Toppling deformation body forming process and fracture development mode analysis method and system

Abstract

The invention relates to the technical field of slope instability data analysis, and discloses a toppling deformation body forming process and fracture development mode analysis method and system.The method comprises the steps that a toppling deformation physical model is constructed according to geographic data, and deformation characteristics of an anti-tilting rock slope under different working conditions are simulated; building a rock stratum fracture development prediction model based on the dumping deformation physical model, inputting a simulation result into the rock stratum fracture development prediction model, and predicting rock stratum dumping deformation bodies and fracture development under different climate conditions; analyzing the prediction result to obtain a fracture development mode; carrying out stability partitioning on the toppling deformation body based on a fracture development mode, and evaluating a stability coefficient; and evaluating the stability coefficient and the real-time monitoring data, and adjusting the rock stratum fracture development prediction model according to an evaluation result. The system comprises a rock stratum data collection module, a prediction simulation module and a prediction result analysis module. According to the invention, the precision of evaluating the stability of the counter-tilting rock slope is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of slope instability data analysis, and in particular to a method and system for analyzing the formation process of toppling deformation bodies and the fracture development mode. Background Art

[0002] Toppling is a typical mode of slope instability, and its failure mechanism is completely different from the common sliding mode. Along with the construction of large-scale engineering projects such as water conservancy and hydropower, open-pit mines, and transportation at home and abroad, the toppling deformation and instability phenomena of rock masses have been widely revealed, becoming a key problem restricting the construction of related projects. At present, there are still no in-depth conclusive results in the research on such slopes, resulting in disputes and difficulties in the process of the engineering community dealing with related problems.

[0003] Prior Art One, a Chinese patent with the patent number: 202411412245.5 discloses a design method and system for a foundation pit retaining structure to prevent water inrush and gushing risks, which relates to the technical field of foundation pit construction safety. Under complex geological conditions, through multi-scale comprehensive exploration technology, the system can comprehensively obtain the information of karst caves and fractures in the karst strata, generate an initial geological data set, and accurately divide the foundation pit area. Compared with the traditional geophysical exploration method, the accuracy of geological forecasting is greatly improved. The system also accurately evaluates the underground risks by analyzing the development degree of karst caves, the filling status of fractures, the bearing capacity of soil layers, and the fluctuation value of the groundwater level, and real-time monitors the deformation of the foundation pit to timely discover potential problems. In addition, the system classifies the water inrush risks to generate different strengthened support retaining structures and densities, as well as the settings of drainage points, which can effectively cope with serious water inrush risks and prevent further damage to the foundation pit. Although it can specifically and effectively reduce the probability of the occurrence of water inrush and collapse problems, thereby ensuring the construction progress and safety; however, the complexity and diversity of the fracture development mode make it impossible to comprehensively and accurately describe and predict the geometric shape parameters of fractures.

[0004] Prior Art Two, a Chinese patent with the patent number 202411396253.5, provides a method for grouting and seepage prevention and reinforcement of the dam body of an underground coal mine reservoir, including: determining the parameters of the coal pillar dam body and the artificial dam body in the underground coal mine reservoir; detecting the coal pillar dam body, delineating the seepage anomaly area of the coal pillar dam body, analyzing the fracture development of the coal pillar dam body, and verifying it through borehole peeping; around the seepage anomaly area of the coal pillar dam body, constructing multiple first grouting boreholes from the side of the coal pillar dam body adjacent to the roadway to the side adjacent to the water storage goaf, and carrying out segmented grouting on the first grouting boreholes; constructing multiple second grouting boreholes in the artificial dam body, and using polyacrylate modified cement materials to grout the second grouting boreholes. Although it can effectively fill the fractures inside the coal pillar dam body, at the joints between the artificial dam body and the roof and floor rock strata and the coal pillar dam body, forming a multi-level segmented grouting and reinforcement anti-seepage structure inside the coal pillar dam body and a two-way grouting and reinforcement anti-seepage structure between the coal pillar dam body and the artificial dam body, improving the density and impermeability of the dam body; however, it lacks stability assessment and ignores the stress-strain relationship of the rock mass and the evolution process of geological disasters.

[0005] Prior Art Three, a Chinese patent with the patent number 202411362131.4, discloses a device and method for measuring the mechanical behavior and fracture characteristics of soil under wet-dry cycles, including four parts: a tensile-compressive specimen box, a data acquisition and image processing system, a high-precision balance, and a water bath temperature control system. Based on the active principle, without applying external loads, only by setting a force sensor on the side of the moving half-box, the process of the force generated by the soil sample during the wet-dry cycle can be continuously measured. Although, through an intelligent program, key parameters that change dynamically during the wet-dry cycle of the specimen are collected, and the taken soil sample images are analyzed by combining the Image J image analysis software and the digital image correlation method (DIC), so that during the wet-dry cycle, both the change process of the morphological characteristics of the soil fracture network can be monitored in real time, and the evolution law of the displacement field during the development and healing of soil fractures can be studied, thereby establishing the internal relationship between the mechanical properties and hydraulic properties of the soil under wet-dry cycle conditions; however, the experimental and observational means are insufficient, and it is difficult to learn the fracture development law of the toppling deformed body.

[0006] Currently, Prior Art One, Prior Art Two, and Prior Art Three have the problems of the complexity and diversity of the fracture development mode, resulting in the inability to comprehensively and accurately describe and predict the geometric shape parameters of fractures, lack of stability assessment, ignoring the stress-strain relationship of the rock mass and the evolution process of geological disasters, and insufficient experimental and observational means, making it difficult to learn the fracture development law of the toppling deformed body. To solve the above problems, the present invention provides a method and system for analyzing the formation process and fracture development mode of a toppling deformed body. Summary of the Invention

[0007] The main object of the present invention is to provide a method and system for analyzing the formation process of toppling deformable bodies and the fracture development pattern, so as to solve the problems in the prior art that the geometric shape parameters of fractures cannot be comprehensively and accurately described and predicted, and the lack of stability assessment.

[0008] To achieve the above object, the present invention provides the following technical solutions:

[0009] A method for analyzing the formation process of toppling deformable bodies and the fracture development pattern, the method for analyzing the formation process of toppling deformable bodies and the fracture development pattern includes:

[0010] Obtain the geographical location, climate and hydrological conditions, topography, geological structure and underlying lithology geographical data of the target area; construct a physical model of toppling deformation according to the geographical data, and simulate the deformation characteristics of the anti-dipping rock slope under different working conditions;

[0011] Based on the physical model of toppling deformation, construct a prediction model for rock layer fracture development, input the simulation results into the prediction model for rock layer fracture development, and predict the toppling deformation of rock layers and fracture development under different climate conditions;

[0012] Analyze the prediction results to obtain the fracture development pattern; based on the fracture development pattern, conduct stability zoning on the toppling deformable body, evaluate the stability coefficient; evaluate the stability coefficient with the real-time monitoring data, and adjust the prediction model for rock layer fracture development according to the evaluation results.

[0013] As a further improvement of the present invention, the process of constructing a physical model of toppling deformation according to the geographical data includes the following steps:

[0014] Obtain the geographical location, climate and hydrological conditions, topography, geological structure and underlying lithology geographical data of the target area; analyze the variation law of the occurrence of rock layers with elevation and horizontal depth based on the on-site detailed investigation data, adit and borehole data;

[0015] Based on the results of analyzing the variation law of the occurrence of rock layers with elevation and horizontal depth, obtain the variation trend of the dip direction of the rock mass with the adit depth and the variation trend of the dip angle of the rock mass with the adit depth; analyze the change of the occurrence of the fracture zone structural plane of the toppling deformable body to obtain the law of change of the occurrence of rock layers caused by fracture;

[0016] Classify the structural planes of the toppling rock mass into primary structural planes, tectonic structural planes and superficial structural planes; according to the material composition characteristics, scale, spatial distribution, development degree and geological significance in engineering of the structural planes, divide the structural planes into five grades; construct a physical model of toppling deformation according to the geographical data, and simulate the deformation characteristics of the anti-dipping rock slope under different working conditions.

[0017] As a further improvement of the present invention, the process of simulating the deformation characteristics of an anti-dipping rocky slope under different working conditions includes the following steps:

[0018] Analyze the weathered layer characteristics of the toppling rock mass according to the structural planes of five grades, and divide the weathered layer characteristics of the toppling rock mass into four major types;

[0019] Judge whether the structural planes are developed as unloading tensile cracks or medium- and gentle-inclination shear cracks inclined along the slope; and divide the unloading zone of the slope rock mass into strong unloading, weak unloading, and deep unloading;

[0020] Based on the obtained weathered layer and its variation law, construct a physical model of toppling deformation, and conduct three excavation simulations according to the deformation characteristics of the anti-dipping rocky slope under different working conditions; record the excavation simulation data.

[0021] As a further improvement of the present invention, the process of predicting the toppling deformation body and crack development of the rock stratum under different climate conditions includes the following steps:

[0022] According to the simulation results of the physical model of toppling deformation, obtain deformation data; based on the physical model of toppling deformation, construct a prediction model for the development of rock stratum cracks, retrieve historical rainfall data and historical deformation data, and analyze the historical rainfall data and historical deformation data to obtain a rainfall data set;

[0023] Divide the rainfall data set into a training set, a validation set, and a test set, and use the training set to train the prediction model for the development of rock stratum cracks; after the training is completed, input the deformation data into the prediction model for the development of rock stratum cracks to predict the toppling deformation body and crack development of the rock stratum under different climate conditions;

[0024] Step 21: According to the prediction results, obtain the toppling deformation body and crack development of the rock stratum under different future climate conditions, and upload the prediction data to the cloud platform; compare the prediction data with the deformation crack development threshold. If it is greater than the threshold, adjust the plan; if it is less than the threshold, conduct an analysis.

[0025] As a further improvement of the present invention, the process of using the training set to train the prediction model for the development of rock stratum cracks includes the following steps:

[0026] Retrieve historical rainfall data and historical deformation data, obtain the relationship data between rainfall and surface cumulative deformation, and fit the relationship data between rainfall and surface cumulative deformation to obtain a fitting sequence;

[0027] Determine and initialize the topological structure of the prediction model for the development of rock stratum cracks. Determine the topological structure of the prediction model for the development of rock stratum cracks according to the number of inputs and outputs. The number of input layer nodes and output layer nodes of the prediction model for the development of rock stratum cracks are determined by the number of input and output parameters respectively;

[0028] Calculate the fitness value, given the evolutionary parameters of the rock stratum fissure development model, obtain the optimal weight threshold, and determine whether the preset conditions are met; if the preset conditions are met, obtain the optimal rock stratum fissure development model, and if the conditions are not met, readjust.

[0029] As a further improvement of the present invention, the process of obtaining the optimal weight threshold specifically includes the following steps:

[0030] Determine the basic parameters of the genetic algorithm from the rock stratum fissure development model, and randomly generate an initial population of the heavy punch scale; select a data range, generate a real number vector of the middle mass individuals within this range, and use it as a chromosome of the genetic algorithm;

[0031] Encode the chromosome to obtain the weight and threshold; initialize the population; calculate the fitness value, given the evolutionary parameters of the rock stratum fissure development model, and use the real number vector as the weight and threshold of the rock stratum fissure development model for assignment.

[0032] Input the training set to train the rock stratum fissure development model, and when the preset accuracy is reached, obtain the output value of the rock stratum fissure development. Use the predicted output and the expected output of the rock stratum fissure development model as the reciprocal of the sum of the squares of the absolute errors as the fitness function.

[0033] As a further improvement of the present invention, the process of using the predicted output and the expected output of the rock stratum fissure development model as the reciprocal of the sum of the squares of the absolute errors as the fitness function includes the following steps:

[0034] Select operators according to the preset ratio, select the chromosomes in each generation of the population based on the selection sequence of the fitness ratio, perform a crossover operation on two chromosomes, and select the genes of the individuals for mutation;

[0035] Obtain the optimal weight threshold, calculate the value of the fitness function, and determine whether the preset end condition is met. If it is met, output the optimal weight and threshold of the optimized rock stratum fissure development model; if not, perform iteration again until the end condition is met;

[0036] According to the training results, obtain the predicted residual sequence, analyze the residual sequence to obtain the fitting value; obtain the final rock stratum fissure development model according to the fitting value; input the deformation data into the rock stratum fissure development model to predict the rock stratum toppling deformation body and fissure development under different climate conditions.

[0037] As a further improvement of the present invention, the process of adjusting the rock stratum fissure development prediction model according to the evaluation results includes the following steps:

[0038] Analyze the distribution characteristics and development patterns of fractures in the prediction results, analyze the number, length, density of fractures and their manifestations under different geological conditions, and obtain the fracture development patterns;

[0039] Introduce shear strength and dip angle geological data, evaluate the stability coefficients of different regions, and correct them in combination with the numerical simulation results; Combine real-time monitoring data with the rock fracture development model to evaluate the accuracy of the rock fracture development model;

[0040] And adjust the parameters of the rock fracture development model according to the real-time monitoring data; Establish a long-term monitoring system, regularly collect and analyze the detection data, evaluate the changing trends of fracture development and slope stability, and upload the changing trends to the cloud platform for visual display.

[0041] As a further improvement of the present invention, the process of evaluating the changing trends of fracture development and slope stability includes the following steps:

[0042] According to the real-time monitoring data, analyze the number, length, density of fractures and the key parameter data of slope stability, and combine the historical data and prediction results to understand the expansion rate and distribution characteristics of fractures;

[0043] Use the collected monitoring data to adjust the parameters of the rock fracture development model; Simulate through the physical model of toppling deformation to evaluate the expansion mode of fractures under different geological conditions and their influence on slope stability;

[0044] Establish a long-term monitoring system, regularly collect and analyze the detection data, continuously evaluate the changing trends of fracture development and slope stability, and upload them to the cloud platform for visual display.

[0045] To achieve the above object, the present invention also provides the following technical solutions:

[0046] A system for analyzing the formation process of a toppling deformation body and the fracture development pattern, which is applied to the method for analyzing the formation process of a toppling deformation body and the fracture development pattern. The system for analyzing the formation process of a toppling deformation body and the fracture development pattern includes:

[0047] A rock stratum data collection module, which is used to obtain geographical location, climate and hydrological conditions, topography and geomorphology, geological structure and underlying lithology geographical data of the target area; Construct a physical model of toppling deformation according to the geographical data, and simulate the deformation characteristics of the reverse-dipping rock slope under different working conditions;

[0048] A prediction and simulation module, which is used to construct a prediction model for rock stratum fracture development based on the physical model of toppling deformation, input the simulation results into the prediction model for rock stratum fracture development, and predict the toppling deformation body and fracture development of the rock stratum under different climate conditions;

[0049] The analysis and prediction result module is used to analyze the prediction results to obtain the fracture development pattern; based on the fracture development pattern, the toppling deformation body is divided into stability zones to evaluate the stability coefficient; the stability coefficient is evaluated with the real-time monitoring data, and the rock stratum fracture development prediction model is adjusted according to the evaluation results.

[0050] The present invention obtains geographical data such as the geographical location, climate and hydrological conditions, topography and geomorphology, geological structure, and underlying lithology of the target area; constructs a toppling deformation physical model according to the geographical data to simulate the deformation characteristics of the anti-dipping rock slope under different working conditions; provides an accurate geographical information basis and reliable data support for subsequent deformation analysis and fracture prediction; the toppling deformation physical model can simulate the deformation characteristics under different working conditions, helping to understand the failure mechanism and deformation law of the anti-dipping rock slope, so as to provide a scientific basis for engineering design. Based on the toppling deformation physical model, a rock stratum fracture development prediction model is constructed. This model inputs the simulation results to predict the development of the rock stratum toppling deformation body and fractures under different climate conditions; it can predict the development of rock stratum fractures under different climate conditions, providing an important reference for slope stability evaluation; the prediction of the fracture development pattern helps to identify potential landslide risk areas, so as to take effective prevention and control measures. Analyze the prediction results to obtain the fracture development pattern, and based on this, divide the toppling deformation body into stability zones to evaluate the stability coefficient. Combine the real-time monitoring data to adjust and optimize the model; use methods such as the method of increasing rock weight for stability calculation, and conduct fracture field analysis through discrete element software to optimize the model parameters. Description of the Drawings

[0051] Figure 1 It is a schematic step flow diagram of an embodiment of the method for analyzing the formation process of the toppling deformation body and the fracture development pattern of the present invention;

[0052] Figure 2 It is a schematic step flow diagram of an embodiment of the method for analyzing the formation process of the toppling deformation body and the fracture development pattern of the present invention for constructing a toppling deformation physical model according to geographical data;

[0053] Figure 3 It is a schematic step flow diagram of an embodiment of the method for analyzing the formation process of the toppling deformation body and the fracture development pattern of the present invention for simulating the deformation characteristics of the anti-dipping rock slope under different working conditions;

[0054] Figure 4 It is a schematic step flow diagram of an embodiment of the method for analyzing the formation process of the toppling deformation body and the fracture development pattern of the present invention for predicting the development of the rock stratum toppling deformation body and fractures under different climate conditions;

[0055] Figure 5Schematic diagram of the steps for training a rock stratum fracture development prediction model using a training set in an embodiment of the method for analyzing the formation process of a toppling deformable body and the fracture development pattern according to the present invention;

[0056] Figure 6 Schematic diagram of the steps for obtaining the optimal weight threshold in an embodiment of the method for analyzing the formation process of a toppling deformable body and the fracture development pattern according to the present invention;

[0057] Figure 7 Schematic diagram of the steps for using the predicted output and the desired output of a rock stratum fracture development model as the reciprocal of the sum of squared absolute errors as a fitness function in an embodiment of the method for analyzing the formation process of a toppling deformable body and the fracture development pattern according to the present invention;

[0058] Figure 8 Schematic diagram of the steps for adjusting a rock stratum fracture development prediction model according to an evaluation result in an embodiment of the method for analyzing the formation process of a toppling deformable body and the fracture development pattern according to the present invention;

[0059] Figure 9 Schematic diagram of the steps for evaluating the changing trends of fracture development and slope stability in an embodiment of the method for analyzing the formation process of a toppling deformable body and the fracture development pattern according to the present invention;

[0060] Figure 10 Schematic diagram of the functional modules in an embodiment of the system for analyzing the formation process of a toppling deformable body and the fracture development pattern according to the present invention;

[0061] Figure 11 Schematic diagram of the structure in an embodiment of the electronic device according to the present invention;

[0062] Figure 12 Schematic diagram of the structure in an embodiment of the storage medium according to the present invention. Detailed implementation manners

[0063] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0064] The terms "first", "second", and "third" in the present invention are only for descriptive purposes and cannot be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, features defined with "first", "second", and "third" may explicitly or implicitly include at least one of such features. In the description of the present invention, the meaning of "a plurality" is at least two, such as two, three, etc., unless otherwise specifically defined. In the embodiments of the present invention, all directional indications (such as up, down, left, right, front, back...) are only used to explain the relative positional relationship and movement conditions between components in a specific posture (as shown in the drawings). If the specific posture changes, the directional indications will also change accordingly. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not limited to the listed steps or units, but optionally further includes steps or units not listed, or optionally further includes other steps or units inherent to these processes, methods, products, or devices.

[0065] Referring to "embodiments" herein means that the specific features, structures, or characteristics described in connection with the embodiments can be included in at least one embodiment of the present invention. The phrase appears in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. Those skilled in the art will explicitly and implicitly understand that the embodiments described herein can be combined with other embodiments.

[0066] As Figure 1 shown, this embodiment provides an embodiment of the method for analyzing the formation process of toppling deformable bodies and the fracture development pattern. In this embodiment, the method for analyzing the formation process of toppling deformable bodies and the fracture development pattern specifically includes the following steps:

[0067] Step S1: Obtain geographical data such as the geographical location, climate and hydrological conditions, topography, geological structure, and underlying lithology of the target area; construct a physical model of toppling deformation according to the geographical data, and simulate the deformation characteristics of the anti-dipping rock slope under different working conditions;

[0068] Step S2: Based on the physical model of toppling deformation, construct a prediction model for rock layer fracture development, input the simulation results into the prediction model for rock layer fracture development, and predict the toppling deformation of the rock layer and the fracture development under different climate conditions;

[0069] Step S3: Analyze the prediction results to obtain the fracture development pattern; conduct stability zoning for the toppling deformable body based on the fracture development pattern, and evaluate the stability coefficient; evaluate the stability coefficient with the real-time monitoring data, and adjust the prediction model for rock layer fracture development according to the evaluation results.

[0070] Preferably, in step S1 of this embodiment, geographical data such as the geographical location, climate and hydrological conditions, topography and geomorphology, geological structure, and underlying lithology of the target area are obtained; a physical model of toppling deformation is constructed based on the geographical data to simulate the deformation characteristics of the anti-dipping rocky slope under different working conditions; an accurate geographical information foundation is provided, providing reliable data support for subsequent deformation analysis and crack prediction; the physical model of toppling deformation can simulate the deformation characteristics under different working conditions, helping to understand the failure mechanism and deformation law of the anti-dipping rocky slope, so as to provide a scientific basis for engineering design. In step S2, a prediction model for the development of rock layer cracks is constructed based on the physical model of toppling deformation. This model inputs the simulation results to predict the development of rock layer toppling deformation bodies and cracks under different climate conditions; step S2 can predict the development of rock layer cracks under different climate conditions, providing an important reference for slope stability evaluation; the prediction of the crack development pattern helps to identify potential landslide risk areas, so as to take effective prevention and control measures. In step S3, the prediction results are analyzed to obtain the crack development pattern, and based on this, the stability of the toppling deformation body is divided, and the stability coefficient is evaluated. Combining real-time monitoring data, the model is adjusted and optimized; methods such as the method of increasing the rock weight are used for stability calculation, and the crack field is analyzed by discrete element software to optimize the model parameters; this embodiment improves the evaluation accuracy of the stability of the anti-dipping rocky slope, and can timely discover potential instability risks; according to the evaluation results, the prediction model for the development of rock layer cracks is adjusted to make it more in line with the actual situation, thereby improving the accuracy and reliability of the prediction.

[0071] Further, as Figure 2 shown, the process of constructing the physical model of toppling deformation according to the geographical data in step S1 specifically includes the following steps:

[0072] Step S11: Obtain geographical data such as the geographical location, climate and hydrological conditions, topography and geomorphology, geological structure, and underlying lithology of the target area; analyze the variation law of the occurrence of rock layers with elevation and horizontal depth based on on-site detailed survey data, adit and borehole data;

[0073] Step S12: Obtain the variation trend of the rock mass dip with adit depth and the variation trend of the rock mass dip angle with adit depth based on the results of analyzing the variation law of the occurrence of rock layers with elevation and horizontal depth; analyze the variation of the occurrence of the fracture zone structural plane of the toppling deformation body to obtain the variation law of the occurrence of rock layers caused by fracture;

[0074] Step S13: Classify the structural planes of the toppling rock mass into primary structural planes, tectonic structural planes, and shallow epigenetic structural planes; divide the structural planes into five grades according to the material composition characteristics, scale, spatial distribution, development degree, and geological significance in the project; construct a physical model of toppling deformation based on geographical data to simulate the deformation characteristics of the anti-dipping rock slope under different working conditions.

[0075] Among them, the first-level structural planes are regional faults and local faults, with an extension length of dozens of kilometers, the width of the fracture zone is generally > 10 m, and there are continuous fault gouges and mylonites in the fault fracture zone; the second-level structural planes are faults with an extension length > 1 km and a fracture zone width > 5 m, and there are continuous fault gouges and mylonites; the third-level structural planes are faults (F), shear displacement zones (Jc), and unloading fissures (Jx) with an extension length of several hundred meters to about one kilometer, or a fracture zone width of 0.2 m - 2 m, and there are continuous or discontinuous fault gouges and mylonites; the fourth-level structural planes are small faults with an extension length of dozens of meters to several hundred meters and a fracture zone width of 0.1 m - 0.5 m; and extrusion surfaces (gm) with an extension length of dozens of meters and a fracture zone width < 0.1 m; joint dense zones (Jm) with an extension length, obvious displacement, and soft filling materials; the fifth-level structural planes are joints and random joints that appear in groups with an extension length ranging from dozens of centimeters to several meters.

[0076] Preferably, in step S11 of this embodiment, geographical data such as the geographical location, climate and hydrological conditions, topography and geomorphology, geological structure, and underlying lithology of the target area are obtained; the on-site detailed investigation data, adit and borehole data are analyzed to study the variation law of the attitude of rock strata with elevation and horizontal depth; comprehensive geological background information is provided, providing basic data support for subsequent analysis; it helps to identify the spatial distribution characteristics and variation law of rock strata, providing a scientific basis for engineering design and construction. In step S12, the variation trend of the dip direction of the rock mass with the adit depth and the variation trend of the dip angle of the rock mass with the adit depth are obtained according to the analysis results; the variation law of the attitude of the rock strata caused by fracture is obtained by analyzing the variation of the attitude of the fault zone structural plane of the toppling deformation body; the variation of the mechanical properties of the rock mass at different depths is revealed, providing important information for understanding the stability of the rock mass; by analyzing the variation law of the fault zone structural plane, the deformation behavior of the rock mass under specific conditions can be predicted, thereby guiding engineering design and construction safety. In step S13, the structural planes of the toppling rock mass are classified into primary structural planes, tectonic structural planes, and superficial structural planes; according to the material composition characteristics, scale, spatial distribution, development degree, and geological significance in the project of the structural planes, the structural planes are divided into five grades; a physical model of toppling deformation is constructed based on the geographical data to simulate the deformation characteristics of the anti-dip rock slope under different working conditions; a detailed classification method of the rock mass structural plane is provided, which helps to accurately evaluate the stability of the rock mass; the constructed physical model can simulate the deformation characteristics of the slope under different working conditions, providing a scientific basis for engineering design and helping to formulate effective prevention and control measures.

[0077] Further, as Figure 3 shown, the process of simulating the deformation characteristics of the anti-dip rock slope under different working conditions in step S13 specifically includes the following steps:

[0078] Step S131: Analyze the weathered layer characteristics of the toppling rock mass according to the five-grade structural planes and divide the weathered layer characteristics of the toppling rock mass into four major types; among them, the four major types are completely weathered, strongly weathered, weakly weathered, and slightly weathered;

[0079] Step S132: Judge that the structural plane development is an unloading tensile crack or a medium-steep dip shear crack inclined along the slope; and divide the unloading zone of the slope rock mass into strong unloading, weak unloading, and deep unloading; among them, the deep unloading zone is the unloading phenomenon with a small opening degree that occasionally develops deeply in the threat and rock mass;

[0080] Step S133: Based on the obtained weathered layer and variation law, construct a physical model of toppling deformation, and conduct three excavation simulations according to the deformation characteristics of the anti-dip rock slope under different working conditions; record the excavation simulation data.

[0081] Preferably, in step S131 of this embodiment, the characteristics of the weathered layer of the toppling rock mass are analyzed according to five hierarchical structural planes; the characteristics of the weathered layer of the toppling rock mass are divided into four major types: completely weathered, strongly weathered, weakly weathered, and slightly weathered; by dividing the weathered layer into different types in this embodiment, the weathering degree of the rock mass can be more accurately described and understood. In step S132, the development of structural planes is judged to distinguish unloading tensile cracks and medium-steep dip shear cracks sloping along the slope; the unloading zone of the slope rock mass is divided into strong unloading, weak unloading, and deep unloading; by distinguishing different types of unloading zones in this embodiment, the deformation behavior of the rock mass under different working conditions can be more accurately predicted, thereby optimizing the engineering design and reducing potential safety hazards. In step S133, based on the obtained weathered layer and its variation law, a physical model of toppling deformation is constructed; three excavation simulations are carried out according to the deformation characteristics of the anti-dipping rock slope under different working conditions; the excavation simulation data is recorded; constructing a physical model and carrying out excavation simulations can simulate the deformation process of the slope under different conditions, thereby verifying the accuracy of the theoretical model.

[0082] Further, as Figure 4 shown, the process of predicting the toppling deformation body and crack development of the rock stratum under different climate conditions in step S2 specifically includes the following steps:

[0083] Step S21: According to the simulation results of the physical model of toppling deformation, deformation data is obtained; a prediction model for the development of rock stratum cracks is constructed based on the physical model of toppling deformation, historical rainfall data and historical deformation data are retrieved, and the historical rainfall data and historical deformation data are analyzed to obtain a rainfall data set;

[0084] Step S22: The rainfall data set is divided into a training set, a validation set, and a test set, and the training set is used to train the prediction model for the development of rock stratum cracks; after training, the deformation data is input into the rock stratum crack development model to predict the toppling deformation body and crack development of the rock stratum under different climate conditions;

[0085] Step S23: According to the prediction results, the toppling deformation body and crack development conditions of the rock stratum under different future climate conditions are obtained, and the prediction data is uploaded to the cloud platform; the prediction data is compared with the deformation crack development threshold. If it is greater than the threshold, an adjustment plan is carried out. If it is less than the threshold, an analysis is carried out.

[0086] Preferably, in step S21 of this embodiment, deformation data of the rock stratum is obtained through simulation of a physical model of tipping deformation. Such a physical model usually combines rheological theory and finite element analysis to accurately predict the deformation behavior of the rock stratum under different conditions; combines historical rainfall data and historical deformation data to construct a comprehensive data set for subsequent model training and verification; provides accurate deformation data, providing a basis for subsequent prediction of crack development; enhances the reliability of the model, and improves the prediction accuracy through the analysis of historical data. In step S22, the historical rainfall data set is divided into a training set, a validation set, and a test set, and the training set is used to train the prediction model for rock stratum crack development; the trained model is used to predict the tipping deformation body and crack development of the rock stratum under different climate conditions, and the validation set and the test set are used to evaluate the model performance; a prediction model that can adapt to different climate conditions is constructed, improving the prediction accuracy and robustness; through the evaluation of the validation and test sets, the effectiveness of the model in practical applications is ensured. In step S23, according to the prediction results, the tipping deformation bodies and crack development conditions of the rock stratum under different climate conditions are uploaded to the cloud platform for real-time monitoring and management; the prediction data is compared with the set deformation crack development threshold. If it exceeds the threshold, an adjustment plan is triggered; if it does not exceed, further analysis is carried out; real-time monitoring of the tipping deformation body and crack development of the rock stratum is realized, improving the timeliness and accuracy of disaster warning; through the threshold comparison mechanism, potential risks can be quickly responded to, reducing the possibility of disasters.

[0087] Furthermore, as Figure 5 shown, the process of using the training set to train the prediction model for rock stratum crack development in step S22 specifically includes the following steps:

[0088] Step S221: Retrieve historical rainfall data and historical deformation data to obtain data on the relationship between rainfall and cumulative surface deformation, and fit the data on the relationship between rainfall and cumulative surface deformation to obtain a fitted sequence;

[0089] Step S222: Determine and initialize the topological structure of the rock stratum crack development model. Determine the topological structure of the rock stratum crack development model according to the number of inputs and outputs. The number of nodes in the input layer and the output layer of the rock stratum crack development model are determined by the number of input and output parameters respectively;

[0090] Step S223: Calculate the fitness value, given the evolutionary parameters of the rock stratum crack development model, obtain the optimal weight threshold, and determine whether the preset conditions are met; if the preset conditions are met, obtain the best rock stratum crack development model, and if the conditions are not met, readjust.

[0091] Among them, the fitting formula for the data on the relationship between rainfall and cumulative surface deformation in step S221:

[0092]

[0093] In the formula, D represents the cumulative surface deformation (unit: millimeter); R i represents the i-th rainfall amount (unit: millimeter); T i represents the duration of the i-th rainfall (unit: hour); S i represents the intensity of the i-th rainfall (unit: millimeter per hour); C represents the initial fracture density of the rock stratum (unit: number per square meter); G represents the terrain slope, unit: degree; α, β, γ, δ, ζ, ∈, η, θ represent fitting parameters, determined by the nonlinear least squares method; this formula is used to fit the relationship between rainfall amount and cumulative surface deformation, considering factors such as rainfall amount, duration, intensity, fracture density, and terrain slope, and enhancing the nonlinear fitting ability through exponential and logarithmic functions;

[0094] Formula for initializing the topological structure of the rock stratum fracture development model in step S222:

[0095]

[0096] In the formula, N in represents the number of nodes in the input layer; N out represents the number of nodes in the output layer; X j represents the j-th input parameter (such as rainfall amount, temperature, etc.); Y j represents the weight adjustment factor of the j-th input parameter; Z represents the thickness of the rock stratum, unit: meter; U) represents the compressive strength of the rock stratum, unit: megapascal; W k represents the k-th output parameter, such as fracture density, deformation amount, etc.; V k represents the weight adjustment factor of the k-th output parameter; Q represents the tensile strength of the rock stratum, unit: megapascal; P represents the initial stress of the rock stratum, unit: megapascal; κ j , λ j , μ, ν, ω k , ξ k , ρ, σ represent weight coefficients. This formula is used to determine the topological structure of the rock stratum fracture development model, and the number of nodes in the input layer and output layer is dynamically adjusted through nonlinear functions (such as hyperbolic tangent, exponential, sine, cosine) to adapt to complex geological conditions.

[0097] Preferably, step S221 of this embodiment involves extracting data on rainfall and surface deformation from historical records; by analyzing the relationship between rainfall and surface deformation, a fitting series is obtained; through the fitting series, the impact of future rainfall on surface deformation can be more accurately predicted, providing a scientific basis for geological disaster warning; providing data support for engineering design, disaster prevention and emergency response, and helping decision makers to formulate more effective measures. In step S222, the number of nodes in the input and output layers of the model is determined by analyzing input parameters (such as geological conditions, hydrological conditions, etc.) and output parameters (such as crack distribution, density, etc.); after determining the topological structure, the model is initialized, including setting initial weights and bias values, so that the subsequent training process can proceed smoothly; this embodiment ensures that the model can effectively capture the key factors of rock fracture development through reasonable topological structure design, thereby improving the model's predictive ability and accuracy; providing technical support for the simulation and analysis of rock fracture development, helping to improve the safety and reliability of engineering design. In step S223, the model's fitness value, representing the model's performance under the current parameters, is calculated using the given evolutionary parameters. This fitness value is then used to determine whether the model meets the preset optimization criteria. If so, the optimal model is considered found. Otherwise, the parameters are adjusted and the model is retrained. Through an iterative optimization process, the optimal solution is gradually approached, improving the model's performance and prediction accuracy. This ensures that the resulting rock fracture development model is highly reliable and practical, accurately reflecting actual geological conditions.

[0098] Further, if Figure 6 As shown, the process of obtaining the optimal weight threshold in step S223 specifically includes the following steps:

[0099] Step S2231: Determine the basic parameters of the genetic algorithm based on the rock fracture development model, randomly generate an initial population of heavyweight size; select a data range, generate a real number vector of individuals in the population within the range, and use it as a chromosome of the genetic algorithm;

[0100] Among them, the chromosome contains all the values and thresholds of the rock fracture development model;

[0101] Step S2232: Encode the chromosome to obtain weights and thresholds; initialize the population; calculate the fitness value, and assign the weights and thresholds of the rock fracture development model using the real number vector as the value;

[0102] Step S2233: Input the training set to train the rock fracture development model to obtain the rock fracture development output value to achieve the preset accuracy, and use the predicted output and expected output of the rock fracture development model as the inverse of the sum of squares of absolute errors as the fitness function.

[0103] Among them, the genetic algorithm initial population generation formula in step S2231:

[0104]

[0105] In the formula, P init represents the initial population; represents the i'-th chromosome; w1, w2, …, w r represents the weight vector; b1, b2, …, b s represents the threshold vector; φ1, φ2 represent the weight phase angles; ψ r represents the weight decay factor; χ1, χ2, …, χ s represents the threshold adjustment factor; q represents the population size; r represents the number of weights; s represents the number of thresholds; this formula is used to generate the initial population of the genetic algorithm, and the weights and thresholds in the chromosome are non-linearly adjusted through trigonometric functions, exponential functions, and logarithmic functions to enhance the diversity of the population;

[0106] Fitness value calculation formula in step S2232:

[0107]

[0108] In the formula, represents the fitness value of the i'-th chromosome; y t represents the t-th expected output value; represents the t-th predicted output value; ω represents the weight regularization coefficient; φ j represents the weight decay factor; ρ' represents the threshold regularization coefficient; σ k represents the threshold adjustment factor; u represents the number of training set samples. This formula is used to calculate the fitness value of the chromosome, combining the prediction error and the regularization term, and dynamically adjusting the influence of the weights and thresholds through the exponential function and the hyperbolic tangent function.

[0109] Fitness function optimization formula in step S2233:

[0110]

[0111] In the formula, represents the newly generated chromosome; represents the current optimal chromosome; represents two randomly selected chromosomes; σ', τ, ρ″, φ represent the adjustment coefficients. This formula is used to generate a new chromosome, introducing periodic changes through sine and cosine functions to enhance the global search ability of the genetic algorithm.

[0112] Preferably, in step S2231 of this embodiment, the basic parameters of the genetic algorithm are determined according to the rock stratum fracture development model, including the population size, chromosome coding method, etc.; a certain number of individuals are randomly generated within the specified data range, and each individual is represented by a real number vector, which contains all the weights and thresholds of the rock stratum fracture development model; the real number vector is used as the chromosome for subsequent operations of the genetic algorithm; in this embodiment, by randomly generating the initial population, an initial solution space is provided for the genetic algorithm, ensuring the diversity of the algorithm; by encoding the weights and thresholds of the rock stratum fracture development model into the chromosome, the optimization of the model parameters is realized, thereby improving the adaptability and accuracy of the model. In step S2232, the chromosome is decoded to extract the weights and thresholds; after the decoding of the chromosome is completed, the entire population is initialized, and the fitness value of each individual is calculated using the evolutionary parameters of the given rock stratum fracture development model; the fitness value is calculated as the reciprocal of the sum of the squared errors between the predicted output and the expected output of the rock stratum fracture development model; in this embodiment, by decoding the chromosome, the parameters obtained by the genetic algorithm are applied to the rock stratum fracture development model, enabling the model to be trained and optimized according to these parameters; the fitness value is calculated to evaluate the performance of each individual in the current problem, providing a basis for subsequent selection, crossover, and mutation operations. In step S2233, the training set is input to train the rock stratum fracture development model until the preset accuracy is reached; the output value of the rock stratum fracture development model is obtained; the reciprocal of the sum of the squared absolute errors between the predicted output and the expected output of the rock stratum fracture development model is used as the fitness function; the model is trained and the parameters are continuously adjusted to enable the model to more accurately predict the development of rock stratum fractures; in this embodiment, by calculating the fitness function, the performance of the model is fed back to guide the genetic algorithm for further optimization.

[0113] Further, as Figure 7 shown, the process of taking the reciprocal of the sum of the squared absolute errors between the predicted output and the expected output of the rock stratum fracture development model as the fitness function in step S2233 specifically includes the following steps:

[0114] Step S22331: Select the operator according to the preset ratio, select the chromosomes in each generation of the population based on the selection sequence of fitness ratio, and perform a crossover operation on two chromosomes, and select the genes of the individuals for mutation;

[0115] Step S22332: Obtain the optimal weight threshold, calculate the value of the fitness function, and judge whether the preset end condition is satisfied. If it is satisfied, output the optimal weights and thresholds of the optimized rock stratum fracture development model; if not, re-iterate until the end condition is satisfied;

[0116] Step S22333: Obtain a predicted residual sequence based on the training results, analyze the residual sequence to obtain a fitted value; obtain the final rock stratum fissure development model according to the fitted value; input the deformation data into the rock stratum fissure development model to predict the rock stratum toppling deformation body and fissure development under different climate conditions.

[0117] Among them, the chromosome selection and crossover formula in step S22331:

[0118]

[0119] In the formula, represents the offspring chromosome; represents the parent chromosome; θ′ represents the crossover angle. This formula is used for chromosome crossover operations, achieving smooth transitions through sine and cosine functions to avoid the impact of mutations on the model performance.

[0120] Preferably, in step S22331 of this embodiment, operators are selected according to a preset ratio, and chromosomes in each generation of the population are selected based on the selection sequence of fitness ratio; crossover operations are performed on two chromosomes, and the genes of individuals are selected for mutation; through the selection operation and crossover operation in this embodiment, the retention of excellent genes and the reproduction of excellent individuals in the population are ensured, thereby improving the overall fitness of the population. The crossover operation promotes gene recombination, increases the diversity of the population, helps avoid local optimal solutions, and improves the global search ability. The mutation operation further increases the diversity of the population, prevents the algorithm from falling into local optimal solutions, and makes the optimization process more comprehensive and effective. In step S22332, the optimal weight threshold is obtained, the fitness function value is calculated, and it is judged whether the preset end condition is satisfied. If satisfied, the optimal weight and threshold of the optimized rock stratum fissure development model are output; if not satisfied, iteration is performed again; in this embodiment, the fitness function value is calculated to evaluate the performance of the current population, and it is determined whether to terminate the algorithm according to the preset end condition. In step S22333, according to the training results, a predicted residual sequence is obtained, the residual sequence is analyzed to obtain a fitted value, the final rock stratum fissure development model is obtained according to the fitted value, and the deformation data is input into the rock stratum fissure development model to predict the rock stratum toppling deformation body and fissure development under different climate conditions; in this embodiment, the fitting effect of the model is evaluated by analyzing the predicted residual sequence, and the model parameters are adjusted accordingly to obtain a better fitted value. The finally formed rock stratum fissure development model can accurately reflect the actual deformation situation and can be used for predicting the rock stratum toppling deformation body and fissure development under different climate conditions.

[0121] Furthermore, as Figure 8 shown, the process of adjusting the rock stratum fissure development prediction model according to the evaluation results in step S3 specifically includes the following steps:

[0122] Step S31: Analyze the distribution characteristics and development patterns of the cracks based on the prediction results, analyze the number, length, density of the cracks, and their performance under different geological conditions, and derive the crack development pattern;

[0123] Step S32: Introducing geological data such as shear strength and inclination, evaluating the stability coefficient of different regions, and performing corrections based on numerical simulation results; combining real-time monitoring data with the rock fracture development model to evaluate the accuracy of the rock fracture development model;

[0124] Step S33: Adjust the rock fracture development model parameters according to the real-time monitoring data; establish a long-term monitoring system, regularly collect and analyze detection data, evaluate the changing trends of fracture development and slope stability, and upload the changing trends to the cloud platform for visual display.

[0125] Preferably, in step S31 of this embodiment, the prediction results are analyzed to identify key parameters such as the number, length, and density of cracks; the performance of cracks under different geological conditions is analyzed, including the morphology, distribution pattern, and relationship with geological structures of the cracks; by analyzing the distribution characteristics and development patterns of cracks, this embodiment can better understand the formation mechanism and development trend of cracks in a specific geological environment, thereby providing basic data support for subsequent stability assessments. In step S32, geological data such as shear strength and inclination are combined to evaluate the stability coefficients of different regions; the model is corrected using numerical simulation results to ensure the accuracy of the model; the real-time monitoring data is combined with the rock crack development model to verify and adjust the model; the accuracy and reliability of the model are improved, making the assessment of rock stability more scientific and accurate. By introducing real-time monitoring data, the model parameters can be dynamically adjusted to improve the real-time and effectiveness of the prediction; correction combined with numerical simulation results can more accurately reflect the development of cracks under actual geological conditions, thereby providing a more reliable basis for engineering design and disaster prevention. In step S33, the parameters of the rock fracture development model are adjusted based on real-time monitoring data; a long-term monitoring system is established to regularly collect and analyze test data; and the changing trends are uploaded to the cloud platform for visualization. The inclusion of real-time monitoring data allows for dynamic adjustment of model parameters, enabling the model to better adapt to actual changes and improving the accuracy and reliability of predictions. Establishing a long-term monitoring system helps continuously track fracture development trends and slope stability changes, providing continuous assurance for project safety. The visualization of data makes monitoring results more intuitive and easy to understand, facilitating analysis and judgment by engineers and decision makers.

[0126] Further, if Figure 9 As shown, step S33 is a process of evaluating the trend of crack development and slope stability, which specifically includes the following steps:

[0127] Step S331: Analyze the key parameter data such as the number, length, density of fissures and slope stability based on the real-time monitoring data, in combination with historical data and prediction results, to understand the expansion rate and distribution characteristics of the fissures;

[0128] Step S332: Adjust the parameters of the rock fissure development model using the collected monitoring data; simulate through the physical model of toppling deformation to evaluate the expansion mode of fissures under different geological conditions and the impact on slope stability;

[0129] Step S333: Establish a long-term monitoring system, regularly collect and analyze the detection data, continuously evaluate the changing trends of fissure development and slope stability, and upload them to the cloud platform for visual display;

[0130] Among them, the visual display includes the fissure expansion path, slope instability risk level and treatment plan.

[0131] Preferably, in step S331 of this embodiment, the key parameter data such as the number, length, density of fissures are obtained through real-time monitoring and analyzed in combination with historical data; statistical analysis methods such as Monte Carlo sampling are used to analyze the expansion rate and distribution characteristics of the fissures; the current monitoring data is compared with the historical data to understand the long-term changing trend of the fissures; by analyzing the expansion rate and distribution characteristics of the fissures in this embodiment, the behavior of the fissures under different conditions can be better understood, thus providing a basis for slope stability evaluation; by combining historical data and real-time monitoring data, the prediction accuracy of the future development of the fissures can be improved, so as to take preventive measures in advance. In step S332, the parameters of the rock fissure development model are adjusted according to the collected monitoring data to reflect the actual situation; the physical model is used to simulate the expansion mode of the fissures under different geological conditions and evaluate its impact on slope stability; by adjusting the model parameters in this embodiment, the model is closer to the actual geological conditions, thus improving the accuracy of the simulation results; and by simulating the expansion mode of the fissures under different geological conditions, the slope stability at different positions can be evaluated, so as to identify potential instability risks. In step S333, a long-term monitoring system is established to regularly collect and analyze the detection data; the monitoring data is uploaded to the cloud platform and visually displayed, including the fissure expansion path, slope instability risk level and treatment plan; through the long-term monitoring system in this embodiment, the changing trends of fissure development and slope stability can be continuously evaluated, and potential problems can be discovered in time; through the visual display in this embodiment, relevant decision-makers can intuitively understand the current state and future trends of the slope, so as to formulate more effective treatment plans.

[0132] Such as Figure 10As shown in the figure, this embodiment also provides an embodiment of a system for analyzing the formation process of toppling deformable bodies and the fracture development pattern. In this embodiment, the system for analyzing the formation process of toppling deformable bodies and the fracture development pattern is applied to the method for analyzing the formation process of toppling deformable bodies and the fracture development pattern as described in the above embodiment.

[0133] The system for analyzing the formation process of toppling deformable bodies and the fracture development pattern includes:

[0134] A rock layer data collection module 1, which is used to obtain geographical data such as the geographical location, climate and hydrological conditions, topography and geomorphology, geological structure, and underlying lithology of the target area; construct a physical model of toppling deformation according to the geographical data, and simulate the deformation characteristics of the reverse-inclined rock slope under different working conditions;

[0135] A prediction simulation module 2, which is used to construct a prediction model for rock layer fracture development based on the physical model of toppling deformation, input the simulation results into the prediction model for rock layer fracture development, and predict the toppling deformation of the rock layer and the fracture development under different climate conditions;

[0136] An analysis and prediction result module 3, which is used to analyze the prediction results to obtain the fracture development pattern; conduct stability zoning on the toppling deformable body based on the fracture development pattern, and evaluate the stability coefficient; evaluate the stability coefficient and the real-time monitoring data, and adjust the prediction model for rock layer fracture development according to the evaluation results.

[0137] Preferably, in this embodiment, the rock layer data collection module provides basic information for subsequent simulation and prediction by obtaining geographical data such as the geographical location, climate and hydrological conditions, topography and geomorphology, geological structure, and underlying lithology of the target area; provides accurate basic data for the construction of the physical model of toppling deformation, ensuring the reliability and accuracy of the model. The prediction simulation module is based on the physical model of toppling deformation, and this module constructs a prediction model for rock layer fracture development; by inputting the simulation results, combined with the toppling deformation of the rock layer and the fracture development under different climate conditions, prediction and analysis are carried out; this embodiment can effectively predict the fracture development of rock layers under different conditions, providing a scientific basis for engineering design and disaster prevention; at the same time, by simulating the fracture development under different climate conditions, the influence of environmental factors on the stability of rock layers can be better understood. The analysis and prediction result module conducts a detailed analysis of the prediction results, identifies the fracture development pattern, and conducts stability zoning on the toppling deformable body based on this. By calculating the stability coefficient and combining the real-time monitoring data, the stability of the rock layer is evaluated. Through the analysis of the fracture development pattern and stability zoning, the stability of the rock layer can be evaluated more accurately, thus providing a scientific basis for engineering decision-making. In addition, the introduction of real-time monitoring data enables the model to be dynamically adjusted, improving the timeliness and accuracy of prediction.

[0138] As Figure 11As shown in the figure, an embodiment of an electronic device is provided in this embodiment. In this embodiment, the electronic device 4 includes a processor 41 and a memory 42 coupled to the processor 41.

[0139] The memory 42 stores program instructions for implementing the layout method of the dumping deformation body formation process and the crack development mode analysis method in any of the above embodiments.

[0140] The processor 41 is configured to execute the program instructions stored in the memory 42 to perform the layout of the dumping deformation body formation process and the crack development mode analysis method.

[0141] Among them, the processor 41 can also be referred to as a CPU (Central Processing Unit, central processing unit). The processor 41 may be an integrated circuit chip with signal processing capabilities. The processor 41 may also be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0142] Furthermore, Figure 12 The figure is a schematic structural diagram of a storage medium according to an embodiment of the present application. The storage medium 5 of the embodiment of the present application stores program instructions 51 that can implement all of the above methods. Among them, the program instructions 51 can be stored in the above storage medium in the form of a software product, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) or a processor to execute all or part of the steps of the methods described in various embodiments of the present application. The foregoing storage medium includes: various media that can store program codes such as a USB flash drive, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk, or an optical disc, or a terminal device such as a computer, a server, a mobile phone, or a tablet.

[0143] In several embodiments provided by the present invention, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of units is only a logical function division, and there may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed mutual coupling or direct coupling or communication connection may be through some interfaces, and the indirect coupling or communication connection of devices or units may be in an electrical, mechanical, or other form.

[0144] In addition, in each embodiment of the present invention, each functional unit can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of a software functional unit. The above is only the embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the content of the specification and drawings of the present invention, or directly or indirectly applied in other related technical fields, shall be included in the patent protection scope of the present invention by the same token.

[0145] The specific embodiments of the invention have been described in detail above, but they are only examples, and the present invention is not limited to the specific embodiments described above. For those skilled in the art, any equivalent modification or substitution of the invention is also within the scope of the present invention. Therefore, all equal transformations, modifications, improvements, etc. made without departing from the spirit and principles of the present invention should be covered by the scope of the present invention.

Claims

1. A method for analyzing the formation process of a toppling deformable body and the crack development pattern, characterized in that The analysis method for the formation process of the toppling deformation body and the fracture development pattern includes: Obtain the geographical location, climate and hydrological conditions, topography, geological structure, and underlying lithology geographical data of the target area; construct a physical model of toppling deformation based on the geographical data, and simulate the deformation characteristics of the anti-dip rock slope under different working conditions; Construct a prediction model for rock layer fracture development based on the physical model of toppling deformation, input the simulation results into the prediction model for rock layer fracture development, and predict the toppling deformation body and fracture development of the rock layer under different climate conditions; Analyze the prediction results to obtain the fracture development pattern; conduct stability zoning on the toppling deformation body based on the fracture development pattern, and evaluate the stability coefficient; evaluate the stability coefficient with real-time monitoring data, and adjust the prediction model for rock layer fracture development according to the evaluation results.

2. The method for analyzing the formation process of the toppling deformable body and the crack development pattern according to claim 1, characterized in that The process of constructing a physical model of toppling deformation based on geographical data includes the following steps: Obtain the geographical location, climate and hydrological conditions, topography, geological structure, and underlying lithology geographical data of the target area; analyze the variation law of the rock layer occurrence with elevation and horizontal depth based on the on-site detailed investigation data, adit, and borehole data; Obtain the variation trend of the rock mass dip with adit depth and the variation trend of the rock mass dip angle with adit depth based on the analysis results of the variation law of the rock layer occurrence with elevation and horizontal depth; analyze the change of the fracture zone structural plane occurrence of the toppling deformation body to obtain the law of rock layer occurrence change caused by fracture; Classify the structural planes of the toppling rock mass into primary structural planes, tectonic structural planes, and superficial structural planes; divide the structural planes into five grades according to the material composition characteristics, scale, spatial distribution, development degree, and geological significance in the project; construct a physical model of toppling deformation based on the geographical data, and simulate the deformation characteristics of the anti-dip rock slope under different working conditions.

3. The method for analyzing the formation process and crack development pattern of a collapsed deformed body according to claim 2, characterized in that: The process of simulating the deformation characteristics of the anti-dip rock slope under different working conditions includes the following steps: Analyze the weathered layer characteristics of the toppling rock mass according to the five-grade structural planes, and divide the weathered layer characteristics of the toppling rock mass into four major types; Judge whether the structural plane development is a unloading tensile fracture or a medium-slope inclined shear fracture with a slope; divide the unloading zone of the slope rock mass into strong unloading, weak unloading, and deep unloading; Based on the obtained weathered layer and variation law, construct a physical model of toppling deformation, and conduct three excavation simulations according to the deformation characteristics of the anti-dip rock slope under different working conditions; record the excavation simulation data.

4. The method for analyzing the formation process of the toppling deformable body and the crack development pattern according to claim 1, characterized in that The process of predicting the toppling deformation body and fracture development of the rock layer under different climate conditions includes the following steps: According to the simulation results of the physical model of toppling deformation, obtain the deformation data; construct a prediction model for rock layer fracture development based on the physical model of toppling deformation, retrieve the historical rainfall data and historical deformation data, and analyze the historical rainfall data and historical deformation data to obtain a rainfall data set; Divide the rainfall data set into a training set, a validation set, and a test set, and use the training set to train the prediction model for rock layer fracture development; after the training is completed, input the deformation data into the rock layer fracture development model to predict the toppling deformation body and fracture development of the rock layer under different climate conditions; In the first step, based on the prediction results, the dumping deformation of the rock formation and the development of fissures under different future climate conditions are obtained, and the prediction data is uploaded to the cloud platform. The prediction data is compared with the threshold of deformation fissure development. If it is greater than the threshold, the adjustment plan is carried out; if it is less than the threshold, analysis is carried out.

5. The method for analyzing the formation process of the toppling deformable body and the crack development pattern according to claim 4, characterized in that, The process of training the rock formation fissure development prediction model using the training set includes the following steps: Retrieve historical rainfall data and historical deformation data to obtain the relationship data between rainfall and surface cumulative deformation, and fit the relationship data between rainfall and surface cumulative deformation to obtain a fitted sequence. Determine and initialize the topological structure of the rock formation fissure development model. Determine the topological structure of the rock formation fissure development model according to the number of inputs and outputs. The number of input layer nodes and output layer nodes of the rock formation fissure development model are determined by the number of input and output parameters respectively. Calculate the fitness value, set the evolution parameters of the rock formation fissure development model, obtain the optimal weight threshold, and determine whether the preset conditions are met. If the preset conditions are met, the best rock formation fissure development model is obtained; if the conditions are not met, readjustment is carried out.

6. The analysis method for the formation process of the toppling deformable body and the crack development pattern according to claim 5, characterized in that The process of obtaining the optimal weight threshold specifically includes the following steps: Determine the basic parameters of the genetic algorithm by the rock formation fissure development model, and randomly generate an initial population of a certain scale. Select a data range, generate a real number vector of individuals in the population within this range, and use it as a chromosome of the genetic algorithm. Encode the chromosome to obtain the weights and thresholds; initialize the population; calculate the fitness value, set the evolution parameters of the rock formation fissure development model, and use the real number vector as the individual to assign values to the weights and thresholds of the rock formation fissure development model. Input the training set to train the rock formation fissure development model. When the preset accuracy is reached, the rock formation fissure development output value is obtained. Use the reciprocal of the sum of the squares of the absolute errors between the predicted output and the expected output of the rock formation fissure development model as the fitness function.

7. The method for analyzing the formation process of the toppling deformation body and the fracture development pattern according to claim 6, characterized in that, The process of using the reciprocal of the sum of the squares of the absolute errors between the predicted output and the expected output of the rock formation fissure development model as the fitness function includes the following steps: Select operators according to the preset ratio, select the chromosomes in each generation of the population based on the selection sequence of fitness ratio, perform crossover operations on two chromosomes, and select the genes of individuals for mutation. Obtain the optimal weight threshold, calculate the value of the fitness function, and determine whether the preset end condition is met. If it is met, output the optimal weights and thresholds of the optimized rock formation fissure development model; if not, perform iteration again until the end condition is met. According to the training results, obtain the prediction residual sequence, analyze the residual sequence to obtain the fitted value; obtain the final rock formation fissure development model according to the fitted value; input the deformation data into the rock formation fissure development model to predict the dumping deformation of the rock formation and the development of fissures under different climate conditions.

8. The method for analyzing the formation process of the toppling deformable body and the crack development pattern according to claim 1, characterized in that, The process of adjusting the rock formation fissure development prediction model according to the evaluation results includes the following steps: Analyze the distribution characteristics and development patterns of the fissures in the prediction results, analyze the number, length, density of the fissures and their performance under different geological conditions, and obtain the fissure development pattern. Introduce shear strength and dip angle geological data, evaluate the stability coefficients of different regions, and correct them in combination with numerical simulation results; combine real-time monitoring data with the rock fracture development model to evaluate the accuracy of the rock fracture development model; Adjust the parameters of the rock fracture development model according to real-time monitoring data; establish a long-term monitoring system, regularly collect and analyze the detection data, evaluate the change trends of fracture development and slope stability, and upload the change trends to the cloud platform for visual display.

9. The method for analyzing the formation process and crack development pattern of a collapsed deformed body according to claim 8, characterized in that: The process of evaluating the change trends of fracture development and slope stability includes the following steps: According to real-time monitoring data, regarding the data of fracture quantity, length, density, and key parameters of slope stability, analyze in combination with historical data and prediction results to understand the expansion rate and distribution characteristics of fractures; Use the collected monitoring data to adjust the parameters of the rock fracture development model; simulate through the physical model of toppling deformation to evaluate the expansion mode of fractures under different geological conditions and the impact on slope stability; Establish a long-term monitoring system, regularly collect and analyze the detection data, continuously evaluate the change trends of fracture development and slope stability, and upload them to the cloud platform for visual display.

10. A system for analyzing the formation process of toppling deformable bodies and the crack development pattern, which is applied to the method for analyzing the formation process of toppling deformable bodies and the crack development pattern as described in any one of claims 1 to 9, and is characterized in that The analysis system for the formation process of the toppling deformation body and the fracture development mode includes: The rock layer data collection module is used to obtain geographical location, climate and hydrological conditions, topography, geological structure, and underlying lithology geographical data of the target area; construct a physical model of toppling deformation according to the geographical data to simulate the deformation characteristics of the reverse-dipping rock slope under different working conditions; The prediction and simulation module is used to construct a prediction model for rock layer fracture development based on the physical model of toppling deformation, input the simulation results into the prediction model for rock layer fracture development, and predict the toppling deformation body and fracture development of the rock layer under different climate conditions; The analysis and prediction result module is used to analyze the prediction results to obtain the fracture development mode; conduct stability zoning for the toppling deformation body based on the fracture development mode and evaluate the stability coefficient; evaluate the stability coefficient with real-time monitoring data and adjust the prediction model for rock layer fracture development according to the evaluation results.

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