4397 results about "Tunnel construction" patented technology

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 provides a tunnel construction safety monitoring and early warning method and system based on multi-dimensional data fusion, and relates to the technical field of construction safety early warning, and the method comprises the steps: obtaining first information which comprises tunnel construction parameters and image information; performing space-time fusion processing and redundancy reduction processing on the first information to generate a four-dimensional hypergraph tensor space of tunnel construction; performing cross-scale analysis and topology extraction on the four-dimensional hypergraph tensor space, and constructing a dynamic heterogeneous graph network; performing space-time diagram convolution processing on the dynamic heterogeneous graph network, and generating a composite risk manifold based on a result obtained by processing and a multi-head attention mechanism; coupling processing is carried out according to the composite risk manifold, stability analysis and critical state judgment are carried out based on the risk phase change hypersurface obtained through processing, and real-time early warning is carried out based on a dynamic early warning boundary of tunnel construction obtained through judgment. According to the invention, the risk identification accuracy and the early warning response timeliness in the tunnel construction environment are 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 discloses a BIM-driven tunnel construction full life cycle management method and system, and relates to the technical field of industrial data processing and analysis, and the method comprises the steps: building a BIM model covering the full life cycle of a tunnel and a data mapping linkage relation; collecting monitoring data, accessing the monitoring data to the BIM model, and performing dynamic linkage evaluation to obtain a construction state result; and constructing a safety management mechanism according to an evaluation result and feeding back field guidance. The technical problem that in tunnel construction full-life-cycle management, a traditional industrial data processing mode cannot achieve full-life-cycle integration and cross-stage linkage of multi-source industrial data in the stages of design, construction and operation and maintenance is solved. The technical effects of full-life-cycle integration and cross-stage linkage processing of the industrial data in tunnel design, construction and operation and maintenance stages, improvement of the comprehensiveness, collaboration and timeliness of industrial data processing, and accurate acquisition of the construction state to optimize on-site safety management are achieved.

The invention provides a mountainous area tunnel construction safety intelligent monitoring and early warning method and system, and relates to the technical field of construction safety monitoring, and the method comprises the steps: collecting visible light and depth images of tunnel surrounding rock, and carrying out the segmentation and extraction of crack features through a depth attention network after image preprocessing and data fusion; extracting parameter time sequence data based on the crack spatial position and the type feature; determining fracture evolution characteristics and critical state parameters by combining wavelet transform and stress-rate coupling analysis; and adopting deep reinforcement learning to calculate the instability probability and generate early warning information. According to the invention, intelligent identification, instability prediction and risk early warning of tunnel surrounding rock cracks are realized, and the safety monitoring accuracy and early warning timeliness are improved.

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

The invention discloses a BIM-based full-period management method and system for tunnel construction organization optimization, and belongs to the technical field of construction organization and cost management. The management method comprises the following steps: establishing a three-dimensional geometric model and a parameterized model of a tunnel based on a BIM technology; performing work decomposition on a construction task, and calculating a construction activity time parameter; calculating the cost of materials, machinery and manpower of each construction unit, and summarizing the total cost of the project; optimizing a construction period and resource allocation under a multi-constraint condition by adopting a genetic algorithm; predicting future process time consumption and construction progress based on an LSTM time sequence prediction model; and integrating dynamic data of the construction site, and updating the BIM model in real time. Through combination with dynamic data, efficient organization and scientific management of the whole tunnel construction process are realized, the construction efficiency can be improved, the engineering cost can be reduced, and the scientificity and rationality of project decision making are remarkably improved.

The invention provides a shield tunnel construction period segment longitudinal dynamic floating prediction method, and relates to the field of shield tunnel segment floating prediction.The method comprises the steps that the static upward floating force and the dynamic upward floating force of a shield tunnel are calculated by considering the timeliness of slurry, and the characteristic of nonlinear change of the rigidity of a circular seam connector is considered; the method comprises the following steps: dividing bending-resistant and shearing-resistant deformation modes of a circular seam joint, further deducing bending rigidity and shearing rigidity of a tenon-type joint in each deformation stage, deducing a shield tail segment longitudinal floating prediction model of the circular seam joint in the shield tunnel construction period, and constructing a nonlinear iterative algorithm of shield tunnel longitudinal deformation according to the theories; and acquiring parameter data of actual engineering, and inputting the parameter data into the duct piece floating prediction model to obtain a duct piece floating value. By means of the method, accurate prediction of floating deformation of the shield tail segment in large shield tunneling can be achieved, an effective scientific basis is provided for large-diameter shield tunneling construction, the problems that parameter decision of shield tunneling machine operators is difficult, and the segment floating control effect is poor are solved, and structural diseases caused by too large floating of the shield tail segment are eliminated.

The invention relates to the technical field of construction management, in particular to a tunnel construction progress real-time tracking system which comprises an environment monitoring module, a progress acquisition module, an anomaly recognition module, a risk assessment module and a resource allocation module. According to the method, through identification of the time sequence and the change amplitude of the tunnel excavation face environment parameters, pre-screening of potential progress interference factors, enhancement of influence factor perception ability and dynamic comparison of environment data and process progress are achieved, the process disjunction phenomenon can be captured in time, and the aging control precision is improved; through linkage analysis of operation time and equipment operation records, association analysis of abnormal positioning, scheduling response and progress offset section resource data is facilitated, operation node-level dynamic evaluation is achieved, allocation imbalance and high-frequency mismatched resources are avoided, operation time sequences are adjusted preferentially, field bottlenecks are relieved, and operation efficiency is improved. And real-time closed-loop linkage between the construction progress and resource configuration is enhanced through information integration and correlation analysis.

The invention discloses a drilling and blasting method tunnel construction safety risk evolution analysis method based on a knowledge graph, and the method comprises the steps: obtaining and preprocessing the text data of a drilling and blasting method tunnel construction accident case, and obtaining a safety risk corpus; a mixed deep learning model is constructed, the safety risk corpus is trained through the mixed deep learning model, and the risk factors and the incidence relation of drilling and blasting method tunnel safety are obtained; constructing a drilling and blasting method tunnel construction safety risk coupling evolution analysis knowledge graph based on the risk factors and the incidence relation, and obtaining a key accident chain and a key edge in combination with a knowledge query and path reasoning algorithm; and quantitatively evaluating independent values and coupling effect intensity of the risk factors by using an interaction matrix algorithm, calculating comprehensive importance scores of the risk factors, and determining key risk points according to the comprehensive importance scores of the risk factors. The problems that in a traditional method, attention to the risk factor coupling effect is insufficient, and accident case data are not fully utilized are effectively solved.

The invention relates to the technical field of tunnel construction monitoring, and discloses a tunnel construction deformation monitoring system based on machine vision and a monitoring method thereof. According to the method, real-time image acquisition is carried out on a tunnel construction structure to obtain a tunnel surface image sequence, and meanwhile, displacement parameters and strain parameters of the tunnel structure are monitored; performing initial deformation prediction according to the displacement parameter and the strain parameter to obtain a predicted deformation parameter; acquiring key area images in the tunnel by means of machine vision equipment, and inputting the key area images into a plurality of deformation feature recognizers respectively by combining the predicted deformation parameters to recognize and obtain actual deformation features; and finally, final deformation trend analysis is performed in combination with the predicted deformation parameters and the actual deformation characteristics, and a tunnel deformation monitoring result is obtained. According to the method, pertinence, comprehensiveness and accuracy of deformation monitoring are improved, the deformation state of the tunnel structure can be reflected more accurately, and an effective technical means is provided for safety monitoring of tunnel construction.

The invention discloses a tunnel construction progress digital twinborn prediction and resource scheduling system and method, relates to the technical field of tunnel construction, and provides the following scheme that the method comprises the steps that real-time monitoring data of a tunnel construction area are collected, the real-time monitoring data comprise equipment position information and process progress quantitative data, and a BIM model of the tunnel construction area is constructed; dividing the equipment position information into a plurality of continuous time windows according to time nodes of the BIM model, and obtaining equipment operation parameters and corresponding energy consumption values in each time window; when the effective energy consumption is calculated, the geological condition grade weight and the construction process reference value are introduced to dynamically correct the initial efficiency coefficient, and meanwhile, the geological condition fluctuation, the equipment performance attenuation and the process connection change in the construction conditions are reflected through the time attenuation coefficient, so that the nonlinear influence of the geological difference and the process adjustment on the energy consumption efficiency is effectively eliminated; the strong relevance between the energy consumption and the progress is established, and a reliable data association basis is provided for progress prediction.

The invention belongs to the technical field of tunnel construction monitoring, and particularly relates to a tunnel primary support deformation detection method and system based on three-dimensional point cloud. The method comprises the following steps: carrying out adaptive feature enhancement processing on an original tunnel initial supporting point cloud, and outputting an enhanced point cloud; constructing an edge vector field of double-plane geometric constraint based on the enhanced point cloud; based on the edge vector field, through a joint objective function containing registration item and deformation detection item weights, an alternating direction multiplier method is adopted to synchronously optimize and solve point cloud registration parameters and a preliminary screening deformation point set; calculating the displacement consistency confidence coefficient through orthogonal projection decomposition by applying the optimal registration parameter and the preliminary screening point set, and performing cross validation and outputting a final deformation point set; and establishing an autoregression model with curvature constraint based on the final deformation point set to carry out primary support deformation spatio-temporal evolution prediction and early warning. According to the method, the precision and reliability of initial support deformation detection in a complex tunnel environment are remarkably improved, registration error transmission is effectively inhibited, and the long-term deformation prediction capability is enhanced.

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 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 embodiment of the invention discloses a tunnel construction scene traffic scheduling method and system based on intelligent traffic, and the method comprises the steps: obtaining a vehicle state data set of each traffic node in a tunnel construction region, carrying out the dynamic scheduling processing of the vehicle state data set, generating a traffic light control instruction set corresponding to each traffic node, scheduling strategy generation processing is executed according to the traffic light control instruction set, a global traffic scheduling strategy of the tunnel construction area is obtained, and the global traffic scheduling strategy is fed back to a traffic light control system of the tunnel construction area so as to indicate the traffic light control system to execute switching operation of traffic node passing states according to a preset time window. Therefore, the problems of scheduling instruction lagging and low resource utilization rate can be improved.

The embodiment of the invention discloses a feature recognition labeling method and system applied to three-dimensional tunnel modeling, and the method comprises the steps: firstly carrying out the preprocessing of an obtained tunnel construction design drawing and point cloud scanning data of a corresponding construction stage, and obtaining a three-dimensional tunnel model data set with a space coordinate alignment relation; the three-dimensional tunnel model data set comprises a multi-source data fused structural point cloud unit and a design drawing vectorized contour line unit; secondly, performing three-dimensional structure feature extraction on the data set to obtain geometric morphology features of a structure point cloud unit and structure correlation features of a contour line unit; then carrying out conjoint analysis on the two features through a deep learning network to generate a feature analysis result containing feature response distribution; and finally, based on the result, identifying and labeling a key structure region in the three-dimensional tunnel model data set, generating a feature identification and labeling result containing feature type labels and spatial position information, and providing more accurate analysis and management basis for tunnel construction.

The invention discloses a tunnel rockburst type prediction method and system based on multi-dimensional mechanism fusion and a medium. Relates to the technical field of tunnel rockburst. Dynamically acquiring multi-dimensional basic information in a tunnel construction process; inverting first proportions of different fracture modes of the micro-seismic event according to the micro-seismic information fusion moment tensor, and performing energy evolution on the acoustic emission information to obtain second proportions of different fracture modes of each acoustic emission event; the dynamic failure weights of different fracture modes are comprehensively obtained; spatial clustering analysis is carried out based on the dynamic damage weight and the multi-dimensional basic information, rockburst type probability models of different rockburst types are constructed, and the probabilities of different rockburst types are predicted; according to the scheme, the rockburst type probability model is constructed in combination with real-time multi-dimensional basic information, dynamic judgment and prediction of rockburst types are achieved, and technical guarantee is provided for deep tunnel construction safety.

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 deep-buried tunnel TBM vibration and excavation unloading fracture micro-seismic discrimination method and system, and relates to the technical field of tunnel construction, and the method comprises the steps: obtaining micro-seismic event data in a target area; spatial classification is carried out according to the micro-seismic event data, and the influence range of the excavation unloading effect is obtained; performing region division according to the influence range to obtain a rock fracture signal after noise reduction; performing multi-layer decomposition on the rock fracture signal by using wavelet transform to obtain a characteristic parameter set; performing energy grading according to the characteristic parameter set to obtain a grading result; constructing a discrimination standard according to the grading result to obtain the discrimination standard; and performing discrimination based on the discrimination standard to obtain a discrimination result. According to the method, the dynamic model is constructed based on the multi-energy-level discrimination standard, main control factor classification is performed on the real-time micro-seismic event, real-time dynamic discrimination of TBM vibration and excavation unloading fracture in the construction process is achieved, and the early warning timeliness of time-delay rock burst is remarkably improved.

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 discloses an intelligent tunnel construction safety monitoring and early warning system and method, and belongs to the technical field of tunnel engineering monitoring. The method comprises the steps that construction quality data and environment data in the tunnel construction process are collected; performing data preprocessing on the data, extracting key features from the preprocessed data for analysis, and generating a daily data analysis report; generating a dynamic threshold of the construction quality data based on a dynamic adaptive threshold optimization model DATOM; and adjusting an early warning trigger condition according to the real-time construction stage label, and sending alarm information to terminal equipment when the construction quality data and the environment data deviate from the threshold value and reach the early warning condition. According to the method, the requirements of different construction stages on dynamic change of the monitoring parameter threshold can be met, and the safety monitoring and early warning precision is relatively high.

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 belongs to the technical field of tunnel engineering, and provides a multi-physics field real-time assimilation simulation and regulation method and system in a tunnel grouting process in order to solve the problem that real-time dynamic simulation and automatic regulation are lacked in existing tunnel construction, and the real-time assimilation simulation and regulation method and system in the tunnel grouting process are provided by utilizing ensemble Kalman filtering and combining real-time monitoring data in the tunnel grouting process. Dynamically correcting parameters of the multi-physical model; time correlation in the slurry condensation process is considered, a time-varying condensation model depicting physical property changes of slurry evolving along with time is integrated, the time-varying condensation model serves as an external function in the time step length to be embedded into the multi-physical field model in correction, and the slurry flowing state is adjusted in a self-adaptive mode through numerical simulation; and generating control parameters of tunnel grouting according to a dynamic simulation result, and realizing closed-loop regulation and control of tunnel grouting. Synchronous linkage of numerical simulation and on-site working conditions is realized.

The invention discloses a tunnel face back break visual analysis method based on laser point cloud, and the method comprises the steps: carrying out the scanning operation of a tunnel through a laser scanner, and collecting the specific original rock three-dimensional point cloud model data in a tunnel construction circulation process; exporting the acquired original rock mass three-dimensional point cloud model data, and performing denoising preprocessing on the original rock mass three-dimensional point cloud model data to obtain tunnel rock mass three-dimensional point cloud model data; the rock mass three-dimensional point cloud model data also need to be subjected to space compression processing, so that the quality of the rock mass three-dimensional point cloud model data is improved; based on the rock mass three-dimensional point cloud model data, a tunnel actually-measured excavation section after construction is obtained; and weighting and calculating tunnel over-excavation and under-excavation values based on the point-to-pole distance between the tunnel design section and the tunnel actually-measured excavation section. According to the method, the back-break depth is directly calculated through the point cloud data and is bound as the point cloud attribute, so that the analysis efficiency and the result intuition are remarkably improved.

The invention discloses an intelligent monitoring method, device and system for tunnel construction gas, and the method comprises the steps: collecting multi-source gas data in real time through a mixed gas monitoring network disposed in a tunnel construction environment, the mixed gas monitoring network comprises a fixed gas monitoring node, a mobile gas monitoring node and a personnel wearable monitoring node; based on the multi-source gas data, constructing a three-dimensional gas concentration field dynamic model covering the whole tunnel construction area by using a data fusion and machine learning algorithm; on the basis of the three-dimensional gas concentration field dynamic model, the spatio-temporal evolution trend of the gas concentration in a future preset time period is predicted, at least one control instruction is generated and executed according to the prediction result, and the control instruction comprises an intelligent early warning instruction and / or a ventilation system adjusting instruction. According to the intelligent monitoring method, device and system, traditional post-event alarm is converted into pre-warning and active intervention, the safety of tunnel construction is remarkably improved, and the emergency response speed is remarkably increased.

The invention is suitable for the field of expressway construction, and provides an expressway tunnel construction safety early warning method and system, and the method comprises the steps: collecting the real-time deformation data of a building structure body and the historical deformation data of the building structure body in a targeted tunnel construction process; acquiring information of a stratum where the targeted tunnel is located; constructing a tunnel strain field distribution prediction model based on the historical deformation data of the building structure body; calculating a deformation control index threshold value based on the information of the stratum where the targeted tunnel is located and historical deformation data of the building structure body; based on the tunnel strain field distribution model and the deformation control index threshold, predicting the deviation trend of the building structure body; based on the deviation trend of the building structure body, early warning prompt information is sent, and comprehensive monitoring and accurate prediction are conducted on targeted tunnel construction. Through real-time data acquisition and analysis, potential safety hazards can be found in advance, and the construction safety is greatly improved. And a scientific quantitative basis is provided for construction by constructing a model and calculating a threshold value, so that the construction quality can be improved.

The invention relates to a CFD-based in-situ 3D printing shield secondary lining constructability optimization method, and belongs to the technical field of tunnel construction. The method comprises the following steps: constructing a joint simulation platform, calibrating the size of a single-layer secondary lining, establishing a CFD simulation geometric model, setting boundary conditions of a physical field, simulating a printing process, and optimizing a construction process based on a simulation result. According to the method, the quality source items of concrete layer-by-layer stacking in the in-situ 3D printing secondary lining process are loaded and simulated by configuring the UDF, and the single-layer secondary lining size calibration result is synthesized, so that the function relationship between the quality source items and absolute coordinates in a closed calculation domain is established; according to the in-situ 3D printing shield secondary lining construction method, the effect of multi-layer printing of fresh concrete along with movement of a spray head is simulated, the overall efficiency of modeling simulation is improved, good universality is achieved, and an in-situ 3D printing shield secondary lining construction scheme with higher constructability can be planned according to different tunnels.