23 results about "Slope failure" patented technology

According to the cold region slope multi-field coupling effect simulation test device and method, the device is composed of a freeze-thaw environment test box, a slope model, an anchor rod axial loading system, a crack water injection assembly and a multivariate data monitoring system, and the multi-physical field coupling effect of freeze-thaw cycle, crack water permeation and long-term creep can be simulated. By dynamically adjusting the temperature gradient and graded loading parameters, data of a stress field, a temperature field and a displacement field can be collected in real time, the influence of different freeze-thaw conditions and creep drawing loads on a slope damage mechanism can be analyzed, a key test means is provided for revealing the strain rate effect, the aging deformation characteristics and the instability criterion of the slope in the cold region, and the test efficiency is improved. The method has an important guiding value for stability evaluation and disaster prevention and control of the cold region slope engineering.

The invention discloses a defective rock mass early warning method based on a multi-fractal spectrum and a damage variable, and belongs to the technical field of rock mass disaster early warning, and the method comprises the steps: extracting a slope rock mass acoustic emission signal time-varying data sequence, and calculating a dynamic self-adaptive sliding window length; according to the time-varying data sequence, a ringing counting time sequence of each sliding window is determined, multi-scale deconstruction is carried out based on Haar wavelets, and multi-resolution measurement of each subinterval in the sliding window is calculated; determining the upper and lower limits of the weight of each multi-resolution measure, and calculating the spectral width of the multi-fractal spectrum of each sliding window; calculating the rock mass damage index and the damage index acceleration of the rock mass damage index at each moment in the rock mass damage process, and establishing a relation curve between the spectral width of the multi-fractal spectrum and the damage index; and determining a jump point of the damage index acceleration and a jump point of the spectral width of the multi-fractal spectrum according to the relation curve, and when the two jump points occur at the same moment, determining that the moment is a slope damage early warning point.

A method for determining the optimal pull-out strength of anchor bolts in reinforced slopes, belonging to the field of slope reinforcement technology, includes: establishing a mathematical model of the anchor bolt-reinforced slope; obtaining curves showing the relationship between the slope safety factor and the pull-out strength of anchor bolts under different numbers and bond lengths; obtaining curves showing the variation of slope failure probability and anchor bolt pull-out strength under different numbers and bond lengths; and obtaining the optimal design value of the pull-out strength of anchor bolts in the reinforced slope. This method introduces the failure probability into anchor bolt support engineering, reflecting the reliability of the slope anchor bolt support engineering through the failure probability; and effectively obtains the relationship between pull-out strength and the number of anchor bolts, bond strength and slope stability coefficient. In slope reinforcement engineering, the pull-out characteristics of anchor bolts are fully utilized. The optimal pull-out strength corresponds to the number of anchor bolts and the bond strength, avoiding waste of engineering materials and improving the economy of the support engineering while ensuring slope safety.

The invention discloses a rock mass parameter inversion method and system based on automatic machine learning, and the method comprises the steps: obtaining temporal and spatial change features from rock mass monitoring data, constructing a feature importance evaluation system, and obtaining a standardized feature vector; according to the coordinated parameter transmission and model synchronization result, rock mass strength and deformation parameters are obtained, a correlation analysis model is adopted to calculate a safety coefficient and a damage probability, and a harmfulness evaluation index is obtained; the method comprises the following steps: acquiring a parameter distribution rule and a change trend, constructing a parameter database system for classified filing and version control, obtaining centralized management platform data, extracting historical tracing information, triggering a dynamic adjustment inversion strategy if the change trend exceeds a preset threshold, and obtaining a real-time updated parameter statistical analysis result for integrated risk prediction. According to the method, the accuracy of rock mass parameter inversion and the real-time performance of engineering risk prediction are remarkably improved, the slope damage probability is reduced, and engineering safety and efficient management are ensured.

The application discloses a method and system for inverting a structure of a seismic-induced soft and hard interbedding anti-inclined ancient landslide stratum, and relates to the technical field of seismic-induced landslide simulation and prevention, which comprises the following steps: reconstructing a pre-destabilization topography to obtain a geological profile of a main sliding direction before destabilization; constructing a plurality of soft and hard interbedding anti-inclined slope models with different soft and hard interbedding thickness ratios by using a discrete element numerical simulation technology based on a damage theory, and then simulating a dynamic failure process to obtain a characteristic line of a simulated slope failure surface; determining an actual topographic line based on unmanned aerial vehicle surveying and mapping data, and calculating a geometric matching degree according to the actual topographic line and the characteristic line of the simulated slope failure surface; selecting a soft and hard interbedding anti-inclined slope model with the largest geometric matching degree, and marking a corresponding soft and hard interbedding thickness ratio as a parameter of a seismic-induced pre-destabilization soft and hard interbedding anti-inclined ancient landslide stratum structure. The application can calculate the parameter of the seismic-induced pre-destabilization soft and hard interbedding anti-inclined ancient landslide stratum structure which cannot be directly obtained through field investigation.

The invention provides a seabed slope stability evaluation method and system, and belongs to the field of landslide disaster prevention and control. The method comprises the following steps: acquiring a digital elevation model (DEM) of a to-be-evaluated area based on a to-be-evaluated seabed slope range, and dividing sample points corresponding to grids; collecting at least five unstable factors of a sample point, constructing a geomechanical model to calculate a safety coefficient, and dividing the sample point into a current stable point and an inspection point; based on the inspection points, sorting contribution values of all parameters of the unstable factors to the safety coefficient, and taking the first three sorted parameters as the inspection factors; and constructing an association stability evaluation model, calculating the failure probability of the current investigation point for each investigation point on the basis of three investigation factors of the investigation point, the nearest neighbor sample point and the next neighbor sample point, drawing a seabed slope failure risk map, and sending out an instability early warning for the investigation point reaching a failure probability threshold. According to the method, the calculation amount of seabed slope stability evaluation is reduced, and the evaluation accuracy is improved.

The invention discloses a slope damage area automatic detection and restoration discrimination method based on deep learning, and the method comprises the following steps: obtaining and preprocessing multi-temporal slope monitoring data, and forming multi-source input data; inputting the multi-source input data into the variant DeepLab segmentation network to generate boundary data of the slope damage area; performing time sequence analysis based on the boundary data of the slope damage area to generate slope damage evolution data; combining the boundary data and the evolution data, extracting the area, the crack width, the displacement rate and the gradient, and forming a repair discrimination parameter set; inputting the repair discrimination parameter set into the improved multi-parameter repair discrimination network, and outputting repair level data; introducing an uncertainty modeling module into the network, and generating repair judgment confidence data; and matching a repair measure library according to the repair level data and the repair judgment confidence data, generating a repair measure recommendation result and outputting a judgment report. The system realizes integration of detection and discrimination, and is suitable for geological disaster monitoring and treatment.

A method and device for assessing the damage caused by acid rain to lime-improved loess soil. The method comprises the following steps: obtaining in-situ improved soil and in-situ loess, performing layered processing, and conducting testing; comparing various test data of the in-situ improved soil and in-situ loess with various test data of the initial improved soil and initial loess to determine the degree of deterioration of the in-situ improved soil; constructing a model test box based on actual slope conditions, sequentially filling the model test box with an in-situ loess slope and an in-situ improved soil slope to simulate slope failure under acidic conditions; obtaining the in-situ improved soil slope and in-situ loess slope, performing layered processing, and conducting testing; comparing various test data of the in-situ improved soil slope and in-situ loess slope with various test data of the initial improved soil and initial loess to determine the degree of deterioration of the in-situ improved soil slope. This design not only provides highly reliable and accurate assessment results, but also is easy to operate and has low cost.

The application provides a photovoltaic slope safety evaluation method considering spatiotemporal variability of multi-source parameters, comprising: collecting geological exploration data and multi-source monitoring data of a photovoltaic slope, determining a random distribution type of initial multi-source parameters, and selecting key monitoring data; performing data matching of multi-source parameters and monitoring data, and screening multi-source parameters and monitoring data by using a MIC algorithm; based on field geological exploration data, a three-dimensional simulation model considering spatiotemporal variability of multi-source parameters of the photovoltaic slope is constructed, and a data set is obtained; based on a 1D-CNN-MCMC deep learning model framework, the data set is trained, and a posterior probability distribution of a safety factor FS of the photovoltaic slope is dynamically updated in combination with field monitoring data; a failure probability P of the photovoltaic slope is calculated f , and safety evaluation and early warning are performed. The application comprehensively considers uncertain factors such as slope geological parameter variability, environment, and photovoltaic structure, uses field multi-source monitoring data, obtains a photovoltaic slope failure probability more in line with actual conditions, and performs safety evaluation and early warning.

The invention provides a disaster protection method and system based on a mining slope, and the method comprises the steps: determining a rock-soil layer parameter of a target mining slope, a goaf geometric parameter and a goaf overlying strata collapse characteristic parameter, and determining a geomechanical model of the target mining slope; analyzing a slope failure mode under the influence of goaf mining by using a geomechanical model to obtain a stability coefficient of a target mining slope; and determining a filling range parameter and a filling slurry design parameter of the goaf according to the stability coefficient. The slope failure modes of the slope under different mining working conditions are simulated through the geomechanical model of the target mining slope, and compared with a single-parameter evaluation mode, the obtained stability coefficient is more accurate. The filling range of the goaf and the design parameters of the filling slurry are determined according to the stability coefficient, it can be ensured that the filling range of the goaf and the performance of the filling slurry are matched with the actual stability requirement of the slope, and the problem of mining slope geological disasters can be effectively solved.

The invention belongs to the technical field of disaster identification and prediction, and provides a slope failure instability mode intelligent identification method and system, and the technical scheme is that the method comprises the steps: taking the engineering feature information subjected to parameterized coding processing in a slope failure instability database as an input parameter, taking the slope failure instability mode as an output parameter, and constructing a plurality of parameter combination schemes; each individual of the genetic algorithm is expressed as an input feature subset, performance evaluation is performed on the slope failure instability mode intelligent identification model trained under different parameter combination schemes, a fitness function of each individual is determined according to a performance evaluation result, and a feature subset corresponding to the optimal individual is found through iterative optimization; and predicting the to-be-identified scheme based on the obtained feature subset corresponding to the optimal individual to obtain a slope failure instability mode prediction result. And the efficiency and the accuracy of the slope failure instability mode identification means are improved.

The invention provides a photovoltaic slope safety evaluation method considering spatial-temporal variability of multi-source parameters, and the method comprises the steps: collecting geological exploration data and multi-source monitoring data of a photovoltaic slope, determining the random distribution type of initial multi-source parameters, and selecting key monitoring data; performing data matching between the multi-source parameters and the monitoring data, and screening the multi-source parameters and the monitoring data by using an MIC algorithm; the method comprises the following steps: constructing a three-dimensional simulation model considering photovoltaic slope multi-source parameter spatio-temporal variability based on field geological exploration data, and obtaining a data set; training a data set based on a 1D-CNN-MCMC deep learning model framework, and dynamically updating posterior probability distribution of a photovoltaic slope safety factor FS in combination with field monitoring data; and calculating a photovoltaic slope failure probability Pf, and carrying out safety evaluation and early warning. According to the method, uncertain factors such as slope geological parameter variability, environment and photovoltaic structure are comprehensively considered, and on-site multi-source monitoring data is utilized, so that the photovoltaic slope failure probability more conforming to the actual situation is obtained for safety evaluation and early warning.

The invention relates to a geotechnical characteristic information parameterization method for intelligent prediction of a slope instability mode. The geotechnical characteristic information parameterization method comprises the following steps: identifying and classifying to-be-processed slope original characteristic information; encoding the text type data and the numeric type data; defining a coding result as an input parameter of the slope instability mode prediction model; obtaining classification labels used for representing different instability modes, and defining the classification labels as output parameters; outputting the data set; the data set is used for constructing and training a slope instability mode prediction model. The method has the advantages that the generated standardized data set is clear in physical significance, rigorous in logic and uniform in format and can be directly called by a machine learning algorithm, a high-quality training basis is provided for an intelligent analysis model, and the training efficiency and prediction accuracy of a slope instability mode intelligent judgment model are remarkably improved.

The application provides a submarine slope stability evaluation method and system, and belongs to the field of landslide disaster prevention and control. The method is based on the range of the submarine slope to be evaluated, acquires a digital elevation model (DEM) of the to-be-evaluated area, and divides sample points corresponding to a grid; at least five unstable factors of the sample points are collected, a geomechanics model is constructed to calculate a safety factor, and the sample points are divided into current stable points and observation points; based on the observation points, the contribution values of all parameters of the unstable factors to the safety factor are sorted, and the top three parameters are taken as observation factors; a correlation stability evaluation model is constructed, for each observation point, based on the three observation factors of the current observation point, the nearest neighbor sample point and the second nearest neighbor sample point, the failure probability of the current observation point is calculated, a submarine slope failure risk map is drawn, and an unstable early warning is issued for the observation points reaching the failure probability threshold. The application reduces the calculation amount of the submarine slope stability evaluation, and improves the evaluation accuracy.

The embodiment of the invention provides a side slope stability state dynamic identification method, device and equipment. The side slope stability state dynamic identification method comprises the following steps: obtaining monitoring data of a target side slope about inclination angle change; determining the slope damage type of the target slope according to the monitoring data and a preset slope damage type judgment standard; performing feature extraction on the monitoring data in response to the fact that the slope damage type of the target slope is an acceleration type; determining the current failure rate of the target slope based on the extracted features; and calling a pre-constructed prediction model to generate a time prediction value when the landslide phenomenon of the target slope occurs based on the damage rate.

The invention discloses a discrete element modeling and numerical simulation method for slope vibration damage. The discrete element modeling and numerical simulation method comprises the following steps: 1) establishing a model box model of slope vibration damage; constructing a two-dimensional numerical simulation box according to the input length and height; 2) giving specified particle radius and dispersion coefficient parameters of the particle model, applying gravity action, and naturally stacking the model; (3) slope elevation data are imported, a cutting tool is used for cutting and layering the initial geometric model, and a slope model is formed; 4) storing the soil body material parameters in a document, and importing the soil body material parameters; 5) giving parameters such as particle radius and dispersion coefficient of the particle model, applying gravity action, and naturally stacking the model; 6) carrying out standard balance to balance the stress, energy, heat and the like of the whole model, and 7) setting different peak accelerations and vibration frequencies, and researching the catastrophe process of the slope damaged by vibration. And discrete element numerical simulation of slope vibration damage is completed.

This invention proposes a slope safety monitoring and early warning method based on intelligent particles. The method includes the following steps: S1. intelligent particle deployment and monitoring system construction; S2. intelligent particle cumulative rotation angle outlier identification based on the Z-score; S3. intelligent particle cumulative rotation angle time registration based on Kalman filtering and cubic interpolation; S4. intelligent particle cumulative rotation angle fusion based on an adaptive fusion algorithm; S5. slope deformation stage classification based on the cumulative rotation angle time series and the tangent angle method; S6. slope failure moment estimation based on the cumulative rotation angle time series and the cumulative rotation angle velocity inverse method. This invention applies intelligent particle sensors to slope safety monitoring and early warning, using the cumulative rotation angle as an indicator for classifying slope deformation stages and estimating slope failure moments. This method can sensitively reflect changes in slope stability and issue timely early warnings.

The present application relates to a kind of considering climate change's rainfall slope instability probability estimation method, which includes following five steps: (I) the collection and downscaling processing of future daily rainfall data in global climate model;(II) the time-varying characteristic quantification of non-stationary rainfall variable under the influence of climate change;(III) the establishment of rainfall interval-rainfall duration alternative random process updating model considering climate change;(IV) rainfall slope vulnerability analysis considering rock-soil parameter uncertainty;(V) estimate the slope instability probability based on total probability theorem convolution rainfall disaster curve and slope vulnerability curve in future predefined time.Compared with prior art, the present application proposes a kind of considering climate change's rainfall slope instability probability estimation new method, realizes the quantitative characterization of non-stationary rainfall caused by climate change effect, and provides an effective method for more accurate estimation of future rainfall slope instability probability.

The application discloses a reinforcing support method for a slide-pile type slope failure mode, and a reinforcing support structure is composed of a steel rail pile structure (1), a prestressed anchor cable structure (2) and a frame beam structure (3) which are combined and connected into a whole; the steel rail pile structure (1) is composed of a row of steel rails which are vertically arranged in an upper slide-pile rock mass (4) of the slide-pile type slope failure structure and deeply penetrate into a stable bedrock in a bedding internal part; the prestressed anchor cable structure (2) is composed of a row of prestressed steel strands which are arranged in a lower rotating rock mass (5) of the slide-pile type slope failure structure and deeply penetrate into the stable bedrock in the bedding internal part; and the frame beam structure (3) is composed of two mutually parallel cross beams (3-1) and a row of vertical beams (3-2) which are in transition with the cross beams (3-1) and are mutually perpendicular to the cross beams (3-1). The application can play a role of "blocking upward and pulling downward" on the slide-pile type rock slope failure, meanwhile, the frame beam can effectively connect all the structures to form a complete reinforcing system, thereby guaranteeing the stability of the slide-pile type failure slope.

This invention relates to a wind-resistant prefabricated foundation for transmission towers in mountainous areas and its assembly method, belonging to the field of power engineering technology. It aims to solve the technical problems of existing mountain transmission tower foundations, such as easy loosening due to freeze-thaw cycles, inconvenient construction, insufficient wind resistance, and slope failure caused by soil erosion. The foundation includes a ground surface, a pit, an anchoring mechanism, a wire mesh foundation I, a wire mesh foundation II, a frozen soil adaptive expansion mechanism, a connecting sleeve, a slope protection mechanism, and foundation columns. Each component is prefabricated in the factory and assembled on-site. The anchoring mechanism achieves automatic anchoring, the wire mesh foundations are arranged in reverse to enhance wind resistance, the frozen soil adaptive expansion mechanism uses its own weight to compensate for freeze-thaw gaps and self-locks, and the slope protection mechanism adapts to soil erosion and adheres closely to the soil surface. This invention eliminates the need for complex grouting, is convenient to construct, achieves freeze-thaw adaptation, strong wind resistance, and prevention of soil erosion, requires no secondary manual maintenance, is suitable for complex working conditions in mountainous areas, and extends the service life of the foundation.

The application discloses a kind of open pit slope radar monitoring data denoising and early warning method, by obtaining the deformation basis data of slope radar monitoring in the process of open pit production, obtain initial deformation matrix sequence;Further, the initial deformation matrix sequence is carried out interval difference operation denoising processing, and the denoised slope deformation difference matrix sequence is obtained, and the denoising method effectively eliminates environmental factor noise;Further, the average value of each matrix element of denoised slope deformation difference matrix sequence is calculated, and the sequence with time characteristics is obtained;Finally, based on critical slowing down theory, the variance and autocorrelation coefficient of time characteristic sequence are calculated, the precursor warning information of slope failure is identified by the rapid change characteristics on the variance and autocorrelation coefficient and time relationship curve, with the advantages of effectively improving the signal-to-noise ratio and early warning accuracy of slope radar monitoring, important significance for open pit safety production, etc., suitable for slope radar monitoring deformation data denoising and early warning in the process of open pit mining.

The invention relates to a wind-resistant fabricated foundation suitable for a power transmission tower in a mountainous area and an assembly method thereof, belongs to the technical field of electric power engineering, and aims to solve the technical problems that an existing power transmission tower foundation in the mountainous area is easy to loosen in freeze-thaw cycle, inconvenient to construct, insufficient in wind resistance and ineffective in slope protection due to soil loss. The foundation comprises a foundation surface, a foundation pit groove body, an anchoring mechanism, a mesh cage foundation I, a mesh cage foundation II, a frozen soil self-adaptive expansion mechanism, a butt joint sleeve, a slope protection mechanism and a foundation stand column, all the parts are prefabricated in a factory and spliced on site, the anchoring mechanism achieves automatic anchoring, the mesh cage foundations are reversely arranged, and the wind resistance rigidity is enhanced. According to the self-adaptive slope protection structure, complex grouting is not needed, construction is convenient and fast, freeze-thaw self-adaption, strong wind resistance and soil loss prevention can be achieved, manual secondary maintenance is not needed, the self-adaptive slope protection structure adapts to complex working conditions of mountainous areas, and the service life of a foundation is prolonged.

The invention relates to the field of rock and road engineering, and discloses a slope stability analysis method considering a soil structure. Comprising the following steps: S1, measuring basic parameters of a soil body, S2, measuring a soil-water characteristic curve, S3, calculating an unsaturated permeability coefficient, S4, analyzing structural parameters of the soil body, S5, constructing a slope numerical simulation model, S6, reducing and setting non-equal proportion coefficients, S7, calculating a comprehensive safety coefficient, and S8, simulating slope failure characteristics. According to the method, soil structure quantitative characterization and unsaturated seepage-stress field action are coupled, the influence of the soil structure is considered in slope stability calculation, and accurate simulation of the slope progressive failure process is achieved.