1713 results about "Shield tunneling" patented technology

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 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 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 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 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 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.

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 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 discloses a layered settlement prediction method and device based on multi-source data fusion and a medium, and relates to the technical field of shield tunnel construction monitoring. The method comprises the following steps: acquiring multi-source monitoring data, and preprocessing the multi-source monitoring data to obtain standard monitoring data; wherein the multi-source monitoring data comprises layered settlement monitoring data, shield construction parameters and geological survey data; inputting the standard monitoring data into a preset layered settlement prediction model to obtain a layered settlement prediction result; wherein the layered settlement prediction result comprises the layered settlement amount and the settlement rate in the unit distance before and after the shield tunneling machine in the unit time in the future; and based on a layered settlement prediction result, triggering graded early warning, and generating a visual decision report. By constructing the layered settlement prediction model, the coupling relationship among the shield construction parameters, the address survey data and the settlement of different depths is excavated, and the settlement of the shield tunneling machine in the unit time in the future and the specific distance before and after the shield tunneling machine is accurately predicted.

The invention belongs to the field of shield tunneling machine deviation rectification track planning, and discloses a shield tunneling machine deviation rectification track high-precision autonomous planning method and system, and the method comprises the steps: determining the minimum turning radius of a shield tunneling machine; carrying out coordinate calculation on the DTA to obtain the coordinate, the direction and the curvature of each point; projecting the starting point state and the end point state of the shield tunneling machine under the Cartesian coordinate system into the established Frenet coordinate system, establishing a correction curve parameterization equation based on a quintic polynomial line type, and converting the state of the shield tunneling machine into the Cartesian coordinate system; through segment pre-typesetting constraint, determining a deviation correction curve end point feasible region; the deviation degree of the deviation correction curve and the DTA and the length and smoothness of the deviation correction curve serve as optimization parameters, normalization processing is carried out according to the threshold value of each target, a comprehensive cost function is established, an improved particle swarm optimization algorithm is used for solving, and a global optimal deviation correction track is output. According to the method, real-time autonomous high-precision deviation rectification track planning of the shield tunneling machine can be realized, the track feasibility is improved, the pose deviation degree is reduced, and the tunnel quality and the construction efficiency are improved.

The invention relates to the technical field of tunnel engineering monitoring and construction, and discloses a shield construction tunnel full deformation prediction method based on artificial intelligence. The method comprises the following steps: acquiring a geological sensing data stream, a construction operation data stream and a historical deformation case library; performing multi-modal data fusion on the geological sensing data flow and the construction operation data flow to generate a construction environment map with consistent time and space; searching a case sequence matched with the current construction environment map from a historical deformation case library, performing feature significance evaluation through the case sequence, and identifying a dominant feature group of full deformation prediction; constructing an algorithm preferential engine based on the dominant feature group; activating an algorithm preferential engine to perform parallel processing on the construction environment map, and generating an adaptability measurement set of each algorithm; integrating the adaptability measurement set and real-time construction limiting conditions, and selecting an optimal prediction algorithm by adopting a trade-off decision-making mechanism; and generating a full deformation prediction model, and integrating the model to a shield construction monitoring platform.

The invention belongs to the technical field of underground engineering intelligent construction, and particularly discloses a shield tunneling performance prediction method and system based on an interpretable BO-LGBM model, and the method comprises the steps: receiving detection data in a shield tunneling process, and determining tunneling parameters, geological parameters and karst parameters based on the detection data, so as to construct an input feature set; constructing an LGBM model, performing multi-stage screening on the input feature set to obtain a comprehensive feature importance sequence, and determining key target features corresponding to model prediction influence according to the comprehensive feature importance sequence; on the basis of key target features, a Bayesian optimization algorithm is used to optimize hyper-parameters of the LGBM model, and a trained BO-LGBM prediction model is obtained; the BO-LGBM prediction model is used for predicting ground surface settlement, tunneling efficiency, specific energy and overexcavation rate of the detection data; and obtaining a performance index based on the trained prediction model so as to evaluate model performance and prediction precision. According to the invention, the model prediction accuracy of the shield performance can be improved.

The invention relates to the technical field of tunnel engineering building materials, in particular to a double-slurry synchronous grouting material based on shield silty-fine sand and a preparation method of the double-slurry synchronous grouting material based on shield silty-fine sand. The liquid A is prepared from, by weight, 20%-30% of cement, 30%-50% of shield silty-fine sand, 5%-10% of bentonite, 5%-15% of mineral admixture, 0.1%-0.5% of stabilizer and 20%-30% of water, and the stabilizer is a water-soluble high-molecular polymer; the solution B is a water glass solution, and the volume ratio of the solution A to the solution B is (10-15): 1; the double-slurry synchronous grouting material further comprises a retarder and a coagulant, the retarder is citric acid, and the coagulant is a nano Ca (OH) 2 crystal nucleus. Bidirectional and gradient regulation and control of the gelation time between 5s and 100s can be realized through the coagulant and the retarder, and diversified requirements of different geological conditions and construction progress on slurry operation time are met.

The invention relates to the technical field of tunnel construction, and discloses a shield tunneling attitude prediction method and system based on data fusion and deep learning, and the method comprises the following steps: S1, cross-data-source time alignment; s2, missing value processing and anomaly detection; s3, input and output feature selection; s4, feature standardization and sliding window design; s5, establishing and predicting a shield attitude prediction model; according to the method, the average absolute error is reduced by 20%, and the overall prediction accuracy reaches 95%; key parameters such as rolling angle prediction goodness of fit is close to 0.99, the shield head horizontal deviation prediction precision is improved by about 28% compared with LSTM / GRU, high-precision prediction can be kept for parameters with small fluctuation or large change through the synergistic effect of time alignment, feature screening, sufficient excavation attitude, tunneling and vibration data, the model structure is simplified, precision and calculation efficiency are considered, and the method is suitable for field deployment; the attitude deviation can be predicted 6 minutes ahead of time, an operator is assisted to adjust parameters, deviation accumulation is reduced, and construction safety is guaranteed.

The invention relates to the technical field of tunnel engineering prefabricated part production, in particular to a large-diameter duct piece independent kiln assembly line conveying prefabricating method and system. S2, a reinforcement cage and an embedded part are installed; s3, concrete is precisely poured and vibrated; s4, pre-curing and surface treatment; s5, carrying out roller way type flow conveying; s6, hydration heat-driven dynamic maintenance is carried out; and S7, demolding and finished product detection. And S8, mold circulation. According to the large-diameter duct piece prefabricating production line, through technical innovation in multiple aspects of mold, conveying, maintenance, control and the like, the core technical problem in existing large-diameter duct piece prefabricating production is solved, high-precision, high-efficiency, low-energy-consumption and green production of large-diameter duct pieces is achieved, and the large-diameter duct piece prefabricating production line has remarkable technical advantages and wide application prospects and is suitable for large-diameter duct piece prefabricating production. The method can be widely applied to infrastructure construction projects such as shield tunnels, open caisson projects and underground comprehensive pipe galleries.

The invention discloses a muck treatment capacity monitoring method and a muck treatment capacity monitoring system, and relates to the technical field of shield construction and muck treatment capacity prediction treatment, and the method comprises the following steps: collecting operation parameters of a shield tunneling machine deslagging and conveying link, calculating a muck flow coupling index, evaluating a deslagging link coupling state, and carrying out optimization; the load coupling index of the shield tunneling machine is calculated, the energy distribution balance is judged, and dynamic adjustment is conducted according to the unbalance state; calculating the energy consumption deslagging propulsion efficiency, evaluating the matching degree, and optimizing the power and conveying parameters of each link; a CNN and LSTM combined multi-scale modeling method is adopted to predict the future muck handling capacity, and online correction is carried out through a sudden change adaptive network; and finally, fusing the standardized data set with the actual operation parameters to generate a unit time muck handling capacity sequence. By means of the method, the deslagging and propelling matching degree of the shield tunneling machine can be improved, the energy utilization efficiency is optimized, and high-precision prediction and real-time regulation and control of the muck handling capacity are achieved.

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

The invention discloses a shield cutter cutting load real-time monitoring and adjusting method and system. The method comprises the following steps: constructing a shield cutter distributed sensing network, and collecting strain, vibration and temperature of a cutter; a wireless transmission network is established, and the collected strain, vibration and temperature of the cutter are transmitted in real time; according to the strain, vibration and temperature, received in real time, of the cutter, cutting load parameters are obtained in a multi-source data fusion mode; based on the obtained cutting load parameters, stratum parameter identification is carried out in combination with a deep learning algorithm; in combination with the stratum parameters and the cutting load parameters, a wear state evaluation model based on fuzzy reasoning is established, and the tool wear state is evaluated; and designing a multi-objective optimization function according to the wear state of the cutter and the stratum parameters, solving the multi-objective optimization function, obtaining optimal shield tunneling machine operation parameters, and performing dynamic adjustment. The cutting load of each cutter in shield construction can be monitored in real time, and the cutter parameters can be adaptively adjusted.

The invention belongs to the technical field of tunnel construction, and discloses a method and a system for determining the supporting force of a shield tunneling machine passing through a river channel height difference stratum.The method comprises the following steps: establishing a model in which a soil body actively damaged in front of a shield tunneling machine excavation face is regarded as a prismoid and a wedge body by combining a wedge body damage theory and a limit equilibrium method; the shield supporting force and the sliding soil body in the limit equilibrium state serve as analysis objects, and the mechanical balance of the shield supporting force and the sliding soil body is analyzed; the riverway undercrossing process is divided into five stages, the excavation face supporting force of each uneven stratum of the undercrossing riverway is calculated, and therefore the magnitude of the shield undercrossing riverway excavation face limit supporting force is determined. According to the method, a wedge-shaped body theoretical calculation model is simplified, complex formulas and tedious calculation steps are avoided, the limit supporting force of a horizontal stratum at the bottom of a river channel, a river channel slope, a non-river-channel bottom layer and junctions of all strata is accurately calculated, supporting force calculation is more suitable for actual engineering conditions, and a foundation is laid for subsequent theoretical analysis and technical development.

The invention discloses a reinforcement learning-based slurry balance shield construction attitude settlement collaborative optimization method and system, electronic equipment and a storage medium. The method comprises the following steps of: constructing state and action characteristics; a control strategy modeling step: constructing a composite reward function which takes incision pressure, partition propulsion pressure, tunneling speed and the like as control variables and fuses ground surface settlement and attitude control performance; designing a control strategy model based on reinforcement learning; a control strategy training step; a control parameter output step; in the shield tunneling process, control parameter setting is dynamically generated through current state information, and cooperative control over settlement and posture is achieved. According to the slurry balance shield construction posture settlement collaborative optimization method and system based on reinforcement learning, the electronic equipment and the storage medium, the adaptive capacity and self-learning capacity of the system can be improved, and the automation, intelligence and construction safety level of the slurry balance shield construction process can be improved.

The invention relates to the technical field of hard rock drilling construction, in particular to a rock breaking hob, a shield tunneling machine cutterhead and a shield tunneling machine. The rock breaking hob comprises a cutter shaft, an annular cutter body and a plurality of pressurizing assemblies; an annular cavity communicated with an external high-pressure water source is formed in the cutter shaft; the annular cutter body is rotationally arranged on the cutter shaft, and a plurality of blind holes are formed in the annular cutter body; the pressurizing assembly comprises a compression block, a plugging block and a first elastic piece; the compression block is in sealed sliding connection with the inner wall of the blind hole to form a compression cavity, and a plurality of first channels used for communicating the compression cavity with the annular cavity are formed in the annular cutter body; a sliding groove, a communicating hole and a water spraying hole are formed in the compression block; the sliding groove communicates with the compression cavity through the communicating hole. The sliding groove communicates with the end face of one end of the compression block through the water spraying hole. The plugging block is slidably connected with the sliding groove and blocks the water spraying hole when located at the end, close to the central axis of the annular cutter body, in the sliding groove. The first elastic piece is used for enabling the compression block to pop out of the blind hole. The rock breaking device has the effects of reducing water source waste and improving the rock breaking efficiency.

The invention provides a karst cave group detection method and device in shield construction, and the method achieves the comprehensive detection of karst caves through a transient electromagnetic and sound wave array composite wave field detection mode, and improves the detection accuracy and detection efficiency of the karst cave group. And feature extraction is performed on the composite wave field detection data through a cross-physical coupling model, a karst cave probability model is established, and quantitative evaluation of a karst cave detection result is realized. When the karst cave possibly exists in the construction direction of the shield tunneling machine, the distribution prediction direction of the karst cave is further determined, and the shield tunneling machine is prevented from entering the karst cave area blindly in the tunneling process. Through the multi-source sensing array carried on the shield tunneling machine, equipment parameters during operation of the shield tunneling machine are collected in real time, potential construction anomalies can be found easily, and the accuracy and timeliness of karst cave detection are improved. Combined analysis is carried out on the composite wave field detection data and the operation parameters of the shield tunneling machine through the risk assessment model, a karst cave risk prediction value is determined, and the accuracy of karst cave risk assessment is improved.

The invention discloses a shield tunnel-oriented duct piece dislocation detection method, which comprises the following steps of: acquiring an original multi-source data set, and constructing a ring-level data packet set indexed according to ring numbers according to pre-stored propulsion encoder information; extracting local features of the circular seam, constructing self-calibration optimization data by utilizing the spatial deviation of an actual circular seam and a designed circular seam, jointly solving external parameters of a sensor and a ring-level attitude correction amount, and generating a circular seam alignment result set containing an accurate circular seam spatial position; mapping the circular seam alignment result set to a polar coordinate domain, and extracting an attention enhancement feature set through a pre-configured parallel patch sensing network; and in combination with geometric information in the circular seam alignment result set and visual features in the attention enhancement feature set, calculating a slab staggering geometric vector decomposed into multidirectional components, and generating a slab staggering classification result set. According to the method, the problem that sensor drifting and tiny slab staggering characteristics are difficult to recognize in long-distance tunneling is effectively solved, and full-process automatic accurate control over the segment assembling quality is achieved.

The invention discloses a boulder dense stratum geological radar detection method based on horizontal directional drilling, which comprises the following steps of: firstly, drilling into a stratum from the earth surface at a smaller incidence angle by adopting the horizontal directional drilling, and drilling along the central axis of a shield tunnel to form a drill hole with the diameter of about 30cm; after drilling is completed, the geological radar is fixed to the rear portion of the drill bit through a rigid connecting device, and the geological radar synchronously advances and rotates along with the drill bit. Four rolling balls are arranged at the bottom of the geological radar to reduce motion resistance and are tightly attached to the inner wall of a drill hole to ensure detection stability. The geological radar is driven by rotation of a drill bit to rotate by 360 degrees for detection, so that stratum information around a drill hole is obtained; then, performing image recognition on drilling slag, and establishing a corresponding relation between a geological radar image and a stratum type so as to optimize interpretation precision of geological radar data; and finally, in combination with geological radar detection data and a drilling deslagging analysis result, generating a boulder distribution profile map, and predicting the boulder distribution condition on the tunneling path of the shield tunneling machine.

The invention discloses a shield tunnel construction geology real-time prediction method and system based on a neural network, and the method comprises the steps: obtaining and preprocessing tunneling parameter data of a shield tunneling machine, inputting the preprocessed tunneling parameter data of the shield tunneling machine into a pre-trained geologic feature extraction model, and outputting geologic feature data; inputting the geologic feature data into a pre-trained geologic feature classification model, and outputting a geologic category; the geological feature extraction model is a neural network model formed by an integrated auto-encoder added with an attention mechanism, and the integrated auto-encoder comprises a plurality of parallel sub-auto-encoders; the geologic feature classification model is a residual CNN neural network model. According to the method, real-time accurate geological prediction can be realized by fully utilizing the tunneling parameters of the whole construction section, the total sample size of data required by training is reduced, the accuracy and timeliness of model prediction are greatly improved, and the geological change condition can be more effectively judged.

The invention discloses a method and a system for evaluating the toughness of an oversized-diameter shield undercrossing airport runway based on a differential settlement and dynamic settlement rule, terminal equipment and a medium, and relates to the technical field of shield engineering. The method comprises the following steps: acquiring engineering information and post-construction settlement and post-construction differential settlement control standards; constructing a three-dimensional finite element model which comprises a plurality of simulation working conditions and a plurality of key characteristic sections and reflects interaction of soil and a structure; simulating sequential tunneling of the shield, obtaining dynamic settlement data and a final settlement value, and calculating a differential settlement amount; substituting the dynamic settlement data into the toughness performance function to obtain a dynamic toughness value and an evolution curve; and calculating a final-state toughness index based on the dynamic toughness value, and judging the toughness grade in combination with a control standard and a preset grade division standard. According to the method, dynamic and final-state quantitative evaluation and grade judgment of runway toughness are realized, a structural performance attenuation rule is clarified, and a scientific basis is provided for construction control optimization and engineering decision.

The invention discloses a real-time multi-objective optimization decision-making method for shield tunneling parameters based on multi-mode NSGA-II (Non-dominated Sorting Genetic Algorithm-II) and LSTM (Long Short Term Memory). Comprising the following steps: acquiring a plurality of data sets, training a constructed shield tunneling parameter prediction model and a tool abrasion loss model, acquiring a tunneling parameter time sequence to be measured and a geological parameter time sequence, and inputting the tunneling parameter time sequence and the geological parameter time sequence into the shield tunneling parameter prediction model to obtain a tunneling parameter prediction value, inputting the tunneling parameter prediction value into a tool wear loss model to obtain a wear loss prediction value of each tool; constructing and solving a tunneling parameter multi-objective optimization model to obtain an optimal solution set of tunneling parameters; and performing minimum similar distance processing according to the optimal solution set, the tunneling parameter prediction value and the wear loss prediction value of each cutter, and taking the to-be-set tunneling parameter corresponding to the minimum similar distance value as the finally-set tunneling parameter at the current moment. According to the method, self-adaption to the geological condition of the tunneling stratum is achieved, the comprehensive tunneling performance of the shield is improved, and the total tunneling time and the construction cost in engineering are saved.

The invention discloses a tunneling parameter optimization and hob abnormal wear avoidance method based on shield cutterhead vibration, and relates to the technical field of tunnel and underground engineering, and the method comprises the steps: collecting three-way vibration signals during tunneling of a shield cutterhead; driving the shield cutter head to execute an in-situ tunneling experiment, and synchronously calculating a vibration signal characteristic value by using the three-way vibration signal; carrying out bin opening detection and statistics on the abrasion type of each hob, and judging the abnormal damage level of the shield cutter head in combination with the material structure information of the hobs; extracting vibration intensity ranges when the shield cutter head is at different abnormal damage grades, and taking the corresponding vibration intensity range when the shield cutter head is at the lowest abnormal damage grade as a target tunneling range; and analyzing whether the vibration intensity of the shield cutter head is in the target tunneling range or not in real time during shield tunneling. According to the invention, the limitation of hysteresis of traditional geological exploration is broken through; hob breakage or eccentric wear accidents caused by geological sudden changes are avoided, and construction safety in complex stratums is remarkably improved.