5466 results about "Tunnel construction" patented technology

The invention discloses a tunnel multi-source fusion dynamic twin surrounding rock intelligent prediction and control method and system, and the method comprises the steps: collecting multi-source heterogeneous data in real time through intelligent drilling equipment, a distributed optical fiber sensing network and a mobile scanning device which are disposed on a tunnel construction surface; inputting the multi-source heterogeneous data into a space-time diagram convolutional network, constructing a space-time correlation model of geological features and construction disturbance parameters, and outputting a coupling feature matrix; based on the coupling characteristic matrix, simulating the dynamic response of the surrounding rock-support system through a streaming voxelization engine and a multi-physics coupling engine, and generating a risk evolution three-dimensional thermodynamic diagram; and inputting the risk evolution three-dimensional thermodynamic diagram into the deep reinforcement learning model, and dynamically optimizing to generate a construction parameter regulation and control instruction meeting a safety constraint condition. According to the technology, a full-link channel of'data perception-mechanical analysis-decision execution 'is opened in a closed-loop mode, and therefore the double risks of'data distortion misjudgment' and'response lag out-of-control 'of a traditional method are avoided.

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 provides a tunnel construction monitoring analysis method and system based on AI video monitoring, and relates to the technical field of artificial intelligence. A tunnel construction original video stream data set is acquired through a multi-view video acquisition device, and a spatio-temporal feature sequence is generated through spatio-temporal feature fusion processing; the spatiotemporal feature unit comprises a pixel region movement track, a personnel wearing identifier and a construction equipment contour feature, then extracting dynamic features of the spatiotemporal feature sequence to obtain a dynamic spatiotemporal feature set, and executing construction personnel statistical processing based on the dynamic spatiotemporal feature set; generating a hole entering personnel quantity statistical result, a personnel distribution statistical result of each construction part and a personnel identity matching result, performing work efficiency comparative analysis according to the personnel statistical result and a preset construction progress plan, and generating a construction progress deviation index and resource allocation optimization strategy; and when the progress deviation index exceeds the first threshold value, early warning is triggered, and a strategy is output to the management terminal, so that effective monitoring and management of tunnel construction are realized.

The invention discloses a real-time surrounding rock deformation monitoring and data acquisition method and system, which is applied to long-distance weak surrounding rock tunnel construction, and comprises the following steps: determining the dynamic change trend of underground water seepage rate and ground stress distribution gradient by adopting a time sequence analysis method; based on the trend, carrying out risk partitioning on the tunnel construction section by adopting a K-means clustering algorithm, determining a deformation sensitive area, and optimizing the spatial distribution of the monitoring points according to the deformation sensitive area; monitoring data are acquired in real time, and when the data fluctuation period exceeds a threshold value, the data acquisition frequency of the corresponding monitoring point is automatically improved; processing high-frequency acquired data by adopting a long-short-term memory network to obtain a real-time surrounding rock deformation prediction result; the prediction result and the multi-source real-time geological parameters are fused, a Bayesian updating method is adopted for processing, a quantitative surrounding rock stability evaluation result is obtained, closed-loop self-adaptive optimization of a monitoring scheme and accurate risk prediction are achieved, and the safety early warning capacity of tunnel construction and the utilization efficiency of monitoring resources are remarkably improved.

The invention discloses a large-diameter shield tunneling parameter optimization method and system based on digital twinning, and relates to the technical field of shield tunnel numerical simulation, machine learning and digital twinning, and the method comprises the steps: obtaining shield tunnel construction site data, and constructing a virtual digital twinning body; building a training sample set based on virtual numerical twinborn generation data; training an agent model by using the training sample set; during construction, actual stratum response data and tunneling parameter data are obtained in real time, the real-time data are input into the soil mass parameter inversion agent model, and the optimal value of the soil mass parameter is rapidly output; the optimal value is input into the virtual digital twin, and dynamic real-time updating of numerical simulation is carried out; and on the basis of the updated soil parameter data and the tunneling parameter data in the next construction step, stratum response prediction data are obtained through the stratum response prediction agent model, so that the tunneling parameter data are adjusted and optimized. According to the invention, more accurate numerical simulation and safe and efficient tunneling of the shield tunnel can be realized.

The invention provides a tunnel construction safety monitoring and early warning method and system based on multi-dimensional data fusion, and relates to the technical field of construction safety early warning, and the method comprises the steps: obtaining first information which comprises tunnel construction parameters and image information; performing space-time fusion processing and redundancy reduction processing on the first information to generate a four-dimensional hypergraph tensor space of tunnel construction; performing cross-scale analysis and topology extraction on the four-dimensional hypergraph tensor space, and constructing a dynamic heterogeneous graph network; performing space-time diagram convolution processing on the dynamic heterogeneous graph network, and generating a composite risk manifold based on a result obtained by processing and a multi-head attention mechanism; coupling processing is carried out according to the composite risk manifold, stability analysis and critical state judgment are carried out based on the risk phase change hypersurface obtained through processing, and real-time early warning is carried out based on a dynamic early warning boundary of tunnel construction obtained through judgment. According to the invention, the risk identification accuracy and the early warning response timeliness in the tunnel construction environment are improved.

The present invention relates to the technical field of tunnel rockburst microseismic monitoring, and provides a strain-structural plane slip-type rockburst failure mechanism analysis method for a drill-and-blast method tunnel. The present invention collects strain-structural plane slip-type rockburst microseismic data during tunnel construction; in light of engineering geological survey data, performs microseismic monitoring and data analysis on strain-structural plane slip-type rockburst that has occurred in a tunnel, divides a strain-structural plane slip-type rockburst risk area, and determines a microseismic rockburst early warning area; and, by means of these data, deeply understands a microseismic activity law of strain-structural plane slip-type rockburst of deep-buried drill-and-blast method tunnels, discriminates respective functions of tensioning and shearing in rock failure processes, comprehensively analyzes the failure mechanism of strain-structural plane slip-type rockburst, and further obtains the failure mechanism of strain-structural plane slip-type rockburst of the deep-buried drill-and-blast method tunnels, providing an important theoretical basis for predicting and preventing rockburst disasters.

The invention discloses a tunnel back break intelligent finishing and surrounding rock stability dynamic evaluation method and system, relates to the field of tunnel construction, and provides a tunneling-oriented back break intelligent finishing and surrounding rock stability dynamic evaluation method which integrates point cloud sensing, geological modeling, finite element analysis and deep learning prediction. And a closed-loop control process from identification, control, modeling, prediction and feedback is constructed. An over-excavation and under-excavation area is identified through a high-precision point cloud and a neural network, a trimming instruction is generated, a three-dimensional model and a support time sequence model are dynamically constructed in combination with geologic features, a variable parameter reduction method is introduced to realize more real stability analysis, and future displacement and a safety coefficient of surrounding rock are predicted by using deep learning. The method can improve the intelligent level of tunneling control and construction safety, and has remarkable engineering application value.

The invention discloses a BIM-driven tunnel construction full life cycle management method and system, and relates to the technical field of industrial data processing and analysis, and the method comprises the steps: building a BIM model covering the full life cycle of a tunnel and a data mapping linkage relation; collecting monitoring data, accessing the monitoring data to the BIM model, and performing dynamic linkage evaluation to obtain a construction state result; and constructing a safety management mechanism according to an evaluation result and feeding back field guidance. The technical problem that in tunnel construction full-life-cycle management, a traditional industrial data processing mode cannot achieve full-life-cycle integration and cross-stage linkage of multi-source industrial data in the stages of design, construction and operation and maintenance is solved. The technical effects of full-life-cycle integration and cross-stage linkage processing of the industrial data in tunnel design, construction and operation and maintenance stages, improvement of the comprehensiveness, collaboration and timeliness of industrial data processing, and accurate acquisition of the construction state to optimize on-site safety management are achieved.

The invention provides a tunnel construction structure health on-line monitoring method and system, and the method comprises the steps: obtaining stress data, crack images and seismic wave signals of tunnel construction, and carrying out the preprocessing; obtaining and constructing stress-crack joint representation mapping, generating a first joint feature vector, and predicting a lining health state of a future # imgabs0 # time step; generating a second joint feature vector, and determining a stratum stress field of the future # imgabs1 # time step according to the second joint feature vector; constructing a tunnel lining damage probability model according to the stress data and the crack image, and determining an overall construction risk score; and dividing the construction risks into a plurality of grades. According to the method, data multi-modal fusion, structure-stratum joint modeling and AI enhanced prediction and early warning are achieved, the defects in the prior art are effectively overcome, the method can be widely applied to engineering scenes such as tunnel construction, subway construction and underground space development, and the construction safety and the structure health monitoring capacity are remarkably improved.

The invention discloses a shield tunnel full-life structure deformation monitoring and early warning method based on digital twinning, and the method comprises the following steps: S1, constructing a tunnel-geological complex model fusing BIM and three-dimensional point cloud technology according to the point cloud data of a tunnel completion section; s2, FBG and incremental sensing are adopted, and full-life-cycle deformation real-time monitoring is conducted on the tunnel; s3, constructing an LGBM proxy model, and updating the deformation prediction of the tunnel structure in the construction period; s4, constructing a GAT-LSTM model, and predicting structural deformation of the tunnel in the service period; s5, constructing a tunnel structure deformation grading early warning method based on a strength reduction and sharp point mutation theory; and S6, constructing a tunnel monitoring and early warning system based on digital twinning. The tunnel-geological complex structure reconstruction is realized by fusing stratum information and high-precision spatial data on the basis of a digital twinning technology, the tunnel construction and service period real-time deformation monitoring and early warning method and the twinning system are provided, and the service safety and failure risk pre-warning of the tunnel structure in the whole life cycle are guaranteed.

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

The invention provides a tunnel disaster source multi-attribute joint inversion method and system based on physical property constraint, and relates to the technical field of tunnel engineering and joint inversion, and the method comprises the steps: constructing a tunnel face three-dimensional geologic model, applying controlled disturbance to surrounding rock physical property parameters, analyzing the change characteristics of earthquake and electromagnetic observation response, and obtaining a tunnel face three-dimensional geologic model; establishing a quantitative mapping relation between the surrounding rock physical property parameter disturbance quantity and the multi-field response signal variable quantity; constructing a multi-dimensional sensitivity evaluation index, and generating a joint sensitivity result; the surrounding rock units are identified and divided, differentiated partition rock physical relationship constraints are constructed, and a physical multi-attribute inversion target of joint inversion is determined based on the partition rock physical relationship constraints; and on the basis of a physical multi-attribute inversion target, a multi-source joint inversion target function is constructed in combination with the joint sensitivity result, joint solving and iterative updating are carried out, and a tunnel face advanced inversion result is output. According to the invention, calamity source fine identification and risk quantitative evaluation in front of the tunnel face during the tunnel construction period are realized.

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

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

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

The invention relates to a fractured rock stratum water-rich tunnel excavation water inrush early warning method and system. The method comprises the steps that real-time monitoring data in the tunnel excavation process are obtained; based on the real-time monitoring data, predicting by using the water flow inrush prediction model, and generating a water flow inrush amount prediction result; analyzing crack propagation, deformation and instability conditions of the surrounding rock of the tunnel under different excavation parameters through the rock mass damage evaluation model in combination with the real-time monitoring data, and generating a rock mass damage evaluation result; generating a construction optimization scheme based on the water flow inflow prediction result and the rock mass damage evaluation result; comparing the real-time monitoring data with a preset safety threshold value, and judging whether a water inrush risk or a surrounding rock instability risk exists or not; if the real-time monitoring data exceed the preset safety threshold value, a safety early warning signal is triggered, and emergency measures are taken immediately. The method has the effect of improving the safety in tunnel construction.

The invention discloses a tunnel full-period construction feature information fusion and quality tracing method and system, and the method comprises the steps: converting the construction data of a tunnel full period into construction feature information through constructing a digital twinborn model of tunnel construction, and carrying out the dynamic weighting according to the importance of different construction feature information, performing fusion to generate high-dimensional construction feature data; representing the high-dimensional construction feature data as a graph structure; analyzing the graph structure by using a graph neural network algorithm GNN to obtain an anomaly detection result of the construction equipment; and constructing the construction data into a knowledge graph, tracing quality data in the construction process by using a knowledge graph reasoning technology, and analyzing potential quality risks in combination with the anomaly detection result, so as to realize risk early warning and full life cycle quality management of the construction process. According to the embodiment of the invention, more comprehensive and accurate data support can be provided for construction management, and the safety of the construction process and the efficiency of quality management are improved.

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

The invention 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 technical field of karst tunnel collapse early warning, in particular to a karst tunnel collapse intelligent early warning method, system and equipment based on micro-seismic multi-precursor characteristics. According to the technical scheme, the method comprises the steps of multi-scale micro-seismic monitoring network construction, multi-modal micro-seismic feature processing, quantum neural network early warning model construction and self-adaptive early warning and risk assessment. According to the invention, through omnibearing data acquisition of the multi-scale micro-seismic monitoring network, different depth sensors and an intelligent transmission mode, monitoring is accurate and stable, through combination of multi-modal feature extraction fusion, time domain, frequency domain and waveform multi-dimensional information mining, surrounding rock conditions are accurately judged, early warning analysis is enhanced by using quantum characteristics through the quantum neural network, and the early warning accuracy is improved. And self-adaptive threshold adjustment and multi-factor risk assessment are matched, accurate early warning is achieved, collapse is efficiently prevented, and tunnel construction safety is powerfully guaranteed.

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

The invention discloses a hydraulic engineering digital design method based on digital twinning, and relates to the technical field of hydraulic engineering design. According to the digital twin-based hydraulic engineering digital design method, design demand information of a to-be-designed diversion tunnel is obtained and input into a pre-trained building recognition model for comprehensive analysis, a spatial layout map of the to-be-designed diversion tunnel is obtained, and a tunnel design digital twin model is constructed; simulation data of the tunnel design digital twinborn model are obtained, simulation analysis is carried out respectively, a tunnel construction risk index and a hydraulic action risk comprehensive index of the tunnel design digital twinborn model are obtained, comprehensive analysis is carried out, and a tunnel feasibility index of the tunnel design digital twinborn model is obtained; according to the method, the tunnel design digital twin model is subjected to design optimization based on the feasibility index, so that a lagging mode of re-evaluation after building is effectively avoided, later frequent reinforcement and reworking are prevented, and the engineering safety coefficient is improved.

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

The invention provides a shield tunnel construction period segment floating amount prediction method, system and device and a medium, and belongs to the technical field of tunnel detection. The method comprises the following steps: acquiring an exploration data set and an actual measurement data set of a shield tunnel section; the exploration data set is input into a shield tunnel mechanical model to be solved, and relevant mechanical parameter values of the shield tunnel section are obtained; determining a physical control equation for reflecting the deformation characteristics of the shield tunnel section according to the Timoshensincaro beam model and related mechanical parameter values thereof; according to the actually measured data set and the physical control equation, training a neural network model to obtain a segment floating quantity prediction model; and obtaining positions of a plurality of segments to be detected in the shield tunnel section and inputting the positions into the segment floating quantity prediction model for analysis to obtain predicted floating quantities of the plurality of segments to be detected. According to the method, a physical mechanism conforming to tunnel construction can be fused into the neural network model to effectively constrain the prediction process, and the reliability of the prediction result of the segment floating amount can be improved.

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

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

The present invention belongs to the technical field of tunnel construction deformation monitoring, and specifically relates to an intelligent monitoring system for tunnel construction deformation, comprising a multi-point deformation monitoring module, a spatiotemporal data fusion module, a spatial correlation analysis module and an intelligent early warning module. A dynamic spatial deformation database containing a time series is constructed by performing multi-point deformation monitoring on tunnel surrounding rocks, and the spatial deformation correlation between monitoring points is analyzed based on the database to construct a spatial deformation correlation network of the tunnel surrounding rocks. When the spatial deformation correlation network exceeds a preset safety threshold, an early warning display is performed, and abnormal deformation trends can be captured in time, the early warning lag time can be significantly shortened, and construction safety can be improved. At the same time, when an early warning is triggered based on tunnel surrounding rock deformation monitoring, local early warnings and global early warnings are distinguished, and early warning results are displayed in three dimensions, which is helpful for priority management, so that construction personnel can take targeted emergency measures according to the early warning level.

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

The invention relates to the technical field of tunnel engineering safety monitoring, in particular to an autonomous controllable three-dimensional tunnel gushing analysis method and system. The method comprises the following steps: collecting multi-source heterogeneous geological, hydrological and construction data, fusing to obtain multi-source heterogeneous data, and constructing a dynamically updated three-dimensional geological-construction coupling digital twinborn model after alignment coding; carrying out multi-scale physical-AI hybrid modeling, correcting real-time data, carrying out water gushing evolution rolling prediction, and outputting prediction data; a three-dimensional visual platform is constructed, and multi-level linkage early warning is started based on a risk threshold value; the system comprises a data fusion module, a water gushing evolution rolling prediction module and a multi-stage linkage early warning module. By fusing geological data, hydrological monitoring data and construction parameters and combining three-dimensional visual rendering and deep learning fluid simulation technologies, the requirements for rapid early warning and timely decision making of water burst disasters in the tunnel construction process are met, and intelligent support is provided for engineering disaster prevention decision making.