Foundation pit depth data prediction method and system based on multi-region bearing capacity correlation

Abstract

The application discloses a kind of based on multi-region bearing capacity association foundation pit depth data prediction method and system, it is related to data processing technical field, method includes: obtaining engineering site, engineering site is divided into n sub-regional, the bearing area of sub-regional is obtained;Bearing capacity data is obtained, and bearing capacity sequence is formed;Sub-regional adjacent sub-regional is obtained and as the associated sub-regional of sub-regional, and according to the bearing capacity sequence of sub-regional and the bearing capacity sequence of associated sub-regional, the sub-association index between sub-regional and associated sub-regional is obtained, and according to the sub-association index between sub-regional and all associated sub-regional, the typical association index of sub-regional is obtained;The eigenvalue depth of sub-regional foundation pit is obtained, and according to the typical association index of sub-regional and the eigenvalue depth of sub-regional foundation pit, the predicted depth of sub-regional foundation pit is obtained, and construction is carried out according to predicted depth of foundation pit.The application has the advantages of dynamic correction, data correlation analysis and good depth prediction effect.

Description

A Method and System for Predicting Foundation Pit Depth Based on Multi-Regional Bearing Capacity Correlation Technical Field

[0001] This invention relates to the field of data processing technology, specifically to a method and system for predicting foundation pit depth based on multi-regional bearing capacity correlation. Background Technology

[0002] In the field of bridge engineering foundation pit construction, the reasonable determination of the foundation pit depth is directly related to the safety of the engineering structure and the construction economy. Current methods for determining foundation pit depth typically rely solely on experience and regulatory requirements. However, bridge engineering sites often have complex geological conditions, with significant differences in soil structure, rock strata distribution, and bearing capacity between different sub-regions. Furthermore, the geological conditions between adjacent sub-regions are correlated; for example, changes in the bearing capacity of one sub-region may be influenced by the geological characteristics of adjacent sub-regions. Differences in soil density between adjacent regions may lead to uneven stress distribution, thus affecting the stability of the foundation pit after excavation.

[0003] Specifically, existing methods for determining the depth of foundation pits only consider the bearing capacity data (such as standard penetration test) of a single construction sub-region for analysis, ignoring the bearing capacity correlation between regions. When the bearing capacity of adjacent sub-regions differs significantly, the depth may be too shallow due to the failure to consider the influence of adjacent regions (failing to meet the overall structural stability requirements and easily leading to the risk of collapse), thus reducing the accuracy and engineering applicability of foundation pit depth prediction. Therefore, it is necessary to effectively integrate the bearing capacity correlation information of adjacent sub-regions in the prediction of foundation pit depth in bridge engineering sites to improve the accuracy of foundation pit depth determination and ensure construction safety and economy. Summary of the Invention

[0004] In view of the technical problems existing in the background art, the present invention provides a method and system for predicting foundation pit depth data based on multi-region bearing capacity correlation.

[0005] A method for predicting foundation pit depth based on multi-region bearing capacity correlation includes: acquiring the engineering site and dividing it into n sub-regions that can be used independently for foundation pit construction, and acquiring the bearing area of ​​each sub-region; acquiring bearing capacity data at multiple depth locations in each sub-region, arranging the bearing capacity data of each sub-region at multiple depth locations in depth order to form a bearing capacity sequence; acquiring the sub-regions adjacent to the i-th sub-region as associated sub-regions of the i-th sub-region, and acquiring sub-correlation indices between the i-th sub-region and each associated sub-region based on the bearing capacity sequence of the i-th sub-region and the bearing capacity sequences of each associated sub-region, and acquiring typical correlation indices of the i-th sub-region based on the sub-correlation indices between the i-th sub-region and all associated sub-regions; acquiring the intrinsic depth of the foundation pit in the i-th sub-region, and acquiring the predicted depth of the foundation pit in the i-th sub-region based on the typical correlation indices and the intrinsic depth of the foundation pit in the i-th sub-region, and carrying out construction according to the predicted depth of the foundation pit.

[0006] Optionally, obtaining the sub-association index between the i-th sub-region and each associated sub-region based on the bearing capacity sequence of the i-th sub-region and the bearing capacity sequences of each associated sub-region includes: obtaining the absolute value of the difference between the bearing capacity data at the same depth in the bearing capacity sequence of the i-th sub-region and the bearing capacity sequences of each associated sub-region and using it as the difference data, and arranging the difference data at different depths in sequence to form a bearing capacity difference sequence; obtaining the sub-association index between the i-th sub-region and each associated sub-region based on the bearing capacity difference sequence between the i-th sub-region and each associated sub-region.

[0007] Optionally, obtaining the sub-association index between the i-th sub-region and each associated sub-region based on the bearing capacity difference sequence between the i-th sub-region and each associated sub-region includes: obtaining a standard deviation threshold based on the bearing area of ​​the i-th sub-region; obtaining the number of difference data exceeding the standard deviation threshold in the bearing capacity difference sequence between the i-th sub-region and each associated sub-region, and recording it as the association number between the i-th sub-region and each associated sub-region; dividing the association number between the i-th sub-region and each associated sub-region by the number of difference data in the bearing capacity difference sequence to obtain the sub-association index between the i-th sub-region and each associated sub-region.

[0008] Optionally, obtaining the standard deviation threshold based on the carrying area of ​​the i-th sub-region includes: obtaining a preset coefficient, multiplying the carrying area of ​​the i-th sub-region by the preset coefficient, and obtaining the standard deviation threshold.

[0009] Optionally, obtaining the typical association index of the i-th sub-region based on the sub-association index between the i-th sub-region and all associated sub-regions includes: obtaining the maximum value among the sub-association indices between the i-th sub-region and all associated sub-regions, and using the maximum value as the typical association index of the i-th sub-region.

[0010] Optionally, obtaining the predicted depth of the foundation pit in the i-th sub-region based on the typical correlation index of the i-th sub-region and the intrinsic depth of the foundation pit in the i-th sub-region includes: obtaining the adjustment ratio, multiplying the product of the intrinsic depth of the foundation pit in the i-th sub-region and the adjustment ratio by the typical correlation index of the i-th sub-region, and obtaining the depth correction amount of the i-th sub-region; adding the intrinsic depth of the foundation pit in the i-th sub-region and the depth correction amount of the i-th sub-region, and obtaining the predicted depth of the foundation pit in the i-th sub-region.

[0011] A foundation pit depth prediction system based on multi-region bearing capacity correlation is also provided. The system includes: a region division module, used to acquire the engineering site and divide the engineering site into n sub-regions that can be used independently for foundation pit construction, and to acquire the bearing area of ​​each sub-region; a data acquisition module, used to acquire bearing capacity data at multiple different depth positions in each sub-region, and to arrange the bearing capacity data of each sub-region at multiple depth positions in depth order to form a bearing capacity sequence; a data processing module, used to acquire the sub-regions adjacent to the i-th sub-region and use them as associated sub-regions of the i-th sub-region, and to acquire the sub-correlation index between the i-th sub-region and each associated sub-region based on the bearing capacity sequence of the i-th sub-region and the bearing capacity sequences of each associated sub-region, and to acquire the typical correlation index of the i-th sub-region based on the sub-correlation index between the i-th sub-region and all associated sub-regions; and a data prediction module, used to acquire the intrinsic depth of the foundation pit in the i-th sub-region, and to acquire the predicted depth of the foundation pit in the i-th sub-region based on the typical correlation index and the intrinsic depth of the foundation pit in the i-th sub-region, and to carry out construction according to the predicted depth of the foundation pit.

[0012] Optionally, the data processing module is further configured to: obtain the absolute value of the difference between the bearing capacity sequence of the i-th sub-region and the bearing capacity data at the same depth in the bearing capacity sequence of each associated sub-region and use it as the difference data, and arrange the difference data at different depths in sequence to form a bearing capacity difference sequence; obtain the sub-association index between the i-th sub-region and each associated sub-region based on the bearing capacity difference sequence between the i-th sub-region and each associated sub-region.

[0013] Optionally, the data processing module is further configured to: obtain a standard deviation threshold based on the bearing area of ​​the i-th sub-region; obtain the number of difference data exceeding the standard deviation threshold in the bearing capacity difference sequence between the i-th sub-region and each associated sub-region, and record it as the number of associations between the i-th sub-region and each associated sub-region; divide the number of associations between the i-th sub-region and each associated sub-region by the number of difference data in the bearing capacity difference sequence, and obtain the sub-association index between the i-th sub-region and each associated sub-region.

[0014] Optionally, the data processing module is also used to: obtain a preset coefficient, multiply the carrying area of ​​the i-th sub-region by the preset coefficient, and obtain the standard deviation threshold.

[0015] The beneficial effects of this invention are reflected in:

[0016] In the entire method for predicting foundation pit depth based on multi-regional bearing capacity correlation, firstly, sub-regions are divided and bearing area is obtained during the sub-region division stage. This ensures the construction independence of each sub-region and provides spatial parameter support for subsequent correlation analysis. Secondly, bearing capacity data acquisition and sequence construction arrange the bearing capacity data of different depths in each sub-region in depth order to form a structured dataset. This lays the foundation for analyzing the bearing capacity differences of strata at the same depth in adjacent regions. By calculating sub-correlation indices and typical correlation indices, the degree of bearing capacity difference between the i-th sub-region and adjacent regions at different depths is quantified. The typical correlation indices focus on the adjacent regions with the most significant differences, making the analysis more targeted and avoiding stability assessment bias caused by ignoring significant local differences. Thirdly, the correlation information of adjacent regions is transformed into specific depth adjustment values, appropriately increasing the predicted foundation pit depth based on the intrinsic depth. This not only compensates for the insufficient depth problem that may be caused by existing methods that only consider single-region geological conditions, but also controls the correction range by adjusting the proportion, avoiding over-excavation and increased construction costs, thus achieving a balance between structural safety and construction economy. Attached Figure Description

[0017] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the accompanying drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn to scale.

[0018] Figure 1 is a schematic diagram of part of the process of S1 to S3 in the foundation pit depth data prediction method based on multi-region bearing capacity correlation of the present invention.

[0019] Figure 2 is a schematic diagram of part of the S3 process in the foundation pit depth data prediction method based on multi-region bearing capacity correlation of the present invention;

[0020] Figure 3 is a schematic diagram of part of the process of S3 to S4 in the foundation pit depth data prediction method based on multi-region bearing capacity correlation of the present invention;

[0021] Figure 4 is a schematic diagram of the steps of the foundation pit depth prediction method based on multi-region bearing capacity correlation of the present invention;

[0022] Figure 5 is a schematic diagram of part of step S3 in the foundation pit depth data prediction method based on multi-region bearing capacity correlation of the present invention;

[0023] Figure 6 is a schematic diagram of part of step S32 in the foundation pit depth data prediction method based on multi-region bearing capacity correlation of the present invention;

[0024] Figure 7 is a schematic diagram of part of step S4 in the foundation pit depth data prediction method based on multi-region bearing capacity correlation of the present invention. Detailed Implementation

[0025] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention 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. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0026] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0027] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, the terms "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0028] As shown in Figures 1 to 4, a method for predicting foundation pit depth based on multi-region bearing capacity correlation is provided. In one embodiment, the method includes:

[0029] S1. Obtain the engineering site and divide the engineering site into n sub-regions that can be used independently for foundation pit construction, and obtain the bearing area of ​​each sub-region;

[0030] S2. Obtain bearing capacity data at multiple depth locations in each sub-region, and arrange the bearing capacity data at multiple depth locations in each sub-region in order of depth to form a bearing capacity sequence;

[0031] S3. Obtain the sub-regions adjacent to the i-th sub-region and use them as associated sub-regions of the i-th sub-region. Obtain the sub-association index between the i-th sub-region and each associated sub-region based on the bearing capacity sequence of the i-th sub-region and the bearing capacity sequence of each associated sub-region. Obtain the typical association index of the i-th sub-region based on the sub-association index between the i-th sub-region and all associated sub-regions.

[0032] S4. Obtain the intrinsic depth of the foundation pit in the i-th sub-region, and obtain the predicted depth of the foundation pit in the i-th sub-region based on the typical correlation index of the i-th sub-region and the intrinsic depth of the foundation pit in the i-th sub-region, and carry out construction according to the predicted depth of the foundation pit.

[0033] In this embodiment, it should be noted that in S1, the first step involves collecting basic information and defining the scope of the bridge engineering site. The engineering site is divided into n (n≥3, ensuring at least 3 sub-regions) sub-regions that can be independently constructed for foundation pits. When dividing the site, the independence of the bridge foundation (e.g., each pier or abutment corresponds to an independent foundation pit construction requirement), the natural boundaries of the site (e.g., the boundary between gentle slopes and flat land formed by topographic undulations, the direction of existing roads or pipelines), and the relative consistency of geological units (e.g., the approximate range of the same soil or rock layer distribution) can be considered. At the same time, it is also possible to consider that the area is sufficient to independently cover the foundation pit design requirements, ensuring that each sub-region can independently carry out foundation pit excavation, support design, and stability monitoring during the construction process, avoiding mutual interference between the construction of different sub-regions.

[0034] Furthermore, it is necessary to obtain the bearing area of ​​each sub-region, that is, the projected area of ​​each sub-region on the horizontal plane. This area is determined through on-site measurement or geometric analysis based on the site topographic map, without considering local elevation differences within the sub-region, and only using its horizontal projected outline as the calculation benchmark. For example, in a cross-river bridge project site, which includes the abutment areas on both banks and the three pier areas in the river, the water area corresponding to each pier foundation is divided into an independent sub-region, and the abutment areas on both banks are divided into sub-regions based on the road boundaries on the banks and the planar area after the site is leveled. The bearing area of ​​each sub-region is calculated by measuring the geometric parameters of its planar outline (such as the side length and radian of the polygon). Among them, the abutment sub-regions near the banks are restricted by the existing flood control dikes, and their planar outlines are irregular in shape. Their bearing area is still determined according to the horizontal projected range inside the flood control dike, providing basic data for subsequent analysis of the bearing capacity correlation between sub-regions and calculation of the standard deviation threshold.

[0035] In S2, bearing capacity data will be collected within each sub-region defined in S1. The data collection targets strata extending downwards from the surface to different depths within each sub-region, with the collection depth not exceeding the intrinsic depth of the excavation pit determined using existing methods for that sub-region. This ensures data coverage of all strata potentially involved in the excavation. Conventional geological exploration methods such as standard penetration tests (SPTs) can be employed. A center point will be selected within each sub-region, and bearing capacity data (e.g., SPT values) will be collected sequentially at different depths, starting from the shallow surface. Each depth will correspond to a separate set of data reflecting the bearing capacity of the strata at that depth. For example, in a land bridge abutment area, the surface consists of a layer of plain fill, followed by a layer of silty clay, a layer of medium sand, and finally a layer of strongly weathered rock corresponding to the intrinsic depth of the foundation pit. During data collection, standard penetration data is collected starting from the surface plain fill and at regular intervals until the top of the strongly weathered rock layer is reached. In a waterway bridge pier area, the exploration location needs to be fixed first using a drilling platform. After penetrating the water cover layer, bearing capacity data is collected at various depths starting from the loose sediments on the riverbed surface. Similarly, the data is controlled within the intrinsic depth of the foundation pit to avoid excessive exploration and increased costs.

[0036] Furthermore, after data collection, the bearing capacity data for each sub-region needs to be arranged sequentially by depth to form a bearing capacity sequence. The arrangement follows a principle of shallow to deep, meaning the first data point in the sequence corresponds to the bearing capacity at the shallowest depth (usually the surface or near the surface) within the sub-region, and subsequent data are arranged in ascending order of depth until the bearing capacity data reaches the deepest depth (not exceeding the intrinsic depth of the foundation pit). This arrangement clearly reflects the bearing capacity variation characteristics of the strata from shallow to deep within the sub-region, with each data point corresponding to a specific depth, forming an ordered dataset. For example, if standard penetration data from multiple depths from shallow to deep were collected for a sub-region, arranged in ascending order of depth, the earlier data in the sequence reflect the bearing capacity of shallow, soft soil layers, the middle data correspond to the bearing capacity of transition layers, and the later data represent the bearing capacity of strata close to the intrinsic depth of the foundation pit. This sequence will serve as the basis for comparing bearing capacity at the same depth with related sub-regions in S3, providing structured data support for subsequent calculations of bearing capacity difference sequences.

[0037] In S3, the first step is to identify the associated sub-regions of the i-th sub-region, i.e., other sub-regions that are geographically directly adjacent to it. The adjacency relationship is defined based on the planar layout of the sub-regions divided in S1, using the direct contact of sub-region boundaries as the criterion, without considering non-directly adjacent areas. For example, in a cross-river bridge project, the sub-region of pier 2 in the river (denoted as i) has associated sub-regions including pier 1 directly adjacent upstream, pier 3 directly adjacent downstream, and abutment A sub-region near the south bank with partially overlapping boundaries; while abutment B sub-region on the north bank is not considered an associated sub-region because it is separated from pier 2 by pier 3.

[0038] After identifying the associated sub-regions, it is necessary to calculate the sub-correlation index between the i-th sub-region and each associated sub-region based on the bearing capacity sequence of each sub-region in S2 arranged in depth order. The specific process is as follows: for the bearing capacity sequence of the i-th sub-region (bearing capacity data from shallow to deep depths) and the bearing capacity sequence of a certain associated sub-region, take the absolute value of the difference between the bearing capacity data at the corresponding depth positions to obtain a series of difference data. These data are arranged in depth order to form a bearing capacity difference sequence, which reflects the bearing capacity difference between the two sub-regions at the same depth.

[0039] Next, sub-correlation indices are calculated based on the bearing capacity difference sequence. This process requires combining the bearing capacity area of ​​the i-th sub-region obtained in S1. First, the standard deviation threshold is determined based on the bearing capacity area of ​​the i-th sub-region. This threshold is obtained by multiplying the bearing capacity area by a preset coefficient. The preset coefficient needs to be determined based on the geological type of the engineering site (e.g., the site of the cross-river bridge is mainly composed of interlayered cohesive soil and sand formed by river alluvial deposits) and regional stability requirements (e.g., the bridge foundation needs to meet long-term bearing capacity and seismic resistance requirements). It is determined through a limited number of preliminary exploration experiments (e.g., small-scale comparative tests are conducted on typical geological profiles within the site) to ensure that the threshold can reasonably distinguish between bearing capacity fluctuations within the normal range and significant differences that may affect stability.

[0040] Then, the number of difference data points in the bearing capacity difference sequence that exceed the standard deviation threshold (i.e., the number of correlations) is counted. This number is then divided by the total number of data points in the difference sequence (i.e., the total number of depth locations collected in S2). The result is the sub-correlation index between the i-th sub-region and the associated sub-region. This index reflects the proportion of bearing capacity differences between the two sub-regions at different depths that exceed the reasonable range. For example, in the bearing capacity difference sequence between pier 2 sub-region (i) and pier 1 sub-region, the difference at some depths (such as the boundary between the silty clay layer and the medium sand layer) exceeds the standard deviation threshold. After counting the number of these depths, dividing by the total number of depths yields the sub-correlation index between the two. Similarly, the sub-correlation index between i and pier 3 sub-region, and between i and abutment A sub-region, can be calculated.

[0041] After obtaining the sub-correlation indices of the i-th sub-region and all related sub-regions, the maximum value needs to be selected as the typical correlation index for that sub-region. The reason for selecting the maximum value is that different related sub-regions have different degrees of influence on the i-th sub-region. The related sub-region corresponding to the maximum value represents the adjacent region with the most prominent difference in bearing capacity characteristics from the i-th sub-region. This prominent difference may be due to different stratigraphic origins (e.g., the No. 1 pier area is close to the riverbank and has a loose silt layer deposited later, while the No. 2 pier area is located in the mainstream area of ​​the river center, and the soil layer is relatively dense after long-term water flow compaction) or local changes in geological structure (e.g., discontinuous rock layer distribution caused by small-scale faults), which may have a more direct impact on stress transfer and stability after foundation pit excavation.

[0042] For example, the sub-correlation indices of sub-area 2 (i) with sub-areas 1, 3 and abutment A are a, b and c, respectively. If the value of a is the largest, then a is taken as the typical correlation index of i. This indicates that the bearing capacity difference between sub-area 2 and sub-area 1 is the highest in multiple depths exceeding the reasonable range, reflecting a more obvious inconsistency in the bearing capacity of the two strata. This inconsistency may lead to sub-area 2 being subjected to uneven stress from sub-area 1 laterally after the foundation pit is excavated. Therefore, the typical correlation index a can more focusedly reflect the influence of adjacent areas that need to be focused on, providing a targeted basis for the subsequent correction of the foundation pit depth.

[0043] In S4, the intrinsic depth of the foundation pit in the i-th sub-region needs to be obtained first. This depth is a basic value determined by existing methods, mainly based on the geological conditions and specifications of the individual sub-region, without considering the bearing capacity relationship between adjacent sub-regions.

[0044] Specifically, determining the intrinsic depth of the foundation pit requires considering the design loads of the bridge foundation (such as the vertical loads and horizontal thrusts of the piers) and the safety factor requirements in engineering specifications. This is achieved through conventional stability calculations to ensure that when the foundation pit is excavated to this depth, the strata within a single sub-region can provide sufficient bearing capacity to support the bridge foundation, preventing settlement or instability due to insufficient bearing capacity. For example, in a land-based bridge abutment sub-region, existing methods, based on the standard penetration data of its surface fill layer, middle silty clay layer, and lower medium sand layer, combined with the design loads of the abutment foundation, calculate that the foundation pit needs to be excavated to a certain depth into the medium sand layer. At this depth, the bearing capacity of the strata, verified by specifications, meets the stability requirements of the single region; this depth is the intrinsic depth of the foundation pit for that sub-region.

[0045] Furthermore, after obtaining the intrinsic depth of the foundation pit, the predicted depth of the foundation pit needs to be calculated by combining the typical correlation index obtained in S3, so that the depth determination process incorporates the bearing capacity correlation information of adjacent sub-regions. The typical correlation index reflects the proportion of the most prominent bearing capacity difference between the i-th sub-region and the adjacent sub-regions. The greater the difference, the higher the value of the index, indicating that the potential impact of the adjacent region on the stability of the current region needs more attention. The selection of the adjustment ratio needs to be determined based on the geological category of the engineering site (such as the cohesive soil area on land and the sandy soil area in water) and the regional stability requirements (such as seismic intensity and groundwater level change range), through a limited number of preliminary experiments (such as selecting sub-regions with different geological conditions within the site for small-scale model tests) to balance safety reserves and construction costs. For example, in the land bridge abutment area, the strata are relatively stable, so a smaller adjustment ratio can be selected. In the water bridge pier area, the water flow may exacerbate the stress unevenness, so a slightly larger adjustment ratio can be selected.

[0046] The calculation logic for depth correction is as follows: based on the intrinsic depth of the foundation pit, the maximum range that can be corrected is determined by combining the adjustment ratio, and then the actual correction range is controlled by typical correlation indicators. The higher the typical correlation indicator, the greater the correction amount, so that the depth adjustment can specifically respond to the prominent differences between adjacent areas.

[0047] Finally, the intrinsic depth of the foundation pit is added to the depth correction to obtain the predicted depth. This predicted depth is appropriately increased from the intrinsic depth to address the uneven stress distribution that may result from differences in bearing capacity between adjacent sub-regions. For example, in the No. 2 pier area (water area) of a cross-river bridge, the typical correlation index stems from the bearing capacity difference with the upstream No. 1 pier area (No. 1 pier is close to the riverbank and has a loose silt layer, causing bearing capacity differences at multiple depths to exceed the standard threshold). The adjustment ratio is selected based on the sandy soil geology and stability requirements of the water area. The calculated depth correction makes the predicted depth slightly deeper than the intrinsic depth, ensuring that after excavation to this depth, the No. 2 pier area can better adapt to the lateral stress influence from the direction of No. 1 pier, making the foundation pit stability more consistent with the geological conditions of the overall project site.

[0048] In summary, the foundation pit depth prediction method based on multi-regional bearing capacity correlation firstly involves dividing the area into sub-regions and obtaining the bearing area during the sub-region division stage. This ensures the construction independence of each sub-region and provides spatial parameter support for subsequent correlation analysis. Secondly, the bearing capacity data acquisition and sequence construction arranges the bearing capacity data of different depths in each sub-region in depth order, forming a structured dataset. This lays the foundation for analyzing the bearing capacity differences of strata at the same depth in adjacent regions. By calculating sub-correlation indices and typical correlation indices, the degree of bearing capacity difference between the i-th sub-region and adjacent regions at different depths is quantified. The typical correlation indices focus on the adjacent regions with the most significant differences, making the analysis more targeted and avoiding stability assessment bias caused by ignoring significant local differences. Thirdly, the correlation information of adjacent regions is transformed into specific depth adjustment values, appropriately increasing the predicted foundation pit depth based on the intrinsic depth. This not only compensates for the insufficient depth problem that may be caused by existing methods that only consider single-region geological conditions, but also controls the correction range by adjusting the proportion, avoiding excessive excavation and increased construction costs, thus achieving a balance between structural safety and construction economy.

[0049] As shown in Figure 5, in one embodiment, obtaining the sub-association index between the i-th sub-region and each associated sub-region in S3 based on the bearing capacity sequence of the i-th sub-region and the bearing capacity sequences of each associated sub-region includes:

[0050] S31. Obtain the absolute value of the difference between the bearing capacity sequence of the i-th sub-region and the bearing capacity data at the same depth in the bearing capacity sequence of each associated sub-region and use it as the difference data. Arrange the difference data at different depths in sequence to form a bearing capacity difference sequence.

[0051] S32. Obtain the sub-association index between the i-th sub-region and each associated sub-region based on the bearing capacity difference sequence between the i-th sub-region and each associated sub-region.

[0052] In this embodiment, it should be noted that in S31, the difference in the formation bearing capacity between the i-th sub-region and the associated sub-region at the same depth is quantified by comparing the bearing capacity data of the i-th sub-region and the associated sub-region.

[0053] Specifically, it needs to be based on the bearing capacity sequence constructed in S2—the bearing capacity data of each sub-region is arranged in order from shallow to deep, and each data point corresponds to a specific depth of strata (such as surface fill layer, middle silty clay layer, lower medium sand layer, etc.). For the i-th sub-region (such as the No. 2 pier sub-region of the cross-river bridge) and a certain related sub-region (such as the No. 1 pier sub-region upstream), it is necessary to match them one by one according to the depth position: take the bearing capacity data (such as standard penetration) at a certain depth of the i-th sub-region, subtract it from the bearing capacity data at the same depth of the related sub-region, and then take the absolute value to obtain the difference data, so as to eliminate the influence of positive and negative directions and only retain the degree of difference.

[0054] For example, the standard penetration test (SPT) data at a depth of 5 meters (silty clay layer) in the No. 2 pier area is subtracted from the SPT data at a depth of 5 meters in the No. 1 pier area, and the absolute value is taken as the difference data at that depth. Arranging the difference data at all depth locations in ascending order creates a bearing capacity difference sequence. This sequence fully reflects the bearing capacity differences of the corresponding strata within the intrinsic depth range from the ground surface to the foundation pit in the two sub-regions, providing structured data support for subsequent assessments of the significance of the differences.

[0055] In S32, based on the bearing capacity difference sequence, the bearing capacity correlation between the i-th sub-region and a single associated sub-region is further quantified. The ultimate goal is to calculate the sub-correlation index to provide a basis for determining subsequent typical correlation indicators. Specifically, by setting the order of judgment criteria, statistical difference degree, and calculation of correlation ratio, the abstract bearing capacity difference is transformed into a quantifiable index.

[0056] First, a standard deviation threshold needs to be determined to distinguish between normal fluctuations in formation bearing capacity and significant differences that may affect stability. Next, the number of data points exceeding this threshold in the difference sequence (i.e., the number of correlations) is counted. These data points represent that the bearing capacity difference between the two sub-regions at corresponding depths has exceeded a reasonable range. Finally, by comparing the number of correlations with the total amount of data in the difference sequence, a sub-correlation index is obtained. This index directly reflects the proportion of significant bearing capacity differences between the two sub-regions at different depths in the form of a ratio.

[0057] For example, in the difference sequence between the No. 2 pier area and the No. 1 pier area, the difference at some depths (such as the junction of the silty clay layer and the medium sand layer) exceeds the standard threshold. After counting the number of these depths and dividing by the total number of depths, the sub-correlation index between the two can be obtained. The higher the value of this index, the more the bearing capacity difference between the two sub-regions exceeds the reasonable range at more depths, and the potential risk of mutual influence also increases accordingly.

[0058] As shown in Figure 6, in one embodiment, obtaining the sub-association index between the i-th sub-region and each associated sub-region in S32 based on the bearing capacity difference sequence between the i-th sub-region and each associated sub-region includes:

[0059] S321. Obtain the standard deviation threshold based on the carrying area of ​​the i-th sub-region;

[0060] S322. Obtain the number of difference data exceeding the standard deviation threshold in the bearing capacity difference sequence between the i-th sub-region and each associated sub-region, and record it as the number of associations between the i-th sub-region and each associated sub-region.

[0061] S323. Divide the number of associations between the i-th sub-region and each associated sub-region by the number of difference data in the bearing capacity difference sequence, and obtain the sub-association index between the i-th sub-region and each associated sub-region.

[0062] In this embodiment, it should be noted that in S321, a standard deviation threshold for judging whether the bearing capacity difference is significant is determined. This threshold needs to be determined by combining the bearing area of ​​the i-th sub-region (obtained in S1) and a preset coefficient. It is the core basis for distinguishing between normal stratum fluctuations and differences that need attention.

[0063] Among them, the bearing area reflects the spatial scale of the sub-region. The larger the area, the more complex the distribution of strata stress, and the allowable bearing capacity fluctuation range needs to be adjusted accordingly. The preset coefficient needs to be determined based on the geological category of the engineering site (such as the different geological characteristics of sandy soil areas in water areas and cohesive soil areas on land in cross-river bridges) and regional stability requirements (such as seismic intensity level, annual variation of groundwater level, etc.) through a limited number of preliminary exploration experiments (such as selecting different geological profiles in the site for small-scale comparative tests) to ensure that the threshold can both filter out meaningless small fluctuations and capture significant differences that may affect stability.

[0064] For example, the bearing area of ​​the No. 2 pier area (water area) of a cross-river bridge is calculated by measuring the planar profile in S1. Since the area is composed of alternating layers of sand and clay formed by river alluvium, the geological conditions are relatively complex. Furthermore, the impact of water flow disturbance caused by ship navigation on the stability of the strata needs to be considered. The preset coefficient is selected from the previous experiments as a value adapted to the water environment. The standard deviation threshold obtained by multiplying the two can reasonably define whether the bearing capacity difference between this sub-region and related sub-regions at various depths needs to be included in the correlation analysis.

[0065] In S322, the number of depth locations with significant differences in the bearing capacity difference sequence (i.e., the number of associations) is counted. These depth locations are the basis for subsequent calculation of sub-association indicators, which directly reflect the potential impact range of the bearing capacity difference between the two sub-regions on stability.

[0066] Specifically, the bearing capacity difference sequence obtained in S31 (difference data arranged in depth order) is compared one by one with the standard deviation threshold determined in S321: if the difference data at a certain depth is greater than the threshold, it indicates that the bearing capacity difference at that depth has exceeded the reasonable fluctuation range, and local instability may occur in the current area after the excavation of the foundation pit due to stress transfer in adjacent areas (such as lateral displacement in sandy soil areas and uneven settlement in cohesive soil areas), and the depth location needs to be included in the associated quantity; if the difference data is less than or equal to the threshold, it is considered as normal stratum fluctuation and is not included in the statistics.

[0067] For example, in the bearing capacity difference sequence between the No. 2 pier area and the No. 1 pier area, the surface fill layer has a small difference and does not exceed the threshold because it is all loose deposits; however, the difference in the middle layer of silty clay exceeds the threshold because the No. 1 area is close to the riverbank and has loose interlayers deposited later; the difference in the lower layer of medium sand also exceeds the threshold because the No. 2 area is located in the mainstream area of ​​the river and the soil compaction is higher. By counting the number of these depth locations that exceed the threshold (e.g., 2 depths for the silty clay layer and 3 depths for the medium sand layer), we can obtain the correlation between the two. The more of this number, the more bearing capacity differences there are at more depths in the two sub-regions that require attention.

[0068] In S323, a sub-association index is obtained by comparing the number of correlations (statistical results from S322) with the total data volume of the bearing capacity difference sequence (the total number of depth locations collected in S2). This index quantifies the prevalence of bearing capacity differences between the i-th sub-region and a single correlated sub-region in a proportional form. The total data volume is the total number of depth locations involved in collecting bearing capacity data for the i-th sub-region in S2 (e.g., if 10 depth locations were collected from the surface to the intrinsic depth of the foundation pit, the total data volume is 10). The number of correlations is the number of depth locations that exceed the standard deviation threshold (e.g., 5). The sub-association index obtained by dividing the two (e.g., 5 / 10) intuitively reflects the degree of correlation between the two sub-regions.

[0069] For example, the number of associations between pier area 2 and pier area 1 is 5, with a total data volume of 10, and the sub-association index is 5 / 10; while pier areas 2 and 3 have more similar geological conditions (both are located in the main channel of the river and have little difference in soil compaction), so the number of associations is 2, and the sub-association index is 2 / 10. This index can clearly compare the degree of influence of different associated sub-regions on the i-th sub-region, providing a quantitative basis for the subsequent selection of typical association indicators.

[0070] In one implementation, obtaining the standard deviation threshold based on the carrying area of ​​the i-th sub-region in step S321 includes:

[0071] Obtain the preset coefficient, multiply the carrying area of ​​the i-th sub-region by the preset coefficient, and obtain the standard deviation threshold.

[0072] In this embodiment, it should be noted that the standard deviation threshold is obtained by multiplying the carrying area of ​​the i-th sub-region (obtained in S1) by a preset coefficient. The most important part is determining the preset coefficient.

[0073] Specifically, when determining the value of the preset coefficient, firstly, the basic value of the geological category is determined according to the site geological survey report: different soil types (such as cohesive soil, sandy soil, gravelly soil) or rock strata (such as strongly weathered rock, moderately weathered rock) correspond to different preset coefficients due to differences in particle composition, density, and mechanical properties; for example, cohesive soil has a lower preset coefficient because of its high cohesion between particles and a smaller range of bearing capacity fluctuation; sandy soil has a higher preset coefficient because of its loose particles and susceptibility to disturbance and a larger range of bearing capacity fluctuation; and strongly weathered rock strata have an even lower preset coefficient because of their relatively intact structure.

[0074] Next, the foundation values ​​are modified based on regional stability requirements, mainly considering three factors: First, seismic intensity. According to the seismic fortification intensity of the site as defined by the design code, the higher the intensity (e.g., 7 degrees and above), the stronger the sensitivity of the stratum stability to bearing capacity differences, requiring a certain increase in the foundation preset coefficient. Second, the range of groundwater level changes. In areas with large annual variations in groundwater level (e.g., seasonal river basins or rainy areas), the softening effect of water on the stratum may exacerbate bearing capacity fluctuations, requiring a step-by-step upward adjustment of the foundation values ​​according to the water level variation level (e.g., ±1m, ±2m). Third, the surrounding environmental loads. If the sub-area is adjacent to existing buildings, roads, or heavy pipelines, the additional loads may lead to uneven distribution of stratum stress, requiring an appropriate increase in the preset coefficient according to the load level (e.g., static load, dynamic load).

[0075] Finally, the coefficients were verified and adjusted through a limited number of field comparative experiments: 3 to 5 typical sub-regions within the site (covering the main geological categories and stability conditions) were selected, and different preset coefficients were set using a trial-and-error method to calculate the standard deviation threshold. The correlation between the "proportion of significant difference depths" in the bearing capacity difference sequence under each threshold and the stability monitoring data (such as sidewall displacement and settlement) after actual foundation pit excavation was statistically analyzed. When the "proportion of significant difference depths" corresponding to a certain preset coefficient showed a strong correlation with stability risk (such as the probability of displacement exceeding the specification limit) (such as a correlation coefficient of 0.7 to 0.8), the coefficient was determined as the final value.

[0076] For example, in a sandy area of ​​a cross-river bridge, the foundation's preset coefficient was 0.3. Due to the earthquake intensity of 7 degrees, it was adjusted upward by 0.1, and due to the annual groundwater level fluctuation of ±1.5m, it was adjusted upward by 0.05. Finally, after experimental verification, it was adjusted to 0.42. At this point, the calculated standard deviation threshold can accurately distinguish the differences in bearing capacity that need attention.

[0077] As shown in Figure 5, in one implementation, obtaining the typical association index of the i-th sub-region based on the sub-association index between the i-th sub-region and all associated sub-regions in S3 includes:

[0078] S33. Obtain the maximum value among the sub-association indicators between the i-th sub-region and all associated sub-regions, and use the maximum value as the typical association indicator of the i-th sub-region.

[0079] In this embodiment, it should be noted that in S33, the typical correlation index is obtained by selecting the maximum value from the sub-correlation indexes of the i-th sub-region and all related sub-regions. Its core purpose is to focus on the adjacent sub-regions that have the most prominent impact on the stability of the current region, so that the subsequent deep correction is more targeted.

[0080] Because the geological conditions of different associated sub-regions differ to varying degrees from those of the i-th sub-region (e.g., some associated sub-regions have lower sub-association indices due to similar stratigraphic origins; others have higher sub-association indices due to the presence of faults or loose interlayers), the associated sub-region corresponding to the maximum value represents the most significant difference in bearing capacity characteristics between the two. This difference may originate from the stratigraphic formation process (e.g., the No. 1 pier area is close to the riverbank and formed a loose silt layer due to later deposition; the No. 2 pier area is located in the river center and formed a dense sand layer due to long-term water flow scouring and compaction) or local geological structures (e.g., small-scale joint development leading to differences in rock strata integrity), which may affect the stability of the current area after the foundation pit is excavated through lateral stress transmission (e.g., deformation of the support structure caused by differential settlement).

[0081] For example, the No. 2 pier area (i) of the cross-river bridge has three related sub-areas: the upstream No. 1 pier area (sub-related index a), the downstream No. 3 pier area (sub-related index b), and the south bank abutment A sub-area (sub-related index c). Among them, a has the highest value because there is a loose silt layer in No. 1 area, b has a lower value because the strata in No. 3 and No. 2 areas are both dense sand layers, and c has a medium value because abutment A is a land cohesive soil area, which is different from the sandy soil in the water area. Selecting a as a typical related index means that the subsequent depth correction will mainly respond to the bearing capacity difference in the No. 1 pier area, so that the predicted depth of the foundation pit is more in line with this prominent influencing factor and avoids the key risk points being covered by the averaging treatment.

[0082] As shown in Figure 7, in one embodiment, obtaining the predicted depth of the foundation pit in the i-th sub-region based on the typical correlation index of the i-th sub-region and the intrinsic depth of the foundation pit in the i-th sub-region in step S4 includes:

[0083] S41. Obtain the adjustment ratio. Multiply the product of the intrinsic depth of the foundation pit in the i-th sub-region and the adjustment ratio by the typical correlation index of the i-th sub-region to obtain the depth correction amount of the i-th sub-region.

[0084] S42. Add the intrinsic depth of the foundation pit in the i-th sub-region to the depth correction amount of the i-th sub-region, and obtain the predicted depth of the foundation pit in the i-th sub-region.

[0085] In this embodiment, it should be noted that in S41, the bearing capacity correlation information of adjacent sub-regions is converted into specific depth adjustment values. Specifically, the depth correction amount of the foundation pit is calculated through the synergistic effect of the adjustment ratio and typical correlation indicators. This ensures that the depth adjustment can respond to the significant differences between adjacent regions while avoiding excessive correction that would increase costs. Specifically, when determining the adjustment ratio, the basic adjustment ratio is first set according to the geological category. In terrestrial cohesive soil regions, due to the relatively dense soil structure and more uniform stress transmission between adjacent regions, a lower basic adjustment ratio is used (e.g., 0.05~0.1). In aquatic sandy soil regions, due to low interparticle friction and easy lateral deformation, a higher basic adjustment ratio is used (e.g., 0.1~0.15). In rock regions, due to high bearing capacity and good stability, a very low basic adjustment ratio is used (e.g., 0.03~0.05). Subsequently, the foundation ratio was adjusted based on the regional stability risk level. Firstly, the bearing capacity difference between adjacent sub-regions was determined using typical correlation indicators calculated by S3 (e.g., low level ≤0.3, medium level 0.3~0.6, high level ≥0.6). The higher the level, the higher the adjustment ratio needed to be (e.g., medium level increased by 0.02, high level increased by 0.05). Secondly, the foundation pit support conditions were considered. Sub-regions using simple support (e.g., slope excavation) had weaker resistance to lateral stress, so the adjustment ratio was increased by 0.03~0.05 compared to areas using composite support (e.g., pile foundation + internal bracing). Finally, optimization was achieved through a limited number of cost-benefit experiments. Two to three representative sub-regions were selected within the site, and the depth correction and predicted depth were calculated according to different adjustment ratios. The support costs (e.g., steel consumption, construction period) and stability benefits (e.g., reduced probability of collapse) during the excavation process were simulated, and a cost-benefit curve was plotted. The adjustment ratio at the inflection point of the curve was selected as the optimal value—at this point, the stability benefit and cost increment brought by each additional unit of adjustment ratio were basically balanced.

[0086] For example, in a cohesive soil area of ​​a land bridge abutment, the foundation adjustment ratio is 0.08. Due to the typical correlation index of 0.5 (medium level), it is adjusted upward by 0.02. No additional upward adjustment is made when using pile support. After cost-benefit experiment, it was found that when the adjustment ratio is 0.1, the support cost increases by 10%, but the collapse risk is reduced by 15%. The benefit is slightly higher than the cost. Therefore, the adjustment ratio is determined to be 0.1.

[0087] Furthermore, the depth correction is based on the intrinsic depth of the foundation pit. First, the theoretical maximum correctable range is determined by "intrinsic depth of foundation pit × adjustment ratio" to avoid uncontrolled correction leading to excessive excavation. Then, by multiplying by the typical correlation index (the maximum value obtained in S3, reflecting the proportion of the most prominent bearing capacity difference with adjacent areas), the correction magnitude is directly linked to the degree of difference. The higher the typical correlation index, the more significant the bearing capacity difference between adjacent areas at greater depths, requiring a larger correction to cope with potential stress unevenness. Conversely, if the typical correlation index is low, it indicates that the difference between adjacent areas is small, and the correction amount is correspondingly reduced.

[0088] For example, the intrinsic depth of the foundation pit in the No. 2 pier area (water area) of the cross-river bridge is determined based on its own sand bearing capacity data. The typical correlation index originates from the difference with the No. 1 pier area upstream (the No. 1 area is close to the riverbank and has a loose silt layer accumulated later, resulting in bearing capacity differences at multiple depths exceeding the standard threshold). The adjustment ratio is selected according to the fluidity of the sand in the water area and the seismic requirements of the bridge foundation. The depth correction amount obtained by the three factors can not only respond to the influence of the No. 1 pier area in a targeted manner, but also avoid increasing the difficulty and cost of underwater excavation due to excessive correction.

[0089] In S42, the final predicted depth of the foundation pit is obtained by adding it to the intrinsic depth of the pit. This allows the depth determination process to fully integrate the bearing capacity correlation information of adjacent sub-regions, forming a comprehensive result that takes into account both the individual conditions of a single region and the influence of multiple regions. The intrinsic depth of the foundation pit serves as a base value, ensuring that the strata within a single sub-region can meet the bearing requirements of the bridge foundation (e.g., the intrinsic depth of the No. 2 pier sub-region is calculated to support its vertical load and horizontal thrust), but it does not consider the uneven stress distribution that may occur in adjacent regions. The depth correction focuses on compensating for this deficiency by quantifying the significant differences between adjacent regions, providing an additional safety reserve for the foundation pit depth. The logic of adding the two is that the predicted depth, on the basis of meeting the bearing capacity of a single region, must further adapt to the correlation influence of adjacent regions, so that the strata after the foundation pit excavation can not only bear its own load, but also resist the lateral stress caused by the differences between adjacent regions (e.g., the lateral compression that the loose silt layer in the No. 1 pier region may exert on the No. 2 region).

[0090] For example, in the area of ​​pier No. 2 of the cross-river bridge, the intrinsic depth of the foundation pit has been determined through standard penetration data and load calculations, which meets the stability requirements of a single area. The depth correction obtained from S41 reflects the depth adjustment value that needs to be supplemented due to the difference in bearing capacity at multiple depths such as silty clay layer and medium sand layer between the area and pier No. 1. After adding the two, the predicted depth of the foundation pit is appropriately increased compared to the intrinsic depth. This increase is not a blind deepening, but a precise control based on typical correlation indicators and adjustment ratios: the increased depth allows pier No. 2 to be excavated to a denser stratum (such as a deeper position in the medium sand layer), so as to better resist the unbalanced stress from pier No. 1 and reduce the risk of foundation pit sidewall deformation caused by differences between adjacent areas.

[0091] A foundation pit depth prediction system based on multi-region bearing capacity correlation is also provided. The system includes:

[0092] The area division module is used to obtain the engineering site, divide the engineering site into n sub-regions that can be used independently for foundation pit construction, and obtain the bearing area of ​​each sub-region.

[0093] The data acquisition module is used to acquire bearing capacity data at multiple different depth locations in each sub-region, and to arrange the bearing capacity data of each sub-region at multiple depth locations in depth order to form a bearing capacity sequence.

[0094] The data processing module is used to obtain the sub-regions adjacent to the i-th sub-region and use them as the associated sub-regions of the i-th sub-region, and to obtain the sub-association index between the i-th sub-region and each associated sub-region based on the bearing capacity sequence of the i-th sub-region and the bearing capacity sequence of each associated sub-region, and to obtain the typical association index of the i-th sub-region based on the sub-association index between the i-th sub-region and all associated sub-regions.

[0095] The data prediction module is used to obtain the intrinsic depth of the foundation pit in the i-th sub-region, and to obtain the predicted depth of the foundation pit in the i-th sub-region based on the typical correlation index of the i-th sub-region and the intrinsic depth of the foundation pit in the i-th sub-region, and to carry out construction according to the predicted depth of the foundation pit.

[0096] In one embodiment, the data processing module is further configured to: obtain the absolute value of the difference between the bearing capacity sequence of the i-th sub-region and the bearing capacity data at the same depth in the bearing capacity sequence of each associated sub-region and use it as the difference data, and arrange the difference data at different depths in sequence to form a bearing capacity difference sequence; obtain the sub-association index between the i-th sub-region and each associated sub-region based on the bearing capacity difference sequence between the i-th sub-region and each associated sub-region.

[0097] In one embodiment, the data processing module is further configured to: obtain a standard deviation threshold based on the bearing area of ​​the i-th sub-region; obtain the number of difference data exceeding the standard deviation threshold in the bearing capacity difference sequence between the i-th sub-region and each associated sub-region, and record it as the number of associations between the i-th sub-region and each associated sub-region; divide the number of associations between the i-th sub-region and each associated sub-region by the number of difference data in the bearing capacity difference sequence, and obtain the sub-association index between the i-th sub-region and each associated sub-region.

[0098] In one implementation, the data processing module is further configured to: obtain a preset coefficient, multiply the carrying area of ​​the i-th sub-region by the preset coefficient, and obtain a standard deviation threshold.

[0099] In this embodiment, it should be noted that the specific method of performing the above-mentioned foundation pit depth data prediction system based on multi-region bearing capacity correlation has been described in detail in the embodiments of the foundation pit depth data prediction method based on multi-region bearing capacity correlation, and will not be elaborated here.

[0100] The preferred embodiments of this disclosure have been described in detail above with reference to the accompanying drawings. However, this disclosure is not limited to the specific details of the above embodiments. Within the scope of the technical concept of this disclosure, various simple modifications can be made to the technical solutions of this disclosure, and these simple modifications all fall within the protection scope of this disclosure.

[0101] It should also be noted that the various specific technical features described in the above embodiments can be combined in any suitable manner without contradiction. To avoid unnecessary repetition, this disclosure will not describe the various possible combinations separately.

[0102] Furthermore, various different embodiments of this disclosure can be combined in any way, as long as they do not violate the spirit of this disclosure, they should also be regarded as the content disclosed in this disclosure.

[0103] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered within the scope of the claims and specification of the present invention.

Claims

1. A method for predicting foundation pit depth based on multi-regional bearing capacity correlation data, characterized in that, include: Obtain the engineering site and divide it into n sub-regions that can be used independently for foundation pit construction, and obtain the bearing area of ​​each sub-region; Obtain bearing capacity data at multiple depths in each sub-region, and arrange the bearing capacity data of each sub-region at multiple depths in depth order to form a bearing capacity sequence; obtain the sub-regions adjacent to the i-th sub-region and treat them as associated sub-regions of the i-th sub-region; obtain the absolute value of the difference between the bearing capacity sequence of the i-th sub-region and the bearing capacity sequences of each associated sub-region at the same depth and use it as the difference data, and arrange the difference data at different depths in sequence to form a bearing capacity difference sequence; obtain the bearing capacity difference sequence between the i-th sub-region and each associated sub-region. The sub-association indexes between each associated sub-region are calculated; the maximum value among the sub-association indexes between the i-th sub-region and all associated sub-regions is obtained, and the maximum value is used as the typical association index of the i-th sub-region; the intrinsic depth of the foundation pit in the i-th sub-region is obtained, and the adjustment ratio is obtained; the product of the intrinsic depth of the foundation pit in the i-th sub-region and the adjustment ratio is multiplied by the typical association index of the i-th sub-region to obtain the depth correction amount of the i-th sub-region; the intrinsic depth of the foundation pit in the i-th sub-region and the depth correction amount of the i-th sub-region are added together to obtain the predicted depth of the foundation pit in the i-th sub-region, and construction is carried out according to the predicted depth of the foundation pit.

2. The method for predicting foundation pit depth based on multi-regional bearing capacity correlation according to claim 1, characterized in that, The step of obtaining the sub-association index between the i-th sub-region and each associated sub-region based on the bearing capacity difference sequence between the i-th sub-region and each associated sub-region includes: obtaining a standard deviation threshold based on the bearing area of ​​the i-th sub-region; obtaining the number of difference data exceeding the standard deviation threshold in the bearing capacity difference sequence between the i-th sub-region and each associated sub-region, and recording it as the association number between the i-th sub-region and each associated sub-region; dividing the association number between the i-th sub-region and each associated sub-region by the number of difference data in the bearing capacity difference sequence, and obtaining the sub-association index between the i-th sub-region and each associated sub-region.

3. The method for predicting foundation pit depth based on multi-regional bearing capacity correlation according to claim 2, characterized in that, The step of obtaining the standard deviation threshold based on the carrying area of ​​the i-th sub-region includes: obtaining a preset coefficient, multiplying the carrying area of ​​the i-th sub-region by the preset coefficient, and obtaining the standard deviation threshold.

4. A foundation pit depth prediction system based on multi-region bearing capacity correlation, characterized in that, The system is used to implement the foundation pit depth data prediction method based on multi-region bearing capacity correlation as described in any one of claims 1 to 3. The system includes: a region division module, used to acquire the engineering site and divide the site into n sub-regions that can be used independently for foundation pit construction, and to acquire the bearing area of ​​each sub-region; a data acquisition module, used to acquire bearing capacity data at multiple different depth locations in each sub-region, and to arrange the bearing capacity data of each sub-region at multiple depth locations in depth order to form a bearing capacity sequence; and a data processing module, used to acquire data related to the i-th sub-region. The adjacent sub-regions are used as associated sub-regions of the i-th sub-region. The sub-association index between the i-th sub-region and each associated sub-region is obtained based on the bearing capacity sequence of the i-th sub-region and the bearing capacity sequences of each associated sub-region. The typical association index of the i-th sub-region is obtained based on the sub-association index between the i-th sub-region and all associated sub-regions. The data prediction module is used to obtain the intrinsic depth of the foundation pit in the i-th sub-region. The predicted depth of the foundation pit in the i-th sub-region is obtained based on the typical association index and the intrinsic depth of the foundation pit in the i-th sub-region. Construction is carried out according to the predicted depth of the foundation pit.

5. The foundation pit depth data prediction system based on multi-region bearing capacity correlation according to claim 4, characterized in that, The data processing module is further configured to: obtain a standard deviation threshold based on the bearing area of ​​the i-th sub-region; obtain the number of difference data exceeding the standard deviation threshold in the bearing capacity difference sequence between the i-th sub-region and each associated sub-region, and record it as the number of associations between the i-th sub-region and each associated sub-region; divide the number of associations between the i-th sub-region and each associated sub-region by the number of difference data in the bearing capacity difference sequence, and obtain the sub-association index between the i-th sub-region and each associated sub-region.

6. The foundation pit depth data prediction system based on multi-region bearing capacity correlation according to claim 5, characterized in that, The data processing module is also used to: obtain a preset coefficient, multiply the carrying area of ​​the i-th sub-region by the preset coefficient, and obtain the standard deviation threshold.

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