Quantitative evaluation method and system for deep unloading of bank slope based on multi-stage evolution model
By using a multi-stage evolution model and utilizing exploration engineering data and geospatial data, the tug-of-war height and opening width are calculated, and a deep unloading cumulative effect index is generated. This solves the problems of relying on expert experience and high-cost data in existing technologies, and realizes regional quantitative evaluation and risk assessment of deep unloading of bank slopes.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- CHINA HYDROELECTRIC ENGINEERING CONSULTING GROUP CHENGDU RESEARCH HYDROELECTRIC INVESTIGATION DESIGN AND INSTITUTE
- Filing Date
- 2026-03-03
- Publication Date
- 2026-05-12
AI Technical Summary
Existing deep unloading assessment methods rely on expert experience, lack standardized algorithms, cannot achieve overall quantitative assessment of bank slopes, and depend on high-cost geophysical data, making it difficult to conduct rapid surveys and cross-sectional comparisons.
Based on a multi-stage evolution model, by acquiring exploration engineering data and geospatial data, the tug-of-war height is calculated, mapped to the valley evolution stages, the cumulative opening width is calculated, the normalized damage intensity index is calculated, and the deep unloading cumulative effect index is generated to achieve a quantitative assessment of the entire region.
This approach transforms the deep unloading of riverbank slopes from point-based qualitative descriptions to regional quantitative evaluations, improving the objectivity and efficiency of the evaluation. The generated quantitative indices have clear physical meanings, supporting engineering comparisons and risk assessments.
Smart Images
Figure CN121786939B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of geological data analysis technology, specifically to a method and system for quantitative assessment of bank slope deep unloading based on a multi-stage evolution model. Background Technology
[0002] The widespread development of "deep unloading" (also known as deep fracturing, deep cracks, or deep unloading) within riverbank slopes is a unique geological phenomenon closely related to the evolutionary history of river valleys. It is distributed within the slightly necrotic rock mass within the normal unloading zone, manifesting as tensile or tensile-shear fractures with opening widths ranging from a few millimeters to tens of centimeters, with alternating relaxed and relatively intact sections. This phenomenon is widely recognized as a key, hidden geological factor affecting the stability of rock masses in engineering projects such as dam foundations, caverns, and slopes. Therefore, a scientific and accurate assessment of deep unloading on riverbank slopes is of paramount importance for the safe site selection and construction of major engineering projects.
[0003] Currently, the evaluation method for deep unloading in engineering practice mainly relies on the expert experience-based comprehensive classification method based on multi-source test data. This method represents the conventional practice in this field, and its typical process is as follows: First, geometric parameters (such as crack width), geophysical parameters (such as P-wave velocity, seismic CT images), and rock mass quality parameters (integrity index, RQD, etc.) of deep unloading are collected at specific exploration points (such as adit exposure surfaces). Then, geological engineers comprehensively review these multi-source heterogeneous parameters and subjectively judge the "relaxation degree" of the rock mass based on their personal experience. Finally, qualitative classification labels such as "strongly relaxed," "moderately relaxed," or "slightly relaxed" are output.
[0004] However, the aforementioned existing technical solutions have the following drawbacks when serving the quantitative needs of macro-level decision-making:
[0005] First, the core decision-making relies on the personal experience of experts, which cannot be refined into standardized, repeatable mathematical algorithms, leading to inconsistent evaluation results and a lack of objective and consistent comparison benchmarks. Second, this method can only describe and classify local points or zones directly exposed by existing exploration projects, failing to effectively aggregate discrete point information to generate quantitative indicators that can continuously characterize the overall risk level of the entire bank slope or project area. Third, it heavily relies on costly and time-consuming exploration methods such as seismic wave testing and detailed core logging to obtain auxiliary parameters, making it difficult to conduct rapid surveys and horizontal comparisons of large-scale candidate areas during the planning stage.
[0006] In summary, while current technologies can achieve "detailed diagnosis" of deep unloading, they completely lack a standardized technical system capable of "macroscopic measurement." Specifically, there is a lack of a solution that does not rely on subjective expert judgment, but utilizes only the most crucial geometric data (opening width and position) to automatically generate a quantitative risk indicator for the entire region through algorithmic models. Summary of the Invention
[0007] This invention aims to address the problems of low efficiency, strong subjectivity, and inability to achieve overall quantitative evaluation of bank slopes in existing deep unloading evaluation methods. It proposes a quantitative evaluation method and system for deep unloading of bank slopes based on a multi-stage evolution model.
[0008] The technical solution adopted by the present invention to solve the above-mentioned technical problems is as follows:
[0009] In a first aspect, the present invention provides a method for quantitative assessment of deep unloading of riverbank slopes based on a multi-stage evolution model, the method comprising:
[0010] Acquire exploration engineering logging data and geospatial data of the target area, extract spatial location information and opening width of each deep unloading object from the exploration engineering logging data, obtain river water level elevation from the geospatial data, and calculate the tug-of-war height of each deep unloading object based on the spatial location information and river water level elevation.
[0011] According to the predefined rules for dividing valley evolution stages, the tug-of-war height of each deep unloading object is mapped to the corresponding valley evolution stage, thereby assigning a stage label to each deep unloading object;
[0012] For each valley evolution phase, the opening widths of all deep unloading objects within that phase are summed to obtain the cumulative opening width reflecting the total geometric damage in that phase.
[0013] For each stage of valley evolution, the number of major exploration projects that effectively control the corresponding elevation range of that stage is obtained, and the normalized damage intensity index of that stage is calculated based on the cumulative opening width and the number of major exploration projects of that stage.
[0014] Based on the normalized damage intensity index of all valley evolution stages, the deep unloading cumulative effect index, which characterizes the deep unloading cumulative development intensity of the target area slope in the valley evolution history, is calculated. The deep unloading of the target area slope is then quantitatively evaluated based on the deep unloading cumulative effect index.
[0015] Furthermore, the valley evolution stages are divided into four stages based on the regional valley terrace sequence and deep unloading development characteristics, and the stage labels correspond to the first stage, the second stage, the third stage, and the fourth stage, respectively.
[0016] The predefined rules for dividing valley evolution stages include:
[0017] When the tug-of-war height of a deeply unloaded object is within the first height range, it is mapped to the first phase;
[0018] When the tug-of-war height of a deeply unloaded object is within the second height range, it is mapped to the second phase;
[0019] When the tug-of-war height of a deeply unloaded object is within the third height range, it is mapped to the third phase;
[0020] When the tug-of-war height of a deeply unloaded object is within the fourth height range, it is mapped to the fourth phase.
[0021] Furthermore, the first height range is greater than or equal to 10 meters and less than 80 meters, the second height range is greater than or equal to 80 meters and less than 160 meters, the third height range is greater than or equal to 160 meters and less than 250 meters, and the fourth height range is greater than or equal to 250 meters.
[0022] Furthermore, the main exploration project is an exploration adit laid out transversely and traversing a dense rock zone; when there is no exploration adit within the corresponding elevation range, the main exploration project is a borehole that reveals deep unloading within the corresponding elevation range.
[0023] Furthermore, the formula for calculating the normalized damage intensity index is as follows:
[0024] ;
[0025] in, For the first Normalized damage intensity index of different stages of valley evolution. For the first The cumulative opening width of the valley during its evolutionary stages. For the first The number of major exploration projects that effectively control the elevation range corresponding to the different stages of valley evolution.
[0026] Furthermore, the formula for calculating the deep unloading cumulative effect index is as follows:
[0027] ;
[0028] in, This is the cumulative effect index of deep unloading. For the first Normalized damage intensity index of different stages of valley evolution. Indicates the total number of periods.
[0029] Furthermore, the deep unloading of the target area's bank slope is quantitatively assessed based on the aforementioned deep unloading cumulative effect index, specifically including:
[0030] The deep unloading cumulative effect index is compared with a preset threshold range, and the deep unloading development intensity level of the target area is output based on the comparison result.
[0031] Furthermore, when the deep unloading cumulative effect index is less than or equal to 10, the corresponding deep unloading development intensity level is low; when the deep unloading cumulative effect index is greater than 10 and less than or equal to 30, the corresponding deep unloading development intensity level is medium; when the deep unloading cumulative effect index is greater than 30, the corresponding deep unloading development intensity level is high.
[0032] Furthermore, the method also includes:
[0033] By comparing the normalized damage intensity index corresponding to each stage of valley evolution, or by calculating the proportion of the normalized damage intensity index corresponding to each stage to the deep unloading cumulative effect index, the contribution of different geological historical stages to the total development intensity of the current deep unloading of the riverbank is determined, and the dominant valley evolution stage is determined based on the contribution.
[0034] Secondly, the present invention provides a quantitative assessment system for deep unloading of riverbanks based on a multi-stage evolution model, used to implement the quantitative assessment method for deep unloading of riverbanks based on a multi-stage evolution model as described in the first aspect, the system comprising:
[0035] The data access and preprocessing module is used to acquire exploration engineering logging data and geospatial data of the target area, extract the spatial location information and opening width of each deep unloading object from the exploration engineering logging data, obtain the river water level elevation from the geospatial data, and calculate the tug-of-war height of each deep unloading object based on the spatial location information and the river water level elevation.
[0036] The geological grading and labeling module is used to map the tug-of-war height of each deep unloading object to the corresponding valley evolution stage according to the predefined valley evolution stage division rules, thereby assigning a stage label to each deep unloading object;
[0037] The cumulative opening width calculation module is used to sum up the opening widths of all deep unloading objects in each valley evolution period to obtain the cumulative opening width that reflects the total geometric damage in that period.
[0038] The normalized damage intensity index calculation module is used to obtain the number of major exploration projects that effectively control the corresponding elevation range for each valley evolution period, and calculate the normalized damage intensity index for that period based on the cumulative opening width and the number of major exploration projects for that period.
[0039] The bank slope deep unloading quantitative assessment module is used to calculate the deep unloading cumulative effect index, which characterizes the deep unloading cumulative development intensity of the bank slope in the target area during the river valley evolution history, based on the normalized damage intensity index of all river valley evolution stages. The deep unloading of the bank slope in the target area is then quantitatively assessed based on the deep unloading cumulative effect index.
[0040] The beneficial effects of this invention are as follows: The quantitative assessment method and system for deep unloading of riverbanks based on a multi-stage evolution model provided by this invention, through the construction of a standardized calculation process based on the evolution stages of the river valley, achieves a fundamental shift from point-based qualitative description to regional quantitative evaluation of deep unloading of riverbanks. This invention only needs to utilize the most easily obtainable data on deep unloading location and opening width from exploration engineering logging, automatically divides the data into stages through an objective tug-of-war height-evolution stage mapping rule, and sequentially performs cumulative, normalization, and synthesis operations, ultimately outputting a deep unloading cumulative effect index characterizing the overall development intensity of the region. This process is entirely driven by preset rules and algorithms, eliminating reliance on expert subjective experience and expensive geophysical data, significantly improving the objectivity, consistency, and efficiency of the evaluation. Simultaneously, the generated quantitative index has clear physical meaning and comparability, and can be used as structured data to directly serve digital decision-making processes such as engineering selection and risk assessment, effectively breaking down the data barriers between geological exploration results and intelligent engineering applications. Attached Figure Description
[0041] Figure 1 A flowchart illustrating the quantitative assessment method for deep unloading of riverbank slopes based on a multi-phase evolution model, provided as an example.
[0042] Figure 2 A schematic diagram of the stages of valley evolution provided for an embodiment;
[0043] Figure 3 This is a schematic diagram of the structure of a quantitative assessment system for deep unloading of riverbanks based on a multi-stage evolution model, provided as an example. Detailed Implementation
[0044] Existing technical evaluation processes suffer from high subjectivity, poor consistency of results, and the inability to generate quantitative indicators characterizing the overall risk level of the engineering area. They also suffer from high data costs, low efficiency, and difficulties in directly digitizing the evaluation results. Therefore, this invention proposes a technical solution.
[0045] In this invention, firstly, by calculating the tug-of-war height, each deep unloading object is anchored within the evolutionary sequence of valley incision. Secondly, using a preset "tug-of-war height-evolutionary stage" mapping rule, the geological history stage to which each object belongs is automatically labeled, thus encoding spatiotemporal information. Then, the deep unloading damage (cumulative opening width) within each evolutionary stage is spatially aggregated, and the number of major exploration projects is introduced as a normalization factor to eliminate the influence of differences in exploration investment, thereby calculating a standardized normalized damage intensity index for each stage. Finally, through the linear superposition of geological damage effects across multiple stages, the staged normalized damage intensity indices are combined into a comprehensive deep unloading cumulative effect index, thereby objectively and quantitatively characterizing all discrete rupture data in a region into a comparable overall macroscopic development intensity value.
[0046] The technical solutions in this embodiment will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.
[0047] Figure 1 A flowchart illustrating a quantitative assessment method for deep unloading of riverbank slopes based on a multi-phase evolution model is shown below. Please refer to [link / reference]. Figure 1 The method includes the following steps:
[0048] Step 1: Data Acquisition and Preprocessing
[0049] Obtain exploration engineering logging data and geospatial data of the target area, extract the spatial location information and opening width of each deep unloading object from the exploration engineering logging data, obtain the river water level elevation from the geospatial data, and calculate the tug-of-war height of each deep unloading object based on the spatial location information and the river water level elevation.
[0050] In practical applications, object data from exploration projects (such as adits and boreholes) and framework data containing digital elevation models (DEMs) and river water level elevations are accessed through a pre-defined data interface. The unique identifier, spatial three-dimensional coordinates, and core feature—opening width—of each deep unloading object are extracted from the object data; the river water level elevation of the project area is obtained from the framework data. Subsequently, the raw data undergoes automated cleaning and coordinate calibration, and based on the spatial elevation of each deep unloading object and the local river water level elevation, its tug-of-war height (the difference between the object's elevation and the river water level elevation) is automatically calculated. This establishes a correlation between discrete geological observation data and a continuous geospatial field, laying the foundation for subsequent analysis.
[0051] Step 2: Automatic marking of geological stages:
[0052] Based on the predefined rules for dividing valley evolution stages, the tug-of-war height of each deep unloading object is mapped to the corresponding valley evolution stage, thereby assigning a stage label to each deep unloading object.
[0053] In this embodiment, the valley evolution stages are divided into four stages based on the regional valley terrace sequence and deep unloading development characteristics, and the stage labels correspond to the first stage, the second stage, the third stage and the fourth stage, respectively.
[0054] The predefined rules for dividing valley evolution stages include:
[0055] When the tug-of-war height of a deeply unloaded object is within the first height range, it is mapped to the first phase;
[0056] When the tug-of-war height of a deeply unloaded object is within the second height range, it is mapped to the second phase;
[0057] When the tug-of-war height of a deeply unloaded object is within the third height range, it is mapped to the third phase;
[0058] When the tug-of-war height of a deeply unloaded object is within the fourth height range, it is mapped to the fourth phase.
[0059] In practical applications, based on predefined rules for dividing valley evolution periods, the tug-of-war height of each deep unloading object is mapped to its corresponding geological history period. Specifically, this embodiment, based on the statistical patterns and regional valley terrace sequences of numerous engineering cases in the high mountain and canyon areas of Southwest China (such as the Baihetan and Jinping I hydropower stations), abstracts the valley incision history into a four-stage general evolution model and establishes a quantitative correspondence between tug-of-war height and period: when the tug-of-war height is greater than or equal to 10 meters and less than 80 meters, it is mapped to the first period; when the tug-of-war height is greater than or equal to 80 meters and less than 160 meters, it is mapped to the second period; when the tug-of-war height is greater than or equal to 160 meters and less than 250 meters, it is mapped to the third period; and when the tug-of-war height is greater than or equal to 250 meters, it is mapped to the fourth period. In practical applications, the above rules are automatically executed, assigning a period label to each deep unloading object. This transforms geological evolution knowledge into computable classification logic, realizing the conversion from spatial objects to spatiotemporal objects.
[0060] Please see Figure 2 , Figure 2 The map marks the location and approximate elevation range of multi-level river terraces (such as terrace I to terrace V), and schematically indicates the exposure locations of several exploration projects (such as PD01 to PD06). Figure 2 The four vertical regions, divided from top to bottom using different filling patterns, represent the four valley evolution stages defined in this invention. Figure 2 The two dashed lines in the figure represent two unloading lines. It should be noted that the tug-of-war height used to determine the phase to which the deep unloading object belongs is not directly given by the vertical axis reading in the figure, but must be calculated: that is, the elevation of the object's location (…). Figure 2 (The vertical coordinate in the graph) minus the river surface elevation at the same location ( Figure 2 (The middle is 70 meters). Figure 2 In the middle, from top to bottom, the figures correspond to the fourth stage (corresponding to the stage from peneplain incision to canyon (level V and above)), the third stage (corresponding to the period when the river incises from level V to level IV), the second stage (corresponding to the period when the river incises from level IV to level III), and the first stage (corresponding to the period when the river incises from level III (or level II) to level I) in the river valley evolution model. This figure clearly reveals the mapping relationship between the tug-of-war height of the deep unloading object and its corresponding geological historical period.
[0061] The above mapping relationship is based on statistics of the relationship between deep unloading of typical hydropower stations and valley evolution in the high mountain and canyon areas of Southwest China. Please refer to Table 1 for details.
[0062] Table 1. Statistical Table of the Relationship between Deep Unloading of Typical Hydropower Stations and Valley Evolution in the High Mountain and Canyon Area of Southwest China
[0063]
[0064] In Table 1, T0 to T6 represent the starting point of the tug-of-war height, the first-level terrace, the second-level terrace, the third-level terrace, the fourth-level terrace, the fifth-level terrace, and the sixth-level terrace (penetration surface), respectively. Table 1 lists the range of the valley terraces corresponding to this period (e.g., T3-T1 indicates the incision from the third-level terrace to the first-level terrace) and the measured tug-of-war height intervals (unit: meters). This data reflects the stages of valley landform evolution. Table 1 also lists the tug-of-war height intervals (unit: meters) of deep unloading development revealed by actual exploration within the elevation range corresponding to this hydropower station period. This data reveals the correlation between the spatial and temporal distribution of deep unloading phenomena and the valley incision events of this period.
[0065] As shown in Table 1, within the same valley evolution stage of each hydropower station, the actual revealed deep unloading tug-of-war heights are mainly concentrated within the corresponding terrace tug-of-war height range for that stage. For example, the deep unloading (71-90 meters) of the first stage of the Baihetan Hydropower Station is distributed within its first-stage terrace range (20-100 meters); the deep unloading (140-155 meters) of the second stage of the Jinping I Hydropower Station is distributed within its second-stage terrace range (125-185 meters). This embodiment, through correlation analysis of the data in the two columns of deep unloading tug-of-war height and terrace tug-of-war height in Table 1, summarizes several tug-of-war height ranges where deep unloading is mainly concentrated, and then standardizes these ranges into four consecutive stage division thresholds.
[0066] Step 3, Accumulation of Staged Damage Intensity:
[0067] For each valley evolution phase, the opening widths of all deeply unloaded objects within that phase are summed to obtain the cumulative opening width reflecting the total geometric damage in that phase.
[0068] In practical applications, the period labels assigned in step 2 are used as a basis. All deeply unloaded objects are automatically partitioned into the corresponding valley evolution period subsets. Then, for each period... The calculation involves arithmetically summing the opening widths (in cm) of all deeply unloaded objects within the subset of this period. The resulting cumulative opening width is... This represents the corresponding geological historical stage (the first stage). This provides a direct geometric measure of the total tensile damage caused by deep unloading of the river valley slope rock mass (period), thereby enabling the compression and conversion from massive discrete microscopic object data to macroscopic staged damage intensity indicators.
[0069] Step 4: Calculation of Normalized Damage Intensity Index:
[0070] For each stage of valley evolution, the number of major exploration projects that effectively control the corresponding elevation range of that stage is obtained, and the normalized damage intensity index of that stage is calculated based on the cumulative opening width and the number of major exploration projects of that stage.
[0071] In this embodiment, the main exploration project is an exploration adit laid out transversely and traversing a dense rock zone; when there is no exploration adit within the corresponding elevation range, the borehole that reveals deep unloading within the corresponding elevation range is taken as the main exploration project.
[0072] The formula for calculating the normalized damage intensity index is as follows:
[0073] ;
[0074] in, For the first Normalized damage intensity index of different stages of valley evolution. For the first The cumulative opening width of the valley during its evolutionary stages. For the first The number of major exploration projects that effectively control the elevation range corresponding to the different stages of valley evolution.
[0075] In practical applications, firstly, for each stage of river valley evolution... The system automatically counts the number of major exploration projects within the corresponding tug-of-war height range for that period, which effectively control the range by traversing the dense rock zone from a transverse direction. Exploration adits are preferred, and if no adits are available, exposed boreholes are used instead. This quantity This serves as a quantitative weight for evaluating the representativeness and reliability of the data sampling for this period. Then, the cumulative span of this period is... (Obtained from step 3) Divide by the corresponding number of major exploration projects The normalized damage intensity index was obtained. This represents the average tensile damage width controlled by a unit of effective exploration work during a specific geological period. Through division, a weighted average is applied to the original cumulative damage based on data reliability, ensuring that the calculated value is consistent across different exploration investment levels in engineering areas, or between periods with different exploration densities within the same engineering area. The values are objectively comparable.
[0076] Step 5: Constructing the cumulative effect index:
[0077] Based on the normalized damage intensity index of all valley evolution stages, the deep unloading cumulative effect index, which characterizes the deep unloading cumulative development intensity of the target area slope in the valley evolution history, is calculated. The deep unloading of the target area slope is then quantitatively evaluated based on the deep unloading cumulative effect index.
[0078] In this embodiment, the formula for calculating the deep unloading cumulative effect index is as follows:
[0079] ;
[0080] in, This is the cumulative effect index of deep unloading. For the first Normalized damage intensity index of different stages of valley evolution. Indicates the total number of periods.
[0081] Specifically, this embodiment will divide the evolutionary stages of each river valley ( In this embodiment The damage events are considered as independent rock mass damage events in a time series, and it is assumed that the damage effects of deep unloading formed at different geological historical stages are preserved and linearly superimposed in the existing riverbank rock mass. Based on this, the normalized damage intensity index corresponding to each valley evolution stage is... The scalar result obtained by summing the results yields the deep unloading cumulative effect index. Deep unloading cumulative effect index The quantitative characterization of the total development intensity of deep unloading that the bank slope in the target area has borne and accumulated during the river valley evolution process, after normalization by exploration control, can be directly used for the overall risk level assessment of the engineering area, or as a key parameter for subsequent risk assessment models and decision support.
[0082] Specifically, the deep unloading cumulative effect index The larger the value, the higher the intensity of deep unloading development on the target area's bank slope, and the more significant the cumulative damage effect experienced by the bank slope rock mass; the deep unloading cumulative effect index. The smaller the value, the lower the intensity of deep unloading development on the target area's bank slope, and the weaker the cumulative damage effect. Therefore, the calculated deep unloading cumulative effect index can be used as a basis for further analysis. The quantitative values enable a quantitative evaluation of the development intensity of deep unloading on the bank slope in the target area, providing a clear and definite scientific basis for engineering decisions.
[0083] In this embodiment, the deep unloading cumulative effect index can also be compared with a preset threshold range, and the deep unloading development intensity level of the target area can be output based on the comparison result. Specifically, when the deep unloading cumulative effect index is less than or equal to 10, the corresponding deep unloading development intensity level is low; when the deep unloading cumulative effect index is greater than 10 and less than or equal to 30, the corresponding deep unloading development intensity level is medium; and when the deep unloading cumulative effect index is greater than 30, the corresponding deep unloading development intensity level is high.
[0084] Specifically, this embodiment is based on statistical analysis of data from several typical hydropower stations in the high mountain and canyon areas of Southwest China (such as Baihetan, Jinping I, and Yebatan). Referring to Table 2, the intensity of deep unloading development on the riverbank slope is divided into three levels: "low," "medium," and "high," and corresponding threshold ranges are determined. When the value is less than or equal to 10, it is judged as low-level; when A value greater than 10 and less than or equal to 30 is judged as intermediate; when A value greater than 30 is considered high-level. In practical applications, this rule automatically outputs the corresponding intensity level label, thereby transforming continuous quantitative indices into intuitive and definite engineering risk classification conclusions, providing a clear and actionable qualitative judgment basis for macro-level decision-making.
[0085] Table 2. Development Degree of Deep Unloading in Typical Hydropower Stations in the High Mountain and Canyon Area of Southwest China
[0086]
[0087] In this embodiment, the method further includes: by comparing the normalized damage intensity index corresponding to each valley evolution stage, or calculating the proportion of the normalized damage intensity index corresponding to each stage to the deep unloading cumulative effect index, determining the contribution of different geological historical stages to the total development intensity of the current deep unloading of the bank slope, and determining the dominant valley evolution stage based on the contribution size.
[0088] Specifically, the normalized damage intensity index for each stage of valley evolution was calculated. and deep unloading cumulative effect index Then, by automatically comparing each period The magnitude of the value, or the calculation of each period. Value in the total index The proportion (contribution rate) of different valley downcutting stages (i.e., different geological historical stages) is used to quantitatively assess the relative contribution of these stages to the overall development intensity of deep unloading on the existing riverbank slopes. For example, by identifying... The period with the largest value, or calculation and The ratio can identify which historical period's valley downcutting event induced the most significant deep unloading damage, thus providing a more refined decision-making basis for targeted engineering surveys and phased design of remediation measures.
[0089] In this embodiment, the deep unloading cumulative effect index can also be displayed intuitively through the indicator dashboard. and each period index Contributions; achieving multi-project area comparison through radar chart analysis. Value and Horizontal comparison of values; spatial risk diagram Value and The values are mapped to the geographic space of the engineering area to form a macro-intensity level distribution map. Simultaneously, all quantitative indicators (D, ...) are... The assessment results and their intensity levels are encapsulated in structured data formats such as JSON and CSV, and output through standard application programming interfaces (APIs). This allows the assessment results to be directly and seamlessly accessed by engineering site selection decision support systems (DSS), project management systems (PM), or digital twin platforms, thereby completely breaking down data silos and achieving data connectivity from quantitative geological assessment to intelligent design and management of upper-level engineering projects.
[0090] In summary, the quantitative assessment method for deep unloading of riverbanks based on a multi-stage evolution model provided in this embodiment combines geological evolution theory with standardized algorithms to construct a fully objective assessment model encompassing data access, automatic staging, cumulative calculation, and normalized synthesis. This embodiment relies solely on the core geometric data of deep unloading (location and opening width), automatically assigning it to its spatiotemporal context using a tug-of-war height-evolutionary stage mapping rule. Normalization is achieved by incorporating the number of exploration projects, ultimately generating a deep unloading cumulative effect index characterizing the overall development intensity of the region. This embodiment completely eliminates reliance on expert subjective experience and expensive geophysical data, significantly improving the objectivity, consistency, and efficiency of the assessment. Furthermore, the generated quantitative index and staging contribution values can be directly used for project selection and risk classification, revealing the dominant historical evolutionary stages and providing standardized data support for engineering decision-making and geological analysis.
[0091] Based on the above technical solutions, this embodiment also proposes a quantitative assessment system for deep unloading of riverbanks based on a multi-stage evolution model, used to implement the quantitative assessment method for deep unloading of riverbanks based on a multi-stage evolution model as described in the embodiment. Please refer to [link to relevant documentation]. Figure 3 The system includes:
[0092] The data access and preprocessing module is used to acquire exploration engineering logging data and geospatial data of the target area, extract the spatial location information and opening width of each deep unloading object from the exploration engineering logging data, obtain the river water level elevation from the geospatial data, and calculate the tug-of-war height of each deep unloading object based on the spatial location information and the river water level elevation.
[0093] The geological grading and labeling module is used to map the tug-of-war height of each deep unloading object to the corresponding valley evolution stage according to the predefined valley evolution stage division rules, thereby assigning a stage label to each deep unloading object;
[0094] The cumulative opening width calculation module is used to sum up the opening widths of all deep unloading objects in each valley evolution period to obtain the cumulative opening width that reflects the total geometric damage in that period.
[0095] The normalized damage intensity index calculation module is used to obtain the number of major exploration projects that effectively control the corresponding elevation range for each valley evolution period, and calculate the normalized damage intensity index for that period based on the cumulative opening width and the number of major exploration projects for that period.
[0096] The bank slope deep unloading quantitative assessment module is used to calculate the deep unloading cumulative effect index, which characterizes the deep unloading cumulative development intensity of the bank slope in the target area during the river valley evolution history, based on the normalized damage intensity index of all river valley evolution stages. The deep unloading of the bank slope in the target area is then quantitatively assessed based on the deep unloading cumulative effect index.
[0097] It is understood that the slope deep unloading quantitative assessment system based on the multi-stage evolution model described in this embodiment is a system for implementing the slope deep unloading quantitative assessment method based on the multi-stage evolution model described in the embodiment. As the system disclosed in the embodiment corresponds to the method disclosed in the embodiment, the description is relatively simple. For relevant parts, please refer to the description of the method. It will not be repeated here.
Claims
1. A quantitative assessment method for deep unloading of riverbank slopes based on a multi-stage evolution model, characterized in that, The method includes: Acquire exploration engineering logging data and geospatial data of the target area, extract spatial location information and opening width of each deep unloading object from the exploration engineering logging data, obtain river water level elevation from the geospatial data, and calculate the tug-of-war height of each deep unloading object based on the spatial location information and river water level elevation. According to the predefined rules for dividing valley evolution stages, the tug-of-war height of each deep unloading object is mapped to the corresponding valley evolution stage, thereby assigning a stage label to each deep unloading object; For each valley evolution phase, the opening widths of all deep unloading objects within that phase are summed to obtain the cumulative opening width reflecting the total geometric damage in that phase. For each stage of valley evolution, the number of major exploration projects that effectively control the corresponding elevation range of that stage is obtained, and the normalized damage intensity index of that stage is calculated based on the cumulative opening width and the number of major exploration projects of that stage. Based on the normalized damage intensity index of all valley evolution stages, the deep unloading cumulative effect index, which characterizes the deep unloading cumulative development intensity of the target area slope in the valley evolution history, is calculated. The deep unloading of the target area slope is then quantitatively evaluated based on the deep unloading cumulative effect index.
2. The method for quantitative assessment of bank slope deep unloading based on a multi-stage evolution model according to claim 1, characterized in that, The valley evolution stages are divided into four stages based on the regional valley terrace sequence and deep unloading development characteristics, and the stage labels correspond to the first stage, the second stage, the third stage and the fourth stage, respectively; The predefined rules for dividing valley evolution stages include: When the tug-of-war height of a deeply unloaded object is within the first height range, it is mapped to the first phase; When the tug-of-war height of a deeply unloaded object is within the second height range, it is mapped to the second phase; When the tug-of-war height of a deeply unloaded object is within the third height range, it is mapped to the third phase; When the tug-of-war height of a deeply unloaded object is within the fourth height range, it is mapped to the fourth phase.
3. The method for quantitative assessment of bank slope deep unloading based on a multi-stage evolution model according to claim 2, characterized in that, The first height range is greater than or equal to 10 meters and less than 80 meters, the second height range is greater than or equal to 80 meters and less than 160 meters, the third height range is greater than or equal to 160 meters and less than 250 meters, and the fourth height range is greater than or equal to 250 meters.
4. The method for quantitative assessment of bank slope deep unloading based on a multi-stage evolution model according to claim 1, characterized in that, The main exploration project is an exploration adit laid out transversely and traversing a dense rock zone; when there is no exploration adit within the corresponding elevation range, the main exploration project is to expose boreholes with deep unloading within the corresponding elevation range.
5. The method for quantitative assessment of bank slope deep unloading based on a multi-stage evolution model according to claim 1, characterized in that, The formula for calculating the normalized damage intensity index is as follows: ； in, For the first Normalized damage intensity index of different stages of valley evolution. For the first The cumulative opening width of the valley during its evolutionary stages. For the first The number of major exploration projects that effectively control the elevation range corresponding to the different stages of valley evolution.
6. The method for quantitative assessment of bank slope deep unloading based on a multi-stage evolution model according to claim 1, characterized in that, The formula for calculating the deep unloading cumulative effect index is as follows: ； in, This is the cumulative effect index of deep unloading. For the first Normalized damage intensity index of different stages of valley evolution. Indicates the total number of periods.
7. The method for quantitative assessment of bank slope deep unloading based on a multi-stage evolution model according to claim 1, characterized in that, The deep unloading of the target area's bank slope is quantitatively assessed based on the deep unloading cumulative effect index, specifically including: The deep unloading cumulative effect index is compared with a preset threshold range, and the deep unloading development intensity level of the target area is output based on the comparison result.
8. The method for quantitative assessment of bank slope deep unloading based on a multi-stage evolution model according to claim 7, characterized in that, When the deep unloading cumulative effect index is less than or equal to 10, the corresponding deep unloading development intensity level is low; when the deep unloading cumulative effect index is greater than 10 and less than or equal to 30, the corresponding deep unloading development intensity level is medium; when the deep unloading cumulative effect index is greater than 30, the corresponding deep unloading development intensity level is high.
9. The method for quantitative assessment of bank slope deep unloading based on a multi-stage evolution model according to claim 1, characterized in that, The method further includes: By comparing the normalized damage intensity index corresponding to each stage of valley evolution, or by calculating the proportion of the normalized damage intensity index corresponding to each stage to the deep unloading cumulative effect index, the contribution of different geological historical stages to the total development intensity of the current deep unloading of the riverbank can be determined, and the dominant valley evolution stage can be determined based on the contribution.
10. A quantitative assessment system for deep unloading of riverbank slopes based on a multi-stage evolution model, characterized in that, The system is used to implement the method for quantitative assessment of bank slope deep unloading based on a multi-stage evolution model as described in any one of claims 1 to 9, the system comprising: The data access and preprocessing module is used to acquire exploration engineering logging data and geospatial data of the target area, extract the spatial location information and opening width of each deep unloading object from the exploration engineering logging data, obtain the river water level elevation from the geospatial data, and calculate the tug-of-war height of each deep unloading object based on the spatial location information and the river water level elevation. The geological grading and labeling module is used to map the tug-of-war height of each deep unloading object to the corresponding valley evolution stage according to the predefined valley evolution stage division rules, thereby assigning a stage label to each deep unloading object; The cumulative opening width calculation module is used to sum up the opening widths of all deep unloading objects in each valley evolution period to obtain the cumulative opening width that reflects the total geometric damage in that period. The normalized damage intensity index calculation module is used to obtain the number of major exploration projects that effectively control the corresponding elevation range for each valley evolution period, and calculate the normalized damage intensity index for that period based on the cumulative opening width and the number of major exploration projects for that period. The bank slope deep unloading quantitative assessment module is used to calculate the deep unloading cumulative effect index, which characterizes the deep unloading cumulative development intensity of the bank slope in the target area during the river valley evolution history, based on the normalized damage intensity index of all river valley evolution stages. The deep unloading of the bank slope in the target area is then quantitatively assessed based on the deep unloading cumulative effect index.