Safety state prediction method for highway slope flexible protection net for highway construction

Abstract

The present application belongs to the technical field of highway slope protection, and provides a highway slope flexible protection net safety state prediction method for highway construction, which comprises obtaining state characteristic data of the highway slope, highway compression characteristic data, state data of the flexible protection net and slope deep displacement data; a slope landslide probability analysis model is established according to the state characteristic data and the compression characteristic data, and a slope landslide comprehensive probability is generated; a state index is generated according to the protection net state data; a landslide impact estimation strength is generated according to the deep displacement data; then a damage analysis model is established in combination with the impact estimation strength, the landslide comprehensive probability and the state index, and a flexible protection net comprehensive damage probability is generated; the safety state of the protection net is predicted accordingly; the present application realizes quantitative evaluation of the damage probability of the flexible protection net by fusing the slope landslide risk, the protection net degradation and the landslide impact strength, and improves the accuracy and practicality of the safety state prediction.

Description

Technical Field

[0001] This invention belongs to the field of highway slope protection technology, and particularly relates to a method for predicting the safety status of flexible protective nets for highway slopes used in highway construction. Background Technology

[0002] Flexible slope protection netting is a key engineering facility for preventing rockfalls and shallow landslides, and its structural integrity and functional status play a decisive role in ensuring highway traffic safety. However, in actual engineering practice, existing technologies have significant limitations in assessing the safety status of flexible protection netting.

[0003] Traditional methods rely primarily on periodic manual inspections. Operators make judgments based on visual observation of the mesh deformation, anchor loosening or detachment, and recording the frequency of rockfall impacts. This manual approach is not only inefficient and costly but also susceptible to the subjective experience of the inspectors. Furthermore, continuous monitoring is difficult to implement in complex terrain or in adverse weather conditions, leading to delays in detecting potential safety hazards. Some improved solutions attempt to introduce slope stability analysis models, using sensors to collect environmental parameters such as slope displacement, rainfall, and soil moisture content to indirectly infer the load that the protective mesh may bear.

[0004] However, these methods typically treat slope stability analysis and the assessment of the protective netting's own condition as independent processes, failing to establish a dynamic correlation mechanism between the two. For example, while slope displacement data can indicate potential landslide risk, it does not consider the impact of the current damage state of the protective netting on its impact resistance, leading to one-sided assessment results that cannot accurately reflect the actual safety margin under combined loads. More significantly, existing technologies generally neglect the additional pressure effect of highway traffic loads on the slope system. The vibration and dynamic pressure generated by frequent vehicle traffic accelerate soil creep, but existing assessment systems lack the quantitative inclusion of this factor. Furthermore, although deep displacement monitoring technology can capture the deformation trend within the slope, existing methods have failed to effectively translate this data into specific impact intensity predictions for the protective netting, making it difficult to accurately quantify the mechanical effects of potential landslide events on the protective netting. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a method for predicting the safety status of flexible protective nets for highway slopes used in highway construction, thus solving the aforementioned problems.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a method for predicting the safety status of flexible protective netting for highway slopes used in highway construction, the method specifically comprising:

[0007] Acquire state characteristic data of highway slopes, highway compression characteristic data corresponding to highway slopes, state data of flexible protection nets for highway slopes, and deep displacement data of highway slopes;

[0008] Based on the state characteristic data of highway slopes and the corresponding highway compression characteristic data, a highway slope landslide probability analysis model is established to generate a comprehensive probability of highway slope landslides.

[0009] Based on the status data of the flexible protection netting on the highway slope, a flexible protection netting status index is generated.

[0010] Based on deep displacement data of highway slopes, the estimated impact intensity of highway slope landslides is generated.

[0011] Based on the estimated impact intensity of highway slope landslides, the comprehensive probability of highway slope landslides, and the state index of flexible protection nets, a damage analysis model is established to generate the comprehensive damage probability of highway slope flexible protection nets.

[0012] Based on the comprehensive damage probability of flexible protection nets for highway slopes, the safety status of flexible protection nets for highway slopes is predicted.

[0013] Based on the above technical solutions, the present invention also provides the following optional technical solutions:

[0014] Further technical solutions: The specific methods for generating the comprehensive probability of highway slope landslides include:

[0015] Based on the condition characteristic data of highway slopes, generate the basic landslide probability of highway slopes;

[0016] Based on the highway compression characteristic data corresponding to the highway slope, a compression degree index is generated;

[0017] Based on the pressure index and the basic landslide probability of highway slopes, a landslide probability analysis model for highway slopes is established to generate a comprehensive landslide probability for highway slopes.

[0018] The specific expression for the highway slope landslide probability analysis model is as follows: ;

[0019] In the expression, This represents the overall probability of landslides on highway slopes. This represents the probability of a landslide on the foundation of a highway slope, and L represents the degree of compression index. This represents the scaling factor for the impact of road pressure.

[0020] Further technical solutions: The specific methods for generating the probability of landslides on the foundation of the highway slope include:

[0021] Through the formula: ;

[0022] Generate the basic landslide probability of highway slopes ;

[0023] In the formula, This represents the normalized value of the i-th state feature data of the highway slope. This represents the curve steepness adjustment factor. This represents the weight coefficient of the i-th state feature data of the highway slope, and n represents the total number of state feature data of the highway slope.

[0024] Further technical solution: The method for generating the pressure index specifically includes:

[0025] Through the formula: ;

[0026] Generate the pressure index L;

[0027] In the formula, This represents the normalized value of the j-th highway compression characteristic data corresponding to the highway slope. This represents the weight coefficient of the j-th highway compression feature data corresponding to the highway slope, and m represents the total number of highway compression feature data corresponding to the highway slope.

[0028] Further technical solution: The specific method for generating the state index of the flexible protective net includes:

[0029] Through the formula: ;

[0030] Generate the state index R of the flexible protective netting;

[0031] In the formula, This represents the normalized value of the state data of the k-th flexible protective netting on the highway slope. This represents the weighting coefficient of the state data of the k-th flexible protective net on the highway slope, and N represents the total number of state data of the flexible protective net on the highway slope.

[0032] Further technical solutions: The method for generating the estimated impact intensity of highway slope landslides specifically includes:

[0033] Through the formula: ;

[0034] Generate estimated impact intensity of highway slope landslides ;

[0035] In the formula, This represents the unit weight of a highway slope. This represents data based on deep displacement of highway slopes. The estimated volume of slope sliding obtained. This represents the average slope angle. This represents the power amplification factor.

[0036] Further technical solution: The method based on deep displacement data of highway slopes The methods for obtaining the estimated volume of slope sliding include:

[0037] Deep displacement data of highway slopes are obtained through deep displacement sensors. Based on deep displacement data of highway slopes The depth and spatial distribution of the sliding surface are identified, and then the block accumulation method is used to divide the sliding area into multiple blocks and sum the volumes of each block, thereby dynamically calculating the estimated volume of the potential sliding body.

[0038] Further technical solutions: The specific method for generating the comprehensive damage probability of the flexible protection net for highway slopes includes:

[0039] Based on the estimated impact intensity of highway slope landslides and the state index of flexible protection nets, the initial damage probability of highway slope flexible protection nets is generated.

[0040] A damage analysis model is established based on the initial damage probability and the comprehensive landslide probability of highway slope flexible protection netting, and the comprehensive damage probability of highway slope flexible protection netting is generated.

[0041] Further technical solution: The method for generating the initial damage probability of the flexible protective netting for highway slopes specifically includes:

[0042] Through the formula: ;

[0043] Generate the initial damage probability of flexible protective netting for highway slopes. ;

[0044] In the formula, This indicates the estimated impact intensity of a landslide on a highway slope. This represents the theoretical impact strength threshold that the flexible protective net can withstand, and R represents the state index of the flexible protective net. This represents the sensitivity coefficient.

[0045] A further technical solution: The expression for the damage analysis model is specifically as follows: ;

[0046] In the expression, This represents the overall probability of damage to flexible protective netting on highway slopes. This indicates the probability of initial damage to the flexible protective netting on highway slopes. This represents the overall probability of landslides on highway slopes.

[0047] This invention provides a method for predicting the safety status of flexible protective netting for highway slopes used in highway construction, which has the following advantages compared with the prior art:

[0048] This invention integrates the state characteristic data of highway slopes, the pressure characteristic data of highways, the state data of flexible protective nets, and deep displacement data to generate a comprehensive damage probability, thereby achieving dynamic prediction of safety status. It has the advantages of improving prediction accuracy and efficiency, realizing dynamic monitoring and quantitative assessment, and timely warning of safety hazards. Attached Figure Description

[0049] Figure 1 This is a flowchart illustrating the method for predicting the safety status of flexible protective netting for highway slopes used in highway construction, as provided by the present invention.

[0050] Figure 2 This is a flowchart illustrating step S20 of the present invention.

[0051] Figure 3 This is a flowchart illustrating step S50 of the present invention. Detailed Implementation

[0052] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0053] The specific implementation of the present invention will be described in detail below with reference to specific embodiments.

[0054] Please see Figure 1 This invention provides a method for predicting the safety status of flexible protective netting for highway slopes used in highway construction, which specifically includes the following steps:

[0055] Step S10: Obtain the state characteristic data of the highway slope, the highway compression characteristic data corresponding to the highway slope, the state data of the flexible protection net of the highway slope, and the deep displacement data of the highway slope.

[0056] Step S20: Based on the state characteristic data of the highway slope and the corresponding highway compression characteristic data, establish a highway slope landslide probability analysis model and generate a comprehensive probability of highway slope landslide.

[0057] Step S30: Generate the flexible protection net status index based on the status data of the flexible protection net on the highway slope;

[0058] Step S40: Generate the estimated impact intensity of highway slope landslides based on the deep displacement data of the highway slope.

[0059] Step S50: Based on the estimated impact intensity of highway slope landslides, the comprehensive probability of highway slope landslides, and the state index of flexible protection nets, establish a damage analysis model and generate the comprehensive damage probability of flexible protection nets for highway slopes;

[0060] Step S60: Based on the comprehensive damage probability of the flexible protection net for highway slopes, predict the safety status of the flexible protection net for highway slopes;

[0061] Among them, the state characteristic data of highway slopes describes the physical and geological conditions of the highway slopes themselves, such as the soil and rock type, slope, slope height, crack distribution, groundwater level, vegetation cover, etc. These data reflect the inherent stability of the slopes.

[0062] The highway compression characteristic data corresponding to highway slopes describes the relevant data on the additional pressure exerted on the slope by highway traffic loads, such as highway traffic flow, vehicle type, overloading situation, pavement structure, etc. These data reflect the impact of external loads on slope stability.

[0063] The status data of flexible protective netting on highway slopes describes the physical condition and performance of the flexible protective netting itself, such as the degree of corrosion of the netting, the number of broken wire ropes, the loosening of anchor points, the amount of deformation of the netting, and the service life. These data reflect the load-bearing capacity and integrity of the protective netting.

[0064] Deep displacement data of highway slopes describes the movement of soil or rock masses in the depth direction within the slope. It is usually obtained through deep displacement sensors (such as inclinometers). This data is an important basis for judging the deep sliding trend and potential landslide size of the slope.

[0065] Specifically, in step S10, the condition characteristic data of the highway slope can be obtained through manual on-site surveys, geological drilling, remote sensing image analysis, or drone aerial photography. For example, geological surveys can be used to obtain soil and rock types and groundwater levels, while manual measurements can be used to obtain slope gradient, slope height, and crack distribution. The highway pressure characteristic data corresponding to the highway slope can be obtained through traffic flow monitoring systems, vehicle weighing systems, or road surface sensors. For example, traffic cameras can be used to count traffic flow, and weighbridges can be used to obtain vehicle load information. The condition data of the flexible protective netting of the highway slope can be obtained through manual inspection records, sensor monitoring (such as strain sensors and corrosion sensors), or periodic inspection reports. For example, inspectors can visually check for netting damage and loose anchor points. Deep displacement data of the highway slope can be obtained through long-term monitoring using deep displacement sensors (such as inclinometers and stratified settlement meters) installed inside the slope. For example, inclinometers can periodically measure displacement changes at different depths.

[0066] In step S20, the landslide probability analysis model can be established using various methods. For example, it can be trained based on historical landslide event data using statistical regression models or machine learning models (such as support vector machines and neural networks) to predict the landslide probability of the slope. Alternatively, deterministic analysis methods, such as the limit equilibrium method or the finite element method, can be used, combined with safety factor calculations, and converted into a probabilistic form. This model couples the slope's intrinsic stability factors (state characteristic data) with external load factors (highway compression characteristic data) to generate a comprehensive landslide probability.

[0067] In step S30, the state index of the flexible protective net can be generated based on a weighted sum of its state data. For example, different weights can be assigned to various state data such as the degree of corrosion of the netting, the number of broken wire ropes, and the loosening of anchor points, and these data can be normalized and then linearly combined to obtain a comprehensive index. This index aims to quantify the current health status and load-bearing capacity of the protective net; a higher value indicates a worse performance of the protective net.

[0068] In step S40, based on the deep displacement data of the highway slope, a preliminary estimate of the size and sliding velocity of the potential landslide body can be made. For example, by analyzing the trend of deep displacement over time, the depth of the sliding surface and the volume of the sliding body can be determined. Based on these estimation results, combined with the slope's physical parameters (such as density and dip angle), dynamic principles or empirical formulas can be used to calculate the potential impact force or impact energy of the potential landslide body on the protective netting, thereby obtaining the estimated landslide impact intensity.

[0069] In step S50, the damage analysis model can employ a reliability-based approach to compare the impact intensity with the bearing capacity of the protective netting, and combine this with the probability of landslide occurrence to calculate the probability of damage to the protective netting. This model comprehensively considers the likelihood of landslide occurrence, the impact intensity of the landslide on the protective netting, and the protective netting's own impact resistance, thereby generating a comprehensive probability reflecting the overall damage risk of the protective netting.

[0070] In step S60, different safety thresholds can be set based on the generated comprehensive damage probability of the flexible protective netting for highway slopes. For example, when the comprehensive damage probability is lower than a certain preset threshold, the protective netting is judged to be in a safe state; when it is higher than the threshold, the protective netting is judged to be in an unsafe state or requires maintenance. This prediction result can provide decision-making basis for highway management departments and guide the daily maintenance, inspection, or reinforcement of the protective netting.

[0071] This invention integrates the state characteristic data of highway slopes, the pressure characteristic data of highways, the state data of flexible protective nets, and deep displacement data to generate a comprehensive damage probability, thereby achieving dynamic prediction of safety status. It has the advantages of improving prediction accuracy and efficiency, realizing dynamic monitoring and quantitative assessment, and timely warning of safety hazards.

[0072] For preferred options, please refer to [link / reference]. Figure 2 The present invention further proposes a method for generating the comprehensive probability of landslides on highway slopes, specifically including:

[0073] Step S21: Generate the basic landslide probability of the highway slope based on the state characteristic data of the highway slope;

[0074] Step S22: Generate a pressure degree index based on the highway pressure characteristic data corresponding to the highway slope;

[0075] Step S23: Based on the pressure index and the basic landslide probability of the highway slope, establish a landslide probability analysis model for the highway slope and generate a comprehensive landslide probability for the highway slope;

[0076] The specific expression for the highway slope landslide probability analysis model is as follows: ;

[0077] In the expression, This represents the overall probability of landslides on highway slopes. This represents the probability of a landslide on the foundation of a highway slope, and L represents the degree of compression index. This represents the scaling factor for the impact of road pressure.

[0078] Step S21 aims to assess the inherent landslide tendency of highway slopes when not subjected to external highway pressure. The conditional data of the highway slope may include, but are not limited to, the slope's soil and rock type, slope gradient, slope height, geological structure, groundwater level, vegetation cover, and historical deformation records. These data reflect the physical and mechanical properties of the slope itself and form the basis for assessing its stability.

[0079] The purpose of step S22 is to quantify the impact of highway traffic loads or other external pressures on the stability of highway slopes. Highway stress characteristic data may include, but are not limited to, traffic flow, vehicle type, overloading status, road surface conditions, the relative position of the highway and the slope, and the additional stress exerted on the slope by the highway structure. These data are directly related to the external dynamic loads borne by the slope.

[0080] Step S23 combines the slope's inherent landslide tendency with the influence of external pressure to form a more comprehensive landslide risk assessment. By establishing an analytical model, the basic landslide probability can be dynamically adjusted to reflect the true risk under different pressure conditions.

[0081] The expression for the highway slope landslide probability analysis model integrates the basic landslide probability and the pressure degree index to calculate the comprehensive probability of highway slope landslides. The highway pressure influence scaling factor is used to adjust the overall impact of highway pressure on the landslide probability, while the min function ensures that the calculated probability value does not exceed 1. The highway pressure influence scaling factor is an adjustment parameter used to calibrate the weight of the highway pressure influence; it can be determined through historical data fitting or expert experience.

[0082] This application's solution refines the prediction of the comprehensive probability of highway slope landslides by systematically integrating the inherent state characteristics of slopes with the external pressure characteristics of highways. First, by analyzing the inherent properties of the slope, its basic landslide probability is quantified, reflecting the slope's stability under ideal conditions. Simultaneously, by assessing the additional pressure exerted on the slope by external factors such as highway traffic loads, a pressure degree index is generated. Subsequently, these two key parameters are substituted into the highway slope landslide probability analysis model. This model cleverly couples the basic landslide probability and the pressure degree index multiplicatively, adjusting the model using a highway pressure influence scaling factor. This ensures that the final comprehensive probability of highway slope landslides not only considers the slope's inherent vulnerability but also dynamically reflects the impact of external loads on its stability. This integrated approach makes the landslide risk assessment more comprehensive and dynamic, providing a more accurate and reliable basis for subsequent predictions of the safety status of flexible protective nets, significantly improving the scientific rigor and practicality of the entire prediction method.

[0083] Through the above technical solution, this application separates and quantifies the inherent state characteristics of the slope and the compression characteristics of the highway, and integrates them using a clear mathematical model. This allows the calculation of landslide probability to not only consider the stability of the slope itself, but also dynamically reflect the influence of external factors such as highway traffic load on slope stability. This solves the problem that traditional methods may ignore the influence of external dynamic loads, resulting in insufficient prediction accuracy. As a result, it provides a more accurate and reliable input for the subsequent prediction of the safety status of flexible protective nets, and significantly improves the scientificity and practicality of the entire prediction method.

[0084] Preferably, the present invention further proposes a method for generating the probability of landslides on the foundation of the highway slope, specifically including:

[0085] Through the formula: ;

[0086] Generate the basic landslide probability of highway slopes ;

[0087] In the formula, This represents the normalized value of the i-th state feature data of the highway slope. This represents the curve steepness adjustment factor. This represents the weight coefficient of the i-th state feature data of the highway slope, and n represents the total number of state feature data of the highway slope.

[0088] Among them, the basic landslide probability of highway slopes represents the possibility of landslides occurring based solely on the inherent geological, hydrological, and topographical characteristics of the slope itself, without considering the influence of external loads (such as highway traffic loads). It is a core indicator for assessing slope stability and provides a benchmark for subsequently introducing the influence of external loads.

[0089] The normalized value of the i-th state characteristic data of a highway slope refers to various physical quantities or indicators describing the current stability of the highway slope, such as slope gradient, slope height, soil and rock type, crack width, groundwater level, vegetation cover, and rainfall. Since these characteristic data have different dimensions and numerical ranges, they need to be normalized to convert them to dimensionless values ​​between 0 and 1 in order to perform calculations and comparisons within a unified mathematical model. This eliminates the influence of dimensions and ensures a fair contribution of different characteristic data to the model. Normalization methods can employ various approaches, such as linear normalization (e.g., max-min normalization) or nonlinear normalization (e.g., Z-score normalization).

[0090] The curve steepness adjustment coefficient is used to adjust the steepness of the exponential function curve, thereby controlling the sensitivity of individual state feature data to the contribution of basic landslide probability. A larger curve steepness adjustment coefficient makes the basic landslide probability more sensitive to changes in state feature data; that is, when the state feature data deteriorates slightly, the landslide probability will increase rapidly. A smaller curve steepness adjustment coefficient indicates lower sensitivity, and the landslide probability changes relatively smoothly. This coefficient can be obtained based on historical data analysis, expert experience, or optimization through model training.

[0091] The weight coefficient of the i-th state feature data of a highway slope reflects the relative importance of different state feature data in influencing the landslide risk of the highway slope. For example, crack width may have a greater impact on landslide risk than vegetation cover, and therefore its weight coefficient will be higher. These weight coefficients can be determined using methods such as the analytic hierarchy process, entropy weight method, principal component analysis, or machine learning algorithms to objectively reflect the role of each feature data in the landslide formation process.

[0092] The total number of state characteristic data for highway slopes, n, represents the total number of independent state characteristic data used to assess the probability of landslides at the foundation of highway slopes. This number depends on the comprehensiveness of data collection and the complexity of model design, and usually selects those key features that have a significant impact on slope stability.

[0093] This application's solution achieves precise quantification of the probability of foundation landslides on highway slopes by introducing a weighted nonlinear combination model based on multiple state characteristic data. Specifically, firstly, various state characteristic data of the highway slope, such as slope, soil and rock properties, groundwater level, and crack development, are collected and converted into uniform normalized values. This normalization process ensures that data of different dimensions and ranges are fairly included in the calculation. Subsequently, for each normalized state characteristic data, a nonlinear transformation is performed using an exponential function. This exponential function can simulate the nonlinear decreasing trend of slope stability as state characteristics deteriorate, where the curve steepness adjustment coefficient allows for adjustment of this sensitivity according to actual conditions. Next, the nonlinear transformation result of each feature is multiplied by its corresponding weighting coefficient. The weighting coefficient reflects the relative importance of different state characteristics to landslide risk, ensuring that key influencing factors dominate the calculation. Finally, all weighted nonlinear transformation results are summed to obtain the foundation landslide probability of the highway slope. This comprehensive approach, which considers multiple factors and employs a non-linear weighted summation method, enables the basic landslide probability of highway slopes to more accurately reflect the inherent landslide risk of slopes without external loads, providing a solid foundation for subsequent calculations of the comprehensive probability of highway slope landslides.

[0094] Through the above technical solution, this application can more precisely capture the complex relationship between slope condition characteristics and landslide risk, making the assessment of basic landslide probability more accurate and objective. This precise basic landslide probability provides a more reliable input for the subsequent calculation of the comprehensive probability of highway slope landslides, thereby significantly improving the overall accuracy and reliability of the safety status prediction of highway slope flexible protection nets, and helping to promptly identify potential risks and take preventive measures.

[0095] Preferably, the present invention further proposes a method for generating the pressure index, specifically including:

[0096] Through the formula: ;

[0097] Generate the pressure index L;

[0098] In the formula, This represents the normalized value of the j-th highway compression characteristic data corresponding to the highway slope. This represents the weighting coefficient of the j-th highway compression feature data corresponding to the highway slope, and m represents the total number of highway compression feature data corresponding to the highway slope.

[0099] Among them, the pressure degree index, by weighted summing of the normalized values ​​of multiple highway pressure characteristic data, can comprehensively reflect the impact of different pressure characteristics on slope stability, thus more flexibly and accurately assessing the pressure degree.

[0100] The purpose of normalization processing for highway compression characteristic data is to convert raw highway compression characteristic data of different dimensions and ranges into unified, dimensionless values ​​for comprehensive calculation. For example, linear normalization (such as Min-Max normalization) can be used to scale the data to the [0,1] interval; or Z-score normalization can be used to convert the data into a distribution with a mean of 0 and a standard deviation of 1. In addition, highway compression characteristic data may include, but are not limited to: average daily traffic flow, proportion of heavy-duty vehicles, pavement load, subgrade settlement rate, pavement crack width change rate, and subgrade pore water pressure change, etc.

[0101] Weighting coefficient This reflects the relative importance of different highway compression characteristics in assessing the compression degree index L. These weighting coefficients can be determined by various methods. For example, they can be assigned by experts in geotechnical engineering and traffic engineering based on their professional knowledge and experience (expert experience method); or they can be calculated based on historical data analysis, the degree of data variation, or expert scoring results through objective weighting methods such as the Analytic Hierarchy Process (AHP) and entropy weighting method.

[0102] The number of highway stress characteristic data points can be determined based on actual monitoring conditions, data availability, and model accuracy requirements. For example, if only traffic flow and pavement load are monitored, m can be 2; if a more comprehensive assessment is needed, more relevant characteristic data can be included to improve accuracy.

[0103] This application first acquires multiple highway stress characteristic data, such as traffic flow, pavement load, and subgrade deformation. To eliminate the influence of dimensional differences and numerical ranges among different characteristic data, these raw data are normalized. Subsequently, corresponding weighting coefficients are assigned based on the degree of influence of each characteristic data on slope stability. By multiplying the normalized characteristic data with their corresponding weighting coefficients and summing the results, a stress intensity index is finally obtained. This index comprehensively reflects the overall stress intensity of the highway, and the larger the value, the more abnormal the stress condition. This quantified stress intensity index is substituted into the highway slope landslide probability analysis model and combined with the basic landslide probability of the highway slope to more accurately assess the comprehensive landslide probability of the highway slope. This quantification method makes the impact of highway stress no longer a qualitative judgment but a precisely calculable numerical value, significantly improving the scientificity and reliability of landslide probability prediction.

[0104] Through the above technical solution, this application enables the calculation of the comprehensive probability of highway slope landslides to be more refined and accurate, avoiding the errors that may be caused by relying solely on qualitative judgments, thereby significantly improving the reliability and scientific nature of the safety status prediction of highway slope flexible protection nets.

[0105] Preferably, the present invention further proposes a method for generating the state index of the flexible protective net, specifically including:

[0106] Through the formula: ;

[0107] Generate the state index R of the flexible protective net;

[0108] In the formula, This represents the normalized value of the state data of the k-th flexible protective netting on the highway slope. This represents the weighting coefficient of the state data of the k-th flexible protective net of the highway slope, and N represents the total number of state data of the flexible protective net of the highway slope.

[0109] The above formula provides a weighted aggregation method for multiple flexible protective net status data, aiming to generate a single index R that comprehensively reflects the overall status of the flexible protective net. By assigning different weights to different status data, this formula can distinguish the importance of each data point to the status of the protective net, thereby achieving a more refined assessment. The weight coefficients of this formula can be determined based on engineering experience, expert evaluation, or historical data analysis. For example, the Delphi method can be used to invite multiple experts to score the importance of each status indicator, and then the weights can be derived by combining them. Alternatively, machine learning algorithms, such as Support Vector Machine (SVM) or Random Forest, can be used to train on historical monitoring data and protective net failure cases to automatically learn and optimize the weight coefficients, thereby improving the predictive accuracy of the index.

[0110] The normalization process for the state data of the kth flexible protective net on the highway slope aims to eliminate the differences in dimensions and numerical ranges between different state data, ensure that all data are comparable when participating in weighted summation, and avoid the dominance of certain data with large numerical ranges in the calculation of the index.

[0111] The weighting coefficients of the state data for the k-th flexible protective netting on highway slopes are used to quantify the relative importance of different flexible protective netting state data to the overall protective netting state assessment. By setting appropriate weights, key factors that have a significant impact on the safety performance of the protective netting can be highlighted. For example, structural damage to the protective netting (such as broken wire ropes or large-area tearing of the mesh) usually has a higher weight than minor surface corrosion. These weighting coefficients can be determined based on practical engineering experience, industry standards, or through data-driven methods (such as principal component analysis or factor analysis) to objectively reflect the contribution of each state indicator.

[0112] The total number of status data for flexible protective netting on highway slopes represents the total number of independent monitoring or testing data items used for comprehensive assessment of the netting's condition. This indicates that the method can integrate multi-dimensional information, thereby providing a more comprehensive and detailed view of the netting's condition. This status data may include, but is not limited to: the area of ​​netting damage, the degree of wire rope corrosion, the loosening of the anchoring system, changes in support rope tension, netting deformation, and material aging indicators. Furthermore, the value of N depends on the actual deployment of the monitoring system and the types of data available. For example, if the monitoring system can acquire three types of data—netting damage rate, wire rope corrosion level, and anchor point displacement—then N is 3.

[0113] Specifically, this scheme first normalizes the raw state data obtained from the state data of flexible protective netting on highway slopes, converting them into normalized values ​​with uniform dimensions. Then, based on the importance of each state data point to the safety performance of the flexible protective netting, corresponding weight coefficients are assigned. These weight coefficients reflect the relative contribution of each state data point to assessing the overall health of the protective netting. Finally, all normalized state data points are multiplied by their corresponding weight coefficients and summed to obtain the flexible protective netting state index. This index comprehensively reflects the overall health of the flexible protective netting, with a larger index value indicating a worse state. In this way, this scheme integrates multi-source, heterogeneous flexible protective netting state data into a single, physically meaningful index, providing accurate and quantitative input for subsequent calculations of the comprehensive damage probability of flexible protective netting on highway slopes, thereby improving the accuracy and reliability of predicting the safety status of flexible protective netting on highway slopes.

[0114] Through the above technical solution, this application effectively solves the problem that traditional assessment methods are unable to comprehensively and objectively reflect the actual health status of the protective net. It can significantly improve the accuracy of the damage analysis model, which makes the prediction of the safety status of the flexible protective net of the highway slope more reliable and timely. This helps managers to discover potential risks earlier and take targeted maintenance measures, thereby ensuring the safety of highway operation.

[0115] Preferably, the present invention further proposes a method for generating the estimated impact intensity of highway slope landslides, specifically including:

[0116] Through the formula: ;

[0117] Generate estimated impact intensity of highway slope landslides ;

[0118] In the formula, This represents the unit weight of a highway slope. This represents data based on deep displacement of highway slopes. The estimated volume of slope sliding obtained. This represents the average slope angle. This represents the power amplification factor;

[0119] Among them, the estimated impact intensity of landslides on highway slopes represents the magnitude of the impact force that a potential landslide body may generate on a flexible protective net. It is a key parameter for assessing the safety status of the protective net. Its concept lies in quantifying the effect of landslide events on the protective structure in order to conduct subsequent damage probability analysis.

[0120] The unit weight of a highway slope refers to the weight of a unit volume of slope soil, reflecting the density characteristics of the slope material. It can be determined by conducting laboratory density tests through on-site sampling, or by making empirical estimates based on information such as soil type and moisture content provided in the geological survey report.

[0121] The estimated volume of slope sliding, derived from deep displacement data of highway slopes, refers to the total amount of soil that may slide when a potential landslide occurs. It can be determined by using deep displacement data obtained from equipment such as ground-penetrating radar and borehole inclinometers, combined with slope topographic data, to perform three-dimensional modeling and analysis, thereby estimating the geometric dimensions and volume of the sliding body. Alternatively, historical landslide case data can be used, combined with current displacement trends, to predict the potential sliding volume using statistical methods.

[0122] The average dip angle of a slope represents the overall slope gradient and is a crucial factor affecting the downsliding energy of a landslide. It can be determined through on-site measurements, calculations using high-precision surveying equipment (such as UAV aerial surveying or lidar scanning) to acquire topographic data, or directly from engineering geological maps.

[0123] The dynamic amplification factor is used to account for dynamic effects that may exist during landslide impact, such as the instantaneous increase in impact force caused by landslide acceleration and collision. It can be determined based on dynamic theory analysis, numerical simulation results, or by referring to relevant engineering specifications and empirical values. For example, it can be determined by consulting engineering manuals or conducting specialized dynamic analysis, based on factors such as the landslide's velocity and impact duration.

[0124] Specifically, the estimated volume combined with unit weight provides information on the total mass of the potential landslide body; the average slope angle determines the gravitational component of the landslide body sliding down the slope, i.e., the potential driving force; and the dynamic amplification factor considers the dynamic effects during the landslide impact process, making the impact intensity assessment more realistic. By multiplying these key physical parameters, the potential impact force of a landslide on the flexible protective net can be quantitatively calculated. This quantification method based on a physical model makes the assessment of the estimated impact intensity of highway slope landslides no longer a simple qualitative judgment, but rather has a clear physical basis and computability, thus providing a more accurate and reliable input for subsequent damage analysis of the flexible protective net, significantly improving the scientificity and accuracy of the entire safety status prediction method.

[0125] Through the above technical solution, this application overcomes the potential inaccuracies of traditional empirical judgments or simplified models, making the prediction of potential landslide impact forces more scientific and accurate. By accurately calculating the predicted intensity of landslide impacts, more reliable input parameters can be provided for subsequent damage analysis of flexible protective nets, thereby significantly improving the accuracy and reliability of predicting the safety status of highway slope flexible protective nets. This helps to promptly identify potential safety hazards in the protective nets, provides decision-making basis for highway maintenance and management departments, and effectively prevents landslide disasters from threatening highway traffic and the lives and property of people.

[0126] It should be further noted that the data is based on deep displacement data of highway slopes. The methods for obtaining the estimated volume of slope sliding include:

[0127] Deep displacement data of highway slopes are obtained through deep displacement sensors. Based on deep displacement data of highway slopes The depth and spatial distribution of the sliding surface are identified, and then the block accumulation method is used to divide the sliding area into multiple blocks and sum the volumes of each block, thereby dynamically calculating the estimated volume of the potential sliding body.

[0128] Specifically, a deep displacement sensor is a device used to monitor displacement inside a slope. It can detect the deformation of deep soil or rock masses in a slope. Its implementation methods may include, but are not limited to: inclinometers, which obtain horizontal displacement at different depths by measuring the tilt changes of the casing inside the borehole; layered settlement gauges, which monitor vertical or horizontal deformation by measuring the relative displacement between reference points at different depths; or fiber optic grating sensors, which use the strain-sensitive characteristics of optical fibers to monitor minute deformations inside the slope in real time.

[0129] The process of obtaining deep displacement data of highway slopes aims to periodically or continuously collect displacement information at different depths inside the slope using the aforementioned deep displacement sensors. This data forms the basis for analyzing slope stability and judging potential sliding trends, and provides the original basis for subsequent identification of sliding surfaces and calculation of sliding volume.

[0130] Identifying the depth and spatial distribution of the sliding surface is a key analytical step based on the acquired deep displacement data of the highway slope. By analyzing the changing trends, gradients, or abrupt changes in displacement data at different depths, areas of shear deformation or concentrated displacement within the slope can be determined, thereby accurately defining the location (depth) of the potential sliding surface and its distribution in three-dimensional space. This can be achieved through data visualization, numerical analysis algorithms, or empirical judgment.

[0131] The block-based accumulation method simplifies the volume calculation of complex, irregular regions into the summation of the volumes of multiple regular or semi-regular blocks. Its function is to decompose the sliding region into easily calculable units when its geometry is irregular, and then sum the volumes of these units to obtain the total volume of the entire sliding region. Specifically, the block-based accumulation method involves dividing the sliding region into multiple blocks and summing the volumes of each block. First, based on the identified depth and spatial distribution of the sliding surface, the potential sliding region is divided into a series of discrete blocks with a certain thickness or width along a certain direction (e.g., perpendicular to the sliding direction or parallel to contour lines). The volume of each block can be calculated based on its geometry (e.g., approximating a prism, wedge, or irregular polygonal cross-section). Then, the volumes of all blocks are summed to obtain the estimated total volume of the sliding region.

[0132] The process of dynamically calculating the estimated volume of potential landslides emphasizes the real-time or periodic updating capability of the calculation. Since slope displacement is a dynamic process, the position of the sliding surface and the shape of the landslide mass may also evolve over time. By continuously acquiring deep displacement data and repeating the above identification and block accumulation process, the estimated volume of potential landslides can be dynamically updated based on the latest displacement data, thus reflecting the latest changes in the slope condition.

[0133] Deep displacement data is acquired dynamically, so the calculation of the estimated volume of the landslide body can also be dynamically updated, ensuring the real-time performance and accuracy of the estimated volume. This method enables the estimated volume in the estimated intensity of landslide impact on highway slopes to more realistically reflect the actual situation of the slope, thus providing a more reliable basis for predicting the safety status of flexible protection nets for highway slopes.

[0134] For preferred options, please refer to [link / reference]. Figure 3 The present invention further proposes a method for generating the comprehensive damage probability of the flexible protection net for highway slopes, specifically including:

[0135] Step S51: Based on the estimated impact intensity of the highway slope landslide and the state index of the flexible protection net, generate the initial damage probability of the flexible protection net for the highway slope.

[0136] Step S52: Establish a damage analysis model based on the initial damage probability of the flexible protection netting for highway slopes and the comprehensive probability of landslides on highway slopes, and generate the comprehensive damage probability of the flexible protection netting for highway slopes;

[0137] Specifically, in step S51, the estimated impact intensity of a highway slope landslide is a key indicator for measuring the magnitude of the physical impact of a potential landslide event on the protective netting. The higher the value, the greater the impact force the protective netting withstands. The flexible protective netting condition index reflects the current health and load-bearing capacity of the netting. For example, wear, corrosion, and loose components will all cause this index to rise, indicating a decline in the performance of the protective netting. The initial damage probability of the highway slope flexible protective netting is the probability that the protective netting may be damaged under the conditions of a landslide impact event.

[0138] In step S52, a damage analysis model is established based on the preliminary damage probability of the flexible protection netting on the highway slope and the comprehensive landslide probability of the highway slope, generating the comprehensive damage probability of the flexible protection netting. The comprehensive landslide probability of the highway slope is an indicator that measures the overall likelihood of a landslide event occurring on the slope; it comprehensively considers the slope's own condition and external pressure conditions. The comprehensive damage probability of the flexible protection netting on the highway slope is the final risk assessment result, representing the overall probability that the flexible protection netting will be damaged by a landslide event within a given time period. The damage analysis model can be established using the principles of conditional probability or joint probability in probability theory.

[0139] The proposed solution decomposes the damage risk assessment of flexible protective netting into two logically clear stages: first, assessing the inherent vulnerability of the netting to impacts (initial damage probability); and then combining this vulnerability with the overall probability of a landslide event (comprehensive landslide probability) to obtain a more comprehensive and accurate comprehensive damage probability. This step-by-step approach allows for a more reasonable consideration of various influencing factors, avoiding information distortion that may result from direct combination, thereby improving the accuracy and reliability of predicting the safety status of flexible protective netting.

[0140] Through the above technical solution, this application can fully consider the interaction between the potential landslide impact intensity and the state of the protective net itself, accurately reflect the vulnerability of the protective net when facing impact, and thus obtain a more comprehensive and accurate comprehensive damage probability of the flexible protective net for highway slopes. This makes the prediction of the safety status of the flexible protective net more scientific and reliable, and provides a more accurate decision-making basis for the maintenance and management of highway slopes.

[0141] Preferably, the present invention further proposes a method for generating the initial damage probability of the flexible protective netting for highway slopes, specifically including:

[0142] Through the formula: ;

[0143] Generate the initial damage probability of flexible protective netting for highway slopes. ;

[0144] In the formula, This indicates the estimated impact intensity of a landslide on a highway slope. This represents the theoretical impact strength threshold that the flexible protective net can withstand, and R represents the state index of the flexible protective net. This represents the sensitivity coefficient;

[0145] The above formula uses an exponential function, which can simulate the non-linear growth of the damage probability as the impact intensity gradually increases or the condition of the protective net gradually deteriorates, thus more realistically reflecting the actual damage situation of the flexible protective net.

[0146] The theoretical impact strength threshold of a flexible protective net refers to the maximum impact force that a flexible protective net can withstand under ideal conditions without structural damage. This threshold is usually determined by the design parameters, material strength, and installation specifications of the protective net, and can be determined through laboratory testing, finite element analysis, or by referring to industry standards.

[0147] The sensitivity coefficient is an adjustable parameter used to adjust how sensitive the initial damage probability is to changes in impact intensity and the condition of the protective netting. This coefficient can be calibrated and optimized based on practical engineering experience, historical damage data, or expert knowledge to better fit the actual situation. For example, when the model needs to be more sensitive to impact and degradation, the sensitivity coefficient can be increased; conversely, the sensitivity coefficient can be decreased.

[0148] Specifically, when the estimated impact intensity of a highway slope landslide is much lower than the theoretical impact strength threshold that the flexible protective netting can withstand, and the state index of the flexible protective netting is low, the probability of initial damage to the protective netting will be very small, close to zero, which is consistent with the expectation of the protective netting under normal working conditions. However, as the estimated impact intensity of a highway slope landslide increases, or the state index R of the flexible protective netting increases (indicating deterioration of the protective netting), the denominator... It will decrease, causing the exponential term to... Rapidly increase, thus making The rapid decrease in the initial damage probability leads to an accelerated upward trend. The sensitivity coefficient further fine-tunes this rate of increase, allowing the model to be calibrated based on actual engineering data to more accurately reflect the damage characteristics of different types of protective netting or under different geological conditions. This nonlinear modeling approach makes the calculation of the initial damage probability more realistic, avoiding the risks of underestimation or overestimation that may occur with traditional linear models. This provides a more solid foundation for subsequently generating the comprehensive damage probability of flexible protective netting for highway slopes.

[0149] Through the above technical solution, this application can more sensitively reflect the sharp increase in damage risk. This not only improves the calculation accuracy of the initial damage probability of highway slope flexible protection nets, but also provides a more reliable input for the subsequent generation of the comprehensive damage probability of highway slope flexible protection nets. Thus, it significantly improves the accuracy and early warning capability of the entire highway slope flexible protection net safety status prediction method, which helps to take timely maintenance measures and ensure highway operation safety.

[0150] Preferably, the present invention further proposes the following expression for the damage analysis model: ;

[0151] In the expression, This represents the overall probability of damage to flexible protective netting on highway slopes. This indicates the probability of initial damage to the flexible protective netting on highway slopes. This represents the overall probability of landslides on highway slopes;

[0152] Among them, the comprehensive damage probability of flexible protection netting on highway slopes represents the overall probability of the flexible protection netting being damaged after considering the possibility of landslides on highway slopes and their impact on the flexible protection netting. Its role is to provide a final and comprehensive risk indicator to guide subsequent safety status prediction and decision-making.

[0153] Specifically, this model multiplies the comprehensive probability of a highway slope landslide with the initial probability of damage to the flexible protective netting. The comprehensive probability of a highway slope landslide reflects the inherent risk of a landslide event on the slope itself, while the initial probability of damage to the flexible protective netting quantifies the conditional risk of the netting failing due to impact once a landslide occurs. By multiplying these two independent but related probabilities, the model logically derives the comprehensive risk of damage to the flexible protective netting in actual operating environments, considering both whether a landslide will occur and whether the netting can withstand the impact after a landslide. This multiplicative relationship reflects the sequential nature of events: the comprehensive probability of damage to the flexible protective netting under the conditions of a landslide and the netting's damage under landslide impact. Therefore, this model makes the prediction of the safety status of the flexible protective netting more comprehensive and accurate.

[0154] Through the above technical solution, this application can accurately reflect that the final damage to the protective net is the result of the combined effect of two events: the occurrence of slope landslide and the failure of the protective net itself. This makes the prediction of the safety status of the flexible protective net more comprehensive and scientific, and provides a more reliable basis for decision-making for the maintenance and management of highway slopes.

[0155] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for predicting the safety status of flexible protective netting for highway slopes used in highway construction, characterized in that, The method specifically includes: Acquire state characteristic data of highway slopes, highway compression characteristic data corresponding to highway slopes, state data of flexible protection nets for highway slopes, and deep displacement data of highway slopes; Based on the state characteristic data of highway slopes and the corresponding highway compression characteristic data, a highway slope landslide probability analysis model is established to generate a comprehensive probability of highway slope landslides. Based on the status data of the flexible protection netting on the highway slope, a flexible protection netting status index is generated. Based on deep displacement data of highway slopes, the estimated impact intensity of highway slope landslides is generated. Based on the estimated impact intensity of highway slope landslides, the comprehensive probability of highway slope landslides, and the state index of flexible protection nets, a damage analysis model is established to generate the comprehensive damage probability of highway slope flexible protection nets. Based on the comprehensive damage probability of flexible protective netting for highway slopes, the safety status of flexible protective netting for highway slopes is predicted.

2. The method for predicting the safety status of flexible protective netting for highway slopes used in highway construction according to claim 1, characterized in that, The specific methods for generating the comprehensive probability of highway slope landslides include: Based on the condition characteristic data of highway slopes, generate the basic landslide probability of highway slopes; Based on the highway compression characteristic data corresponding to the highway slope, a compression degree index is generated; Based on the pressure index and the basic landslide probability of highway slopes, a landslide probability analysis model for highway slopes is established to generate a comprehensive landslide probability for highway slopes. The specific expression for the highway slope landslide probability analysis model is as follows: ; In the expression, This represents the overall probability of landslides on highway slopes. This represents the probability of a landslide on the foundation of a highway slope, and L represents the degree of compression index. This represents the scaling factor for the impact of road pressure.

3. The method for predicting the safety status of flexible protective netting for highway slopes used in highway construction according to claim 2, characterized in that, The specific methods for generating the basic landslide probability of the highway slope include: Through the formula: ; Generate the basic landslide probability for highway slopes ; In the formula, This represents the normalized value of the i-th state feature data of the highway slope. This represents the curve steepness adjustment factor. This represents the weight coefficient of the i-th state feature data of the highway slope, and n represents the total number of state feature data of the highway slope.

4. The method for predicting the safety status of flexible protective netting for highway slopes used in highway construction according to claim 2, characterized in that, The specific methods for generating the pressure index include: Through the formula: ; Generate the pressure index L; In the formula, This represents the normalized value of the j-th highway compression characteristic data corresponding to the highway slope. This represents the weight coefficient of the j-th highway compression feature data corresponding to the highway slope, and m represents the total number of highway compression feature data corresponding to the highway slope.

5. The method for predicting the safety status of flexible protective netting for highway slopes used in highway construction according to claim 1, characterized in that, The specific methods for generating the status index of the flexible protective net include: Through the formula: ; Generate the state index R of the flexible protective netting; In the formula, This represents the normalized value of the state data of the k-th flexible protective netting on the highway slope. This represents the weighting coefficient of the state data of the k-th flexible protective netting on the highway slope, and N represents the total number of state data of the flexible protective netting on the highway slope.

6. The method for predicting the safety status of flexible protective netting for highway slopes used in highway construction according to claim 1, characterized in that, The specific methods for generating the estimated impact intensity of highway slope landslides include: Through the formula: ; Generate estimated impact intensity of highway slope landslides ; In the formula, This represents the unit weight of a highway slope. This represents data based on deep displacement of highway slopes. The estimated volume of slope sliding obtained. This represents the average slope angle. This represents the power amplification factor.

7. The method for predicting the safety status of flexible protective netting for highway slopes used in highway construction according to claim 6, characterized in that, The data based on deep displacement of highway slopes The methods for obtaining the estimated volume of slope sliding include: Deep displacement data of highway slopes are obtained through deep displacement sensors. Based on deep displacement data of highway slopes The depth and spatial distribution of the sliding surface are identified, and then the block accumulation method is used to divide the sliding area into multiple blocks and sum the volumes of each block, thereby dynamically calculating the estimated volume of the potential sliding body.

8. The method for predicting the safety status of flexible protective netting for highway slopes used in highway construction according to claim 1, characterized in that, The specific methods for generating the overall damage probability of the flexible protection net for highway slopes include: Based on the estimated impact intensity of highway slope landslides and the state index of flexible protection nets, the initial damage probability of highway slope flexible protection nets is generated. A damage analysis model is established based on the initial damage probability and the comprehensive landslide probability of highway slope flexible protection netting, and the comprehensive damage probability of highway slope flexible protection netting is generated.

9. The method for predicting the safety status of flexible protective netting for highway slopes used in highway construction according to claim 8, characterized in that, The specific methods for generating the initial damage probability of the flexible protective netting for highway slopes include: Through the formula: ; Generate the initial damage probability of flexible protective netting for highway slopes. ; In the formula, This indicates the estimated impact intensity of a landslide on a highway slope. This represents the theoretical impact strength threshold that the flexible protective net can withstand, and R represents the state index of the flexible protective net. This represents the sensitivity coefficient.

10. The method for predicting the safety status of flexible protective netting for highway slopes used in highway construction according to claim 8, characterized in that, The specific expression of the damage analysis model is as follows: ; In the expression, This represents the overall probability of damage to flexible protective netting on highway slopes. This indicates the probability of initial damage to the flexible protective netting on highway slopes. This represents the overall probability of landslides on highway slopes.

Images