states of different positions of rock mass and the distance position of the center of a blade is obtained; the influence of section penetration is introduced, and based on the influence of the damage difference between a vertical crack area and a dense nuclear area in the hob rock breaking process, the damage state of the hob section away from the center of a blade within the range of a hob lower contact area and the damage distribution state of the hob lower hard rock side in the interface vertical crack expansion process are determined; and determining an expression of stress change of the transition section based on a CSM model, and realizing impact load prediction when the shield hob passes through the upper-soft and lower-hard composite stratum in combination with the influence of damage of the lateral shearing region. The damage effect and cavity expansion model theory is introduced for the first time, the compatibility of the prediction model and the actual situation is improved, and the abnormal damage rate of the hob is reduced.

The invention relates to the technical field of shield tunnel construction intelligent control, in particular to a shield tunneling machine tunneling speed intelligent prediction method based on multi-algorithm collaborative optimization. The method comprises the following steps: firstly, acquiring operation parameters and geological and environmental parameters of the shield tunneling machine in real time through a multi-source sensor and an industrial bus; carrying out localized cleaning, normalization and feature extraction on the data by utilizing an edge computing device; further, integrating particle swarm optimization, a genetic algorithm, a sparrow search algorithm and a starvation game search algorithm, and realizing global automatic optimization of the hyper-parameters of the bidirectional long-short-term memory network through a parallel independent optimization and result aggregation strategy; and finally, predicting the tunneling speed in real time by using the optimized model. According to the method, the problems of low prediction precision, weak model generalization capability, dependence on manpower on hyper-parameter optimization and the like caused by insufficient multi-source heterogeneous data processing capability are effectively solved, the prediction accuracy, the adaptability and the engineering practical value are improved, and reliable support is provided for safe and efficient propulsion of shield construction.

The invention discloses a tunnel reinforcing steel bar trolley with a single-layer pull net and a construction method of the tunnel reinforcing steel bar trolley. The trolley comprises a portal assembly, a feeding mechanism and a supporting mechanism. The feeding mechanism comprises arch-shaped rails arranged at the two ends of the portal assembly, first telescopic arms connected to the two arch-shaped rails in a sliding mode and a reinforcing steel bar clamping unit connected to the two first telescopic arms, and a plurality of reinforcing steel bar clamping stations are arranged on the reinforcing steel bar clamping unit in the longitudinal direction of the portal assembly at intervals. The supporting mechanisms are arranged on the portal assembly at intervals in the circumferential direction and comprise supporting rods and at least two second telescopic arms, the fixed ends of the second telescopic arms are connected to the portal assembly at intervals in the longitudinal direction, the supporting rods are connected with the telescopic ends of the second telescopic arms, and a plurality of steel bar positioning stations are arranged on the supporting rods at intervals in the longitudinal direction. The device is applied to the technical field of tunnel construction, laying and binding of outer-layer reinforcing steel bars and inner-layer reinforcing steel bars can be conducted in sequence through the single-layer pulling net, operation is convenient and safe, and the quality requirement for reinforcing steel bar binding can be effectively met.

The invention relates to the technical field of safety engineering, in particular to a tunnel unfavorable geologic body recognition system and method based on fused geological information, and the method comprises the steps: data collection and preprocessing, geological information quantification, sample set construction and enhancement, Faster-RCNN model construction of fused geological information, model training and optimization, and unfavorable geologic body intelligent recognition. The intelligent recognition of the unfavorable geologic body of the tunnel is realized by fusing the geological information and the seismic wave method imaging data and utilizing the convolutional neural network model, the recognition precision and robustness are effectively improved, and effective technical support is provided for tunnel construction safety early warning.

The invention discloses a full-circle needle beam trolley and method based on compressed air energy storage and relates to the technical field of tunnel construction, the full-circle needle beam trolley comprises a needle beam assembly, and supporting legs are arranged at the bottoms of the front end and the rear end of the needle beam assembly; the template assembly is arranged outside the needle beam assembly in the front-back direction of the needle beam assembly, and the template assembly at least comprises a first template group and a second template group; the beam frame assembly is arranged on the outer side of the needle beam assembly, and a supporting piece is arranged between the beam frame assembly and the template assembly so as to connect the beam frame assembly and the template assembly; guide mechanisms are arranged at the front end and the rear end of the needle beam assembly, so that the beam frame assembly drives the supporting piece and the template assembly to slide forwards and backwards along the needle beam assembly under the traction of the guide mechanisms. According to the embodiment of the invention, modular trolley, closed-loop sensing and automatic welding / grouting are realized, template positioning and welding seam and grouting quality are ensured, and leakage and reworking risks are reduced.

The invention discloses a tunnel segment joint quality high-precision detection method and system based on an image stabilization mechanical arm. The device comprises a circular track patrol platform, the self-adaptive image stabilization mechanical arm and a macro-micro fusion detection module. The circular rail cruising platform comprises a cruising platform pedestal and a guide rail, the guide rail is installed on an H-shaped beam at the tail of the shield tunneling machine through a cushioning connecting piece, the guide rail is of a circular structure and is located in the shield tunneling machine, and the cruising platform pedestal is arranged on the guide rail and can move in the circumferential direction of the guide rail; one end of the self-adaptive image stabilization mechanical arm is mounted on the cruising platform pedestal, and the macro-micro fusion detection module is mounted at the other end of the self-adaptive image stabilization mechanical arm, so that the macro-micro fusion detection module synchronously moves along with the cruising platform pedestal and is arranged facing a joint of a segment to be mounted and a mounted segment. The invention relates to the technical field of tunnel construction, and can solve the problems of low efficiency, high risk and lagging of manual detection and large interference, low precision and poor flexibility of fixed equipment detection in the prior art.

An anchor rod mounting trolley belongs to the technical field of tunnel construction and comprises a movable chassis, a grouting system and an operation system. The operating system comprises a working arm, a hanging basket device and a propelling device; the grouting system and the working arm are both installed on the movable chassis, the hanging basket device is installed on the working arm, and the propelling device is installed on the hanging basket device. The grouting system is used for injecting grout into an anchor rod hole; the working arm is used for adjusting the positions of the hanging basket device and the propelling device relative to the anchor rod hole; and the propelling device is used for inserting the anchor rod body into the grouted anchor rod hole. The mechanical degree can be improved, the labor intensity is reduced, the operation efficiency is improved, and the construction cost is reduced.

The invention provides a construction tunnel surrounding rock stability evaluation method based on digital twinning and a related device. The method comprises the following steps: constructing a tunnel digital twinning model of a tunnel construction section; geological data and real-time progress position data of a target mileage segment in the construction process are obtained; mapping the geological data to a digital twinborn model, and updating soil body unit parameters; according to the real-time progress position data, synchronously updating activation states of a soil body unit and a supporting structure unit in the model by adopting a state change method; and extracting a surrounding rock stability index based on the updated digital twinborn model, evaluating the stability of the current construction tunnel surrounding rock in combination with a safety threshold to obtain a construction tunnel surrounding rock stability evaluation result, and constructing a tunnel construction process dynamic mapping-oriented digital twinborn model by utilizing the description capability of a finite element method on structural mechanical behaviors. Geological conditions and construction progress information which are obtained in real time on site are used as input, and the construction period structure safety risk of the drilling and blasting method tunnel is evaluated and predicted.

The invention provides a karst tunnel construction transparent intelligent decision-making system, and relates to the field of tunnel construction intellectualization, and the system comprises a dynamic risk analysis and intelligent early-warning module which constructs a dynamic working condition portrait based on karst tunnel construction site information, and uses artificial intelligence and deep learning technologies to obtain a karst tunnel construction transparent intelligent decision-making module; constructing a karst tunnel risk comprehensive evaluation system by combining fuzzy hierarchical evaluation, a user-defined rule engine and expert opinions to calculate a dynamic risk entropy value, predicting a future dynamic risk entropy value by combining a sliding time window mechanism based on a deep learning model and the dynamic working condition portrait, and determining an early warning level according to the risk entropy value; and the intelligent scheduling and decision support module performs scheduling scheme optimization according to the early warning level and the intelligent optimization objective function, continuously monitors the execution condition of the scheduling scheme, and evaluates the execution effect of the scheduling scheme. And the transparency, datamation and intelligent management level of the whole karst tunnel construction process and the early warning capability and response efficiency of sudden geological disasters are improved.

The invention relates to the technical field of tunnel construction geological disaster early warning, and discloses a tunnel construction geological disaster early warning method and system. According to the method, multi-source geological monitoring data and environment dynamic parameters of a tunnel construction area are collected; and then through data fusion and feature engineering processing, data contradiction and redundancy are eliminated, and a standardized training data set is formed. And then training and verifying a disaster risk prediction model by using the data set and adopting a machine learning algorithm so as to obtain an optimized model to accurately predict the disaster risk. And performing risk value prediction on different geological structure units by applying the optimization model, generating a risk distribution map, and determining an optimal geological structure unit through a search algorithm. Based on this, numerical simulation of geological disaster triggering and propagation is executed, a disaster evolution rule is analyzed, and a path simulation data set is output. And finally, performing multi-objective optimization on the data set according to a safety early warning objective, generating a geological disaster early warning scheme, and performing iterative calibration.

The invention provides equipment and a method for testing the emission amount of harmful gas in a tunnel, and relates to the technical field of tunnel construction. Comprising a sleeve, a gas parameter testing unit installed on the sleeve, a water tank communicated with the interior of the sleeve and a gassing module installed on the water tank, and the gassing module is connected with the gas parameter testing unit; one end of the sleeve can be dynamically sealed with the drill rod, and the other end of the sleeve is of an opening structure; the gassing module is used for separating out gas in water and conveying the gas to the gas parameter testing unit; and the gas parameter testing unit is used for detecting the concentration and flow velocity of harmful gas in the sleeve and the concentration of harmful gas separated out from water. A gas parameter testing unit is arranged to monitor the data change of gas gushing out of the drill hole in real time; the gassing module can separate out gas in water in real time and convey the gas to the gas parameter testing unit to measure the concentration of dissolvable gas, so that the calculation of the emission amount is more comprehensive and accurate.

The invention discloses a tunnel gas outburst risk prediction method and system based on multi-source heterogeneous data, and relates to the technical field of safety monitoring and early warning of gas tunnel construction. The method comprises the steps that historical construction data of a gas tunnel are obtained, and influence factors related to the absolute emission amount of tunnel gas are determined; establishing a multi-dimensional gas absolute emission quantity prediction data set; building a deep learning prediction model of a parallel cross hybrid architecture based on a convolutional neural network and a Transform network; performing training optimization on the deep learning prediction model by using the multi-dimensional gas absolute emission prediction data set; and predicting to-be-detected gas tunnel construction data by using the optimized deep learning prediction model, and generating gas outburst risk grade prediction and early warning information in combination with a preset gas outburst risk grade threshold. According to the method, efficient, accurate and real-time gas outburst risk prediction is realized through a CNN-Transform framework.