An intelligent prediction method for earthquake slope instability probability based on coupling of physical mechanism and neural network model

Abstract

The present invention relates to the technical field of intelligent prediction of geological disasters, and specifically discloses a method for intelligent prediction of earthquake slope instability probability based on the coupling of physical mechanisms and neural network models. The method analyzes slope stability through research methods such as numerical simulation, indoor model tests, and slope monitoring, collects and integrates seismic motion data, slope geometric parameters, and stability states, and constructs a database based on physical mechanisms. Through the database, seismic motion spectrum characteristics, pulse characteristics, and time domain / time-frequency characteristics and cumulative displacement values of slope displacement, acceleration, stress, etc. are extracted. Slope instability indicators and thresholds are determined based on database cases. A neural network method based on Bayesian optimization is used to establish an intelligent prediction model for slope instability probability, thereby realizing intelligent prediction of earthquake slope instability probability and providing a theoretical basis and technical support for the prevention and mitigation of earthquake slope disasters.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent prediction of geological disasters, and in particular to an intelligent prediction method for earthquake slope instability probability based on the coupling of physical mechanism and neural network model. Background Art

[0002] Earthquake-induced landslides are one of the most severe geological hazards, posing a significant threat to human life, infrastructure, and the ecological environment. Slope stability analysis is a core component of landslide risk prediction and prevention, and its accuracy and timeliness directly impact the scientific nature of earthquake emergency response decisions.

[0003] Traditional methods primarily rely on physical models for stability assessment, but these methods present certain complexities and limitations. Currently, research on slope stability relies heavily on mechanical analysis based on physical models (e.g., the limit equilibrium method and the finite element method), which require extensive detailed slope data (e.g., geometric characteristics and geotechnical parameters). However, the dynamic response of landslides to strong earthquakes is complex, and these methods are limited in their applicability when faced with large-scale regional assessments or when high-resolution data is lacking. Landslide prediction relies on multi-source data (seismic motion, topography, geology, and monitoring), which exhibit significant differences in their spatiotemporal distribution. For example, seismic motion data primarily utilizes time-domain and frequency-domain features, while topography and geometric parameters are static spatial data. This heterogeneity complicates the integration and feature extraction of multidimensional information, making it difficult for traditional prediction models to fully utilize this information.

[0004] With the development of artificial intelligence (AI) technology, machine learning models have begun to demonstrate significant advantages in slope hazard prediction, particularly in their ability to process large amounts of data. However, these data-driven models often lack an understanding of physical mechanisms, resulting in insufficient interpretability of their predictions, which limits their credibility and applicability in practical applications.

[0005] Therefore, there is an urgent need for a new method for intelligent monitoring, risk assessment and earthquake disaster prevention and control of slope hazards. Summary of the Invention

[0006] To solve the problems existing in the prior art, the present invention provides an intelligent prediction method for the probability of earthquake slope instability based on the coupling of physical mechanisms and neural network models. Through physical mechanism modeling, data-driven feature extraction and Bayesian optimization neural network modeling technology, it provides a theoretical basis and technical support for the prediction of earthquake-induced landslides. It is suitable for intelligent monitoring, risk assessment and earthquake disaster prevention and control of slope disasters, and solves the problems mentioned in the above background technology.

[0007] To achieve the above object, the present invention provides the following technical solution: an intelligent prediction method for earthquake slope instability probability based on the coupling of physical mechanism and neural network model, comprising the following steps:

[0008] S1. Database construction based on earthquake slope stability analysis;

[0009] S2, feature extraction of ground motion and slope response data;

[0010] S3. Determination of earthquake slope stability evaluation index and instability threshold;

[0011] S4, data preprocessing and eigenvalue selection;

[0012] S5. Establishment of intelligent prediction model for earthquake slope instability probability;

[0013] S6. Intelligent prediction of earthquake slope instability probability.

[0014] Preferably, in step S1, the following is specifically included:

[0015] S11. Collect earthquake motion data worldwide, including acceleration time histories, conduct earthquake motion characteristic analysis, and obtain information on peak earthquake acceleration and velocity, Arias intensity, frequency, epicenter distance, and focal depth;

[0016] S12. Collect relevant data for seismic slope stability analysis based on numerical simulation and shaking table model testing methods, and obtain slope response characteristic data including slope geometry, geological conditions, load, acceleration, displacement, and stress distribution;

[0017] S13. Collect real-time monitoring data based on on-site slopes, including displacement and strain data;

[0018] S14. Integrate the above data to build a database for earthquake slope stability analysis, and mark the slope stability status corresponding to each set of data, that is, mark stable as 0 and unstable as 1.

[0019] Preferably, in step S2, data feature extraction is performed based on the constructed database, specifically including the following:

[0020] S21. Seismic data characteristics: Fourier transform (FFT) is used to extract the frequency domain characteristics of seismic motion and slope response, perform spectrum analysis, and calculate the energy distribution in different frequency bands. Based on the improved M&P wave method, the seismic pulse components are identified and extracted. The time-frequency characteristics of seismic motion are used to calculate the pulse energy rate and perform pulse effect characteristic analysis. The calculation formula for pulse seismic motion identification is:

[0021] E R =-(2.68+0.1416×PGVp -0.0162×ER p +0.0002852×PGV p 2

[0022] -0.005476×ER p ×PGV p +0.0005197×ER p 2 )

[0023]

[0024] Among them, E R is the pulse index; PGVp is the pulse peak velocity; ERp is the pulse energy rate; when the pulse index E R When it is greater than 0, the earthquake motion is a pulse earthquake motion; t s and t e and represent the start time and end time of the velocity pulse respectively; E(t) represents the accumulated energy of the ground motion at time t;

[0025] S22. Slope response feature extraction from numerical simulation and model tests: Extract time domain features from displacement, acceleration, and stress curves, including maximum value, root mean square (RMS), and standard deviation; and extract the cumulative displacement of the slope under earthquake action.

[0026] S23. Monitoring data feature extraction: Calculate the rate of change from the displacement and acceleration time series data, including the displacement change rate and velocity change rate.

[0027] Preferably, in step S3, the following is specifically included:

[0028] Through statistical analysis of stable and unstable samples in the database and slope response characteristics, the displacement critical value under specific earthquake motion conditions is calculated and the displacement threshold is determined; the earthquake acceleration time history is extracted to calculate the slope critical acceleration, and the relationship between the acceleration value and slope instability is analyzed to determine the slope critical acceleration; the stress distribution is extracted and the critical stress value is calculated;

[0029] Based on the index thresholds of displacement, acceleration and stress, a multidimensional index system for slope instability is defined. When any stability index reaches or exceeds the instability threshold, the slope is considered to have reached a critical state of instability; when all indicators are below their thresholds, the slope is in a stable state.

[0030] Preferably, the expression for judging the indicator threshold is as follows:

[0031]

[0032] Among them, State represents the slope stability state, which takes the value of "1" or "0"; D represents the cumulative displacement; A represents the maximum acceleration; S represents the maximum stress; D crit is the instability threshold of cumulative displacement; A crit is the acceleration instability threshold; S crit is the instability threshold of stress.

[0033] Preferably, in step S4, the following is specifically included:

[0034] S41. Based on the database constructed in step S1 and the data features extracted in step S2, a large number of slope characteristic parameters and ground motion intensity parameters are obtained, and these parameters are used as influencing factors to perform standardized data preprocessing to ensure that the numerical ranges of the data features are consistent;

[0035] The slope characteristic parameters include slope height, slope gradient, and rock and soil strength; the seismic intensity parameters include peak value, Arias intensity, and pulse energy rate;

[0036] S42, using the slope instability multidimensional index system and threshold judgment expression established in step S3, determining the stability state, i.e., stable is 0 and unstable is 1, and using the stability state as the target value / label;

[0037] S43. Using correlation analysis, factors with correlations less than 0.8 between each influencing factor and other influencing factors are retained. Then, the importance of these retained influencing factors is evaluated based on the random forest algorithm. Influencing factors with importance less than 0.1 or the lowest importance relative to others are removed, and the remaining influencing factors are used as the eigenvalues required for model establishment.

[0038] Preferably, the calculation formula of the impact factor importance is as follows:

[0039]

[0040] Among them, Importance(X i ) is the impact factor X i The importance of N is the number of impact factors, G split is the gain contributed by the factor, and n is the nth influencing factor.

[0041] Preferably, in step S5, the feature values and target values / labels in step S4 are used as a data set, and the data set is randomly divided into 5 parts using 5-fold cross validation. 4 of the parts are selected as training sets and 1 as a test set, and the training set is brought into the convolutional neural network (CNN) algorithm to build a prediction model.

[0042] Bayesian optimization is introduced into the algorithm to automatically adjust the model's hyperparameters, including convolution kernel size, number of convolution kernels, pooling kernel size, pooling method, number of neurons, number of layers, learning rate, and L2 regularization parameter;

[0043] Model testing: Each test uses accuracy, recall rate, and AUC curve to evaluate the stability and generalization ability of the model on the test set. Finally, the average value of each test is taken to obtain the trained model.

[0044] Preferably, in step S6, the closer the slope instability probability value is to 1, the more likely the slope is to become unstable, and the closer the value is to 0, the more stable the slope is.

[0045] The present invention has the following beneficial effects: Through numerical simulation, indoor model testing, and slope monitoring, it constructs a physical mechanism-driven database. Using database examples, it extracts seismic spectrum characteristics, time-domain / time-frequency characteristics, slope displacement, and stress accumulation characteristics. This, combined with a Bayesian optimization-based neural network model, enables accurate prediction of slope stability. By integrating the advantages of physical models with the flexibility of data-driven approaches, this method significantly improves the reliability and scientific nature of landslide prediction, providing technical support for earthquake disaster prevention and mitigation. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 Schematic diagram of the flow of an intelligent prediction method for earthquake slope instability probability based on the coupling of physical mechanism and neural network model in an embodiment of the present invention. DETAILED DESCRIPTION

[0047] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0048] The present invention provides a technical solution: an intelligent prediction method for earthquake slope instability probability based on the coupling of physical mechanism and neural network model, such as Figure 1 As shown, the following steps are included:

[0049] S1. Construction of database based on earthquake slope stability analysis.

[0050] In step S1, the specific steps include:

[0051] S11. Collect earthquake motion data (acceleration time history) worldwide and analyze earthquake motion characteristics to obtain information including peak value (peak acceleration and velocity), intensity (Arias intensity), frequency, epicenter distance, and focal depth;

[0052] S12. Collect relevant data for seismic slope stability analysis based on numerical simulation and shaking table model testing methods, and obtain slope response characteristic data including slope geometry, geological conditions, load, acceleration, displacement, and stress distribution;

[0053] S13. Collect real-time monitoring data based on on-site slopes, including displacement and strain data;

[0054] S14. Integrate the above data to construct a database for earthquake slope stability analysis, and mark the slope stability status corresponding to each set of data (stable is marked as 0, unstable is marked as 1).

[0055] S2, feature extraction of ground motion and slope response data;

[0056] In step S2, data feature extraction is performed based on the constructed database, specifically including the following:

[0057] S21. Seismic data characteristics: Fourier transform (FFT) is used to extract the frequency domain characteristics of seismic motion and slope response, perform spectrum analysis, and calculate the energy distribution in different frequency bands. Based on the improved M&P wave method, the seismic pulse components are identified and extracted. The time-frequency characteristics of seismic motion are used to calculate the pulse energy rate and perform pulse effect characteristic analysis. The calculation formula for pulse seismic motion identification is:

[0058] E R =-(2.68+0.1416×PGV p -0.0162×ER p +0.0002852×PGV p 2

[0059] -0.005476×ER p ×PGV p +0.0005197×ER p 2 )

[0060]

[0061] Among them, E R is the pulse index; PGVp is the pulse peak velocity; ERp is the pulse energy rate; when the pulse index E R When it is greater than 0, the earthquake motion is a pulse earthquake motion; t s and t eand represent the start time and end time of the velocity pulse respectively; E(t) represents the accumulated energy of the ground motion at time t;

[0062] S22. Slope response feature extraction from numerical simulation and model tests: Extract time domain features from displacement, acceleration, and stress curves, including maximum value, root mean square (RMS), and standard deviation; and extract the cumulative displacement of the slope under earthquake action.

[0063] S23. Monitoring data feature extraction: Calculate the rate of change from the displacement and acceleration time series data, including the displacement change rate and velocity change rate.

[0064] S3. Determination of earthquake slope stability evaluation index and instability threshold;

[0065] In step S3, the specific steps include:

[0066] By statistically analyzing the stable and unstable samples in the database in step S1 and the slope response characteristics in step S2, the displacement critical value under specific earthquake motion conditions is calculated to determine the displacement threshold; the earthquake acceleration time history is extracted to calculate the slope critical acceleration, and the relationship between the acceleration value and slope instability is analyzed to determine the slope critical acceleration; the stress distribution is extracted to calculate the critical stress value;

[0067] Based on the index thresholds of displacement, acceleration and stress, a multidimensional index system for slope instability is defined. To ensure the safety of slope engineering, when any stability index reaches or exceeds the instability threshold, the slope is considered to have reached a critical state of instability; when all indicators are below their thresholds, the slope is in a stable state.

[0068] The expression for indicator threshold judgment is as follows:

[0069]

[0070] Among them, State represents the slope stability state, which takes the value of "1" or "0"; D represents the cumulative displacement; A represents the maximum acceleration; S represents the maximum stress; D crit is the instability threshold of cumulative displacement; A crit is the acceleration instability threshold; S crit is the instability threshold of stress.

[0071] S4, data preprocessing and eigenvalue selection;

[0072] In step S4, the specific steps include:

[0073] S41. Based on the database constructed in step S1 and the data features extracted in step S2, a large number of slope characteristic parameters (slope height, slope gradient, rock and soil strength) and earthquake intensity parameters (peak value, Arias intensity, pulse energy rate) are obtained, and these parameters are used as influencing factors to perform standardized data preprocessing to ensure that the numerical ranges of the data features are consistent;

[0074] S42: Based on step S3, slope stability evaluation indicators such as displacement, acceleration, and stress are obtained, and the stability state is determined (stable is 0, unstable is 1) using the slope instability multidimensional indicator system and threshold judgment expression established in step S3, and the stability state is used as the target value / label;

[0075] S43. Using correlation analysis, factors with correlations less than 0.8 between each influencing factor and other influencing factors are retained. Then, the importance of these retained influencing factors is evaluated based on the random forest algorithm. Influencing factors with importance less than 0.1 or the lowest importance relative to others are removed, and the remaining influencing factors are used as the eigenvalues required for model establishment.

[0076] The calculation formula for the impact factor importance is as follows:

[0077]

[0078] Among them, Importance(X i ) is the impact factor X i The importance of N is the number of impact factors, G split is the gain contributed by the factor, and n is the nth influencing factor.

[0079] S5. Establishment of intelligent prediction model for earthquake slope instability probability;

[0080] In step S5, the feature values and target values / labels in step S4 are used as a dataset, and the dataset is randomly divided into five parts using K-fold cross-validation. Four of the parts are selected as training sets and one as a test set. The training set is then fed into a convolutional neural network (CNN) algorithm to build a prediction model.

[0081] Bayesian optimization is introduced into the algorithm to automatically adjust the model's hyperparameters, including convolution kernel size, number of convolution kernels, pooling kernel size, pooling method, number of neurons, number of layers, learning rate, and L2 regularization parameter;

[0082] Model testing: Each test uses accuracy, recall, and AUC curves to evaluate the model's stability and generalization ability on the test set. Finally, the average of each test is taken to obtain the trained model. This provides the final performance evaluation of the model.

[0083] S6. Intelligent prediction of earthquake slope instability probability.

[0084] When seismic data and slope characteristic data are obtained, the slope instability probability can be obtained using the neural network model trained in S5. The closer the value is to 1, the more likely the slope is to become unstable, and the closer it is to 0, the more stable the slope is.

[0085] The present invention provides a theoretical basis and technical support for the prediction of earthquake-induced landslides through physical mechanism modeling, data-driven feature extraction and Bayesian optimization neural network modeling technology, and is suitable for intelligent monitoring of slope hazards, risk assessment and earthquake disaster prevention and control.

[0086] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or apparatus comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or apparatus comprising the element.

[0087] The terms used in the embodiments of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The singular forms "a", "an", "the" and "the" used in the embodiments of the present invention and the appended claims are also intended to include plural forms unless the context clearly indicates otherwise.

[0088] It should be understood that the term "and / or" as used herein is merely a description of the relationship between associated objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A exists alone, A and B exist simultaneously, or B exists alone. Furthermore, the character " / " in this document generally indicates that the associated objects are in an "or" relationship.

[0089] The word "if," as used herein, may be interpreted as "at the time of" or "when" or "in response to determining" or "in response to detecting," depending on the context. Similarly, the phrases "if it is determined" or "if (stated condition or event) is detected" may be interpreted as "when it is determined" or "in response to the determination" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)," depending on the context.

[0090] The references to "first" and "second" in the embodiments merely distinguish similar objects and do not represent a specific ordering of the objects. It is understood that the specific order or precedence of "first" and "second" can be interchanged where appropriate. It should be understood that the objects distinguished by "first" and "second" can be interchanged where appropriate, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein.

[0091] Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments, or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. An intelligent prediction method for earthquake slope instability probability based on the coupling of physical mechanism and neural network model, characterized by: The steps include: S1. Database construction based on earthquake slope stability analysis; S2. Extraction of seismic motion and slope response data features: Data feature extraction based on the constructed database, specifically including the following: S21. Seismic data characteristics: Fourier transform (FFT) is used to extract the frequency domain characteristics of seismic motion and slope response, perform spectrum analysis, and calculate the energy distribution in different frequency bands. Based on the improved M&P wave method, the seismic pulse components are identified and extracted. The time-frequency characteristics of seismic motion are used to calculate the pulse energy rate and perform pulse effect characteristic analysis. The calculation formula for pulse seismic motion identification is: AND R =-(2.68+0.1416×PGV p -0.0162×ER p +0.0002852×PGV p 2 -0.005476×ER p ×PGV p +0.0005197×ER p 2 ) Among them, E R is the pulse index; PGVp is the pulse peak velocity; ERp is the pulse energy rate; when the pulse index E R When it is greater than 0, the earthquake motion is a pulse earthquake motion; t s and t e and represent the start time and end time of the velocity pulse respectively; E(t) represents the accumulated energy of the ground motion at time t; S22. Slope response feature extraction from numerical simulation and model tests: Extract time domain features from displacement, acceleration, and stress curves, including maximum value, root mean square (RMS), and standard deviation; and extract the cumulative displacement of the slope under earthquake action. S23, monitoring data feature extraction: calculating the rate of change from the displacement and acceleration time series data, including the displacement change rate and velocity change rate; S3. Determination of earthquake slope stability evaluation index and instability threshold; S4, data preprocessing and eigenvalue selection; S5. Establishment of intelligent prediction model for earthquake slope instability probability; S6. Intelligent prediction of earthquake slope instability probability.

2. The method for intelligent prediction of earthquake slope instability probability based on coupling of physical mechanism and neural network model according to claim 1 is characterized by: In step S1, the specific steps include: S11. Collect earthquake motion data worldwide, including acceleration time histories, conduct earthquake motion characteristic analysis, and obtain information on peak earthquake acceleration and velocity, Arias intensity, frequency, epicenter distance, and focal depth; S12. Collect relevant data for seismic slope stability analysis based on numerical simulation and shaking table model testing methods, and obtain slope response characteristic data including slope geometry, geological conditions, load, acceleration, displacement, and stress distribution; S13. Collect real-time monitoring data based on on-site slopes, including displacement and strain data; S14. Integrate the above data to construct a database for earthquake slope stability analysis, and mark the slope stability status corresponding to each set of data, that is, mark stable as 0 and unstable as 1.

3. The method for intelligent prediction of earthquake slope instability probability based on coupling of physical mechanism and neural network model according to claim 1 is characterized by: In step S3, the specific steps include: Through statistical analysis of stable and unstable samples in the database and slope response characteristics, the displacement critical value under specific earthquake motion conditions is calculated and the displacement threshold is determined; the earthquake acceleration time history is extracted, the slope critical acceleration is calculated, and the relationship between the acceleration value and slope instability is analyzed to determine the slope critical acceleration; Extract stress distribution and calculate critical stress value; Based on the index thresholds of displacement, acceleration and stress, a multidimensional index system for slope instability is defined. When any stability index reaches or exceeds the instability threshold, the slope is considered to have reached a critical state of instability; when all indicators are below their thresholds, the slope is in a stable state.

4. The method for intelligent prediction of earthquake slope instability probability based on coupling of physical mechanism and neural network model according to claim 3 is characterized by: The expression for judging the indicator threshold is as follows: Among them, State represents the slope stability state, which takes the value of "1" or "0"; D represents the cumulative displacement; A represents the maximum acceleration; S represents the maximum stress; D crit is the instability threshold of cumulative displacement; A crit is the acceleration instability threshold; S crit is the instability threshold of stress.

5. The method for intelligent prediction of earthquake slope instability probability based on coupling of physical mechanism and neural network model according to claim 4 is characterized by: In step S4, the specific steps include: S41. Based on the database constructed in step S1 and the data features extracted in step S2, a large number of slope characteristic parameters and ground motion intensity parameters are obtained, and these parameters are used as influencing factors to perform standardized data preprocessing to ensure that the numerical ranges of the data features are consistent; The slope characteristic parameters include slope height, slope gradient, and rock and soil strength; the seismic intensity parameters include peak value, Arias intensity, and pulse energy rate; S42, using the slope instability multidimensional index system and threshold judgment expression established in step S3, determining the stability state, i.e., stable is 0 and unstable is 1, and using the stability state as the target value / label; S43. Using correlation analysis, factors with correlations less than 0.8 between each influencing factor and other influencing factors are retained. Then, the importance of these retained influencing factors is evaluated based on the random forest algorithm. Influencing factors with importance less than 0.1 or the lowest importance relative to others are removed, and the remaining influencing factors are used as the eigenvalues required for model establishment.

6. The intelligent prediction method for earthquake slope instability probability based on coupling of physical mechanism and neural network model according to claim 5 is characterized by: The calculation formula for the impact factor importance is as follows: Among them, Importance(X i ) is the impact factor X i The importance of N is the number of impact factors, G split is the gain contributed by the factor, and n is the nth influencing factor.

7. The method for intelligent prediction of earthquake slope instability probability based on coupling of physical mechanism and neural network model according to claim 5 is characterized by: In step S5, the feature values and target values / labels in step S4 are used as a data set, and the data set is randomly divided into 5 parts using 5-fold cross-validation. 4 of the parts are selected as training sets and 1 as a test set. The training set is then fed into the convolutional neural network (CNN) algorithm to build a prediction model. Bayesian optimization is introduced into the algorithm to automatically adjust the model's hyperparameters, including convolution kernel size, number of convolution kernels, pooling kernel size, pooling method, number of neurons, number of layers, learning rate, and L2 regularization parameter; Model testing: Each test uses accuracy, recall rate, and AUC curve to evaluate the stability and generalization ability of the model on the test set. Finally, the average value of each test is taken to obtain the trained model.

8. The method for intelligent prediction of earthquake slope instability probability based on coupling of physical mechanism and neural network model according to claim 1 is characterized by: In step S6, the closer the slope instability probability value is to 1, the more likely the slope is to become unstable, and the closer the slope instability probability value is to 0, the more stable the slope is.

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