461 results about "Tunnel safety" patented technology

The present invention relates to a deformation dynamic prediction and early warning method for a tunnel under construction based on multi-source data fusion, and mainly solves the technical problem of large difficulty in evaluating tunnel safety states using macroscopic analysis. The present invention comprises: step 1, dividing dynamic prediction and early warning of an entire tunnel construction process into three stages; step 2, determining a safety level standard framework, and constructing an index system; step 3, constructing identification intervals of each index of an index layer in different safety states; step 4, specifying a relative weight distribution of each index; step 5, calculating distances from an interval to the identification intervals for different safety states on the basis of an interval distance formula, and generating interval distance vectors; step 6, converting the interval distance vectors into similarity vectors on the basis of a similarity formula, carrying out normalization processing, and constructing an analysis matrix; step 7, implementing same-level index data fusion by calculating a product of a relative weight vector of each index of the same layer and the analysis matrix layer by layer, and obtaining a safety level probability distribution of an entire section of a tunnel at a time point; and step 8, dynamically tracking a dominant state of a tunnel safety level, and performing dynamic prediction and early warning on the entire tunnel construction process.

The invention discloses a tunnel fire position positioning and fire source risk analysis method and system, and relates to the technical field of tunnel fire detection, and the method comprises the steps: periodically collecting image, temperature, smoke, harmful gas concentration, infrared radiation and humidity data at each monitoring point in a tunnel, and dynamically adjusting the monitoring frequency according to the humidity; feature extraction is performed on the collected data, a factor set and a fuzzy evaluation set for fire judgment are constructed, and a fuzzy algorithm is utilized to calculate the membership degree of each feature to fire evaluation; a comprehensive membership value vector is generated in combination with the feature weight and is used for judging whether a fire occurs or not and performing preliminary positioning; initial monitoring equipment and front and back adjacent equipment are set, and the fire position is further accurate through fuzzy analysis of data of the front and back adjacent equipment; performing risk assessment on the fire according to the fire position and the abnormal pixel point proportion; intelligent detection, precise positioning and risk analysis of the tunnel fire are realized, and the tunnel safety management level is effectively improved.

The invention belongs to the technical field of tunnel safety monitoring, and particularly relates to a rock stratum stress-deformation coupled tunnel safety real-time dynamic modeling method, which comprises the following steps of: embedding a rock mass mechanical relationship into a neural network model as a hard constraint, and establishing explicit mapping of monitoring data and a physical field, a model parameter field is dynamically optimized by adopting ensemble Kalman filtering real-time general field detection data, and a data driving and physical mechanism collaborative deduction mechanism is designed, so that long-term prediction error accumulation is effectively inhibited; a stress field, a deformation field and other physical quantities deduced and output by the model are directly utilized to calculate disaster risk indexes with clear mechanical significance, and a physically interpretable early warning decision is realized by fusing fuzzy reasoning and multi-source risks; the technical problems of physical misalignment, weak long-term generalization and poor early warning interpretability of a data-driven model are solved.

The invention discloses a subway tunnel structure service performance analysis method and device, and the method comprises the steps: building a prediction model database based on physical experiments and numerical simulation, and determining main factors which affect the service process of a tunnel structure through an interpretable machine learning method; through coupling with a physical equation, the service performance of the tunnel structure is accurately evaluated under the small sample condition, and the characteristic that in an existing machine learning method, the prediction precision is not high under the small sample condition, and a black box is avoided; according to the tunnel structure service performance prediction model constructed by the invention, the subway service performance (safety performance) can be qualitatively evaluated objectively, so that the service state of the lining structure is monitored, evaluated and pre-warned, and the service performance of the tunnel structure can be predicted under the evaluation framework of monitoring-analysis-evaluation-pre-warning-disposal-decision making. Problems existing in a current structure are identified and processed, and a solid technical guarantee is provided for tunnel safety and continuous service.

The invention discloses a tunnel crack detection method and a tunnel crack detection system, and belongs to the field of tunnel safety detection.The tunnel crack detection method comprises the steps that a tunnel inspection robot is utilized to conduct inspection in a tunnel to obtain complete three-dimensional data in the tunnel so as to construct a three-dimensional map in the tunnel, and the position of each crack area in the three-dimensional map is determined; determining an inspection tour area corresponding to each crack area and executing path planning, and generating an optimal inspection tour path of the unmanned aerial vehicle; the optimal inspection path enables the unmanned aerial vehicle to sequentially pass through each inspection area and then leave the tunnel after entering the tunnel under the condition that obstacle avoidance conditions are met, and the total time is shortest; enabling the unmanned aerial vehicle to move according to the optimal inspection path, and scanning the crack region by using the depth camera when the unmanned aerial vehicle moves to one inspection region so as to obtain fine three-dimensional data of the crack region; and performing analysis processing on the fine three-dimensional data of the crack area to complete tunnel crack detection. According to the invention, the efficiency and precision of long tunnel crack detection can be improved.

The invention relates to the technical field of highway tunnel safety monitoring, and discloses a highway tunnel intelligent fire safety early warning system, which comprises a data acquisition and preprocessing module used for acquiring video monitoring data, environment sensor data and vehicle positioning data in a tunnel in real time; the dynamic risk assessment module is used for constructing a risk field model based on vehicle trajectory and smoke diffusion space-time coupling; the emergency resource elastic adaptation module is used for automatically adjusting the induction screen and the fire fighting equipment according to the risk intensity; according to the method, the limitation of traditional threshold alarm is avoided, fire early warning nodes are greatly advanced through multi-source data dynamic fusion, long-term stability of a monitoring benchmark is guaranteed by utilizing an optical flow compensation and pollution self-repairing mechanism, and the self-adaptive migration of a calculation task is realized by utilizing the optical flow compensation and pollution self-repairing mechanism. And the initiative and reliability of tunnel safety prevention and control are obviously improved.

The invention provides a bridge and tunnel geological disaster monitoring method and system based on an unmanned aerial vehicle, and relates to the technical field of geological disaster monitoring, and the method comprises the steps: obtaining the data of a sensor carried by the unmanned aerial vehicle; performing space-time alignment and confidence coefficient weighting on the three-dimensional point cloud data, the image data and the flight attitude data through a space-time weighted covariance fusion model to obtain fusion deformation feature data; inputting the data into a wavelet-Volterra series coupling prediction algorithm, performing nonlinear deformation prediction on the key part, and outputting a future displacement prediction value; generating a geological disaster risk probability graph according to the predicted value and a preset risk threshold value; and based on the probability graph, dynamically optimizing the flight speed, height and flight path of the unmanned aerial vehicle through a Bayesian adaptive flight path planning module, and outputting a real-time monitoring path instruction. Through multi-modal fusion and dynamic path planning, the geological disaster monitoring precision and the cruising ability of the unmanned aerial vehicle are improved, and the method is suitable for safety protection of bridges and tunnels in complex terrains.

The invention discloses a gas pipeline and subway safety distance optimization method based on numerical simulation, and relates to the technical field of subway tunnel safety, and the method comprises the steps: simulating gas pipeline explosion data through a KFX Exsim gas explosion model; establishing a fluid-solid coupling model, a soil dynamic model and a thermodynamic coupling model, and coupling the three models to obtain a multi-physics coupling model; based on a multi-physics field coupling model, performing decomposition and region division on a numerical simulation task in combination with a parallel computing framework, and simulating the influence of gas pipe explosion on the subway; based on a numerical simulation result, evaluating the safety of the tunnel under the action of the explosion load; based on a safety evaluation result, optimizing a safety distance between the gas pipeline and the tunnel; according to the method, the fluid-solid coupling model, the soil dynamic model and the thermodynamic coupling model are established and coupled to form a multi-physical field coupling model comprehensively covering the explosion environment, and accurate simulation of complex behaviors of the subway tunnel in the explosion environment is achieved.

The invention provides a high-speed rail tunnel safety monitoring method and system. The method comprises the following steps: carrying out normalization processing on original time series data to construct a multi-dimensional time series data set; the multi-dimensional time sequence data set is input into a pre-trained LSTM model, the structural state of the tunnel key part in a future time window is predicted, and the structural state comprises the surrounding rock deformation trend, the lining stress peak value, the water seepage risk grade and the crack severity grade; and according to the prediction result of the structure state and a preset mapping relationship between the structure state and the early warning level, triggering hierarchical early warning, and generating a corresponding tunnel maintenance strategy instruction. According to the technical scheme provided by the invention, intelligent sensing, accurate prediction and active maintenance decision making of the tunnel structure state can be realized, and the timeliness and preventability of high-speed rail tunnel safety management are improved.

The invention provides a goaf tunnel lining structure safety evaluation method and system under heavy rainfall, and relates to the technical field of underground and tunnel engineering.The method comprises the steps that a tunnel evaluation index is generated by obtaining rainfall intensity, rainfall duration, geological characteristics, soil permeability and other data; the method comprises the following steps: analyzing interaction of rainfall moisture on lining water pressure and surrounding soil deformation, calculating surrounding rock stress, calculating water pressure generated after rainfall permeation, quantifying dynamic influence of the water pressure on the lining water pressure, and calculating a safety coefficient of a lining structure by integrating a tunnel evaluation index, the surrounding rock stress and the water pressure after permeation. And obtaining lining displacement, crack development speed and water flow permeation data, generating a risk coefficient, comparing the risk coefficient with a safety coefficient, and judging whether to give out early warning or not. According to the method, related data of the tunnel lining are comprehensively analyzed, a tunnel safety evaluation and early warning mechanism is established, and the safety of the goaf tunnel lining structure under the heavy rainfall condition is ensured.

The invention provides a tunnel event and warning lamp acousto-optic linkage strategy generation system based on AI decision, and relates to the technical field of intelligent traffic, and the method comprises the steps: collecting tunnel traffic event data, vehicle structured data, vehicle coordinates, vehicle flow, vehicle speed, and brightness data inside and outside a tunnel in real time, so as to obtain multi-source heterogeneous data; according to the multi-source heterogeneous data, taking the data matching degree, the space-time consistency and the event confidence as multi-objective optimization dimensions, constructing a weighted objective function, generating a Pareto optimal solution set through a multi-objective optimization algorithm, screening the solution set based on space-time constraint conditions, and removing conflict hypotheses; and determining the final event type and the structured event description of the three-dimensional geographic coordinates. The problems of large illumination energy consumption and poor system linkage in existing tunnel safety management are solved.

The invention relates to the technical field of geotechnical engineering and risk assessment, discloses a tunnel base subsurface erosion disaster-caused risk assessment method and system, and aims at solving the problems that in the prior art, due to the fact that static expert experience is relied on, the dynamic adaptability of a model is insufficient, and complex incidence relation cognition is limited. Comprising the steps that multi-source heterogeneous monitoring data are collected and preprocessed, a tunnel space relevance model based on a graph neural network is constructed, subsurface erosion process time sequence dynamics is learned in combination with a gating circulation unit network, and subsurface erosion disaster-causing risk indexes are generated; and when the risk exceeds a threshold value, deconstructing the risk factor by using an SHAP algorithm. Through adoption of the technical scheme, accurate, dynamic and interpretable evaluation of tunnel base subsurface erosion disaster-caused risks can be realized, the accuracy, timeliness, objectivity and practicability of evaluation are improved, and intelligent decision support is provided for tunnel safety operation and maintenance.

The invention relates to the technical field of tunnel defect identification, in particular to a tunnel defect identification method based on a dielectric distribution diagram. According to the method, on the basis of AI rock-soil perspective radar field monitoring, gprMax simulation and laboratory electromagnetic data, a two-dimensional dielectric distribution diagram is generated through variational Bayesian inversion fusion. Through multi-scale spectral clustering and expert knowledge, a potential abnormal region is automatically identified, and then accurate classification and identification of a defect region are realized by adopting graph form constraint propagation and integrating a Transform-graph neural network model. And finally, projecting an identification result to an original image, and generating a visual defect labeling layer and a structured report. According to the invention, high-precision, automatic, visual and structured detection of tunnel structure defects is realized, the intelligent level and risk early warning capability of tunnel safety operation and maintenance are significantly improved, and the digitization and intelligent process of tunnel operation and maintenance management is promoted.

The invention discloses a weak surrounding rock tunnel deformation intelligent monitoring method and system. The monitoring method comprises the following steps: acquiring original time sequence data of tunnel deformation monitoring; decomposing the original time sequence data to obtain decomposed time sequence data; obtaining a feature vector set based on the decomposed time sequence data; the feature vector set is input into a tunnel deformation monitoring model, a tunnel deformation monitoring result is obtained, the tunnel deformation monitoring model is constructed through a support vector regression model and is obtained through training of a training set, and the training set is historical monitoring data. Through fusion application of various algorithms, the tunnel deformation monitoring precision and reliability are remarkably improved, and powerful support is provided for safe operation of the tunnel.

The invention discloses a comprehensive intelligent monitoring decision-making system and method for an overlapping shield tunnel system. The system comprises an information acquisition system, an iterative optimization state real-time evaluation system and an intelligent maintenance decision-making system. Firstly, a preset model is trained through data such as numerical calculation; operating measured data to train a feedback model, and weighting output results of the two models to obtain a tunnel deformation predicted value; then establishing an experience overlapping tunnel safety evaluation system and an iterative optimization evaluation system, performing deep association on the tunnel state, damage and maintenance decision through association degree intelligent real-time analysis, realizing tunnel state real-time monitoring, tunnel damage real-time capturing and tunnel maintenance decision real-time updating, performing intelligent matching on maintenance measures, and performing real-time monitoring on the tunnel state, the tunnel damage and the maintenance decision. And the prediction model and the safety evaluation system are iteratively optimized according to operation effect feedback. According to the invention, real-time perception, intelligent diagnosis and maintenance of the whole life cycle overall service performance of the overlapping shield system are realized, and the long-term service quality of the overlapping shield tunnel structure is improved.

The invention belongs to the technical field of underground engineering construction, and provides a shield tunneling parameter optimization method and system based on historical data mining, and the technical scheme is as follows: constructing a shield tunnel geologic model based on an obtained geological survey report and advanced geological forecast data; tunneling safety grading is conducted on the shield tunneling surrounding rock based on the shield tunnel geologic model, and a dynamic geologic model is obtained; based on the dynamic geologic model, the site construction environment and related specifications, set shield construction task information is obtained; according to the dynamic geologic model and set shield construction task information, corresponding shield tunneling parameter information is determined, shield tunneling efficiency under the current shield tunneling parameters is analyzed, if the shield tunneling efficiency does not reach the set level, the shield tunneling parameters are adjusted according to the shield tunneling efficiency condition under the current shield tunneling parameters, and the shield tunneling efficiency is adjusted according to the shield tunneling efficiency condition under the current shield tunneling parameters. And guiding field construction according to the optimized shield tunneling parameters. The shield tunneling efficiency is improved, and the engineering cost is reduced.

The invention relates to the technical field of tunnel safety monitoring, in particular to a tunnel potential safety hazard detection method and system based on point cloud data, and the method comprises the steps: employing a three-dimensional laser scanning device, carrying out the movable scanning in a tunnel along the longitudinal direction, and obtaining the original three-dimensional point cloud data of a tunnel lining surface structure; denoising and registering the original three-dimensional point cloud data to generate an overall three-dimensional point cloud model representing the current lining structure state of the tunnel; inputting the overall three-dimensional point cloud model into a trained tunnel crack recognition neural network model, and recognizing and positioning cracks of the tunnel lining surface structure; and quantitative calculation of geometric parameters is carried out based on the identified crack, and a tunnel crack analysis report is generated. According to the method, automation, quantification and intelligentization of tunnel crack detection are realized, the three-dimensional geometric parameters of the crack can be accurately calculated, and the time sequence development of the crack can be tracked and the future trend can be predicted.

The invention discloses an intelligent tunnel multi-modal data collaborative management method and device based on deep learning, and relates to the technical field of artificial intelligence, and the method comprises the steps: determining the spatial topological relation of a multi-source heterogeneous sensor in a tunnel, mapping the multi-modal data to a digital twin unified coordinate system, and carrying out the timestamp compensation, thereby obtaining a digital twin unified coordinate system; and multi-modal alignment data is formed. And extracting space-time and topological features of the data, calculating a feature correlation degree, embedding the data into a tunnel semantic space according to the feature correlation degree, evaluating environmental complexity and generating a modal weight coefficient. And carrying out safety evaluation on the joint feature vector obtained by weighting, and judging the safety state of the tunnel, thereby overcoming the limitation of single-mode monitoring in a complex environment.

The invention provides a coal mine roadway surrounding rock stability monitoring and early warning method and device, and relates to the technical field of roadway safety monitoring. Acquiring a plurality of monitoring values respectively corresponding to each monitoring index monitored by a plurality of continuous observation stations of the roadway in the current monitoring period; based on the plurality of monitoring values of each monitoring index and historical monitoring data of each observation station, determining a plurality of parameter values corresponding to each monitoring index of each observation station; determining a parameter threshold value of each parameter value of each monitoring index; determining a risk level corresponding to each monitoring index of each observation station based on a parameter threshold value of each parameter value of each monitoring index and each parameter value of the corresponding monitoring index of each observation station; and determining the risk level distribution of the roadway based on the risk level corresponding to each monitoring index of each observation station. Therefore, scientific monitoring and early warning of roadway surrounding rock instability are realized, and timeliness and accuracy of coal mine roadway surrounding rock stability monitoring and early warning are improved.

The invention relates to the technical field of subway tunnel safety detection, in particular to a YOLO-V8 subway tunnel water leakage detection method and system based on an improved Transform. The method comprises the following steps: step 02, acquiring point cloud data of a subway tunnel target section, expanding the point cloud data through projection, generating a two-dimensional water leakage image, and preprocessing the two-dimensional water leakage image; step 04, inputting the preprocessed two-dimensional water leakage image into a pre-trained water leakage detection model, and outputting a detection result based on the water leakage detection model; and 06, according to a detection result output by the water leakage detection model, determining the position and severity of the water leakage disease in the target section of the subway tunnel. According to the method, higher water leakage detection robustness identification precision can be kept in a dark and disordered subway tunnel environment, and the method has a high practical value.

The invention relates to the technical field of intelligent transportation, and particularly discloses a multi-sensor data fusion method based on adaptive Kalman filtering, which is suitable for vehicle trajectory tracking in a tunnel scene. The method comprises the steps of sensor deployment and data acquisition, data preprocessing and anomaly detection, multi-target data association, adaptive filtering fusion and state estimation. By introducing an anomaly detection mechanism and dynamically adjusting a filtering estimation error covariance, the influence of abnormal data such as a multipath effect and electromagnetic interference is effectively suppressed, and the tracking precision and stability of the position, speed and trajectory of the vehicle are improved. The method is remarkably superior to a traditional filtering algorithm in a complex tunnel environment, has the technical advantages of high-precision tracking, high robustness, high adaptability, high real-time performance, comprehensive functions and the like, and can remarkably improve the reliability and performance of a tunnel vehicle monitoring system; the method is suitable for multiple key fields of tunnel safety early warning, accident prevention, traffic flow optimization, emergency response and the like.

The invention discloses a tunnel trolley supporting system and method intelligently adapting to geological conditions, and belongs to the technical field of tunnel safety supporting. The device comprises a supporting truss, a rigid supporting mechanism and a flexible supporting mechanism. Wherein the flexible supporting mechanism comprises an air bag which is used for preliminarily supporting the surrounding rock of the tunnel; the rigid supporting mechanism comprises a mounting frame, a plurality of air chambers are arranged in the mounting frame, and adjusting assemblies are arranged in the air chambers; the adjusting assembly comprises a piston plate and a piston rod located on the piston plate, and the free end of the piston rod penetrates through the mounting frame, then extends into the air bag and is connected with a supporting plate. The supporting plates are used for carrying out secondary supporting on the tunnel surrounding rock from the interior of the air bag, the supporting pressure of each supporting plate can be adjusted through the inflation pressure in the corresponding air chamber so as to adapt to different geological conditions, good self-adaptive capacity is achieved, and dynamic distribution of the surrounding rock pressure is achieved.

The invention provides a traffic tunnel preventive monitoring and risk early warning system based on an unmanned aerial vehicle group, and relates to the technical field of traffic tunnel safety monitoring, and the method comprises the steps: laying reference mark points on the inner surface of a tunnel as monitoring nodes, collecting the crack width, surface temperature and deformation of each node through the unmanned aerial vehicle group, and calculating the crack width, surface temperature and deformation of each node; and constructing a multi-dimensional feature vector and calculating a space-time weighted distance in combination with the space coordinates and the timestamps. And adjusting a neighborhood search radius according to the tunnel curvature, identifying a risk region through a density clustering algorithm, and marking unclustered abnormal nodes as isolated risk points. And dividing risk levels based on comparison of the structure safety degree index and a dynamic threshold value, and registering a result with the BIM model to realize three-dimensional visualization. Trend early warning is triggered for an area with monotonically decreasing monitoring safety index for three continuous times, full-process automatic monitoring from data acquisition, intelligent analysis to risk early warning is realized, and the accuracy and timeliness of tunnel structure health monitoring are remarkably improved.

The invention provides a tunnel blasting vibration reduction method and system based on asymmetric slotting, relates to the technical field of geotechnical engineering numerical simulation, and provides an integrated intelligent blasting vibration reduction system of asymmetric slotting structure optimization-quantum genetic algorithm collaborative optimization-model dynamic regulation. Triple fusion of a physical mechanism, a mathematical algorithm and an information technology of proximity tunnel blasting vibration control is creatively achieved, the bottleneck that a traditional method depends on experience trial and error, model static lag and vibration control and construction efficiency are difficult to consider at the same time is broken through, vibration speed control precision is improved, tunneling efficiency is improved, and meanwhile the construction efficiency is improved. The dynamic adaptability to complex geological conditions is remarkably enhanced, and a new generation of integrated solution is provided for safe, efficient and intelligent construction of the adjacent tunnel.

The invention discloses a rock mass fracture early warning method for a hydraulic tunnel, belongs to the technical field of tunnel safety monitoring, and can solve the problem that an existing method cannot specifically recognize isolated impact noise and rock mass real fracture signals in the construction period of the hydraulic tunnel under the background of strong noise. The method comprises the following steps: S1, acquiring an original micro-seismic signal synchronously acquired by a sensor array in a hydraulic tunnel; s2, detecting a micro-seismic event from the original micro-seismic signal to obtain an event signal set, and determining a reference channel signal according to the event signal set; s3, extracting cross correlation coefficient peak values of event signals of each channel in the event signal set and reference channel signals, and counting the number of channels of which the cross correlation coefficient peak values exceed a cross correlation coefficient threshold value as the number of coherent channels; and S4, calculating a frequency band energy ratio of the reference channel signal, and generating a rock mass fracture early warning signal when the number of coherent channels and the frequency band energy ratio meet preset conditions. The method is used for rock mass fracture early warning of the hydraulic tunnel.

The invention discloses a track type tunnel safety monitoring and emergency response inspection robot, and belongs to the technical field of tunnel safety in traffic safety, all-weather monitoring is carried out through cameras and various environmental sensors, the system can detect abnormal conditions such as illumination, temperature, smoke and flame in a tunnel in real time, an alarm is quickly given out, and the safety of the tunnel is ensured. The system effectively improves the coverage range and response speed of safety monitoring in the tunnel, can automatically position flames and launch dry powder balls for fire extinguishing emergency treatment when a fire disaster is detected, gives an alarm to the rear vehicle through the loudspeaker, has independent emergency response capability, and is safe and reliable. And the inspection robot moves on the side wall of the tunnel based on the track, and the track type movement ensures that the robot can cover the whole tunnel area according to a preset path, so that accurate and comprehensive monitoring is realized, and blind areas are reduced.

The invention discloses a tunnel detection data informatization management platform based on the Internet of Things. The tunnel detection data informatization management platform comprises a data acquisition module, a data processing module, an informatization management module, a real-time early warning module and a linkage feedback module. Through the synergistic effect of the five modules, various state data in a tunnel can be collected, the collected data are subjected to standardization processing and fusion association, the data standardization degree is improved, data islands are eliminated, and information can be efficiently integrated, so that the sharing level and the deep analysis and utilization level are improved; the method is advantaged in that effective decision support is provided for tunnel management operation, effective processing and analysis can be further carried out on integrated data, key information can be rapidly and accurately extracted from massive detection data, comprehensive evaluation and prediction of the tunnel structure safety state are facilitated, and increasing tunnel safety operation requirements are satisfied.

The invention relates to the technical field of tunnel engineering, and provides a smoke exhaust design method for an adjacent tunnel with a limited boundary of a shading shed, and the method comprises the steps: obtaining the critical smoke exhaust amount of a smoke exhaust system of a target tunnel section, determining the optimal smoke blocking height of a fan-shaped smoke blocking plate according to the critical smoke exhaust amount, obtaining the rated smoke exhaust amount of each smoke exhaust assembly, and obtaining the optimal smoke blocking height of the fan-shaped smoke blocking plate; the number of smoke exhaust assemblies is determined according to the critical smoke exhaust amount and the rated smoke exhaust amount, and the smoke exhaust system is configured according to the number of the smoke exhaust assemblies and the optimal smoke blocking height of the fan-shaped smoke baffles. After the smoke exhaust system is started, the number of fans needing to be increased in the downstream tunnel can be determined according to the actual fluid channeling ratio. The safety of the downstream tunnel can be improved, the evacuation environment of the downstream tunnel can be improved, the number of fans required by the downstream tunnel can be reduced, and the engineering cost is saved. According to the obtained number of the draught fans, the evacuation environment of the downstream tunnel can be effectively improved, excessive number of the draught fans can be prevented, and the engineering cost is effectively optimized.

The invention discloses a tunnel safety assessment method based on a deep learning technology, and the method comprises the steps: collecting safety monitoring data in a tunnel, and carrying out the preprocessing of the collected safety monitoring data; outputting time sequence characteristics based on a long short-term memory (LSTM) network and / or a Transform model of a converter; local space feature extraction is carried out based on a convolutional neural network CNN; outputting global structure features based on a graph neural network GNN; performing joint feature extraction on the time sequence features, the local spatial features and the global structure features based on a multi-input deep neural network DNN and / or a gated loop unit GRU model, and outputting joint features; and classifying the joint features based on a support vector machine (SVM) and / or the DNN, and outputting a safety assessment level of the tunnel. Through effective fusion of multiple deep learning models, multi-dimensional data features can be comprehensively considered, and the accuracy and real-time performance of tunnel safety assessment are improved.

The invention discloses a shield tunnel segment relative displacement monitoring system and a calculation method, and relates to the technical field of civil engineering. The system comprises an optical fiber fixing point, a zigzag optical fiber, an optical fiber demodulator and a computer, the optical fiber fixing point is used for fixing an optical fiber at a set position on the tunnel segment; the zigzag optical fibers are arranged in three optical fiber arrangement modes to form an optical fiber monitoring network of two parallel optical fibers at the upper part and the lower part of the segment and a V-shaped optical fiber in the middle; the optical fiber demodulator is respectively connected with the zigzag optical fibers in the three layout forms and the computer, and is used for acquiring the strain magnitude of the three zigzag optical fibers in the tunnel deformation process, obtaining the length variation of the optical fibers among the optical fiber fixing points, and calculating the opening amount, the slab staggering amount and the corner deformation among the segments. According to the invention, opening, slab staggering and corner deformation of tunnel segments can be comprehensively monitored in real time, so that tunnel risks and hidden dangers can be monitored more comprehensively, and the safety and stability of a tunnel are ensured.