941 results about "Karst" patented technology

The invention provides an underground water pollution source tracing and evaluation method for a riverside water source in a karst area, and the method comprises the following steps: collecting and preprocessing multi-source information, and obtaining a multi-source data set; a water quality sample is collected and analyzed, the surface water-underground water interaction process and pollution characteristics are comprehensively analyzed, and a pollution source type is obtained; performing space-time analysis on the multi-source data set to obtain a pollution activity time period and a potential pollution source area; constructing a karst area hydrological model, simulating the hydrological characteristics of the karst area, constructing a pollutant migration model, and simulating the migration process of pollutants; and constructing a traceability algorithm, tracking pollutants, confirming a pollution source, carrying out risk partitioning and risk assessment, and carrying out visual display of a result. According to the invention, interaction between surface water and underground water can be effectively assessed, accurate traceability of pollutants and multi-angle accurate identification of pollution sources can be realized, and management efficiency can be optimized through reliable risk assessment prediction.

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 discloses a tunnel blasting simulation method and system for a karst area, and relates to the field of tunnel blasting simulation, and the method comprises the steps: building a three-dimensional geologic model of the karst area, and generating an initial ground stress distribution rule through numerical simulation; generating refined initial crustal stress field data; calculating stress wave intensity and time sequence data generated by explosion; acquiring stress wave propagation data; calculating the surrounding rock damage degree and the crack initial position and direction; simulating the surrounding rock crack propagation process, and calculating the penetration probability and the propagation speed of the crack near the karst cave in combination with the karst cave characteristic parameters; analyzing the surrounding rock stability and damage evolution trend of the karst cave area; and optimizing explosive source item parameters and stress wave propagation boundary conditions, and generating a karst area tunnel blasting simulation result. According to the method, the influence of the karst cave on stress wave propagation is accurately simulated, powerful support is provided for optimizing a blasting scheme and improving construction safety and efficiency, and the method has important guiding significance on tunnel engineering under complex geological conditions.

The invention discloses a karst cave pile foundation grouting real-time control system based on optical fiber sensing and AI dynamic optimization, which relates to the field of pile foundation grouting real-time control and comprises a data acquisition module, an intelligent analysis module, a dynamic optimization module and a feedback control module. Normalized optical fiber input vectors and geological input vectors are obtained by analyzing karst cave optical fiber sensing data and geological parameter data, fusion is performed through a double-branch convolutional network, a grouting diffusion prediction model is constructed through a convolutional neural network, the slurry diffusion radius and the filling saturation are output, the model is constructed based on an isolated forest algorithm, and the grouting diffusion prediction model is constructed. The method comprises the following steps: calculating abnormal score early warning, establishing a mapping model, and dynamically adjusting the weight to obtain a Pareto optimal solution set adjusted in each construction stage, and a closed-loop control module can perform timely early warning adjustment on an accident according to the Pareto optimal solution set adjusted by real-time progress and rescreening parameters, can sense data in real time to form closed-loop feedback, and meets the requirements of engineering economy.

The invention relates to the technical field of karst soil monitoring, in particular to a karst key zone soil profile intelligent monitoring system and method. The intelligent monitoring system for the karst soil profile integrates a multi-layer sensor network and edge calculation and specially attacks the soil monitoring problem of a karst area, and the sensor network accurately collects moisture, temperature, pH value, organic matter and microorganism data; the data processing module receives and preprocesses in real time to ensure efficient transmission of data; the deep learning module constructs evolution, community and moisture models to improve the analysis precision; the decision support module generates health assessment and irrigation suggestions and assists in scientific decision making; the system overcomes the bottleneck of poor adaptability of traditional monitoring, reduces energy consumption, relieves communication burden, provides powerful technical support for research and management of karst soil, and realizes accurate, efficient and intelligent monitoring.

The invention provides a pile foundation pore-forming quality control method under complex karst geology, which comprises the following steps: generating a karst cave distribution feature set and a stratum abnormal point set based on karst geology survey data; then, determining a karst cave development trajectory sequence set, carrying out risk partitioning, and identifying a high-risk karst cave development region set; according to the method, a high-risk hole site set and a high-risk stratum node set are determined, and then a karst geological risk map is constructed. And based on the map, generating a pore-forming process path set and a pore-forming risk thermodynamic diagram, and superposing the pore-forming process path set and the pore-forming risk thermodynamic diagram to the pile foundation construction plan to form a pile foundation pore-forming risk thermodynamic diagram for visually displaying construction risks and guiding pile foundation construction. According to the method, the quality and safety of pile foundation hole forming under the complex karst geological condition can be improved, and the construction risk is reduced.

The invention discloses a karst underground river system multi-source pollution comprehensive quantitative traceability method, which comprises the following steps: S1, multi-dimensional sample collection and new pollutant screening: carrying out multi-period sampling on a water body, measuring conventional water quality indexes and nitrogen and oxygen isotopes, screening new pollutants, and carrying out targeted quantification on characteristic new pollutants; s2, principal component analysis dimension reduction and pollution factor identification: carrying out principal component analysis on conventional indexes and new pollutant data, extracting principal components of which the cumulative contribution rate exceeds 85%, identifying composite pollution factors, and eliminating secondary interference factors; s3, nitrogen and oxygen isotope auxiliary verification and denitrification elimination: eliminating denitrification interference by utilizing the ratio of delta 15N-NO3 <-> to delta 18O-NO3 <-> in combination with underground water burial depth and runoff velocity, and distinguishing the nitrate source as a chemical fertilizer, soil nitrogen or sewage and wastewater; and S4, PMF model optimization and pollution source quantification: inputting the conventional indexes and the new pollutant concentration matrix into a PMF model, and analyzing the contribution rate of each pollution source.

The invention provides a rapid karst cave modeling method and system based on multi-source data, and the method comprises the steps: collecting multi-source data including geological exploration data, remote sensing data and unmanned aerial vehicle aerial photography data, and carrying out the preprocessing of the multi-source data; fusing the preprocessed data by adopting a weighted fusion or principal component analysis method; extracting shape features, size features and position features from the fused data; the extracted karst cave features are converted into point cloud data or grid data, and a karst cave model is constructed through three-dimensional modeling software; performing vertex editing and patch adjustment on the initial model, and correcting the model form in combination with expert experience; and comparing the constructed karst cave model with actual geological data, and if an error exceeds a preset threshold value, iteratively adjusting model parameters until a precision requirement is met. According to the method, efficient extraction, accurate modeling and dynamic optimization of the karst cave geological information can be realized.

The invention provides a karst geological environment monitoring method and system, and relates to the technical field of geological environment monitoring. According to the method, a sensor array is deployed at a preset monitoring node of a target karst area, and multi-source data including geological, environmental and chemical sensing data are collected. The monitoring nodes are equipped with edge computing devices, and real-time noise reduction and feature extraction are carried out on data to form a sensing feature set. And uploading the feature set to a cloud, and carrying out dynamic risk assessment to predict the surface collapse probability. And establishing an adaptive early warning mechanism according to the probability, and generating a comprehensive monitoring report. Through technical innovation and multidisciplinary fusion, the defects of an existing karst monitoring system in the aspects of real-time performance, comprehensiveness, early warning precision and environmental adaptability are overcome, technical indexes are remarkably improved, and efficient and reliable technical support is provided for prevention and treatment of geological disasters in the karst area.

The invention provides a karst area pile foundation intelligent positioning and precise construction method which comprises the steps that a karst development characteristic data set of a target engineering area is obtained, and the karst development characteristic data set comprises a three-dimensional geological exploration data set and a karst attribute data set; and preprocessing the data set to obtain a karst preprocessed data set. And based on a feature extraction model, performing feature extraction on the preprocessed three-dimensional geological exploration data set to generate a three-dimensional geological feature map. And identifying the map through a karst cavity identification model to generate a karst cavity distribution thermodynamic diagram. And karst cave feature conversion processing is conducted on the karst preprocessing data set, and a pile foundation construction feature data set is obtained. And according to the karst cavity distribution thermodynamic diagram, constraint processing is conducted on the pile foundation construction characteristic data set, and a pile foundation constraint boundary diagram is generated. And outputting a pile foundation end hole positioning coordinate set according to a pile foundation positioning optimization algorithm and the pile foundation constraint boundary diagram. The construction safety and accuracy of the pile foundation in the karst area can be improved, and the construction risk and cost are reduced.

The invention relates to a pile foundation reinforcement method and system based on a karst area, and belongs to the technical field of foundation treatment and reinforcement. The pile foundation reinforcement method comprises the steps that a karst area pile foundation monitoring network is established, and karst cavity distribution characteristic parameters and underground water dynamic change parameters are obtained; constructing an adaptive evaluation model, and setting an initial real-time feedback correction parameter vector; based on a spatio-temporal evolution rule, generating a karst development comprehensive complexity index of dynamic evolution; a grading response mechanism is established, and pile foundation reinforcement early warning and response measures of corresponding grades are triggered; and constructing an intelligent decision-making system, and continuously optimizing an evaluation model and a reinforcement strategy through a feedback mechanism. According to the method, the karst development comprehensive complexity index is generated by introducing the underground water dynamic change parameters and combining the karst cavity distribution characteristic parameters, the evaluation of the geological environment is expanded from a static time point to a dynamic evolution process, and the comprehensiveness and accuracy of the evaluation are improved.

The invention discloses a reservoir identification method fusing seismic attribute inversion and ancient karst form modeling, and relates to the technical field of reservoir identification and modeling, and the method comprises the steps: carrying out the multi-modal fusion processing of a standardized image data set through an image sensor, obtaining the multi-modal fusion image data, executing the high-precision texture sampling and space alignment processing, and obtaining a multi-modal image data set; generating a cross-modal image fusion matrix; inputting the cross-modal image fusion matrix into an image topological graph structure construction process, establishing an image block node relationship and a spatial connection edge weight, and generating an ancient karst topological graph; and coupling the karst earthquake joint matching graph with the comprehensive reservoir attribute data, establishing an effective reservoir evaluation model, and generating a multi-stage reservoir prediction layer set through weighted fusion and multi-parameter spatial analysis. By constructing an ancient karst topological graph and a karst earthquake joint matching graph, spatial modeling of a complex karst structure and cross-modal correlation analysis of earthquake abnormal attributes are realized.

A design method for preventing and controlling water inrush catastrophe in tunnels and techniques for optimizing actual tunnel construction, specifically: discretizing calculation model into material points, setting virtual boundary layers outside boundary of calculation model; selecting size of horizon of material points to form neighborhood matrix of material points; making crustal stress equivalent to stress boundary condition, making karst cave water pressure equivalent to normal pressure, and converting displacement constraint and tunnel support into displacement boundary conditions; solving speed and displacement of material point, determining whether bonds of all material points meet failure condition, recording local damage situations; after initial balance calculation is stable, simulating tunnel construction process by using material point dormancy method; and according to physical values of optimization parameters of actual tunnel construction obtained by simulation results that meet the requirements of water inrush catastrophe prevention and control, implementing the optimized actual tunnel construction.

The invention discloses a sea water area karst cave group transient electromagnetic collaborative investigation data processing method and system, and the method comprises the steps: transmitting a wide frequency spectrum excitation signal to the seabed through a shipborne transient electromagnetic transmitting system, and synchronously collecting a secondary field attenuation signal responded by a seabed medium through a pull-type receiving array, meanwhile, a distributed sensor network is arranged at a preset seabed node to collect background noise base data; performing motion attitude compensation on the secondary field attenuation signal to generate transient electromagnetic response data; constructing an adaptive filter based on the background noise base data, performing noise suppression processing on the transient electromagnetic response data, and outputting noise suppression data; and performing space-time alignment on the noise suppression data of the towed array and the seabed node data to form a three-dimensional space sampling data set. According to the method, high-precision fusion of deep and shallow detection data and quantitative evaluation of the stability of the karst cave are realized, and the detection reliability and the engineering risk early warning capability of the complex ocean karst area are remarkably improved.

The invention discloses a geotechnical engineering investigation and analysis method and system based on multi-source data fusion, and belongs to the technical field of geological prospecting, and the method comprises the steps: collecting multi-source heterogeneous data of drilling holes in a karst area, and generating a comprehensive feature vector of the drilling holes; according to the drilling comprehensive feature vector, establishing a graph attention network, and generating an update feature; according to the updated features, generating an exploration prediction result of geotechnical engineering; establishing a dynamic adjustment model, and generating a model weight set value of the graph attention network; adjusting the model weight of the graph attention network according to the model weight set value of the graph attention network; according to the method, the multi-source heterogeneous data of the drill holes are converted into the comprehensive feature vectors of the drill holes, so that the problems of data isomerism and information islands are solved; in combination with a dynamic graph attention network, the model weight is optimized in real time, and the accuracy and adaptivity of karst cave distribution prediction under the complex karst geological condition are remarkably improved; and meanwhile, continuous iterative optimization can be carried out on the model, and the geotechnical engineering investigation accuracy of the karst area is improved.

The invention discloses a karst development area geological disaster identification method and system based on artificial intelligence, relates to the field of geological disaster early warning and risk assessment, and solves the problems that it is difficult to integrate geology and related data, it is difficult to construct a model to predict disaster categories based on historical disaster cases and real-time monitoring data, and the risk assessment efficiency is improved. The technical problem of lack of association of a prediction result with a geographic space to generate a risk distribution map and dynamically update a high-risk area is solved; comprising the following steps: introducing multi-source heterogeneous data, and combining historical geological disaster cases; preprocessing the data; extracting underground water level change rate, soil humidity and vibration frequency characteristics; combining a plurality of features into a feature matrix, and projecting the feature matrix to a principal component space; after training the model by using a random forest algorithm, optimizing the constructed model by combining cross validation with a Bayesian algorithm; the model prediction result is converted into a risk level value through a mapping rule; and generating a risk level distribution map based on the GIS data.

The invention discloses a high-pressure jet grouting pile construction optimization control method and system, and belongs to the technical field of pile foundation construction. According to the method, the pile body strength is predicted based on the XGBoost regression model, the HSIO hybrid swarm intelligent optimization algorithm is combined with the artificial bee colony algorithm and the seathead optimization algorithm, the construction parameter combination is dynamically adjusted, and optimal matching of the target strength and the real-time construction state is achieved. The method solves the problems of parameter setting deviation and non-uniform pile forming quality caused by the fact that traditional construction depends on artificial experience, is particularly suitable for high-precision construction requirements under complex geological conditions (such as karst areas and deep and thick sludge layers), remarkably improves the pile body strength stability, construction efficiency and quality controllability, and reduces the construction cost. And meanwhile, material waste and personal error risks are reduced.

The invention discloses a tunnel water inrush monitoring and early warning method under the water-rock coupling effect, and relates to the technical field of karst tunnels. Through the design of the method, the positions of karst caves and fractures of tunnels containing complex geological conditions such as karst caves and fractures under the water-rock coupling effect can be detected, the real-time change of water-containing fracture stress is detected, address disaster influence factors are analyzed based on machine learning to obtain evaluation indexes of the address disaster influence factors, and the evaluation indexes of the address disaster influence factors are analyzed. Therefore, a tunnel water inrush monitoring and early warning system is established, and a more accurate and rapid early warning method is provided for the tunnel water inrush problem.

The invention relates to the technical field of tunnel construction, and discloses a coal mine goaf karst tunnel construction method which comprises the following steps: S1, performing three-dimensional scanning on the axis and the periphery of a tunnel by adopting a high-frequency geological radar in combination with aerial survey of an unmanned aerial vehicle, and accurately positioning the boundary of a goaf, the development form of a karst cave and filler distribution; and S2, integrating a seismic wave reflection method, drilling and coring data and a geological sketch result, constructing a three-dimensional geological model, and dynamically predicting a potential water inrush and mud inrush risk area. According to the method, a high-frequency geological radar is combined with aerial survey of an unmanned aerial vehicle, three-dimensional scanning is conducted on the axis and the periphery of a tunnel, geological radar data, a point cloud model and tunnel design BIM are integrated, the boundary of a goaf (the error is smaller than or equal to 0.3 m) and the karst cave form are marked, and the collapse risk level is predicted based on rock mass mechanical parameters (compressive strength and fracture density) and hydrological data (seepage paths). Therefore, the method has the advantages of advanced detection, multi-source data fusion and the like, and the resolution of geological forecast is improved.

The present invention discloses a cave-type data center constructed based on an underground river, as well as its construction method and operation method, belonging to the technical field of cave-type data center construction. The data center includes a mountain body, in which an underground river is developed, and the underground river flows through a karst cave in the mountain body. On one side or both sides of the underground river in the mountain body, a number of equipment storage tunnels are provided. The number of the equipment storage tunnels are respectively connected to the karst cave through a slope connecting tunnel. An equipment cabinet and a liquid heat exchanger are arranged in the equipment storage tunnel. A river water conveying assembly is connected to the liquid heat exchanger. One end of the river water conveying assembly far away from the liquid heat exchanger passes through the slope connecting tunnel and extends into the underground river. The liquid cooling method is used to cool and dissipate heat from the equipment cabinet. This direct temperature control method significantly improves the heat dissipation and cooling effect on the IT equipment cabinet. At the same time, the heat exchange efficiency is high, and the operation energy consumption of the data center can be reduced.

The invention relates to a composite foundation settlement calculation method considering karst rock-soil interface grouting, which is characterized in that n layers of covering layers are arranged on a limestone karst area, the thicknesses of soil layers are respectively h1, h2,..., hn, the compression moduli of the corresponding soil layers are respectively Es1, Es2,..., Esn, the pile length of a composite foundation reinforcing pile is L, and the m layer is reinforced; the distance between the bottom face of the reinforcing pile group and the bottom face of each soil layer is Zm, Zm + 1,... and Zn, after karst grouting is considered, the compression modulus increasing coefficients of the soil layers above the bedrock face are lambda1, lambda2,... and lambdan, the lambdan is larger than or equal to 1.0, and the total settlement of the composite foundation is the product of the sum of the settlement S1 of the reinforcing area and the settlement S2 of the underlying layer and the settlement experience correction coefficient ms. According to the method, a composite foundation settlement calculation formula is improved, the soil layer compression modulus improvement coefficient lambda is introduced into the settlement calculation process, the synergistic effect of karst grouting and composite foundation reinforcement is comprehensively considered, and the settlement calculation precision of the high-speed rail karst roadbed composite foundation is remarkably improved.

The invention belongs to the technical field of karst geology bridge foundation construction and foundation treatment, and discloses a strong development karst geology bridge pile foundation grading treatment and composite pile forming construction method, which realizes on-demand treatment and grading construction through advanced geological detection and karst grade division and reasonable selection of construction modes according to different karst cave types. The problems of material waste and excessive grouting caused by construction are effectively avoided; a conventional cast-in-place pile is directly adopted in a micro-pore area, the process is simplified, and the cost is saved; for isolated karst caves and through karst caves, low-pressure grouting and synchronous pouring are designed correspondingly, the treatment difficulty is lowered, and the field resource utilization rate is increased; the construction method has extremely high field adaptability, can flexibly deal with complex and changeable karst structures, effectively realizes segmented independent grouting by adopting a multi-segment isolation guide pipe structure and combining with an intelligent grouting-pouring synchronous control system, and prevents slurry streaming or concrete loss between different construction depths.

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 geological disasters, and discloses a geological disaster monitoring analysis method and device, electronic equipment and a storage medium. The method comprises the following steps: deploying a sensor in a drill hole in a target geological area; acquiring an output signal of the sensor, and processing the output signal to obtain an acquired digital signal with a unified format; analyzing according to the acquired digital signals to obtain karst development characteristic data, karst dynamic change data and ground subsidence data; performing calculation according to the acquired digital signals, the karst development characteristic data and the karst dynamic change data through a karst disaster prediction model to obtain karst disaster risk index data; calculating correlation between the land subsidence data and the acquired digital signals to obtain land subsidence correlation data; and displaying the karst disaster risk index data and the land subsidence correlation data in a preset visual mode. According to the method, geological disasters are accurately monitored, and efficient geological disaster data analysis and risk assessment are realized.

The embodiment of the invention relates to the technical field of pile foundation construction, and particularly discloses a karst pile foundation slurry leakage prevention early warning method and system. According to the embodiment of the invention, the pile foundation construction area is determined, and geological radar detection, electrical exploration and drilling exploration are carried out on the pile foundation construction area to obtain geological detection data; monitoring deployment is carried out, real-time monitoring data is acquired, a three-dimensional monitoring model is established, and visual monitoring display is carried out; extracting a plurality of key feature data in the real-time monitoring data, and importing the key feature data into a preset slurry leakage model to obtain a slurry leakage prediction probability; and according to the slurry leakage prediction probability, determining a slurry leakage early warning grade, and according to the slurry leakage early warning grade, carrying out early warning alarm and emergency treatment. According to the method, a synergistic leaking stoppage mechanism of the pH sensitive pneumatic capsule and the geological bonding agent is innovatively designed, directional enrichment of nano materials is driven through the instantaneous negative pressure field effect, in-situ strengthening reaction based on rock mass mineral characteristics is triggered, and dependence of a traditional grouting process on the fracture form is broken through.

The invention relates to the technical field of karst space exploration, in particular to a karst space exploration method based on multi-source data mutual feedback inversion. The system comprises a data acquisition module used for acquiring multi-source geological data of a research area; the data preprocessing module is used for preprocessing the collected multi-source geological data; the multi-source data mutual feedback inversion module is used for constructing a geologic model of a karst space, and comprises a background value determination unit used for measuring a bedrock resistivity background value; the interpretation method construction unit is used for performing mutual feedback inversion on the multi-source data, generating a karst structure network in combination with geological information input and an artificial intelligence algorithm, searching an optimal path through hydrogeological simulation, and predicting a karst space; the karst space modeling module is used for constructing a three-dimensional geologic model of a karst space; and the visual output module is used for visually displaying the three-dimensional geologic model of the karst space. According to the technical scheme, the precision, reliability and efficiency of karst space exploration can be improved.

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 relates to the technical field of tunnel engineering safety prevention and control, and provides a gypsum rock tunnel water disaster risk evaluation and prevention and control scheme selection method which comprises the following steps: step 1-3, based on parameters such as rock mass structural integrity, stratum karst development degree, water yield property, groundwater corrosivity, gypsum rock mineral composition, expansibility and corrosion property, establishing a grading evaluation system, dividing corresponding grades and determining influence indexes R1-R8; 4, adopting an AHP method, an entropy weight method and a CRITIC method to perform multi-dimensional weight calculation on the subjectivity, objectivity and conflict influence indexes, correcting abnormal weights in combination with expert experience and verification rules, generating a comprehensive score, dividing risk grades, and matching graded prevention and control measures; and 5, optimizing and adjusting prevention and control measures in real time through a parameter risk level dynamic judgment and construction method matching mechanism. According to the method, multi-source parameter dynamic empowerment can be fused, and a full-life-cycle prevention and control method with accuracy, timeliness and self-adaptability is provided for tunnel engineering.

The invention discloses a construction method of an E-shaped portal rigid frame structure under the condition of multi-level height limitation, the E-shaped portal rigid frame structure is hoisted and constructed under the condition of multi-level height limitation, and the multi-level height limitation comprises eight levels of heights from an area A to an area H. Construction planning is conducted, specifically, the E-shaped portal rigid frame structure is vertically divided into three vertical areas including an area I, an area II and an area III, and each vertical area is transversely divided into five transverse areas; step 2, filling a karst cave: filling a cave body by using sleeve valve pipe grouting according to filling requirements; 3, pipe pile construction is conducted, specifically, pile driving is conducted in place in the construction area through a pile machine; 4, foundation construction is conducted, specifically, foundation construction is completed in the construction area; and 5, steel structure construction is conducted, specifically, construction of five transverse areas is sequentially conducted in the three vertical areas at the same time. According to the construction method, smooth construction of the portal rigid frame structure within the limited hoisting height is ensured, and the requirements for safety, quality and construction period efficiency are met.

The invention relates to the technical field of geological engineering, in particular to a tunnel karst development degree intelligent evaluation method and device and a medium, and the method comprises the steps: obtaining a multi-source fusion data set; generating a karst development fractal evaluation comprehensive index; determining a tunnel karst key detection area; constructing a three-dimensional medium distribution model of the karst area; carrying out karst area risk grade classification, and establishing a geological risk area prediction model; according to the method, the comprehensiveness and the accuracy of karst development characteristics are remarkably improved by combining multi-source data and a fractal theory; a detection path is dynamically optimized by using a reinforcement learning model, detection blind areas are reduced, and the detection efficiency of key areas is improved; noise reduction and fusion are performed on the data through the generative adversarial network, so that the accuracy of model construction is enhanced; based on the three-dimensional medium distribution model and the risk prediction model, graded evaluation and dynamic prediction of the risk of the karst area are effectively realized.