908 results about "Pore water pressure" patented technology

To significantly improve the prediction accuracy, response speed and management efficiency of large river bank slope disasters, an integrated bank slope monitoring method based on multi-parameter collaborative recognition is proposed. The solution includes step S1 of synchronously collecting data on bank slope displacement, pore water pressure, inclination angle, vibration frequency and environmental temperature and humidity to form an original monitoring dataset and construct a multi-parameter collaborative recognition network; step S2 of using a multi-modal data fusion algorithm to generate a fusion data matrix including spatiotemporal correlation features and perform spatiotemporal data alignment and outlier cleansing; step S3 of combining a geomechanical parameter library and a past disaster case library to output a risk level map and perform dynamic risk assessment model analysis; and step S4 of triggering a multi-level early warning mechanism and generating linked control commands including treatment suggestions to perform multi-level early warning and linked control.

The invention discloses a soft soil foundation deformation prediction method and system based on big data, and relates to the technical field of rock and soil monitoring. InSAR satellite data, Beidou GNSS displacement data, optical fiber strain data and meteorological and geological parameters are integrated through a multi-source sensing network. ERA5 reanalysis data is adopted to establish an atmospheric delay compensation function, and dynamic sliding window filtering and strain gradient constraint are combined to realize data space-time alignment and anomaly cleaning. Based on a generalized Kelvin creep constitutive model, environment coupling functions of temperature, humidity and pore water pressure are fused to dynamically correct model parameters, and the creep response characterization capability in a complex environment is enhanced. And performing distributed joint training on the regionalized geological data through a federated learning framework, fusing differential privacy encryption and a node credibility verification mechanism, realizing safety aggregation and migration optimization of cross-regional data, and generating a geological partition adaptive deformation prediction result. The method effectively improves the reliability of soft soil foundation deformation 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 digital intelligent management method and system for hydraulic engineering construction, particularly relates to the technical field of hydraulic engineering construction management, and is used for solving the problems of regulation lag and insufficient environmental disturbance adaptability of an existing method under the action of multi-physics field coupling. According to the method, a concrete three-dimensional temperature field model is established, initial regulation and control parameters are generated, and a heat conductivity coefficient under the seepage influence is corrected based on space-time relevance of pore water pressure and temperature data; the constraint boundary is dynamically adjusted in combination with the construction progress and the material strength curve, a cooling water pipe coordinated regulation and control matrix is constructed, and target flow instructions of space differentiation are generated through the included angle between the distance and the water flow direction; setting a lag time threshold and optimizing an execution time sequence according to the thermal inertia parameter, splitting the threshold and allocating a weight coefficient when the environmental wind speed suddenly changes, and finally updating the model parameter and the regulation and control instruction; accurate temperature control response and abnormal heat conduction suppression under multi-field coupling are achieved, and the structural safety and construction efficiency under complex working conditions are improved.

The invention discloses a slope structure safety monitoring method and system based on a distributed sensing technology, and the method comprises the steps: carrying out the distributed monitoring of the surface and deep part of a slope, and obtaining monitoring data; data transmission is carried out on the monitoring data through a wireless network; preprocessing and analyzing the transmitted monitoring data to obtain a monitoring data set; according to the monitoring data set, a safety evaluation model of the slope structure is established, and slope surface displacement, deep stress and pore water pressure are comprehensively considered in the safety evaluation model; based on the safety evaluation model, obtaining a quantitative evaluation result of the slope safety state; and establishing a slope disaster early warning model, and performing trend analysis and threshold judgment on the quantitative evaluation result through the slope disaster early warning model to realize early warning and real-time early warning of the slope disaster. The monitoring method provided by the invention has the characteristics of distribution, multiple parameters, long-term stability and high reliability, can comprehensively sense the change of the slope safety state, and provides a reliable basis for disaster early warning.

The invention discloses a safety risk assessment system based on slope construction, relates to the technical field of slope construction, constructs a complete system composed of a data acquisition module, a dynamic disturbance identification module, a risk assessment module and an intelligent early warning and decision-making module, and acquires multi-source data such as displacement, cracks, pore water pressure, seismic disturbance and blasting vibration at high frequency. The time sequence analysis and disturbance feature extraction algorithm is combined to realize the rapid identification of the sudden disturbance event; risk score prediction is further carried out based on a polynomial regression model, and multi-level early warning is generated through comparison with a set threshold value; and finally, by combining the construction state, the support parameters and the disturbance intensity, a safety control suggestion is dynamically generated, so that the response speed and prediction precision of the system to the sudden slope instability risk are effectively improved, and real-time perception and intelligent management and control of the risk in the slope construction process are realized.

The invention relates to the technical field of soft soil area building engineering construction monitoring, and discloses a soft soil area static pressure pile soil squeezing characteristic time effect monitoring and analysis method, which comprises the following steps: arranging a multi-source sensor array in a construction area, and collecting signals such as soil displacement and pore water pressure; and after noise filtering, time sequence alignment and data normalization preprocessing, a layered soil body finite element model is established. A mathematical model with the soil squeezing effect associated with time is constructed in combination with time sequence analysis, a prediction model based on machine learning is used for predicting soil body response, and model parameters are adjusted through an optimization algorithm. And judging whether convergence occurs or not according to a model prediction error so as to dynamically adjust the sensor array and form a closed-loop iterative analysis process. The method can comprehensively and accurately monitor and analyze the soil squeezing characteristic time effect of the static pressure pile in the soft soil area, provides a scientific basis for construction decision making, and guarantees engineering safety and quality.

The present disclosure relates to the technical field of landslide hazard model tests, and in particular to a landslide model test method and device for realizing multi-variable synchronous monitoring. In the test device, a soil slope simulation unit is configured to pile up a soil slope and simulate different inclinations of the slope. A rainfall simulation unit is configured to simulate rainfall of different intensities and induce a landslide. A runoff-seepage separation unit is configured to realize separation of runoff from seepage of the slope under the effect of rainfall. A monitoring module is configured to monitor runoff and seepage flow rates of the slope, changes in water content and pore water pressure within the slope, and a morphological change of the slope under the effect of rainfall, to obtain a mechanism of rainfall-induced landslide deformation and damage based on changes in the flow rate.

The invention relates to the technical field of slope engineering, in particular to a settlement deformation prediction method and system for a reinforced soil slope building structure. The method comprises the following steps: acquiring a soil body infiltration line position parameter of a reinforced soil slope, an interface friction coefficient of a reinforced material and a soil body, a soil body grain grading curve and environment temperature and humidity duration information, and obtaining initial characteristic data of a slope system state; constructing a heat-force-water multi-field coupling monitoring network in the soil-reinforced material interface area according to the initial characteristic data of the slope system state, and obtaining the interface shear stress, the interface normal strain, the pore water pressure gradient and the temperature gradient of each monitoring point to obtain interface action characteristic data. According to the method, the environment disturbance probability density model based on the temperature fluctuation and the sudden change of the water content is constructed, the influence of a complex environment on the slope stability can be quantitatively described, and the robustness of the model is improved.

The invention relates to the technical field of design analysis, in particular to a soft soil deep foundation pit earth excavation construction scheme planning method and system.The soft soil deep foundation pit earth excavation construction scheme planning method comprises the following steps that a three-dimensional geological model is built based on geological exploration data, and rheological parameters, shear strength parameters and permeability coefficients of all soil layers are extracted to serve as layering parameters; according to the layering parameters and preset supporting structure rigidity parameters, a soil body rheological rate compensation coefficient is calculated, and an earthwork block excavation scheme with the grading thickness difference is generated; and in the excavation process, the displacement rate and pore water pressure data of the supporting structure are collected in real time, and when a monitoring value exceeds a preset threshold value, a dynamic adjustment instruction of the block excavation scheme is triggered. According to the method, the layering thickness and the blocking form are intelligently controlled according to different sensitivity levels, the matching coping capacity for soft soil instability inducements is improved, and the limitation that a single physical parameter dominates in traditional blocking design is broken through.

The invention discloses a tubular pile reinforced composite foundation bridgehead differential settlement monitoring method and system, and belongs to the technical field of foundation treatment and foundation engineering. Comprising the following steps: acquiring soil pressure data, settlement data, displacement data and pore water pressure data of a pipe pile reinforced composite foundation bridgehead, respectively performing feature extraction, and adaptively fusing a plurality of feature extraction results by utilizing correlation to generate a fused feature vector; the fusion feature vector is processed through a trained settlement prediction model, and a settlement prediction result is obtained with spatial-temporal features as guidance; wherein the settlement prediction model comprises an adaptive residual connection LSTM network layer, a hierarchical space-time attention mechanism network layer, a full connection layer and a linear layer which are connected in sequence. Comprehensive monitoring of a complex foundation environment can be realized, and the precision of settlement prediction is improved; the problems that the settlement mechanism is not deeply considered in existing settlement monitoring, and the settlement prediction accuracy needs to be improved are solved.

The present invention provides an intelligent construction control method, apparatus, and system for splitting jet grouting mixing piles, relating to geotechnical engineering. The method includes receiving real-time perception information from a construction information self-perception system, which includes drilling depth, drilling rig output power, drill bit torque, drill rod axial force, and pore water pressure of the drill bit. This information is input into a trained machine-learning model for stratum information, establishing a nonlinear implicit correspondence relationship between the perception information and construction stratum conditions, and outputting real-time stratum state information. The real-time stratum state information is then input into a trained self-matching machine-learning model for optimal construction parameters, establishing a nonlinear implicit correspondence relationship between the stratum state information and optimal construction parameters, and outputting current optimal construction parameters. This invention enables real-time intelligent inversion of stratum information and dynamic self-optimization control of construction parameters.

The invention provides a rainfall and earthquake coupled slope simulation method and system. The method comprises the following steps: constructing a coupling numerical model through multi-physics field data of a slope; the dynamic coupling effect of rainfall and earthquake is simulated by initializing a seepage field and loading earthquake power; the stability of the simulation result is comprehensively evaluated through iteration of a strength reduction method, and whole-process coupling from seepage to dynamic response of the slope is achieved. SeismoSignal is used for processing seismic waves, and the problem that the value of kinematics parameters is not zero when the seismic action is finished is avoided; the allowable maximum negative pore water pressure boundary is assigned to the slope surface of the side slope, the actual steady-state seepage field of the side slope is restored, and the seepage field in the side slope under different rainfall conditions is accurately calculated; by adopting an unsaturated soil dynamic seepage model for a seepage field, Rayleigh damping is introduced and an earthquake load is applied, so that cross-time scale coupling is realized. The method is simple and convenient to operate, high in calculation efficiency and accurate in calculation result, and has practical significance and value in research of the problem in the rock-soil field.

The invention discloses a dike safety monitoring and maintenance system and method based on multi-sensor fusion, and belongs to the technical field of hydraulic engineering safety. Comprising a pore water pressure sensor, a horizontal displacement sensor, a vertical displacement sensor, a seepage monitoring sensor, a data acquisition and transmission module and a data acquisition center. And the precision of the sensor is high, for example, the precision of a pore water pressure sensor is within + / -0.1 kPa. And the transmission module adopts a hybrid communication network which is mainly composed of an optical fiber and is assisted by ZigBee and LoRa wireless. The data acquisition center is provided with a high-performance processor and the like. The method comprises the steps of data acquisition, preprocessing, anomaly detection and early warning, risk assessment, maintenance strategy formulation and the like. Data are processed through joint filtering, dynamic normalization and other algorithms, the safety state is judged through clustering analysis and a composite early warning model, and risks are evaluated by integrating multiple factors. According to the method, the accuracy, scientificity and effectiveness of dike safety monitoring and maintenance are remarkably improved, and a powerful guarantee is provided for hydraulic engineering safety management.

The invention provides a landslide prediction method and system based on big data analysis, and relates to the technical field of big data analysis, and the method comprises the following steps: obtaining multi-source geological data of a target region, constructing a three-dimensional geological model, and dividing slope units; calculating an initial safety coefficient of each slope unit by adopting a limit equilibrium method and Monte Carlo simulation; acquiring real-time monitoring data, establishing an inverse analysis optimization model, and inverting and calibrating physical and mechanical parameters; updating calibration parameters to the model, coupling rainfall infiltration and underground water seepage simulation, dynamically updating a pore water pressure field, and calculating a real-time dynamic safety coefficient; and according to a comparison result of the dynamic safety coefficient and a preset threshold value, determining the stable state of the slope and sending out corresponding early warning. According to the method, through multi-source data fusion, parameter dynamic inversion calibration and real-time hydrological and mechanical coupling simulation, accurate and dynamic evaluation and graded early warning of slope stability are realized, and the accuracy and timeliness of landslide prediction are remarkably improved.

The invention discloses a settlement prediction system based on soft soil creep, which relates to the technical field of settlement prediction, and dynamically calculates stress increment, total stress increment and pore water pressure by collecting the loading rate and underground water level change of a foundation in real time, and further calculates vertical effective stress based on the intermediate variables. Therefore, an accurate void ratio calculation formula is constructed. The systematized process not only improves the precision of settlement prediction, but also enhances the adaptability of the model to the change of actual working conditions. Specifically, through collection and processing of real-time data, the method can quickly respond to changes of foundation conditions, and it is ensured that model parameters are always in the optimal state. Calculating the void ratio by using the initial model parameters and evaluating the difference between a predicted value and an actual observed value in combination with an error function, so as to realize iterative optimization of the initial model parameters and finally obtain a corrected empirical model. The robustness and reliability of the model are remarkably improved, and settlement prediction is more accurate and reliable.

The invention discloses a dynamic geological survey system and a construction simulation method. The dynamic geological survey system comprises an intelligent sensing and data acquisition module and a multi-source data fusion processing module, the intelligent sensing and data acquisition module comprises a Raman scattering optical fiber strain sensor array, a three-component seismometer monitoring network and a distributed optical fiber pressure sensing subsystem; the Raman scattering optical fiber strain sensor array, the three-component seismometer monitoring network and the distributed optical fiber pressure sensing subsystem form a multi-dimensional sensing network for shield construction geological monitoring. By means of the integrated intelligent sensing and data acquisition module, geological changes in the shield construction process can be monitored. The Raman scattering optical fiber strain sensor array, the three-component seismometer monitoring network and the distributed optical fiber pressure sensing subsystem jointly form a multi-dimensional sensing network, geological strain, microseismic events, pore water pressure and other key information can be captured, and comprehensive geological data support is provided for construction.

The invention discloses a monitoring and early warning system for geotechnical engineering, and relates to the technical field of geological disaster early warning. A monitoring and early warning system for geotechnical engineering comprises a data acquisition module, a data processing module, a model construction module and a disaster early warning module. According to the method, the displacement and the shear stress of the rock-soil body are predicted through the displacement-hydrological-geological coupling prediction model, so that the comprehensive influence of the mechanical state of the rock-soil body and environmental factors on geotechnical engineering disasters can be more comprehensively reflected, and the coupling effect of various monitoring data can be comprehensively considered; therefore, prediction errors and uncertainty caused by data missing or model singleness can be effectively reduced, hydrological data are processed through causal convolution, the long-term dependency relationship among the hydrological data can be effectively captured, the relevance and hysteresis among the water level, the rainfall and the pore water pressure can be reflected, and the prediction accuracy of the hydrological data is improved. Therefore, the accuracy of model prediction can be remarkably improved, and the early warning accuracy of the monitoring and early warning system is also improved.

The invention discloses a penetration discrimination method and system suitable for a double-layer dam foundation, and the method comprises the steps: S1, obtaining the geological parameters of a double-layer dam, and forming a geological parameter set; s2, constructing a seepage numerical model of the double-layer dam foundation, simulating the seepage field distribution condition of the double-layer dam foundation at each grid node, and obtaining seepage field data; s3, grid node seepage force is calculated, and a potential seepage failure area is determined; s4, monitoring pore water pressure and seepage flow changes of the preset monitoring points in the potential seepage failure area; s5, constructing a seepage failure judgment model, and outputting a seepage judgment result; and S6, performing grading processing according to the judgment result of seepage failure, and outputting grade seepage early warning information through a seepage grading model. According to the method, judgment, real-time monitoring and graded early warning of seepage damage of the double-layer dam foundation are achieved, and the safety of the heterogeneous double-layer dam is guaranteed.

The invention relates to the field of river bank slope monitoring, in particular to an integrated bank slope monitoring method based on multi-parameter collaborative awareness, which comprises the following steps: S1, multi-parameter collaborative awareness network construction: collecting bank slope displacement, pore water pressure, inclination angle, vibration frequency and environment temperature and humidity data to form an original monitoring data set; s2, data space-time alignment and abnormal value cleaning: generating a fusion data matrix including space-time correlation features by adopting a multi-modal data fusion algorithm; s3, dynamic risk assessment model analysis: outputting a risk level map in combination with the geomechanical parameter library and the historical catastrophe case library; s4, multi-stage early warning and linkage control: triggering a multi-stage early warning mechanism and generating a linkage control instruction including a disposal suggestion; powerful support can be provided for accurately carrying out large-scale river bank slope collapse monitoring, real-time risk assessment and efficient emergency response, and remarkably improving the prediction accuracy, response speed and management efficiency of large-scale river bank slope disasters.

The invention discloses a three-dimensional crustal stress field visualization construction method under a complex geological structure condition, and relates to the technical field of crustal stress field analysis. Comprising the following steps: in a to-be-analyzed area, obtaining characteristic data such as density and Poisson ratio of a surface rock-soil layer, a sedimentary layer, a parent material layer and a bedrock layer through well drilling; applying seismic waves, acquiring longitudinal wave and transverse wave speeds of each rock-soil layer by using a geophone, and calculating a shear modulus and a bulk modulus; and establishing a finite element model to analyze the strain tensor under the load, and calculating the stress tensor in combination with the modulus. Calculating pore water pressure, and correcting the stress tensor to obtain effective stress. Fault feature information is obtained through a geological report, and the stress tensor is further corrected to obtain accurate and effective stress. And constructing a three-dimensional geologic model and dividing grids, mapping accurate and effective stress to each grid unit, and completing visual construction of the crustal stress field by utilizing visual software. Safety and economic benefits are effectively improved, and risks caused by geological factors are reduced.

A digital twin-based intelligent slope monitoring method, system, and storage medium are provided. The method includes obtaining point cloud data of a surface of a slope, creating a digital twin model; dividing the digital twin model to generate a plurality of first grids, calculating a displacement change value of each first grid during a target duration; defining the first grid with a displacement change value greater than a first threshold as a first region, dividing the target duration into a plurality of first durations with different lengths; setting a plurality of rainfall patterns, determining an optimal pattern for each first duration, calculating representative rainfall, constructing a prediction model, predicting representative rainfall for a future duration based on the optimal pattern and the representative rainfall of the first duration; generating a safety factor of the first region during the future duration based on the representative rainfall and a pore water pressure.

The invention relates to the field of disaster monitoring, and provides a slope disaster real-time monitoring and early warning method and system, and the method comprises the steps: laying a distributed optical fiber sensing network, a Beidou GNSS receiver, a miniature pore water pressure gauge and an inclinometer at a target slope earth surface position and a deep position, and obtaining internal and external cooperative monitoring data; extracting and calculating a plurality of branch characterization values in the internal and external cooperative monitoring data; calculating and generating multi-source fusion feature data based on the plurality of branch representation values; establishing a dynamic threshold monitoring model, and calculating and generating a real-time early warning threshold; establishing a multi-level linkage early warning mechanism based on the real-time early warning threshold value; and internal and external cooperative monitoring is realized through a multi-mode sensor network, and slope state information is comprehensively captured. Multi-source data fusion processing improves data quality and reliability, a dynamic threshold model accurately adapts to different working conditions, and a multi-level linkage early warning mechanism ensures timely response. The accuracy and timeliness of slope disaster early warning are greatly improved on the whole, and disaster loss is effectively reduced.

The invention provides a solid waste storage yard safety risk early warning method, and belongs to the technical field of solid waste storage yard safety risk, and the method comprises the steps: firstly building a three-dimensional structure model through a geological radar, determining an initial key region, and laying a multi-layer monitoring network comprising a deep displacement meter, a pore water pressure meter and an optical fiber temperature sensor on the basis; the pollution risk is evaluated by applying a percolate migration and diffusion mechanism model, an MSLNN multi-level neural network prediction model is established to analyze the stability of a pile body, and a multi-level early warning threshold value is set. The key innovation lies in that parameters of a sensor node arrangement equation set are dynamically adjusted through a balance judgment function, a closed-loop iteration mechanism is formed, the monitoring network layout and the early warning model are continuously optimized until convergence conditions are met, and the safety risk early warning accuracy of the solid waste storage yard is remarkably improved.

The invention discloses a soft foundation treatment method under the condition of reinforced flow-state muddy soft soil. According to the intelligent soft foundation treatment method, an intelligent monitoring and control system is introduced, the whole soft foundation treatment method achieves accurate monitoring and automatic adjustment of the construction process, and remarkable beneficial effects are achieved. Firstly, key parameters such as pore water pressure, soil deformation and underground water level are monitored in real time, so that engineers can quickly respond to changes of soil properties and timely adjust preloading load and vacuum degree, construction efficiency is optimized, and unnecessary construction period delay is reduced. And secondly, due to automatic data acquisition and processing, the accuracy and speed of data processing are greatly improved, personal errors are reduced, and the construction quality is ensured. In addition, the intelligent system can give out early warning in time when potential risks are found, construction safety is guaranteed, and the possibility of accidents is reduced.

The invention belongs to the field of soil rock engineering, and provides a three-surface integrated geologic structure modeling method for a deep and large foundation pit in a river-adjacent region, which comprises the following steps: acquiring physical and mechanical parameters and hydrogeological information of the deep and large foundation pit in the river-adjacent region; dividing spatial distribution of different soil layers and rock stratums; describing the influence of water level time variation on the pore water pressure and subsurface erosion of the surrounding rock of the foundation pit through an unstable seepage equation; dividing a finite element or finite difference grid, and applying a dynamic water level and ground load boundary condition; gradually unloading and analyzing the evolution of an underground water flow field and a soil-rock deformation field in each excavation step, and enabling the water surface-ground-soil-rock surface coupling effect to penetrate through the whole excavation process; and numbering the support members, sequentially simulating local failures of the support members, recording the maximum horizontal displacement, the maximum vertical displacement or the change curve of the safety coefficient along with the number of the failure members after each failure of the foundation pit, quantifying the redundancy of the support system, and identifying key members. According to the scheme, the accuracy of the model is improved.

The invention discloses an intelligent data acquisition and optimization control system applied to a mine filling system, and relates to the technical field of computer automatic control, the system comprises an ore body stress analysis unit, a filling slurry solid phase mass analysis unit, a filling body physical property analysis unit and a filling control and analysis unit; the ore body stress analysis unit is used for establishing a three-dimensional ore body geometric model according to an ore body geological exploration result, dividing the three-dimensional ore body geometric model into finite element grids and calculating the solid phase mass concentration of filling slurry; the filling body physical property analysis unit is used for establishing a rheological model of the filling slurry according to the solid phase mass concentration of the filling slurry; and the filling control and analysis unit is used for calculating the filling stability according to the three-dimensional stress tensor field of the ore body and the pore water pressure of the filling slurry to obtain a stability coefficient. The filling speed can be intelligently controlled, the construction stability is evaluated in real time, and the safety and high efficiency of the filling construction process are ensured.

The invention discloses a slope deformation monitoring system applied to a power transmission and transformation project, relates to the technical field of slope deformation monitoring, realizes high-precision slope displacement calculation by fusing optical fiber voltage signals and Beidou positioning data, constructs an inclination angle prediction model by combining historical inclination angle data, and introduces residual analysis to identify a structure deviation trend. The system dynamically calculates soil stress attenuation based on pore water pressure data, generates a threshold value correction factor driven by a softening coefficient, and then adaptively adjusts a displacement acceleration early warning threshold value to realize adaptation to different geological states. A closed-loop risk identification mechanism is established by monitoring double indexes of displacement acceleration and inclination angle prediction residual error, and the method has the advantages of being timely in response, high in identification precision, low in false alarm rate and the like, and is particularly suitable for power transmission and transformation scenes with complex geological conditions and large rainfall intensity changes.

The invention discloses a seismic signal-based soil-structure co-evolution seismic liquefaction analysis device and method, and the method comprises the steps: obtaining geological and seismic data of a target region, and constructing a geometric ratio, time ratio and acceleration ratio co-scaling system based on a Bockingham Pi similarity theory; a seismic liquefaction multi-field coupling evolution device is constructed to evolve the soil liquefaction process of a target area, a distributed optical fiber sensor array and a micro pore pressure meter group are adopted to form a millimeter-level spatial resolution monitoring network, and a high-speed camera system is matched to synchronously capture pore water pressure, structural strain and a soil shear band expansion process. Total element time sequence correlation analysis of vibration triggering, seepage developing and structural catastrophe is achieved, the influence degree of the total element time sequence correlation analysis on the earthquake is further analyzed, and a direct basis is provided for engineering restoration priority and a reinforcement scheme.

The embodiment of the invention discloses a numerical simulation method for water-rich stratum foundation pit engineering settlement. The method comprises the steps that computational grids and soil mass parameters of a water-rich stratum foundation pit engineering numerical model are obtained; carrying out numerical calculation on the computational grid to obtain distribution conditions of stress and a seepage field; calculating a permeability coefficient and a compression coefficient in the current soil body state; calculating dynamic pore water pressure and steady-state pore water pressure; calculating soil deformation according to the compression coefficient, the dynamic pore water pressure and the steady-state pore water pressure; and carrying out consolidation analysis according to the permeability coefficient. According to the embodiment, the consolidation deformation condition of the soil body in the complex construction process can be accurately predicted.