A method for predicting settlement of soft ground for embankment construction

Abstract

This invention relates to the field of water conservancy engineering data analysis technology, specifically to a method for predicting settlement of soft foundations in dam construction. The method includes: acquiring load stress data and soil moisture data for each detection point; determining the foundation softness at each detection point based on the correlation between the load stress data and soil moisture data, combined with the difference in soil moisture data between the detection point and length detection points at the same time; obtaining the soil permeability coefficient at each detection point at each time based on the distance distribution between each detection point and each width detection point in the corresponding width detection point sequence, combined with the differences in load stress data and soil moisture data at different times for each width detection point; and adjusting the grayscale system method based on the foundation softness and soil permeability coefficient to predict foundation settlement. This invention improves the accuracy of predicting settlement of soft foundations.

Description

Technical Field

[0001] This invention relates to the field of water conservancy engineering data analysis technology, specifically to a method for predicting settlement of soft foundations for dam construction. Background Technology

[0002] As key structures in water conservancy, transportation, and other infrastructure, the stability of dams directly affects flood control safety, water resource allocation, and the economic and social development of surrounding areas. However, when constructing dams in areas with weak foundations, the high natural water content, high compressibility, and low shear strength of the soil make it prone to significant uneven settlement under load, potentially leading to risks such as dam cracking, seepage instability, and even dam failure. Therefore, how to conduct more accurate and intelligent settlement prediction of weak foundations has become a current technical challenge in the engineering field.

[0003] Existing methods for predicting foundation settlement mainly rely on measured settlement data and use the grey system method to predict foundation sedimentation by combining historical settlement data. However, when a soft foundation is subjected to load pressure, the pore water content in the foundation soil changes, water is squeezed out, and the foundation settles and deforms. However, changes in load pressure around dam facilities can interfere with the soil moisture content at the monitoring points, resulting in low long-term monitoring stability of the grey system method and thus low accuracy of settlement prediction results for soft foundations. Summary of the Invention

[0004] To address the issue that changes in load pressure around dam facilities can interfere with soil moisture content at monitoring points when using the grey system method for foundation settlement prediction, leading to low accuracy in settlement prediction results for weak foundations, this invention aims to provide a settlement prediction method for weak foundations in dam construction. The specific technical solution adopted is as follows:

[0005] For each section along the length of the dam, load stress data and soil moisture data are obtained at each detection point at each time along the width of the dam. Each detection point corresponds to a sequence of length detection points and a sequence of width detection points.

[0006] Based on the correlation between the load stress data and soil moisture data at each test point, and combined with the differences in soil moisture data at the same time between each test point and each length test point in the corresponding length test point sequence, the degree of foundation softness at each test point is obtained.

[0007] Based on the distance distribution between each detection point and each width detection point in the corresponding width detection point sequence, combined with the differences in load stress data of each width detection point at different times and the differences in soil moisture data of each detection point at different times, the soil permeability coefficient of each detection point at each time is obtained.

[0008] Based on the aforementioned foundation softness and soil permeability coefficient, the gray-scale system method is adjusted, and the adjusted gray-scale system method is used to predict foundation settlement.

[0009] Preferably, the step of obtaining the foundation softness of each test point based on the correlation between the load stress data and soil moisture data of each test point, combined with the difference in soil moisture data of each test point and each length test point in the corresponding length test point sequence at the same time, specifically includes:

[0010] Based on the correlation between the load stress data and soil moisture data at each detection point at each time, the probability of weak foundation at each detection point is obtained.

[0011] Based on the difference between the soil moisture data of each detection point at each time point and the soil moisture data of each detection point of the corresponding length in the sequence of detection points at the same time point, the soil difference feature value of each detection point is obtained.

[0012] The product between the probability of a weak foundation and the soil difference characteristic value at each test point is normalized to obtain the degree of foundation softness at each test point.

[0013] Preferably, the step of determining the likelihood of a weak foundation at each testing point based on the correlation between the load stress data and soil moisture data at each testing point at each time step specifically includes:

[0014] The load stress data of each detection point at all times is obtained to form the load stress sequence of each detection point, and the soil moisture data of each detection point at all times is obtained to form the soil moisture sequence of each detection point.

[0015] The negative correlation between the Pearson correlation coefficient between the load stress sequence and the soil moisture sequence at each test point is used as the probability of a weak foundation at each test point.

[0016] Preferably, the step of obtaining the soil difference feature value for each detection point based on the difference between the soil moisture data of each detection point at each time point and that of each detection point of the corresponding length in the sequence of detection points at the same time point specifically includes:

[0017] The soil difference characteristic value of each detection point is obtained by summing the absolute values ​​of the differences between each detection point and the soil moisture data of each detection point in the corresponding length detection point sequence at each same time.

[0018] Preferably, the step of obtaining the soil permeability coefficient of each detection point at each time step based on the distance distribution between each detection point and each width detection point in the corresponding width detection point sequence, combined with the differences in load stress data of each width detection point at different times and the differences in soil moisture data of each detection point at different times, specifically includes:

[0019] The detection point and each width detection point in the corresponding width detection point sequence are located on the same side of the central axis of the embankment along the width direction;

[0020] Based on the distance distribution between each detection point and each width detection point in the corresponding width detection point sequence, combined with the degree of difference in load stress data between different times before each width detection point, and the distance between each detection point and the central axis of the dam section where the detection point is located, the soil impact index of each detection point at each time is obtained.

[0021] Based on the differences in soil moisture data and load stress data between each detection point at each time point and other times before that time point, the soil porosity characteristic value of each detection point at each time point is obtained.

[0022] The product between the soil impact index and the soil porosity characteristic value at each monitoring point at each time step is normalized to obtain the soil permeability coefficient at each monitoring point at each time step.

[0023] Preferably, the step of obtaining the soil impact index for each detection point at each moment based on the distance distribution between each detection point and each width detection point in the corresponding width detection point sequence, combined with the degree of difference in load stress data between different moments before each width detection point, and the distance between each detection point and the central axis of the embankment section where the detection point is located, specifically includes:

[0024] Based on the negative correlation coefficient between the straight-line distance between each detection point and each width detection point in the corresponding width detection point sequence, and combined with the difference in load stress data between different times before each width detection point, the degree of influence of the width detection point on the soil moisture at each time in the width detection point sequence of each detection point is obtained.

[0025] The product of the soil water impact level at each monitoring point at each time step and the straight-line distance between each monitoring point and the central axis of the dam section where the monitoring point is located is normalized to obtain the soil impact index at each monitoring point at each time step.

[0026] Preferably, the step of obtaining the degree of soil moisture influence of each width detection point in the width detection point sequence at each moment based on the negative correlation coefficient of the straight-line distance between each detection point and each width detection point in the corresponding width detection point sequence, combined with the difference in load stress data between different moments before each width detection point, specifically includes:

[0027] Take any given moment as the target moment, and record each moment before the target moment as a reference moment;

[0028] The sum of the absolute values ​​of the differences between the load stress data of each width detection point at every two different historical moments is used as the load difference coefficient of each width detection point.

[0029] The load difference coefficient is summed with the ratio between each detection point and the straight-line distance between each width detection point in the corresponding width detection point sequence to obtain the degree of soil water influence at each detection point at the target time.

[0030] Preferably, obtaining the soil pore characteristic value of each detection point at each time step based on the differences in soil moisture data and load stress data between each detection point at each time step and other times before that time step specifically includes:

[0031] The absolute value of the difference between the soil moisture data at the target time and the soil moisture data at each reference time is calculated to obtain the first data difference of each detection point at each reference time; the absolute value of the difference between the load stress data at the target time and the load stress data at each reference time is calculated to obtain the second data difference of each detection point at each reference time.

[0032] Based on the first and second data differences of each detection point at each reference time, the soil porosity characteristic value of each detection point at the target time is obtained. The first data difference is positively correlated with the soil porosity characteristic value, and the second data difference is negatively correlated with the soil porosity characteristic value.

[0033] Preferably, the adjustment of the grayscale system method by combining the foundation softness and soil permeability coefficient specifically includes:

[0034] The normalized result of the product between the soil permeability coefficient and the foundation softness at each detection point at each time step is used as an adjustment coefficient, and the time response function in the gray system method is adjusted using the adjustment coefficient.

[0035] Preferably, adjusting the time response function in the grayscale system method using the adjustment coefficient specifically includes:

[0036] The product of the adjustment coefficient and the time response function in the grayscale system method is used as the adjusted time response function.

[0037] The embodiments of the present invention have at least the following beneficial effects:

[0038] This invention first establishes detection points at different locations along both the width and length directions, providing a data foundation for subsequent comprehensive analysis of the lateral and longitudinal variation characteristics of each detection point. Then, it analyzes the correlation between soil moisture changes and load stress changes at each detection point. Combined with the soil moisture differences between each detection point and other detection points along the length of the embankment, it comprehensively analyzes the foundation weakness at each detection point. By analyzing the data variation characteristics of load stress and soil moisture at each detection point to determine if they conform to the characteristics of a weak foundation, the probability of each detection point belonging to a weak foundation is characterized. Furthermore, by analyzing the distance distribution between each detection point and other detection points along the width of the embankment, as well as the differences in load stress and soil moisture, it analyzes the influence of soil moisture flow from the surrounding area on the detection points, quantifying the soil moisture infiltration at each detection point to obtain the soil permeability coefficient. Finally, by combining the foundation softness and soil permeability coefficient, the grey system method is adjusted, which corrects the prediction results at detection points significantly affected by weak foundations, thereby improving the accuracy of settlement prediction for weak foundations. Attached Figure Description

[0039] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0040] Figure 1 This is a flowchart of the steps of a method for predicting settlement of weak foundations for dam construction provided by the present invention;

[0041] Figure 2 This is a schematic diagram of the layout of detection points in a cross section provided by the present invention;

[0042] Figure 3 This is a flowchart of the steps in the method for obtaining the degree of soil softness provided by the present invention;

[0043] Figure 4 This is a schematic diagram of the direction of soil moisture transport provided by the present invention;

[0044] Figure 5 This is a flowchart of the steps involved in obtaining the soil permeability coefficient provided by the present invention. Detailed Implementation

[0045] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a method for predicting settlement of weak foundations for dam construction based on the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0046] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0047] The specific implementation scenario addressed by this invention is as follows: During dam construction, to avoid the risk of dam deformation, leakage, or even dam failure due to uneven foundation settlement, it is necessary to predict foundation deposition during dam construction. Furthermore, for soft foundations, due to the high soil moisture content, the moisture content changes at various monitoring points are easily affected by changes in the pore water content of the surrounding soil. Therefore, it is necessary to analyze the changes in soil moisture content of the soft foundation to determine the impact of moisture content changes at surrounding monitoring points on the moisture content at those monitoring points, adjust the parameters of the grey prediction model, and thus achieve accurate prediction of settlement on soft foundations, resulting in better temporal stability and more accurate prediction results.

[0048] The following describes in detail, with reference to the accompanying drawings, a specific scheme for predicting settlement of soft foundations for dam construction provided by the present invention.

[0049] Please see Figure 1 The diagram illustrates a flowchart of a method for predicting settlement of weak foundations for dam construction, provided by an embodiment of the present invention. The method includes the following steps:

[0050] Step S100: Under each cross section along the length of the dam, acquire the load stress data and soil moisture data at each detection point at each moment along the width of the dam. Each detection point corresponds to a sequence of length detection points and a sequence of width detection points.

[0051] Because the dam has a large volume and the foundation of a soft soil has poor overall stability, the bearing capacity of the foundation varies greatly at different locations. Furthermore, in actual implementation scenarios, the dam is usually a trapezoidal structure, and the load stress generated by the dam on the foundation is different at different locations under the same cross section. Therefore, multiple data sampling points are evenly set at multiple locations on the foundation to perform characteristic analysis on the settlement of the soft soil foundation.

[0052] Specifically, along the length of the dam, starting from one side, each cross-section is uniformly acquired at equal intervals. Under each cross-section, different detection points are uniformly set along the width of the dam at equal intervals, with all detection points located on the same horizontal plane of the foundation. In this embodiment, the interval between two adjacent test cross-sections can be set to a range of 20 to 30 meters. Within the same cross-section, the interval between two adjacent detection points is set to 5 meters. The implementer can adjust this setting according to the specific implementation scenario, but it is necessary to ensure that the distance settings are the same during the prediction process in the same stage. Figure 2 The diagram shows the layout of the detection points in a cross-section, where circles represent detection points.

[0053] At each monitoring point, load stress and soil pore water content were collected. Data was collected once a day at the same designated time each day, and the collected data was transmitted in real-time to the data monitoring center for storage via signal lines. The load stress and soil pore water content collected at each monitoring point at each time were standardized to obtain load stress data and soil moisture data, respectively.

[0054] It should be noted that the dataset used to predict the settlement of soft foundations during dam construction can be set by the implementer according to the specific implementation scenario, such as a monitoring period of 15 days.

[0055] More specifically, the methods for collecting load stress and soil pore water content are well-known techniques and will only be briefly introduced here. Soft ground refers to foundations mainly composed of silt, silty soil, fill, miscellaneous fill, or other highly compressible soil layers. Such foundations have excessively high natural water content and low bearing capacity. To avoid disturbing the soil during sampling and causing distortion of test results, this experiment uses an in-situ spiral plate load test to detect load stress. The spiral plate load test is suitable for soft soil, general cohesive soil, silt, and sandy soil. This method involves spiraling a bearing plate to a predetermined depth below the ground surface and applying stress through a force transmission rod, measuring the settlement of the bearing plate, and then analyzing the stress condition of the foundation. Soil pore water content is obtained through the resistance method. A TDR probe is buried in a closed space, and the measured value is converted into soil water content using a measuring instrument.

[0056] like Figure 2 As shown, the dashed line represents the central axis of the current cross-section, meaning the cross-section exhibits a symmetrical trapezoidal distribution. For each cross-section, there are six detection points along the width of the embankment. Each detection point is numbered sequentially from left to right. The detection points with the same number in other cross-sections form a length detection point sequence for each detection point. For example, for detection point number 1 in the current cross-section, all detection points with the same number 1 in other cross-sections form the length detection point sequence for detection point number 1 in the current cross-section. Within the same cross-section, all other detection points located on the same side of the cross-section as each detection point form the width detection point sequence for each detection point. For example... Figure 2 In the middle, the left side of the dashed line is the same side, and the right side of the dashed line is also the same side.

[0057] It should be understood that, assuming any detection point is designated as the target detection point, each length detection point in the target detection point sequence belongs to a different cross-section from its corresponding target detection point, and the length detection point sequence does not contain the target detection point. Similarly, each width detection point in the target detection point sequence belongs to the same cross-section as its corresponding width detection point and is located on the same side of the cross-section.

[0058] Step S200: Based on the correlation between the load stress data and soil moisture data of each detection point, and combined with the difference in soil moisture data of each detection point and each length detection point in the corresponding length detection point sequence at the same time, the degree of foundation softness of each detection point is obtained.

[0059] During the construction of a dam, as the construction height gradually increases, the width of the dam gradually decreases, resulting in a trapezoidal cross-section. This causes the load stress at adjacent testing points on the same cross-section to vary at each stage, but the load along the same longitudinal line of the dam remains the same with each increase in load during construction. When the degree of load stress variation is the same, the more drastic the change in soil pore water content (i.e., the greater the decrease in soil pore water content), the more significant the soil deformation and the greater the degree of foundation settlement at that testing point, indicating a greater degree of foundation weakness.

[0060] Based on this characteristic, the foundation weakness at each testing point is assessed from two aspects. First, the relationship between changes in load stress and soil moisture content at each testing point is analyzed to determine if it conforms to the changing trends of a weak foundation. Second, the degree of data variation among testing points on the same longitudinal straight line in other cross-sections is analyzed to determine if it matches the characteristics of a weak foundation.

[0061] As a concrete example, such as Figure 3 As shown, the method for obtaining the degree of soil softness at each test point can be implemented by steps S201 to S203.

[0062] Step S201: Based on the correlation between the load stress data and soil moisture data of each detection point at each time, the probability of weak foundation at each detection point is obtained.

[0063] The self-weight of the dam exerts a downward load stress on the foundation. As the dam is constructed, this load stress continuously increases. In soft soil foundations, the soil has large pores and a high pore water content. With the increasing downward load stress, the foundation soil is compressed, gradually expelling the pore water. This reduces the soil's porosity, causing it to gradually compact and undergo compression deformation, i.e., foundation settlement. As the load stress further increases, the soil is further compressed, and the pore water content gradually decreases. In other words, there is a negative correlation between the load stress on a soft foundation and the pore water content; the greater the load stress on the soil, the greater the compression and the lower the pore water content.

[0064] Therefore, by analyzing the correlation between load stress data and soil moisture data at each test point in each section of the dam, the greater the negative correlation between the two, the greater the likelihood that the test point is located on a weak foundation.

[0065] Specifically, load stress data at all times for each testing point are used to form the load stress sequence for each testing point, and soil moisture data at all times for each testing point are used to form the soil moisture sequence for each testing point. The negative correlation result of the Pearson correlation coefficient between the load stress sequence and the soil moisture sequence for each testing point is used as the probability of weak foundation for each testing point.

[0066] For ease of description, this embodiment uses any one detection point as an example. The Pearson correlation coefficient ranges from -1 to 1. When the Pearson correlation coefficient corresponding to the target detection point is less than 0, it indicates that the relationship between the load stress sequence and the soil moisture sequence is negatively correlated. When the Pearson correlation coefficient corresponding to the target detection point is greater than 0, it indicates that the relationship between the load stress sequence and the soil moisture sequence is positively correlated. The closer the value is to 0, the weaker the correlation. Therefore, the closer the Pearson correlation coefficient corresponding to the target detection point is to -1, the stronger the negative correlation between the load stress at the location of the target detection point and the water content in the soil pores, and the greater the possibility that the foundation at the location of the target detection point is weak.

[0067] As a specific example, when the Pearson correlation coefficient between the load stress sequence and the soil moisture sequence of the target detection point is less than 0, the absolute value of the Pearson correlation coefficient between the load stress sequence and the soil moisture sequence of the target detection point is taken as the probability of the target detection point having a weak foundation.

[0068] When the Pearson correlation coefficient between the load stress sequence and the soil moisture sequence at the target detection point is greater than or equal to 0, a preset value is used as the probability of the target detection point having a weak foundation. The preset value is a very small positive number, such as 0.01, indicating that under this condition, the probability that the relationship between the load stress and the water content in the soil pores at the target detection point conforms to the characteristics of a weak foundation is extremely small.

[0069] Step S202: Based on the difference between the soil moisture data of each detection point at each time point and the soil moisture data of each detection point of the corresponding length in the sequence of detection points at the same time point, obtain the soil difference feature value of each detection point.

[0070] Specifically, the sum of the absolute values ​​of the differences between each detection point and the soil moisture data of each length detection point in the corresponding length detection point sequence at each same time is calculated to obtain the soil difference characteristic value of each detection point.

[0071] In the sequence of length detection points corresponding to detection point z, each length detection point represents a monitoring location belonging to the same load change as detection point z. Under the same load change, the more drastic the change in the pore water content of the foundation soil at the longitudinal position of detection point z, that is, the greater the difference in soil moisture data between detection point z and each corresponding length detection point at the same time.

[0072] As a concrete example, the method for calculating soil differential characteristic values ​​can be expressed as follows: ,in Y represents the soil difference characteristic value at detection point z, Y represents the number of length detection points in the length detection point sequence, and N represents the number of time points. This represents the soil moisture data at detection point z at time i. This represents the soil moisture data at the y-th length detection point corresponding to detection point z. The larger the value, the more pronounced the deformation of the soil structure at the location of test point z under load stress, indicating that the foundation at test point z is more prone to deformation. The soil difference characteristic value characterizes the degree to which the difference in soil moisture data between each test point and test points of the same length under the same load variation conforms to the characteristics of a weak foundation.

[0073] Step S203: Normalize the product between the probability of a weak foundation and the soil difference characteristic value at each test point to obtain the degree of foundation softness at each test point.

[0074] The normalization method is a well-known technique and will not be discussed in detail here. For example, the minimization normalization method can be used. The correlation between the load stress data and soil moisture data at each test point location, combined with the degree to which the same test point conforms to the characteristics of foundation weakness, comprehensively characterizes the degree of weakness at each test point location.

[0075] Step S300: Based on the distance distribution between each detection point and each width detection point in the corresponding width detection point sequence, combined with the differences in load stress data of each width detection point at different times and the differences in soil moisture data of each detection point at different times, the soil permeability coefficient of each detection point at each time is obtained.

[0076] The construction of dams involves layer by layer, with the width gradually decreasing upwards, resulting in a trapezoidal cross-section. At the center line of each dam layer, the downward load stress is greater, decreasing towards the edges. During construction, the downward load at the center is greater than that at the edges. This downward stress at the center of the dam cross-section causes water in the pores of the weak foundation soil to flow towards the edges. Figure 4The study shows the direction of soil moisture transport. As the moisture flows, it disturbs the water content in the pores of the foundation soil at the edge, making the decrease in water content in the pores of the foundation soil at the edge less obvious or even showing an increase in water content.

[0077] When the load changes at the same rate, considering the stress changes at other locations on the same cross section around the test point, the change in load stress at adjacent locations will cause a change in the water content in the pores of the foundation soil at the test location. The greater the change in load stress in the direction of the dam center on the same cross section, and the closer the test point is to the center, the greater the impact on the water content in the pores of the foundation soil at the test point.

[0078] Based on this characteristic, the first aspect is to analyze the degree of influence of soil moisture changes at each detection point on the same side of the same section by comparing the soil moisture changes at each detection point with those at other detection points on the same side. The second aspect is to analyze the changes in soil pore characteristics at each location by comparing the changes in soil moisture data and load stress data at each detection point at different times. The combined analysis results of the two aspects are used to measure the infiltration of soil pore water content in the soft foundation at the location of each detection point.

[0079] As a concrete example, such as Figure 5 As shown, the method for obtaining the soil permeability coefficient at each detection point at each time step can be implemented by steps S301 to S303.

[0080] Step S301: Based on the distance distribution between each detection point and each width detection point in the corresponding width detection point sequence, combined with the degree of difference in load stress data between different times before each width detection point, and the distance between each detection point and the central axis of the dam section where the detection point is located, the soil impact index of each detection point at each time is obtained.

[0081] It should be understood that the detection points and the corresponding width detection point sequence are located on the same side of the central axis along the width direction of the embankment. Considering that the embankment cross-section is trapezoidal and exhibits a symmetrical structure along both sides of the central axis, the downward load stress generated during the embankment construction also exhibits a symmetrical characteristic along the central axis. When the soft foundation is subjected to downward load stress, the squeezing and movement of soil pore water also moves from the center to both sides. Therefore, the detection points of the foundation on the same side of the embankment cross-section along the central axis are selected for characteristic analysis.

[0082] Specifically, based on the negative correlation coefficient between the straight-line distance between each detection point and each width detection point in the corresponding width detection point sequence, and combined with the difference in load stress data between different times before each width detection point, the degree of soil water influence of each width detection point in the width detection point sequence at each time is obtained. The product of the soil water influence degree corresponding to each detection point at each time and the straight-line distance between each detection point and the central axis of the embankment section where the detection point is located is normalized to obtain the soil impact index for each detection point at each time.

[0083] More specifically, any given moment is taken as the target moment, and each moment before the target moment is recorded as a reference moment; the sum of the absolute values ​​of the differences between the load stress data of each width detection point at every two different historical moments is taken as the load difference coefficient of each width detection point; the load difference coefficient is accumulated with the ratio between each detection point and the straight-line distance between each width detection point in the corresponding width detection point sequence to obtain the degree of soil water influence of each detection point at the target moment.

[0084] As a concrete example, if we take time t as the target time, then every time before time t is a reference time. Taking detection point z as an example, the influence of detection point z on soil moisture at the target time can be expressed as:

[0085]

[0086] in, This indicates the degree of influence of the soil moisture at monitoring point z at the target time, where t represents the t-th time. This represents the straight-line distance between the detection point z and the centerline of the dam section where the detection point z is located. This indicates the number of width detection points in the width detection point sequence corresponding to detection point z. This represents the straight-line distance between detection point z and the corresponding x-th width detection point. This represents the load stress data of the x-th width detection point corresponding to detection point z at time t. This represents the load stress data of the x-th width detection point corresponding to detection point z at the v-th reference time. This is the normalization function.

[0087] This indicates the load stress change at the x-th width detection point at different times. The larger the value, the faster the dam construction speed and the greater the slope of the dam. The greater the load stress change transmitted to the foundation at that width detection point, the greater the compression and aggravation of the soft foundation at that width detection point, and the more the pore water of the foundation soil at that width detection point flows to the surrounding area, thus having a greater impact on the surrounding moisture content.

[0088] This indicates the degree of influence of the distance between the detection point z and the x-th width detection point. The closer the detection point z is to the x-th width detection point, the larger this value, indicating that the location of the detection point z is more influenced by the x-th width detection point, corresponding to the degree of influence on soil moisture. The larger the value, the better.

[0089] Furthermore, considering the influence of soil moisture and the location distribution of monitoring point z, the closer monitoring point z is to the edge of the embankment, the greater the distance between monitoring point z and the central axis. The larger the value of z, the more water flows from the soil pores of the weak foundation of the dam to the edge, and the greater the impact on the water content at the location of the detection point z.

[0090] Based on this, the degree of influence of soil moisture represents the extent to which the moisture content at each monitoring point is affected by the movement of soil moisture at other monitoring points on the same side of the same cross section.

[0091] Step S302: Based on the differences in soil moisture data and load stress data between each detection point at each time and other times before that time, obtain the soil pore characteristic value of each detection point at each time.

[0092] Secondly, considering the compression of the weak foundation by the surrounding load stress at each testing point, the water content in the soil pores at the location of testing point z will be affected. When the downward load stress at the location of the testing point changes little, but the water content at the location of the testing point still changes, and the greater the change in water content, the greater the impact of the load pressure change of the surrounding embankment construction on the point, and the greater the permeability coefficient of the water content in the soil pores of the weak foundation at the location of the testing point.

[0093] Specifically, the method for obtaining soil porosity characteristic values ​​is as follows: The absolute value of the difference between the soil moisture data at the target time and the soil moisture data at each reference time for each detection point is calculated to obtain the first data difference for each detection point at each reference time; the absolute value of the difference between the load stress data at the target time and the load stress data at each reference time for each detection point is calculated to obtain the second data difference for each detection point at each reference time; based on the first and second data differences for each detection point at each reference time, the soil porosity characteristic value for each detection point at the target time is obtained, wherein the first data difference is positively correlated with the soil porosity characteristic value, and the second data difference is negatively correlated with the soil porosity characteristic value.

[0094] As a concrete example, taking the data of detection point z at time t as an example, where time t is the target time, the calculation process of the soil pore characteristic value of detection point z at time t can be expressed as follows:

[0095]

[0096] in, This represents the soil porosity characteristic value of the detection point z at time t. This represents the soil moisture data at monitoring point z at time t. This represents the soil moisture data at the v-th reference time before the t-th time point z. This represents the load stress data at detection point z at time t. This represents the stress data at the v-th reference time before the t-th time at the detection point z.

[0097] It should be noted that, For the first data difference, The second data difference is represented by the addition of 1 to the denominator in the above formula to prevent the value of the second data difference from being 0, which would affect the calculation result.

[0098] The first data difference reflects the change in soil pore water content at test point z at different times, while the second data difference reflects the change in load stress at test point z at different times. A larger value for the first data difference indicates more significant water migration in the soil pores at that test point. Conversely, a smaller value for the second data difference indicates that when the load pressure at test point z changes less, the change in soil pore water at the same test point is greater, thus indicating a greater influence of surrounding loads on the foundation at test point z. In this case, the soil pore characteristic value at the test point will be larger.

[0099] Thus, the soil porosity characteristic value of each detection point at each time moment represents the degree of influence of the surrounding load on the location of the corresponding detection point.

[0100] Step S303: Normalize the product between the soil impact index and the soil porosity characteristic value at each detection point at each time step to obtain the soil permeability coefficient at each detection point at each time step.

[0101] Firstly, the soil impact index characterizes the degree to which each monitoring point is affected by soil moisture changes at the same monitoring point on the same side at each time. Secondly, the soil porosity characteristic value characterizes the degree of soil porosity characteristics of the soft foundation at the location of each monitoring point at each time. Combining the results of these two aspects, the permeability coefficient of soil pore water content in the soft foundation at the location of each monitoring point is measured. The soil permeability coefficient characterizes the water infiltration of the foundation soil at the location of each monitoring point at each time.

[0102] The higher the soil pore water content corresponding to the soil influence index at the testing point, the more the testing point is affected by the soil moisture changes at the same side testing point. At the same time, the higher the value of the soil pore characteristic value, the greater the influence of the surrounding load on the moisture content changes at the testing point. The higher the value of the soil pore water permeability coefficient of the foundation at the location of testing point z.

[0103] It should be noted that the normalization method is a well-known technique and will not be discussed further here.

[0104] Step S400: Based on the degree of foundation softness and soil permeability coefficient, the grayscale system method is adjusted, and the adjusted grayscale system method is used to predict foundation settlement.

[0105] The soil of soft foundations is mostly composed of silt, silty soil, fill, and miscellaneous fill. The porosity of this soil differs significantly from that of hard rock foundations. A higher permeability coefficient at the location of the testing point indicates greater soil porosity. During dam construction, the soil at this location is more susceptible to higher surrounding loads, interfering with settlement predictions. Therefore, based on the degree of foundation weakness at testing point z, and further considering the impact represented by the soil permeability coefficient, an adjustment coefficient is obtained, representing the degree of adjustment required for each testing point.

[0106] Specifically, the normalized product of the soil permeability coefficient and the foundation softness at each monitoring point at each time step is used as the adjustment coefficient for each monitoring point at each time step. This adjustment coefficient is then used to adjust the time response function in the gray-scale system method. The product of the adjustment coefficient and the time response function in the gray-scale system method is then used as the adjusted time response function, which is used to construct a prediction model for predicting the foundation settlement of the system.

[0107] The larger the soil permeability coefficient and the larger the soil looseness value at the location of the detection point z at time t, the greater the influence of the surrounding environment on the detection point z at time t, and the more serious the soil looseness at the location of the detection point z. At this time, the greater the degree of adjustment required for the detection point z at time t, that is, the larger the value of the adjustment coefficient.

[0108] Because the influence of the surrounding environment on the foundation at the test point is easily overlooked when using the grey system method to predict the settlement of soft foundations, the value of the time response function in the grey system method is adjusted by adjusting the coefficients to make the prediction results of foundation settlement more accurate.

[0109] It should be noted that the grey system method for predicting settlement of soft foundations is a well-known technique, and will only be briefly introduced here. The specific operation of using the grey system method for foundation settlement prediction is as follows:

[0110] (1) The original settlement data is accumulated once to generate a settlement increment time series, eliminating random fluctuations.

[0111] (2) Based on the settlement increment time series, a GM(1,1) model for settlement prediction is established. This step can be referred to in the literature "Settlement Analysis and Prediction of Soft Foundation in Binhai District, Tianjin Based on Grey System" by Pang Shaowei.

[0112] (3) After establishing the GM(1,1) model, solve for the time response function of the detection point z. .

[0113] (4) The product of the adjustment coefficient and the time response function is used as the adjusted time response function to realize the adjustment operation of the time response function, thereby eliminating the interference of the movement of soil pore water caused by load compression on the detection point, and thus the prediction error caused by the change of moisture content at the detection point.

[0114] (5) The settlement of each detection point is predicted using the adjusted time response function.

[0115] In summary, existing methods for predicting foundation settlement rely on historical settlement data at monitoring points, employing a grey system method. However, for weak foundations in dam construction, where dams are typically trapezoidal, different locations on the foundation experience varying load stresses. These stresses exert different degrees of pressure on the pore water in the soil, and the varying pore water content under these stresses leads to different stages of settlement. Furthermore, the foundation is susceptible to the influence of surrounding soil moisture flow, resulting in nonlinear settlement variations. Consequently, the grey system method exhibits poor temporal stability and significant prediction errors in this scenario. Therefore, this invention analyzes the impact of surrounding soil moisture flow on monitoring points based on changes in load stress and soil pore water content. By incorporating an assessment of the soil weakness at the monitoring point location, the time function of the grey system method is modified to improve the accuracy of settlement prediction for weak foundations.

[0116] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application 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 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 this application, and should all be included within the protection scope of this application.

Claims

1. A method for predicting settlement of soft foundations for dam construction, characterized in that, The method includes the following steps: For each section along the length of the dam, load stress data and soil moisture data are obtained at each detection point at each time along the width of the dam. Each detection point corresponds to a sequence of length detection points and a sequence of width detection points. Based on the correlation between the load stress data and soil moisture data at each test point, and combined with the differences in soil moisture data at the same time between each test point and each length test point in the corresponding length test point sequence, the degree of foundation softness at each test point is obtained. Based on the distance distribution between each detection point and each width detection point in the corresponding width detection point sequence, combined with the differences in load stress data of each width detection point at different times and the differences in soil moisture data of each detection point at different times, the soil permeability coefficient of each detection point at each time is obtained. Based on the aforementioned foundation softness and soil permeability coefficient, the gray-scale system method is adjusted, and the adjusted gray-scale system method is used to predict foundation settlement, including: The normalized result of the product between the soil permeability coefficient and the foundation softness at each monitoring point at each time point is used as the adjustment coefficient; Adjusting the time response function in the grayscale system method GM(1,1) model using the adjustment coefficient includes: multiplying the adjustment coefficient by the time response function in the grayscale system method GM(1,1) model as the adjusted time response function; The settlement at each detection point is predicted using the adjusted time response function.

2. The method for predicting settlement of soft foundations for dam construction according to claim 1, characterized in that, The method involves determining the soil softness at each testing point based on the correlation between load stress data and soil moisture data at each testing point, combined with the differences in soil moisture data at the same time between each testing point and each length testing point in the corresponding length testing point sequence. Specifically, this includes: Based on the correlation between the load stress data and soil moisture data at each detection point at each time, the probability of weak foundation at each detection point is obtained. Based on the difference between the soil moisture data of each detection point at each time point and the soil moisture data of each detection point of the corresponding length in the sequence of detection points at the same time point, the soil difference feature value of each detection point is obtained. The product between the probability of a weak foundation and the soil difference characteristic value at each test point is normalized to obtain the degree of foundation softness at each test point.

3. The method for predicting settlement of soft foundations for dam construction according to claim 2, characterized in that, The possibility of weak foundation at each testing point is determined by the correlation between the load stress data and soil moisture data at each testing point at each time, specifically including: The load stress data of each detection point at all times is obtained to form the load stress sequence of each detection point, and the soil moisture data of each detection point at all times is obtained to form the soil moisture sequence of each detection point. The negative correlation between the Pearson correlation coefficient between the load stress sequence and the soil moisture sequence at each test point is used as the probability of a weak foundation at each test point.

4. The method for predicting settlement of soft foundations for dam construction according to claim 2, characterized in that, The process of obtaining soil difference feature values ​​for each detection point based on the difference between the soil moisture data of each detection point at each time point and the soil moisture data of each detection point of the corresponding length in the sequence of detection points at the same time point specifically includes: The soil difference characteristic value of each detection point is obtained by summing the absolute values ​​of the differences between each detection point and the soil moisture data of each detection point in the corresponding length detection point sequence at each same time.

5. The method for predicting settlement of soft foundations for dam construction according to claim 1, characterized in that, The soil permeability coefficient of each detection point at each time moment is obtained based on the distance distribution between each detection point and each width detection point in the corresponding width detection point sequence, combined with the differences in load stress data of each width detection point at different times and the differences in soil moisture data of each detection point at different times. Specifically, this includes: The detection point and each width detection point in the corresponding width detection point sequence are located on the same side of the central axis of the embankment along the width direction; Based on the distance distribution between each detection point and each width detection point in the corresponding width detection point sequence, combined with the degree of difference in load stress data between different times before each width detection point, and the distance between each detection point and the central axis of the dam section where the detection point is located, the soil impact index of each detection point at each time is obtained. Based on the differences in soil moisture data and load stress data between each detection point at each time point and other times before that time point, the soil porosity characteristic value of each detection point at each time point is obtained. The product between the soil impact index and the soil porosity characteristic value at each monitoring point at each time step is normalized to obtain the soil permeability coefficient at each monitoring point at each time step.

6. A method for predicting settlement of soft foundations for dam construction according to claim 5, characterized in that, The method involves determining the soil impact index at each detection point at each time step based on the distance distribution between each detection point and each width detection point in the corresponding width detection point sequence, combined with the degree of difference in load stress data between different times before each width detection point, and the distance between each detection point and the central axis of the embankment section where the detection point is located. Specifically, this includes: Based on the negative correlation coefficient between the straight-line distance between each detection point and each width detection point in the corresponding width detection point sequence, and combined with the difference in load stress data between different times before each width detection point, the degree of influence of the width detection point on the soil moisture at each time in the width detection point sequence of each detection point is obtained. The product of the soil water impact level at each monitoring point at each time step and the straight-line distance between each monitoring point and the central axis of the dam section where the monitoring point is located is normalized to obtain the soil impact index at each monitoring point at each time step.

7. A method for predicting settlement of soft foundations for dam construction according to claim 6, characterized in that, The method involves using the negative correlation coefficient between the straight-line distance between each detection point and each width detection point in the corresponding width detection point sequence, combined with the difference in load stress data between different times before each width detection point, to obtain the degree of soil moisture influence of each width detection point in the width detection point sequence at each time step. Specifically, this includes: Take any given moment as the target moment, and record each moment before the target moment as a reference moment; The sum of the absolute values ​​of the differences between the load stress data of each width detection point at every two different historical moments is used as the load difference coefficient of each width detection point. The load difference coefficient is summed with the ratio between each detection point and the straight-line distance between each width detection point in the corresponding width detection point sequence to obtain the degree of soil water influence at each detection point at the target time.

8. A method for predicting settlement of soft foundations for dam construction according to claim 7, characterized in that, The method involves obtaining the soil porosity characteristic value of each detection point at each time step based on the differences in soil moisture data and load stress data between each detection point at each time step and other times before that time step, specifically including: The absolute value of the difference between the soil moisture data at the target time and the soil moisture data at each reference time is calculated to obtain the first data difference of each detection point at each reference time; the absolute value of the difference between the load stress data at the target time and the load stress data at each reference time is calculated to obtain the second data difference of each detection point at each reference time. Based on the first and second data differences of each detection point at each reference time, the soil porosity characteristic value of each detection point at the target time is obtained. The first data difference is positively correlated with the soil porosity characteristic value, and the second data difference is negatively correlated with the soil porosity characteristic value.

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