2441 results about "Shield tunneling" patented technology

The invention discloses a seabed karst stratum tunnel shield data monitoring and analyzing method and system. The method comprises the steps that shield construction mechanical parameters, karst stratum acoustic emission signals and seawater osmotic pressure data are synchronously collected; shield construction mechanical parameters, karst stratum acoustic emission signals and seawater osmotic pressure data are subjected to seepage-stress coupling constraint, a hydraulic connectivity strength index is generated through fracture network seepage tensor inversion, and the filler migration risk level is judged based on pore water pressure dynamic mutation characteristics. And fusing the cutterhead torque frequency spectrum characteristic and the propulsive force fluctuation characteristic to generate a shield disturbance entropy value. According to the method, the hydraulic connectivity index is generated through seepage stress coupling inversion, the grouting path and the rheological parameters are dynamically optimized based on the fracture probability field, shield tunneling, seepage sudden change and grouting prevention and control cooperation is achieved, and the problem of prevention and control disjunction is radically solved.

The invention provides a shield tunnel longitudinal seam joint recessive damage diagnosis method based on multi-mode ultrasonic guided waves, and the method comprises the steps: applying orthogonal polarization in a specific region of a longitudinal seam joint, and forming a space-time coupled guided wave interference pattern in the whole region of the longitudinal seam joint; the acquired guided wave interference map is utilized to separate baseline interference characteristics and abnormal disturbance components caused by hidden damage through multi-physics field reverse decoupling, and the abnormal disturbance components are represented as energy abnormal attenuation and phase distortion in a specific polarization direction; constructing a three-dimensional damage sensitive vector field in the longitudinal seam joint; and carrying out dynamic manifold topology analysis by utilizing the generated three-dimensional damage sensitive vector field, identifying topological characteristics in the three-dimensional damage sensitive vector field, and tracking a migration path and an intensity evolution trajectory of the topological characteristics along with the change of the detection condition. According to the method, full-dimension diagnosis and evolution prediction of the hidden damage of the longitudinal joint connector of the shield tunnel are realized.

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

The invention provides an automatic pavement settlement monitoring method and system based on a shield undercrossing airport runway. The dynamic database is constructed by collecting operation parameters such as shield tunneling machine propelling pressure, cutterhead torque and synchronous grouting pressure in real time and combining three-dimensional deformation data obtained through laser scanning. Strain sensing units are arranged in the soil layers to form a layered monitoring network, strain changes of the soil layers of different depths are collected, and spatial correlation is established between the strain changes and the tunneling position. And analyzing a spatial attenuation rule through a dynamic association processing unit, and generating a multi-dimensional feature data set. And establishing a settlement prediction model, fusing the shield dynamic load sequence and the layered strain conduction characteristics, and outputting settlement distribution information. And the system automatically compares the settlement difference value of the adjacent areas with a preset safety threshold value, and generates a graded early warning signal after verification of the three-dimensional deformation data space. According to the technical scheme provided by the invention, real-time three-dimensional prediction and spatial verification type graded early warning of settlement of the airport runway are realized.

The invention discloses a multi-mode perception and reinforcement learning engineering machinery intelligent regulation and control method and system, and belongs to the technical field of intelligent control and industrial automation. According to the system, stratum data within the range of 20-50 meters in front of a shield tunneling machine are collected in real time through multi-mode sensing equipment such as a distributed optical fiber sensor, a cutterhead vibration sensor and an electromagnetic wave radar, and original data are processed by adopting a wavelet-Fourier combined noise reduction algorithm; and inputting the processed multi-modal data into a reinforcement learning intelligent decision-making model based on a CNN-LSTM hybrid network architecture. The model is trained through a dynamic reward function, and weight coefficient combinations can be automatically switched according to different construction scenes. According to the system, parameters such as the rotating speed, the thrust and the grouting amount of a shield cutter head are regulated and controlled in real time through the self-adaptive control module, and the slurry utilization rate is increased through the gradient pulse grouting technology. The precision, efficiency and safety of shield construction are remarkably improved, and meanwhile energy consumption and construction cost are reduced.

The invention belongs to the field of mud cake removal of shield tunneling machines, and discloses a shield cutterhead residue soil property sensing and intelligent mud cake removal system and method, and the system comprises a sensing module, a modifier spraying module, a regulation and control module and an intelligent terminal; the sensing module is used for collecting mechanical, chemical and thermodynamic signals in a soil bin of the shield tunneling machine in real time; the modifier spraying module is used for intelligently regulating and controlling the proportion of a modifier so as to inhibit mud cake adhesion; the regulation and control module is used for dynamically optimizing a modifier proportioning parameter and a cutterhead tunneling strategy according to the sensing data; and the intelligent terminal is used for displaying the mud cake distribution state and receiving a manual intervention instruction. According to the method, three-dimensional form dynamic analysis and accurate targeted removal of the mud cake can be realized.

The invention provides a shield adaptability adjusting method based on a stratum structure, which comprises the following steps: acquiring geological parameters of a construction area of a shield tunneling machine through drilling and geophysical prospecting, and arranging sensors at key positions of the shield tunneling machine to acquire construction parameters of the shield tunneling machine in real time; a convolutional neural network CNN is adopted to analyze the geological parameters to identify stratum features, and a geological model is constructed based on the identified stratum features; constructing a disaster risk index system, and predicting a disaster risk based on the geological model and the construction parameters by adopting Logistic regression and a random forest algorithm; according to the predicted disaster risk, the cutterhead configuration of the shield tunneling machine is dynamically adjusted, and tunneling parameters are optimized or a muck improvement scheme is adopted; by monitoring geological parameters, construction parameters and environmental changes in real time, a multistage early warning mechanism is adopted to trigger emergency response measures, and construction safety is guaranteed. According to the method, the safety and efficiency of shield construction can be improved, the geological disaster risk is reduced, and reliable technical guarantee is provided for underground space development.

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 provides a shield tunneling machine cutter cylinder state monitoring method based on a multi-sensor information fusion technology. The shield tunneling machine cutter cylinder state monitoring method comprises the steps that multi-physical field synchronous data collection is carried out; self-adaptive signal preprocessing: processing the multi-physics field time sequence signal to obtain multi-physics field synchronization time sequence data; multi-physics field feature space construction: processing the multi-physics field synchronization time sequence data to respectively obtain time domain feature data, frequency domain feature data and time-frequency domain feature data; deep collaborative feature extraction and state evolution modeling: obtaining a multi-physics field coupling feature sequence of the spatio-temporal context through multi-physics field collaborative perception feature coding; global space-time dependency collaborative modeling is adopted, and an enhanced space-time feature sequence is obtained; and state evolution trajectory interpretation and classification decision. According to the scheme, a traditional linear stacking mode of data acquisition-preprocessing-feature extraction-fusion classification is broken through, and a closed-loop intelligent sensing method with physical mechanism guidance, multi-modal data deep coupling and feature self-coevolution is constructed.

The invention provides a shield tunnel dynamic settlement compensation construction method based on an adaptive optimization algorithm, and the method comprises the steps: collecting the ground surface settlement, soil stress and underground water level data in real time through an Internet of Things sensor, achieving the data preprocessing and feature extraction in combination with an edge calculation node, constructing a three-dimensional geologic model, integrating the historical engineering data through transfer learning, and achieving the dynamic settlement compensation of a shield tunnel. The method comprises the following steps: identifying a high-risk area by using a clustering algorithm, designing a hybrid adaptive optimization framework with fusion of random forest and incremental learning, dynamically adjusting shield tunneling speed and soil bin pressure construction parameters, introducing an adaptive step length mechanism to cope with geological complexity change, and identifying a settlement abnormal mode through Fourier transform. Precise compensation is achieved in combination with a layered grouting strategy, the pressure of a soil bin is dynamically adjusted based on a hydraulic system, a closed-loop feedback mechanism is established, the predicted deviation rate is compared with an actual monitoring value, model parameters and the compensation strategy are continuously optimized, the settlement control precision is improved, and the construction risk is reduced.

The invention discloses a method for predicting shield tunneling parameters under a composite stratum, which comprises the following steps of: collecting and sorting shield tunneling historical data under the composite stratum, including geological parameters and construction parameters, and performing abnormal value elimination, missing value filling and data standardization; the geological parameters and the geometric parameters are combined according to the tunneling ring number, and a quantitative geological matrix of each ring is generated; the geometric parameters represent the position of the soil layer relative to the tunnel excavation face by adopting 1-5 codes, and input dimension unification is ensured through zero filling alignment; a CNN-Attention-SVR model is constructed, and the CNN-Attention-SVR model is trained; new geological data are input into the trained model, tunneling parameters are predicted, and construction adjustment is guided; reliable technical support and decision basis are provided for tunnel construction under the composite stratum condition, and therefore the overall safety and economical efficiency of shield construction are improved.

The invention relates to the field of rock-soil particle motion trail acquisition, and discloses a rock-soil particle motion trail acquisition method and system in a shield tunneling process, and the method comprises the steps: multi-source data collection and fusion preparation, rock-soil particle preliminary identification and classification, unmarked motion tracking algorithm initialization, and real-time motion trail calculation and correction. The method comprises the steps of uncertain parameter real-time estimation, simulation and actual measurement data comparison verification, motion trail visualization and analysis report generation, and adaptive optimization and feedback control. According to the method, multi-source data acquisition and fusion preparation is made firstly, a data foundation is laid, then through links of rock-soil particle identification and classification, motion tracking algorithm initialization and the like in sequence, functions of particle motion tracking, trajectory calculation and correction, parameter estimation and the like are realized by utilizing multiple means, and through simulation comparison verification, visualization and self-adaptive optimization feedback control, the real-time performance of the system is improved. And finally, the construction efficiency and quality are improved, construction decision is assisted, construction is guaranteed, and data management is facilitated.

The invention discloses a shield tunneling machine axis predictive control method and system, electronic equipment and a storage medium, the shield tunneling machine axis predictive control method is based on data driving modeling, system dynamic characteristics contained in shield tunneling axis control related data are fully excavated, the predictive control precision is high, and the predictive control precision is high. A GRU (Gated Recurrent Neural Network) model and an ARX (Autoregressive Regressive) model with exogenous variables are combined to construct a guide deviation dynamic characteristic model to predict the guide deviation, the advantage that the GRU is good at processing a long-interval and long-delay time sequence is fully utilized, and the correlation characteristics before and after the shield guide deviation data time sequence are fully excavated; the model not only has the global nonlinear description capability, but also has the capability of describing the time sequence correlation characteristics of the system, the dynamic characteristics of the system are more comprehensively described, and the model is a nonlinear model which is more in line with the guiding actual working characteristics of the shield tunneling machine, and is higher in generalization capability and higher in precision.

The invention discloses a shield tunneling intelligent control method and system based on a ground-tunnel-machine-information-human adaptation level, and relates to the crossing field of underground engineering intelligent construction and information technology. Setting a first-level index and a second-level index; dividing the shield construction state into three levels of intelligent adaptation levels by combining the total score of the construction state with a dynamic threshold value; setting four targets of attitude intelligent control, settlement intelligent control, tunneling efficiency optimization and construction abnormity diagnosis; a machine learning prediction model is constructed, and the construction state parameters corresponding to the targets are predicted in real time; according to the shield intelligent adaptation level, the man-machine cooperation modes are divided into three classes of A level, B level and C level; and comparing actual data with a prediction result of the machine learning model, and dynamically correcting the control target parameters. Through real-time sensing of geological, mechanical and environmental multi-dimensional construction states and in combination with a dynamic weight distribution strategy, precise evaluation of the shield construction state is achieved, and meanwhile self-adaptive switching of the man-machine control right is achieved.

The invention discloses a metro shield tunnel crack width refined numerical simulation method under surface loading, and the method comprises the following specific steps: describing the overall stress deformation characteristics of a soil body-shield tunnel structure based on a stratum-structure method, and simulating a macroscopic equivalent model by adopting a continuous medium model, and the equivalent stiffness reduction effect caused by the influence of longitudinal seams and circular seams is fully considered, the most unfavorable section position along the shield tunnel is determined, so that the key section is replaced with a refined model considering local hand hole weakening, multi-contact and built-in steel bars, and the shield tunnel crack evolution characteristics are described by adopting a total strain crack model. And obtaining the millimeter-scale crack width of the target tunnel structure based on a load-structure method. According to the analysis method, the millimeter-scale crack width of the shield tunnel under the action of ground surface loading can be accurately obtained, the structure safety state can be rapidly evaluated in combination with current specifications, and an important basis is provided for operation and maintenance of the subway shield tunnel.

The invention discloses an intelligent control method and system for muddy water treatment of a muddy water shield tunnel, and relates to the technical field of muddy water treatment.The method comprises the steps that physical characteristic data and chemical characteristic data of muddy water are collected in real time, preliminary treatment is conducted through edge computing nodes, and standardized muddy water characteristic data flow is output; carrying out preliminary screening operation on muddy water according to an intelligent separation strategy, and screening out medium-particle impurities; through self-adaptive dynamic vortex regulation and control, the muddy water rotational flow speed and the muddy water pressure are regulated, and medium particle impurities are subjected to rotational flow separation; carrying out deep separation treatment on medium-particle impurities subjected to cyclone separation, and outputting clear water and deposited mud cakes; clear water flows back to the shield tunneling machine through pumping equipment to be recycled, and deposited mud cakes are subjected to dehydration treatment. According to the invention, medium particle impurities are accurately separated according to the change of muddy water characteristics by utilizing a regulation and control method of dynamically adjusting the rotational flow speed and pressure, so that the muddy water separation efficiency is effectively improved, and the energy consumption is reduced.

The invention provides a shield excavation face deformation monitoring method and system for undercrossing an airport runway, and the method comprises the steps: generating a pressure load response characteristic based on the pressure fluctuation time sequence data of a cutter head hydraulic cylinder in a shield tunneling machine in combination with dynamic load distribution data and static load distribution data; the deformation track of the shield excavation face and the displacement change of a bottom soil body undercrossing the airport runway are obtained, point cloud registration processing is carried out, registered data are obtained, and two-dimensional deformation monitoring data of the shield excavation face and the bottom soil body in the horizontal direction and the vertical direction are extracted; based on the pressure load response characteristics, the two-dimensional deformation monitoring data and the surface settlement monitoring data of the undercrossing airport runway, obtaining a deformation trend prediction result of the shield excavation face; according to the method, high-precision real-time monitoring and trend prediction of collaborative deformation of the shield excavation face and the runway structure in the underpass airport runway scene are achieved, and runway operation safety and shield construction accurate control are guaranteed.

The invention discloses a device judging whether or not an earth pressure balance shield earth chamber is full. The device comprises a pressure detector disposed on the upper portion of a front shield earth chamber of the earth pressure balance shield; the signal output end of a pressure sensor is connected with an upper computer; the upper computer judges whether or not the chamber is full; if not, a warning signal is sent to an alarm unit. The device has the advantages that whether or not the earth chamber is full can be judged anytime during earth pressure balance shield tunneling, full-chamber shield tunneling can be ensured and the application of full-chamber tunneling decreases the danger of collapse ahead.

The invention relates to the technical field of tunnel segment data monitoring and analysis, and relates to a shield tunnel segment loading test platform data monitoring and analysis method. According to the method, the position coordinates of the identification points in the segment seam area are captured and decomposed into normal, tangential and rotary displacement components, real-time synchronous quantification of the seam opening amount, the dislocation amount and the torsion angle is achieved, and meanwhile historical test data and a real-time sequence are fused based on the timestamp alignment technology; a segment joint deformation evolution trend chart is predicted through multi-parameter coupling, and a real-time sequence is combined to carry out collaborative judgment to generate a segment joint area deformation danger degree index, so that deformation trend dynamic accurate prediction and danger degree accurate evaluation are realized. And performing correlation analysis on the identified maximum curvature position, the curvature change rate and the deformation danger degree index of the segment joint area, outputting the safety margin of the segment structure, and triggering a multi-stage early warning mechanism, thereby comprehensively evaluating the overall structure safety state of the shield tunnel segment.

The invention relates to the technical field of tunnel engineering and intelligent construction, in particular to a shield tunneling real-time control method based on a random forest and a particle swarm optimization algorithm. The method comprises the steps that initial tunneling parameters are generated through a parameter recommendation random forest model according to geology and tunnel geometric parameters, and model hyper-parameters are optimized through a sparrow optimization algorithm; carrying out settlement prediction by utilizing the settlement prediction random forest model; if the predicted value exceeds the limit, carrying out iterative optimization by adopting a particle swarm optimization algorithm and taking the initial parameter as a starting point, and searching a global optimal tunneling parameter combination meeting the settlement requirement; finally, the optimized parameters are issued to the shield tunneling machine to be executed, the model is continuously updated based on real-time construction data, and closed-loop control is formed. According to the method, intelligent recommendation and real-time optimization of tunneling parameters can be realized, the ground surface settlement control precision and the system response speed are improved, the dependence on artificial experience is effectively reduced, and the self-adaptive capability and the intelligent level of shield construction under complex geological conditions are enhanced.

The invention discloses a shield muck modified slurry grouting reinforcement effect evaluation method based on multi-data fusion, and belongs to the field of shield construction grouting. The method comprises the steps that during field monitoring, a monitoring section is selected according to tunnel geology and structure characteristics, and a sensor is installed; acquiring data of soil pressure, displacement and pore water pressure, calculating corresponding change rate and gradient, and analyzing change trend. According to the numerical simulation, a three-dimensional model is established based on geological exploration and the like to determine parameters, shield propulsion and grouting reinforcement are simulated, and stratum deformation, soil stress and slurry diffusion and permeation data are obtained. And in the data fusion link, the two types of data are subjected to standardization processing, grouting reinforcement effect grades are divided to determine a prior probability, a probability distribution model is established for monitoring indexes, and a posterior probability is calculated through a Bayesian formula to determine an evaluation result. According to the method, various data are integrated, the reinforcement effect can be comprehensively and accurately evaluated, a scientific decision basis is provided for shield tunnel construction, the construction quality and safety are improved, and waste soil resource utilization is optimized.

The invention discloses a multi-modal data fused shield tunneling machine cutter state fault prediction system, which comprises a state acquisition module for acquiring multi-modal state data, decomposing the multi-modal state data into time and space related characteristics, and obtaining optimal state data through low-rank matrix decomposition and compression; the anomaly simulation module is used for collecting fault state data, constructing an improved lightweight weight migration network to perform cross-domain feature extraction to obtain a cross-domain common feature vector, and obtaining common features through a feature alignment algorithm based on adversarial training on the basis of the cross-domain common feature vector and the fault state data; and the fault prediction module is used for performing fault prediction through a space-time cross attention dynamic migration network on the basis of the obtained optimal state data and common characteristics, so that long sequence characteristics can be captured when the cutter is abnormal, the calculation amount can be reduced when the cutter is normal, and the real-time performance and the accuracy of fault prediction are improved.

The invention discloses an intelligent adjusting system and method for soil layer penetrating power of a shield tunneling machine. The intelligent adjusting system comprises a sensor array, wherein the sensor array is annularly and uniformly distributed around a cutter head of the shield tunneling machine; the risk level module is used for calculating a similarity matrix between section rings by adopting a Gaussian kernel function, dividing performance communities through a Louvain algorithm and outputting geological risk levels and corresponding coordinates; and the intelligent decision module integrates a multilayer convolutional neural network, fuses sensing data and a network analysis result in real time, and generates dynamic instructions of the cutterhead rotating speed, the propelling speed and the grouting pressure. The shield tunneling machine soil layer penetrating power intelligent adjusting system achieves real-time monitoring and intelligent analysis of multi-dimensional information such as soil layer hardness, pore water pressure, ground stress and temperature by integrating a high-precision multi-dimensional sensing module, a risk level module and an intelligent decision module. The system can automatically adjust the cutterhead rotating speed, the propelling speed and the grouting pressure of the shield tunneling machine according to the geological risk grade.

The invention discloses a shield tunnel ground subsidence real-time prediction method considering spatio-temporal information, and the method comprises the steps: constructing an input feature system, including classifying model input into geometric information, multi-ring geological condition information, multi-time step shield parameter information and historical subsidence information; a multi-source information fusion model architecture is designed, geometric information, geological condition information, shield operation parameter information and historical settlement information serve as input of the model, and feature extraction is conducted through multiple encoders. Then feature fusion is carried out, nonlinear mapping is carried out by using a residual network (ResNet), and finally a real-time settlement prediction value is output; according to the scheme, the space-time characteristics of ground subsidence induced by tunneling are deeply excavated, and real-time prediction of subsidence of any position in a disturbance range is realized through deep fusion of multi-source characteristics.

The invention discloses a shield tunnel connection channel portal construction method based on mechanical excavation, and relates to the technical field of shield tunnel connection channel construction, and the shield tunnel connection channel portal construction method comprises the steps that a steel sleeve is installed at a connection channel portal, and portal sealing is achieved; a glass fiber reinforced concrete pipe piece is pre-buried in the opening of the connection channel, and based on the multi-stage construction strategy, the pipe jacking machine entering and exiting construction task is executed in sequence; prefabricated jacking pipe joints are adopted for channel tunneling and splicing, the size parameters of the jacking pipe joints meet the fireproof evacuation function requirement, and the jacking pipe joints are spliced in a staggered joint mode without wedge-shaped quantity; steel jacking pipe joints are arranged at the head end and the tail end of the connecting channel, and arc-shaped steel plates are pre-buried; and in the T-joint section where the transverse channel and the main tunnel intersect, an arc-shaped steel plate is adopted for welding a tunnel portal connector, and an emergency grouting pipe is arranged on the arc-shaped steel plate, so that end-dividing grouting reinforcement is achieved. Reference is provided for shield tunnel connection channel mechanical method portal construction, the structural strength of a holing area is effectively improved, and the operation stability is improved.

Disclosed is an energy-saving control method for a shield tunneling machine, the method comprising: when an advance speed of a shield tunneling machine changes during a tunneling process, according to the change in the advance speed, adjusting one or more of a spoil discharge system (7), a fluid system (10) and an electrical system (11). The spoil discharge system (7) performs adjustment in the following method: calculating a corresponding amount of excavated spoils according to the advance speed, then calculating a theoretical spoil discharge speed according to the amount of excavated spoils, and a matched spoil discharge system (9) adjusting the spoil discharge speed thereof according to the theoretical spoil discharge speed. The fluid system (10) performs adjustment in the following method: according to the magnitude of the advance speed of the shield tunneling machine, the fluid system (10) adjusts the flow rate of a fluid in the corresponding fluid system. The electrical system (11) performs adjustment in the following method: according to the magnitude of the advance speed of the shield tunneling machine, the electrical system adjusts the active power of a transformer of the electrical system. The present application can solve the problems of high apparatus energy consumption and severe energy waste of shield tunneling machines caused by large apparatus installation power, numerous auxiliary apparatuses, unreasonable design, etc. in the prior art. Also disclosed is a system using the method.

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 provides a layered freezing and layered excavation method for butt joint of large-diameter shield tunnels, and relates to the technical field of shield tunnel engineering.During butt joint of the large-diameter shield tunnels, the butt joint faces are layered in sequence from top to bottom, and the procedures of coring and drilling, freezing pipe driving, positive freezing, cutterhead disassembling and excavation are conducted on each layer; after the coring and drilling of the upper layer are finished, the coring and drilling of the next layer are immediately started, and the butt joint surfaces are sequentially layered from top to bottom through the layered budget model, so that the working procedures of coring and drilling, freezing pipe driving and positive freezing of the next layer are simultaneously completed before all the working procedures of the upper layer are completed; the time for coring, drilling, freezing pipe driving and equipment mounting and debugging of other layers except the top layer is saved, the construction period for taking more time for drilling and freezing pipe driving on the shield tunneling machine is saved, and therefore the whole construction period is shortened.

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 shield construction, and provides a shield synchronous grouting control method and system fusing tunneling parameters and geological information, and the method comprises the steps: obtaining the tunneling parameters of a shield tunneling machine and the geological information of a front tunnel in real time; the obtained tunneling parameters and geological information are analyzed and processed, the mapping relation among the tunneling parameters, the geological information and the grouting parameters is determined, and grouting parameter predicted values are obtained; and according to the obtained grouting parameter prediction value, grouting parameters are automatically adjusted, and synchronous grouting of the shield is controlled.