307 results about "Loess" patented technology

The invention discloses a loess tunnel surrounding rock deformation monitoring method, and relates to the technical field of civil engineering and geological engineering, and the method comprises the steps: integrating a spiral winding type optical fiber sensor, a double-cavity humidity compensation vibrating wire sensor and a microseismic array, and capturing surrounding rock strain, vibration and geological activities; the vibrating wire sensor suppresses humidity interference through a silicone oil damping medium and self-adaptive excitation frequency, and the optical fiber sensor is fixed through a pre-embedded silica gel sleeve to adapt to surrounding rock deformation; edge computing nodes are deployed on the inner wall of the tunnel, an FPGA chip and a lightweight GRU model are integrated, optical fiber strain, a micro-seismic energy spectrum and laser point cloud displacement field data are fused in real time, the strain gradient is analyzed, and a crack propagation probability cloud picture is generated; redundant optical fiber link switching is combined with a six-degree-of-freedom mechanical arm to realize breakpoint self-repairing, and a self-cleaning air curtain is integrated to inhibit dust adhesion; and constructing a geological parameter library based on a BIM-GIS fusion platform, driving a finite element-discrete element coupling model to dynamically update boundary conditions, and predicting surrounding rock deformation and collapse risks.

The invention discloses a loess slope stability intelligent monitoring method and system, and the method comprises the steps: carrying out the regional differentiation modeling of multi-source data of a target region, and outputting slope change data; according to the period-by-period displacement time sequence data, a slope deformation trend is obtained through prediction based on an LSTM algorithm; and inputting the slope change data, the slope deformation trend and the meteorological rainfall data into a slope stability evaluation model to predict a safety coefficient, and if the safety coefficient is lower than a preset threshold value, issuing early warning information. According to the loess slope stability intelligent monitoring method and system provided by the invention, data such as gradient change and deformation trend are accurately extracted, multi-source data and meteorological rainfall data are fused by means of cross-modal attention, a random forest model is used for learning a multi-factor coupling rule, and finally multi-factor data prediction is integrated to obtain a safety coefficient for early warning. The problem of low early warning reliability caused by difficulty in accurately evaluating the slope stability by fusing multiple factors such as gradient, deformation and meteorological rainfall can be solved.

The invention relates to the field of seepage analysis, and particularly discloses a loess slope seepage three-dimensional observation device and method under circulating seepage, and the method comprises the steps: constructing a loess seepage slope physical simulation experiment model, collecting an experiment sample image by using an image collection device, and analyzing the attribute parameters of the device to determine whether to adjust the device; preprocessing, bilateral filtering and temperature difference analysis are carried out on the collected experiment image, preprocessing process parameters are monitored, preprocessing precision factors are evaluated according to the parameters, and whether secondary processing is carried out or not and temperature abnormal areas are separated or not is judged; matching a temperature mapping parameter set based on the precision factor, evaluating the mapping accuracy, and correcting as required; finally, an initial three-dimensional temperature field is generated in combination with sensor data, and a permeation path is generated for three-dimensional rendering; according to the method, through multi-step cooperation, three-dimensional observation and analysis of loess slope seepage are achieved, a systematic technical scheme is provided for research of loess slope seepage characteristics, and improvement of the accuracy and the visualization level of slope seepage research is facilitated.

The invention relates to the technical field of rainfall type loess side slope instability early warning methods, and discloses a rainfall type loess side slope instability early warning method and system based on multi-source data fusion, and the method comprises the steps: S1, arranging a sensor network on a side slope, and collecting rainfall capacity, surface displacement and soil pressure data in real time; s2, preprocessing the collected multi-source data, including data cleaning, missing value filling and data standardization; and S3, extracting characteristic indexes capable of reflecting the stability state of the slope from the preprocessed data. Environmental data of the loess slope, such as rainfall, evaporation capacity, temperature and humidity, are acquired by using a sensor network; the deformation data comprises earth surface displacement, deep displacement and crack width; the mechanical data comprises soil pressure, pore water pressure and soil moisture content; image data: a slope surface image and a crack development image are collected, an early warning model is constructed based on a machine learning algorithm, and data features can be automatically learned.

The invention provides a GNSS-based mountain area highway loess slope stability evaluation method and system, and relates to the technical field of loess slope stability evaluation.The method comprises the steps that firstly, a slope area is divided into grids, geologic feature data of each grid are obtained, super nodes are divided according to the geologic feature data, and the super nodes are divided into super nodes; monitoring points are determined, and the soil moisture content is monitored, so that the moisture content of the soil at different depths is analyzed; secondly, the original displacement is monitored through a GNSS, displacement correction is carried out based on the Fredld unsaturated soil shear strength theory, and the actual displacement is obtained; the influence of the accumulated rainfall on the soil body strength parameters is analyzed, and the soil body strength parameters are corrected in real time; coupling the corrected soil body strength parameter with the actual displacement, calculating a safety coefficient, and judging an early warning state according to the rainfall sensitivity coefficient; and finally, comprehensively judging the stability of the slope according to the early warning state. According to the method, through multi-parameter comprehensive analysis, the accuracy of slope stability evaluation is improved.

The invention relates to an iron tower foundation safety analysis method considering the dry-wet cycle effect. The method comprises the following steps that 1, a soil body strength degradation constitutive model is established; 2, determining iron tower foundation structure parameters and soil body parameters; 3, establishing an iron tower foundation-soil body three-dimensional geometric model by utilizing three-dimensional modeling software and rock-soil finite element simulation software; 4, applying a load transmitted to the iron tower foundation by the power transmission iron tower in the iron tower foundation-soil body three-dimensional geometric model, defining initial parameters, and establishing a rainwater seepage and dry-wet cycle coupling effect analysis model; 5, analyzing and predicting the cycle index when the safety coefficient of the iron tower foundation is reduced to a threshold value from the two aspects of tower foundation ultimate bearing performance and iron tower foundation-soil body interaction on the basis of a rainwater seepage and dry-wet cycle coupling effect analysis model; according to the method, an efficient and accurate numerical analysis means can be provided for safety assessment of the power transmission tower foundation in the collapsible loess area.

The invention discloses an accurate monitoring system for linear deformation of a loess tunnel portal side upward slope, and relates to the technical field of geotechnical engineering and surveying and mapping. The system comprises a sensing network layer, a data acquisition and transmission layer, a cloud platform and data analysis layer and an application display layer, displacement data of discrete monitoring points are reconstructed into continuous full-field displacement and strain distribution through Kriging interpolation, then a displacement vector field is regarded as a whole to be analyzed, the maximum shear strain field of the displacement vector field is calculated, an abnormal area with the deformation gradient abrupt change is accurately positioned, and the deformation gradient abrupt change accuracy is improved. The abnormal point sets are merged into independent clusters according to spatial proximity and deformation consistency, and the geometric trajectory of a potential slip crack surface or crack is accurately outlined through curve fitting, so that the technical path fundamentally changes the limitation of point-mode monitoring, and can automatically identify a hidden linear failure trend which is difficult to find by naked eyes in a non-contact manner, so as to improve the detection accuracy of the hidden linear failure trend. And refined data support is provided for slope stability evaluation.

The invention relates to the technical field of geological disaster prevention and control, and discloses an early warning method for loess slope geological disaster prevention and control, which comprises the following steps: arranging stress sensors and pore water pressure gauges at different depths of a loess slope, and collecting and preprocessing ground stress and pore water pressure data; constructing a ground stress change gradient index and a pore water pressure index of the loess slope; scanning the loess slope by using a laser scanner, generating a three-dimensional point cloud, performing point cloud registration, and then calculating a point cloud gradient to obtain a vegetation point cloud of the loess slope; a Kriging interpolation method is adopted to carry out interpolation calculation on the elevation value of the vegetation point cloud, and the average value, the maximum value and the difference value of the elevation difference are utilized to carry out local correction on the interpolation process to obtain the elevation value of the vegetation point cloud after elevation difference correction so as to construct a vegetation elevation variation coefficient index; and finally, constructing a comprehensive early warning model for geological disaster prevention and control of the loess slope to carry out geological disaster early warning. According to the method, the accuracy of early warning of the loess slope geological disasters is improved.

The invention provides a method suitable for water retention of soil with different textures. In order to better perform size matching between the biochar and soil particles, particle size grading is performed on the biochar according to the size of soil aggregates, and three kinds of biochar with the particle size larger than 2.0 mm, the particle size ranging from 0.25 mm to 2.0 mm and the particle size smaller than 0.25 mm are obtained. The three kinds of biochar with different particle sizes are used in different types of soil respectively, and 5% of biochar with the particle size of 0.25-2.0 mm is selectively applied according to the water retention requirement of the arid area loam farmland; according to the drainage requirements of loam waterlogging areas with more rainfall, 5% of biochar with the particle size larger than 2.0 mm is selectively applied; and 5% of biochar with the particle size smaller than 0.25 mm is selectively applied for water and soil conservation of sandy soil in the arid region. By scientifically regulating and controlling the particle size and the application amount of the biochar, the biochar is matched with the specific properties of the soil, so that the water retention capacity of the soil is improved, and the soil efficiency is improved.

The invention relates to the technical field of geotechnical engineering material modification and industrial solid waste resource utilization, in particular to an industrial solid waste curing agent formula screening method for salinized loess improvement, which comprises the following steps: constructing a curing system with different industrial solid waste (such as fly ash, slag and steel slag) composite proportions, identifying the salt type and salt concentration of salinized loess, and determining the salt concentration of the salinized loess. The method comprises the following steps: preparing, maintaining and curing a sample, carrying out unconfined compressive strength test and X-ray diffraction (XRD) analysis, extracting peak areas of main reaction products, and calculating a structure-performance coupling mechanism index Mi = S / f7 (S is the XRD peak area, and f7 is the compressive strength of 7 days). The Mi value and the 28-day strength are used as judgment criteria, and a preferable formula with good structural product master control and strength development is screened out. The screening method provided by the invention has the advantages of distinguishable mechanism, quantifiable index, generalizable process and the like, and is suitable for formula design and engineering application of the industrial solid waste-based curing agent under the conditions of different salt concentrations and ion types.

The invention provides a field slope stability monitoring method based on acoustic emission, and belongs to the technical field of field slope stability monitoring. In order to solve the technical problems of large soil mass fracture signal attenuation, strong noise interference and poor real-time performance of traditional monitoring in the existing loess slope instability monitoring, the adopted technical scheme is as follows: an inclined hole channel is drilled in the main sliding direction of a slope body by adopting a hydraulic guide drilling machine; vertically inserting the manufactured passive waveguide device from the top surface of the slope top, burying the manufactured active waveguide device into the drilled inclined hole channel, and burying the surface of a slope body by using an in-situ soil body; the acoustic emission probes of the passive waveguide devices and the active waveguide devices are connected with a collection device through signal lines, and the acoustic emission probes send induced acoustic emission signals to the collection device; the acquisition device receives the acoustic emission signals, then preprocesses the acoustic emission signals, extracts time-frequency domain features, and transmits the time-frequency domain features to the cloud system through a wireless network; the method is applied to field slope stability monitoring.

The invention provides a method for predicting settlement of a transformer substation in a collapsible loess area based on a neural network, and relates to the technical field of geological engineering monitoring. Then, settlement characteristic indexes such as collapsibility, bearing capacity influence and deformation stability are obtained through analysis, a prediction model is established with soil mass mechanical data as input and the settlement characteristic indexes as output, the model performance is optimized through a particle swarm optimization algorithm and a loss function, and finally a fitness function is constructed according to the settlement characteristic indexes. And calculating the settlement fitness value of each sub-region, evaluating the settlement amount in combination with load time sequence data, setting a settlement threshold value, and carrying out risk marking on the sub-regions exceeding the threshold value. The settlement risk of the loess area where the transformer substation is located is effectively evaluated and controlled by establishing the settlement characteristic index prediction model and the dynamic monitoring mechanism.

The invention provides a slope displacement monitoring method and system for a high and steep slope in a loess area, and the method comprises the steps: carrying out the preprocessing of data through a point cloud denoising algorithm according to an initial point cloud data set, filtering noise points and irrelevant reflection points caused by a complex terrain, and generating an optimized refined point cloud data set; aiming at the refined point cloud data set, constructing a digital surface model of the side slope by utilizing a three-dimensional reconstruction algorithm, carrying out local detail correction on the model in combination with soil loosening characteristics of a loess area, and determining a preliminary morphological structure of the side slope; performing time sequence data acquisition on the preliminary morphological structure to obtain slope three-dimensional model data of multiple time periods, and analyzing the dynamic evolution trend of morphological change to obtain slope deformation feature distribution; according to the slope deformation characteristic distribution, a finite element analysis method is adopted to conduct simulation calculation on stress distribution of the high and steep slope, the stability risk of a deformation area is judged, and potential landslide risk points are determined.

The invention relates to the technical field of geotechnical engineering safety evaluation, and discloses a collapsible water sensitivity index evaluation method based on loess engineering. The method comprises the following steps: acquiring time sequence data of multiple monitoring points of a loess engineering site to form a fusion feature vector; driving the collapsibility mode discrimination model, separating steady-state and transient collapsibility mode features from the collapsibility mode discrimination model, generating a water sensitivity evolution graph according to the features, and constructing a logic decision tree; and performing incremental dynamic coupling analysis, matching the decision tree path with a collapsing development path in a historical engineering case library, calculating a path similarity weight, and performing weighted correction on the water sensitivity evolution graph according to the path similarity weight to obtain a corrected graph. And deducing collapse risk probability distribution under a plurality of time nodes in the future based on the corrected map. Integrating the atlas, the decision tree path and the risk distribution to form a final loess engineering collapsibility water sensitivity evaluation report. According to the invention, mechanism separation and dynamic intelligent prediction of the collapsing process are realized.

The invention belongs to the technical field of mine goaf filling materials, and particularly discloses flow-state solidified loess for coal mine filling and a preparation method and application of the flow-state solidified loess. According to the method, rich loess resources in the loess plateau area are used for preparing the flow-state solidified loess for coal mine filling, the coal mine recovery rate can be increased, the filling material cost is reduced, the problem that stones in the loess plateau area are insufficient is solved, the toughness and dry shrinkage resistance of the solidified flow-state solidified loess can be improved, and the problem that the loess is soft and swelled when meeting water is effectively solved. The curing agent for the fluid-state cured loess is developed according to the characteristics of loess of loess plateau and the characteristics of early strength, fluidity, pumpability, crack resistance and the like required by filling of the fluid-state cured loess, the alkali excitation mechanism, the pozzolanic effect, the secondary hydration reaction and other mechanisms are utilized, and proper functional components such as early strength, water retention and crack resistance are compounded, so that the curing agent for the fluid-state cured loess is prepared. The curing agent can effectively solve the common problems of air shrinkage, easy cracking, low strength and the like when a commercially available curing agent is used for preparing flow-state cured loess.

The invention discloses a loess collapsibility evaluation method and system based on a geophysical prospecting test technology, and relates to the field of land exploration, and the method comprises the steps: laying regular detection grids in a to-be-evaluated loess site, and constructing a site geographic coordinate system; collecting Rayleigh wave signals in real time, inverting a transverse wave velocity profile, and collecting apparent resistivity data; a shallow soil sample is drilled, soil property parameters are measured, and a model is constructed according to the mapping relation between the field geophysical prospecting parameters and the soil property parameters; inputting the acquired data into the wave velocity-water content correlation model and the resistivity-dry density correlation model to generate a water content distribution field and a dry density distribution field; and calculating the collapsibility of loess in each detection grid based on a geophysical prospecting response equation of the collapsibility coefficient, outputting a collapsibility coefficient contour cloud picture, and judging the collapsibility grade of each grid and the distribution range of collapsed loess. The method has the advantages that the collapsibility distribution field and the contour cloud picture are generated through accurate non-intrusive data acquisition and model analysis, and a scientific basis is provided for engineering construction.

The invention discloses a method for cooperatively improving seepage and corrosion resistance of loess through MICP-biogel, and belongs to the technical field of slope protection. The problems that sediment generated when loess is solidified through an existing MICP technology is uneven in distribution, meanwhile, due to brittleness of the sediment and deformation discordance between the sediment and a reinforced soil body, the anisotropy characteristic of the reinforced soil body is obvious, and durability is poor are solved. The anti-seepage and anti-corrosion method which is environment-friendly, non-toxic and harmless, simple and convenient in process, low in cost, convenient and fast to construct and capable of being used in a large area is provided for the loess slope through the steps of gel material preparation, mineralized bacterium liquid preparation, slope surface treatment, dry powder spraying, bacterium liquid spraying and film covering maintenance.

The invention discloses a dynamic monitoring and management system for land subsidence of an oil pipeline in a loess area. The dynamic monitoring and management system comprises a basic sensing module, an intelligent analysis module and a decision execution module, the basic sensing module is used for monitoring ground surface deformation, soil humidity and pipeline surrounding environment parameters along a pipeline in a collapsible loess area in real time; the intelligent analysis module is used for analyzing the data acquired by the basic sensing module to judge whether the ground subsidence occurs or not; if the ground subsidence occurs, calculating the subsidence amount, further calculating the suspension length of the pipeline, analyzing the stress condition of the pipeline, and calculating a comprehensive risk index R; and the decision execution module implements automatic risk assessment and emergency response according to a grading early warning strategy based on a result output by the intelligent analysis module. According to the management system, all-weather dynamic monitoring of loess collapse deformation along the oil pipeline is achieved, early warning is issued in time when potential hazards occur, and emergency management is executed.

The invention discloses a device and method for simulating the freezing and thawing cycle process of collapsible loess in the natural environment, and belongs to the technical field of geotechnical engineering.The device comprises a test box provided with a sample groove, a rainfall simulation system is arranged on the upper portion in the test box, an underground water seepage simulation system is arranged on the lower portion in the test box, and the sample groove is connected with a loading system; a weighing system is arranged at the bottom in the test box, the weighing system is connected with an underground water seepage simulation system, a settlement and internal damage monitoring system is arranged between the weighing system and the rainfall simulation system, and a freezing and thawing circulation system is arranged on the inner side wall of the test box. According to the invention, the natural environment of the loess plateau area can be simulated, and the strength change of the strong salinized soil in the dry-wet process can be tested and researched under the combined action of various factors such as rainfall, load, underground water seepage and freeze-thaw cycle.

A method for determining dynamic strength of loess through average particle size and macropore ratio comprises the following steps: sampling and determining basic physical properties of a soil sample, preparing an artificial structural loess sample, determining loess n, # imgabs0 # under a microstructure, and determining the dynamic strength of the loess according to the average particle size and the macropore ratio. The method comprises the following steps: establishing a three-dimensional model of dynamic characteristics and a microstructure of loess under the action of a dynamic load by utilizing a particle analysis test and a mercury injection test, and fitting a loess sample n, # imgabs1 # formula; fitting a relation formula, establishing a model, and fitting a dynamic internal friction angle, dynamic cohesion, average particle size and macropore proportion formula through data; and finally, measuring the dynamic strength of the loess, and performing simple qualitative verification on the dynamic strength index of the loess by using the neural network according to the given data in the step 4 and adopting the Moire-Coulomb strength theory. The test precision and efficiency are improved.

The invention relates to the technical field of geological exploration, and discloses a thick loess highland coal field three-dimensional seismic exploration method, which comprises the following steps: S101, acquiring an artificial seismic source shot seismic record and superficial Rayleigh wave detector data; s102, performing grid division on the target area, and setting parameters to be inverted; s103, performing vertical ray path time estimation, multi-layer medium dispersion forward modeling and post-stack energy estimation; s104, generating a multi-target error function according to the head wave arrival time observation value, the Rayleigh wave observation dispersion curve, the theoretical ray path time, the phase velocity and the post-stack energy; s105, dividing the target region into three sub-regions, and performing differential evolution iteration until local convergence; s106, establishing a plurality of parallel annealing chains, setting chain temperatures, progressively decreasing the chain temperatures, optimizing to-be-inverted parameters, and outputting optimal to-be-inverted parameters; and S107, calculating the static correction time difference and the absorption compensation coefficient of each acquisition point. The coal seam structure imaging precision and the exploration data reliability under the complex earth surface condition are improved.

The invention discloses a foundation construction method in house building construction, and relates to the technical field of building construction. The method comprises the following steps: step 1, geological investigation and site pretreatment; 2, a shallow soft soil layer is treated; 3, constructing a deep composite pile foundation; 4, laying a composite mattress layer; 5, integrating an intelligent monitoring system; and step 6, quality detection and acceptance. Compared with the prior art, the construction method is more economical and environmentally friendly, the utilization rate of the building residue soil is high, the lime consumption is reduced, carbon emission is reduced, and the material cost is saved; the anti-seismic performance is improved, the anti-seismic intensity of the structure is improved through the elastic concrete layer, and the seismic energy absorption rate is increased; the construction efficiency is improved, and the equipment dismounting time is reduced by the pile casing hole-forming process, so that the construction period is shortened; the adaptability is wider, and the method is suitable for various complex geologies such as collapsible loess, mucky soil and miscellaneous fill.

The invention belongs to the technical field of river and lake water ecological restoration, and particularly discloses equipment and a method suitable for river and lake endogenous polluted sediment treatment, the equipment suitable for river and lake endogenous polluted sediment treatment comprises a material stirring unit and a curing granulation unit, the material stirring unit comprises a horizontal stirring machine box and is used for stirring and mixing raw materials, and the curing granulation unit is used for curing and granulating the raw materials; one side of the horizontal stirring machine box is connected with a loess spiral feeder, a bottom mud spiral feeder and a curing agent spiral feeder, and a loess crusher is connected between the loess spiral feeder and the horizontal stirring machine box. According to the method, the polluted bottom mud is efficiently converted into the ecological restoration matrix which is resistant to water flow scouring and stable in physical structure through a curing granulation process, the matrix can be directly applied to an underwater environment, and the core problem that existing ecological planting soil prepared through bottom mud recycling cannot be stabilized in a water area is solved; and a key matrix material is provided for in-situ construction of an underwater ecological system such as an underwater forest.

The invention discloses a loess slope top power transmission tower instability prediction method based on the landform effect, and belongs to the technical field of power transmission tower stability prediction.The loess slope top power transmission tower instability prediction method comprises the following steps that landform parameter data and landform parameter data of a loess slope around a power transmission tower foundation are obtained, and correction preprocessing of the parameter data is carried out; discrete element software is adopted to establish geometric models of the loess slope, wherein the geometric models comprise three-dimensional structure models of the earth surface, the loess layer and the tower footing; introducing a fluid-solid coupling algorithm, carrying out finite element numerical analysis on the geometric model, and simulating a deformation failure process of the loess slope and an interaction relationship of power transmission tower foundation stress through dynamic simulation and time coupling; and calculating stress field distribution of the power transmission tower foundation through finite element numerical analysis, and verifying and optimizing the model in combination with field monitoring data. According to the method, the limitation of the prior art in the aspect of loess slope power transmission tower instability prediction is overcome, the accuracy, real-time performance and comprehensiveness of prediction are realized, and the disaster loss is reduced.

The invention discloses a deep learning-based collapsibility grade automatic discrimination method. The method comprises the following steps of S1, generating a preprocessed loess geological engineering index data set; s2, obtaining a loess sampling unit graph structure data set with a weighted adjacent matrix; s3, forming a loess sampling unit weighted graph structure data set with node attributes; s4, constructing a multi-target comprehensive fitness function; s5, randomly generating differential flower pollination algorithm population individuals, and performing global search and local search on the differential flower pollination algorithm initial population to obtain an optimal graph attention network hyper-parameter vector and an optimal node feature selection mask vector; and S6, obtaining an optimized graph attention network model, deducing the loess sampling unit weighted graph structure data set with node attributes, and outputting a collapsibility grade prediction result of each sampling unit. According to the method, the influence of misjudgment of different collapsibility levels on engineering safety is dynamically evaluated, so that the algorithm is guided to preferentially improve the recognition accuracy of high-risk levels.

The invention discloses a threshold judgment method for eliminating loess collapsibility through microwave irradiation, relates to the technical field of data prediction and updating, and is used for solving the problem of insufficient collapsibility control effect evaluation means when the structural change is not obvious, and the method comprises the following steps: acquiring and preprocessing a collapsible loess sample to obtain initial physical information data of the collapsible loess sample; microwave irradiation experiment conditions are set, and the collapsibility coefficient change trend of loess under the conditions is calculated. And judging the change of the internal structure according to trend evaluation whether a temperature distribution analysis method is adopted, if the change is weak, further obtaining the microstructure characteristics of the sample and the change of mineral components, and comprehensively evaluating the collapsibility reduction degree. If the reduction degree is high, void ratio parameters are introduced to carry out quantitative analysis on the collapsibility change, and a scanning electron microscope or X-ray diffraction analysis is selected to verify whether the collapsibility change reaches an expected control target or not, so that the stability and safety of loess are ensured.

The invention provides a landslide identification method and system for a collapsible loess slope, a terminal and a storage medium, and the method comprises the steps: inputting a loess slope collection image into a landslide identification model, carrying out the haze concentration estimation and defogging processing, obtaining a collection recovery image, carrying out the feature extraction of the collection recovery image, and obtaining a collection feature map; carrying out landslide identification on the loess slope collection image according to the collection feature map to obtain a collection identification result, and carrying out parameter updating on the landslide identification model according to the collection identification result; and inputting the to-be-identified slope image into the converged landslide identification model for landslide identification to obtain a landslide identification result. According to the embodiment of the invention, defogging processing is carried out on the loess slope acquisition image through the haze estimation value, so that the converged landslide identification model can effectively carry out defogging on the to-be-identified slope image, and the landslide identification accuracy is improved.

The invention relates to the technical field of foundation treatment, in particular to a loess collapsible foundation treatment method based on porosity dynamic monitoring, which comprises the following steps: carrying out initial investigation on a loess collapsible foundation to obtain an initial investigation result, including the soil particle specific gravity, the moisture content, the density, the initial porosity and the collapsibility coefficient of foundation loess; according to the initial investigation result, a foundation treatment scheme is designed, and the scheme comprises determination of the treatment depth, the treatment range and a selected treatment method; foundation treatment is conducted according to the scheme, and in the foundation treatment process, the change of the loess porosity is monitored in real time through a porosity monitoring device; and according to the real-time monitoring data of the porosity, a foundation treatment scheme is adjusted or additional treatment measures are taken until the foundation porosity reaches a preset range and the foundation meets design requirements. The dynamic change of the loess porosity can be monitored in real time, foundation treatment measures can be adjusted in time according to the change of the porosity, and the loess collapsible foundation treatment effect and reliability can be improved.

The invention provides a loess slope deformation monitoring data processing method based on MEMS inertial sensor data and GNSS data, and relates to the technical field of slope deformation monitoring, and the method specifically comprises the following steps: calculating relative displacement and relative displacement, generating an accumulated displacement amount, and collecting rainfall characteristic parameters; iteratively optimizing the clusters and the cluster center points, confirming the number of each cluster sample, merging or splitting the clusters when a number threshold is not met until a preset threshold is met, and determining a final cluster and a cluster center point; determining a cluster to which the feature vector in the current time period belongs, and screening out similar historical time periods; and predicting the accumulated displacement in the next time period, and carrying out instability risk grade evaluation. According to the method, the clustering iteration method is adopted to continuously update the clustering and the clustering center point, the clustering structure is dynamically adjusted, the situation of excessive subdivision or excessive combination of the clustering is effectively avoided, and the loess slope instability risk level can be accurately predicted by means of the dynamic clustering optimization mechanism.

The invention discloses a loess high fill compaction degree nondestructive detection method, relates to the technical field of civil engineering and geological detection, and is used for solving the problems that the detection precision is insufficient, the adaptability to loess characteristics is poor, and the requirement of loess compaction degree nondestructive evaluation cannot be met. An earth surface electromagnetic wave reflection signal and soil particle vibration frequency data are collected to analyze a compaction degree deviation index, when the deviation index is higher than a judgment threshold value, a spectrum analysis algorithm is adopted to calculate compaction degree spectrum characteristics of different depths and positions, and if the spectrum characteristics are consistent, soil particle distribution characteristics and humidity change frequencies are further collected; the compaction uniformity is comprehensively analyzed, the compaction non-uniformity rate is calculated when the uniformity is low, a proper detection algorithm is selected according to the compaction non-uniformity rate to judge whether the compaction non-uniformity problem exists or not, the calculation complexity is reduced, and the judgment accuracy is improved.