A pile foundation bearing capacity intelligent monitoring and evaluation method for static load test

Abstract

The application discloses a pile foundation bearing capacity intelligent monitoring and evaluation method for static load test, which comprises the following steps: real-time receiving load-displacement data stream generated from the static load test; based on the real-time received load-displacement data stream, calculating the settlement rate under the current loading level and the load-displacement curve slope change rate at the current time, and calculating the stability index; according to the stability index, determining the stability state level, and generating corresponding early warning information according to the stability state level; after the static load test is completed, obtaining the complete load-displacement data curve, and combining the pile foundation design parameters and the geological survey data to calculate the multi-dimensional quality index; based on the complete load-displacement data curve, determining the measured value of the single pile vertical ultimate bearing capacity, and according to the multi-dimensional quality index, generating a confidence report for evaluating the confidence of the ultimate bearing capacity result. Through real-time intelligent monitoring and data quality quantitative evaluation, the test safety and the result reliability are effectively improved.

Description

Technical Field

[0001] This application relates to the field of pile foundation engineering testing technology, and in particular to an intelligent monitoring and evaluation method for pile foundation bearing capacity used in static load tests. Background Technology

[0002] The static load test of pile foundation refers to the field test method of applying vertical loads to the top of the pile step by step, observing the settlement of the pile top over time, and determining the vertical compressive bearing capacity of a single pile by analyzing the load-settlement (Qs) relationship. It is also the most direct and reliable method for determining the vertical compressive bearing capacity of a single pile at present, and is widely used in pile foundation testing of various building projects.

[0003] Traditional static load testing methods rely on manual observation of load-displacement curves, making it difficult to determine the working state of the pile-soil system in real time and quantitatively. This is because pile foundation failure is sudden and often only becomes apparent after failure has occurred, posing safety hazards. Furthermore, traditional static load tests typically lack quantitative assessment of the standardization of the testing process and the quality of the data, making it impossible to determine the reliability of the test results. Summary of the Invention

[0004] The purpose of this application is to provide an intelligent monitoring and evaluation method for pile foundation bearing capacity in static load tests, which can realize real-time intelligent monitoring and data quality evaluation to improve the safety and reliability of static load tests.

[0005] Firstly, this application provides an intelligent monitoring and evaluation method for the bearing capacity of pile foundations used in static load tests, employing the following technical solution: Acquire pile foundation design parameters and geological survey data, and receive load-displacement data streams generated from static load tests in real time; Based on the real-time received load-displacement data stream, calculate the settlement rate under the current loading level and the rate of change of the slope of the load-displacement curve at the current moment. The stability index is calculated based on the settlement rate and the rate of change of the slope of the load-displacement curve. Based on the stability index, the stability level is determined by comparing the preset stable state threshold range, and corresponding early warning information is generated according to the stability level. After the static load test, complete load-displacement data curves are obtained. Based on the complete load-displacement data curves, combined with pile foundation design parameters and geological survey data, multidimensional quality indicators are calculated. The multidimensional quality indicators include the proportion of stable time, the coefficient of variation, and the goodness of fit. Based on the complete load-displacement data curve, the measured value of the vertical ultimate bearing capacity of a single pile is determined, and a confidence report is generated to evaluate the credibility of the ultimate bearing capacity result according to multi-dimensional quality indicators.

[0006] Through the above technical solution, the settlement rate and the rate of change of the curve slope are calculated in real time and integrated into a stability index for graded early warning. At the same time, a multi-dimensional quality assessment system is constructed based on the proportion of stable time, the coefficient of variation, and the goodness of fit, and a confidence report is generated. Under the premise of fully complying with current specifications, the static load test of pile foundation can be intelligently monitored throughout the entire process, and the data quality can be quantitatively assessed and the bearing capacity result can be determined. This significantly improves the safety and data reliability of the static load test.

[0007] Optionally, the load-displacement data stream includes timestamps, load data, and displacement data. The calculation of the settlement rate under the current loading level and the rate of change of the load-displacement curve slope at the current moment, based on the real-time received load-displacement data stream, includes: The load data and displacement data are treated as data pairs and arranged in chronological order to form a data set, which is denoted as the loading data set. Determine the current load level and extract a subset of data belonging to the current load level from the loaded data set, denoted as the effective data set; Settlement rate is calculated by linear regression based on the change of displacement data over time in the effective dataset. Based on the load increment of the current loading level and the number of data pairs in the effective data set, obtain an adaptive sliding window; Based on the loaded dataset, an adaptive sliding window is used to perform linear regression on the data pairs within the window to calculate the rate of change of the slope of the load-displacement curve.

[0008] Optionally, an adaptive sliding window can be obtained based on the load increment of the current loading level and the number of data pairs in the valid data set, including: Based on the load increment of the current loading level, the width of the sliding window is obtained through a preset proportional coefficient; Centered on the current load value, determine the candidate load range according to the width of the sliding window; The number of data pairs whose load values ​​fall within the candidate load interval in the loaded dataset is counted. If the number of data pairs reaches the preset minimum fitting number, the candidate load interval is used as an adaptive sliding window. If the number of data pairs does not reach the preset minimum fitting number, then the preset target number of consecutive data pairs are selected in reverse time order with the current time as the endpoint, and the load value interval formed by the selected data pairs is used as an adaptive sliding window.

[0009] Optionally, the pile foundation design parameters include the pile foundation type, and the calculation of the stability index based on the settlement rate and the rate of change of the load-displacement curve slope includes: The settlement rate and the rate of change of the slope of the load-displacement curve are normalized to generate a stability score. Based on the pile foundation type, the corresponding foundation allocation weight is obtained by matching through a preset weight allocation database; Based on the stability score, a stability index is generated by weighted aggregation using basic allocation weights.

[0010] Optionally, the calculation of multidimensional quality indicators based on complete load-displacement data curves, combined with pile foundation design parameters and geological survey data, includes: For each load level, the duration from when the load level reaches the preset stability standard to the end of the load level is obtained, which is taken as the stability duration of the load level. Calculate the ratio of the sum of the stability durations of each load level to the total test duration, and use this as the percentage of stability time. For each load level, the displacement data during the stable duration is extracted, and the ratio of the standard deviation to the mean of the displacement data is calculated as the dispersion coefficient of that load level. The average of the dispersion coefficients of all loading levels is then taken as the overall dispersion coefficient. Based on the pile foundation design parameters and geological survey data, a theoretical load-displacement model curve is constructed. The load-displacement data curve is compared with the theoretical load-displacement model curve to calculate the goodness of fit.

[0011] Optionally, the pile foundation design parameters include pile diameter, pile length, and pile body elastic modulus; the geological survey data includes characteristic values ​​of side skin friction and end resistance of each soil layer; and the construction of a theoretical load-displacement model curve based on the pile foundation design parameters and geological survey data includes: Based on the pile length, the pile body is discretized into multiple micro-segments along the depth direction; Based on the characteristic value of the side friction resistance of the soil layer where each micro-element is located, the pile side load transfer function of each micro-element is established. Based on the characteristic value of the end resistance of the soil layer at the pile tip, a load transfer function at the pile tip is established. Based on the static equilibrium principle of each micro-segment of the pile under axial load, the differential equation of the pile body is constructed by using the pile diameter and the elastic modulus of the pile body. Based on the pile side load transfer function and the pile end load transfer function, the differential equation of the pile body is solved iteratively to calculate the pile top settlement under each load level and generate theoretical load-displacement model curves.

[0012] Optionally, determining the measured value of the vertical ultimate bearing capacity of a single pile based on the complete load-displacement data curve includes: Based on the complete load-displacement data curve, feature parameters are extracted, including the maximum stable load, the total settlement before ultimate loading, and the load value at the curve inflection point. Determine whether the total settlement before the ultimate loading exceeds the preset settlement verification threshold; If not, the maximum stable load shall be taken as the measured value of the ultimate bearing capacity; If so, obtain the last load value before termination and determine whether the load value at the curve inflection point exceeds the last load value before termination. If the load value at the inflection point of the curve exceeds the last load value before termination, then the last load value before termination shall be taken as the measured value of the ultimate bearing capacity. If the load value at the curve inflection point does not exceed the last load value before termination, then the load value at the curve inflection point will be taken as the measured value of the ultimate bearing capacity.

[0013] Optionally, generating a confidence report assessing the reliability of the ultimate bearing capacity result based on multidimensional quality indicators includes: The stability time percentage, the coefficient of variation, and the goodness of fit were normalized to obtain the corresponding quality scores. Based on the pile foundation type, the weight coefficients of each quality indicator are obtained through a preset weight allocation rule; The overall confidence score is calculated by weighting and summing each quality score with its corresponding weight coefficient. Based on the comprehensive confidence score, a confidence report containing the confidence level and the measured value of the ultimate bearing capacity is generated through a pre-set confidence level mapping table.

[0014] Secondly, this application provides an intelligent monitoring and evaluation system for pile foundation bearing capacity used in static load tests, comprising: The data acquisition module 101 is used to acquire pile foundation design parameters and geological survey data, and to receive load-displacement data streams generated from static load tests in real time. The real-time monitoring and early warning module 102 is used to calculate the settlement rate under the current loading level and the rate of change of the slope of the load-displacement curve at the current moment based on the real-time received load-displacement data stream. Based on the settlement rate and the rate of change of the slope of the load-displacement curve, it calculates the stability index. Based on the stability index, it compares the results with a preset stable state threshold range to determine the stable state level and generates corresponding early warning information based on the stable state level. The data quality assessment module 103 is used to obtain a complete load-displacement data curve after the static load test, and to calculate multi-dimensional quality indicators based on the complete load-displacement data curve, combined with pile foundation design parameters and geological survey data. The multi-dimensional quality indicators include the proportion of stable time, the coefficient of variation, and the goodness of fit. The bearing capacity determination and report generation module 104 is used to determine the measured value of the vertical ultimate bearing capacity of a single pile based on the complete load-displacement data curve, and generate a confidence report to evaluate the credibility of the ultimate bearing capacity result according to multi-dimensional quality indicators.

[0015] Thirdly, this application provides a computer-readable storage medium storing a computer program that can be loaded by a processor and executed as described above for an intelligent monitoring and evaluation method of pile foundation bearing capacity for static load testing.

[0016] In summary, this application firstly calculates the settlement rate and curve slope change rate in real time and integrates them into a stability index, which can quantitatively assess the working state of the pile-soil system and issue early warnings in the early stages of stiffness degradation, effectively ensuring test safety. Secondly, through multi-dimensional indicators, the test data is quantitatively evaluated from three perspectives: process standardization, data stability, and mechanical rationality, providing a reliable basis for bearing capacity determination results. Furthermore, it integrates multiple judgment criteria in the technical specifications for building foundation pile testing into a unified automated judgment process, eliminating subjective differences in manual judgment and improving the objectivity and consistency of pile foundation bearing capacity determination results. Attached Figure Description

[0017] Figure 1 This is a flowchart of an intelligent monitoring and evaluation method for pile foundation bearing capacity for static load testing provided in an embodiment of this application; Figure 2 This is a flowchart provided in the embodiments of this application for calculating the settlement rate under the current loading level and the rate of change of the slope of the load-displacement curve at the current moment; Figure 3 This is a flowchart of calculating the stability index based on the settlement rate and the rate of change of the slope of the load-displacement curve, provided in the embodiments of this application. Figure 4 This is a flowchart provided in the embodiments of this application for determining the measured value of the vertical ultimate bearing capacity of a single pile based on a complete load-displacement data curve; Figure 5 This is a schematic diagram of an intelligent monitoring and evaluation system for pile foundation bearing capacity used in static load tests, provided in an embodiment of this application. Detailed Implementation

[0018] The following is in conjunction with the appendix Figure 1 - Appendix Figure 5 This application will be described in further detail below.

[0019] This application provides an intelligent monitoring and evaluation method for pile foundation bearing capacity in static load tests. See [link to relevant documentation]. Figure 1 This includes the following steps: S100: Acquire pile foundation design parameters and geological survey data, and receive load-displacement data streams generated from static load tests in real time.

[0020] S200: Based on the real-time received load-displacement data stream, calculate the settlement rate under the current loading level and the rate of change of the slope of the load-displacement curve at the current moment.

[0021] S300. Based on the settlement rate and the rate of change of the slope of the load-displacement curve, the stability index is calculated.

[0022] S400. Based on the stability index, the stable state level is determined by comparing the preset stable state threshold range, and corresponding early warning information is generated based on the stable state level.

[0023] After the S500 static load test, complete load-displacement data curves are obtained. Based on these curves, combined with pile foundation design parameters and geological survey data, multidimensional quality indicators are calculated.

[0024] S600: Based on the complete load-displacement data curve, determine the measured value of the vertical ultimate bearing capacity of a single pile, and generate a confidence report to evaluate the credibility of the ultimate bearing capacity result according to multi-dimensional quality indicators.

[0025] In this embodiment of the application, the pile foundation design parameters and corresponding geological survey data of the tested pile foundation will first be obtained, which can be obtained through pile foundation design drawings and geotechnical engineering survey reports.

[0026] Among them, the pile foundation design parameters include pile foundation type, pile diameter, pile length and pile body elastic modulus; pile foundation type, such as friction pile, end-bearing pile, etc.; pile body elastic modulus, which reflects the ability of the pile body material to resist elastic deformation.

[0027] Geological survey data includes soil layer distribution, side friction characteristic value, and end resistance characteristic value. The side friction characteristic value refers to the standard value of the ultimate skin friction between each soil layer on the side of the pile and the pile body, reflecting the lateral restraint capacity of the soil on the pile body. The end resistance characteristic value refers to the standard value of the bearing capacity of the soil at the pile end bearing layer under the ultimate state, reflecting the compressive strength of the soil at the pile end.

[0028] After the static load test begins, the load-displacement data stream generated by the static load test will be received in real time. Specifically, the data is generated by the pile foundation static load test analyzer. The pile foundation static load test analyzer is the core engineering equipment for detecting the vertical bearing capacity of the pile foundation. Its core function is to accurately collect load data and displacement data and generate load-displacement curves. The load data represents the actual vertical force borne by the pile foundation during the loading process; the displacement data, also known as the settlement, represents the amount of vertical deformation generated by the pile foundation during the loading process.

[0029] The signal acquisition interface of the pile foundation static load test analyzer connects to two types of core sensors: a load sensor, installed between the loading device and the pile foundation, used to collect real-time load values; and a displacement sensor, symmetrically arranged at the top of the pile, used to collect the settlement of the pile foundation.

[0030] In this embodiment of the application, after obtaining the real-time received load-displacement data stream, the settlement rate under the current loading level and the rate of change of the slope of the load-displacement curve at the current moment are calculated based on the real-time received load-displacement data stream.

[0031] The static load test employs a progressively increasing, equal-volume loading method, with each load increment approximately 1 / 10 of the estimated ultimate bearing capacity. During the test, the load is increased from one level to the next, gradually increasing until the termination condition is met. Therefore, the loading level here refers to the current load level or the current loading level number. For example, a graded load is... =250kN, first-level load value =500kN, second-level load value =750kN, Level 3 load value =1000kN, and so on. Here, each load value represents the target load value applied to the top of the pile during the test, that is, the load is increased from the current value to the target value and kept constant, and then the settlement of the pile top is observed.

[0032] Settlement rate usually refers to the rate of change of pile top settlement over time within a complete loading stage; the rate of change of the slope of the load-displacement curve reflects the trend of stiffness change of the pile-soil system in the current loading stage.

[0033] Specifically, see Figure 2 Based on the real-time received load-displacement data stream, the settlement rate under the current loading level and the rate of change of the slope of the load-displacement curve at the current moment are calculated, including the following steps: S210. Take the load data and displacement data as data pairs and form a data set in chronological order, denoted as the loading data set.

[0034] S220. Determine the current load level and extract the data subset belonging to the current load level from the loaded data set, denoted as the effective data set.

[0035] S230. Based on the change of displacement data over time in the effective dataset, the settlement rate is calculated by linear regression.

[0036] S240. Based on the load increment of the current loading level and the number of data pairs in the effective data set, obtain an adaptive sliding window.

[0037] S250. Based on the loaded dataset, an adaptive sliding window is used to perform linear regression on the data pairs within the window to calculate the rate of change of the slope of the load-displacement curve.

[0038] Since the load-displacement data stream includes not only load data and displacement data but also corresponding timestamps, the load data and displacement data can first be treated as data pairs and arranged in chronological order to form a data set, which is denoted as the load data set.

[0039] Then, determine the current loading level and extract the data subset belonging to the current loading level from the loading data set, which is denoted as the effective data set. Here, the data subset belonging to the current loading level refers to the set of data pairs whose load values ​​are stable within the preset allowable fluctuation range of the target load value of the current loading level and whose stable duration exceeds the preset time threshold.

[0040] For example, if the current loading level is level one, the corresponding load value is... =500kN, the preset allowable fluctuation range is 2%, and the preset time threshold is 2 minutes. Then, the data belonging to the current loading level in the loading data set, that is, the data whose load value is in the range of [500 (1-2%), 500 (1+2%)], and whose load value remains in the range for 2 minutes after first entering the range.

[0041] Then, based on the changes in displacement data over time in the effective dataset, the settlement rate is calculated using linear regression.

[0042] Settlement rate calculations typically employ a sliding time window approach, which uses data from the most recent period for calculation. For example, data from the most recent T minutes (T=30) is used as the time window to extract data from the valid dataset. It is assumed that this time window includes... Each data pair contains a displacement value. and the corresponding timestamp The change of displacement data over time can be expressed as: .

[0043] By using time t as the independent variable and displacement s as the dependent variable, a univariate linear regression model can be established. Where 'a' represents the regression slope and 'b' represents the regression intercept, 'a' can be calculated using the least squares method, and can be expressed as: in, and α represents the average values ​​of time and displacement, respectively, and the regression slope α is the settlement rate of the current loading stage.

[0044] Next, the rate of change of the slope of the load-displacement curve is calculated. Unlike the settlement rate, the rate of change of the slope describes the change in the load dimension. Therefore, a load window is required, which selects only data pairs within a limited range near the current load for calculation, in order to maintain sensitivity to local morphological changes in the load-displacement curve.

[0045] In addition, within the same loading level, each loading process includes two stages: the first is the transient loading stage where the load rises from the target value of the previous level to the target value of the current level. During this stage, the load variation range is large and the data density within the unit load interval is high, that is, the data is dense in the load dimension; the second is the stable observation stage where the load remains near the target value of the current level. During this stage, the load variation range is small and the data is sparse in the load dimension. Obviously, a fixed load window cannot simultaneously adapt to the different needs of the initial loading stage (data dense) and the later loading stage (data sparse).

[0046] Therefore, in this embodiment, an adaptive sliding window is obtained based on the load increment of the current loading level and the number of data pairs in the effective data set.

[0047] Specifically, based on the load increment of the current loading level and the number of data pairs in the effective data set, an adaptive sliding window is obtained, including the following steps: S241. Based on the load increment of the current loading level, obtain the width of the sliding window through a preset scaling factor.

[0048] S242. Using the current load value as the center, determine the candidate load range according to the width of the sliding window.

[0049] S243. Count the number of data pairs whose load values ​​fall within the candidate load interval in the loaded data set. If the number of data pairs reaches the preset minimum fitting number, the candidate load interval is used as an adaptive sliding window.

[0050] S244. If the number of data pairs does not reach the preset minimum fitting number, then select the preset target number of consecutive data pairs in reverse time order with the current time as the endpoint, and use the load value interval formed by the selected data pairs as the adaptive sliding window.

[0051] First, based on the load increment of the current loading level, the width of the sliding window is obtained through a preset proportional coefficient. Here, the load increment of the current loading level refers to the increment from the previous level load (the starting load of this level) to the current load value. Let the starting load value of the current loading level be denoted as... The load value at the current moment is Then the load increment of the current loading level is The width of the sliding window is .

[0052] The preset scaling factor α is a scaling factor that maps the load increment to the width of the sliding window. That is, the width of the sliding window is proportional to the amount of load that has been completed in the current loading level. The preset scaling factor α ranges from 0.5 to 2.0 and can be selected according to the pile foundation type, data acquisition frequency or test purpose.

[0053] Then, centering on the current load value, and according to the width of the sliding window, determine the candidate load interval, which can be represented as follows: .

[0054] For example, the current process is in the third stage of loading (target load value 1000kN), with an initial load value of... =750kN, current load value =900kN, which means it is in the transient loading phase, and the load of the current loading stage increases. =150kN, preset proportional coefficient is 1.0, sliding window width is 150kN, candidate load range is [825, 975].

[0055] Next, the number of data pairs in the loaded dataset whose load values ​​fall within the candidate load interval is counted. If the number of data pairs reaches the preset minimum fit number, the candidate load interval is used as an adaptive sliding window. Here, the preset minimum fit number refers to the minimum number of data pairs required to ensure the statistical significance and reliability of the linear regression results, for example, set to 8.

[0056] If the number of data pairs does not reach the preset minimum fitting number, then select the preset target number of consecutive data pairs in reverse time order, with the current time as the endpoint, and record the minimum load value among the selected data pairs. The maximum load value is Then the interval This serves as an adaptive sliding window. The preset target number here is a minimum number of data pairs to ensure that enough data can be obtained for linear regression analysis even in cases of data sparseness.

[0057] Once the adaptive sliding window is determined, linear regression can be performed on the data pairs within the window based on the loaded dataset. First, the instantaneous slope of the current load-displacement curve is calculated, and then the rate of change of the load-displacement curve slope is calculated based on the rate of change of the instantaneous slope with time or load.

[0058] Extract all data pairs within the adaptive sliding window from the loaded dataset, denoted as... , m is the number of data pairs within the adaptive sliding window, with the load value Q as the independent variable and the displacement s as the dependent variable, for By performing a univariate linear regression, we can obtain the regression slope k, which is the instantaneous slope of the load-displacement curve at the current moment.

[0059] By recording the instantaneous slope at multiple moments, an instantaneous slope sequence can be obtained. Based on the instantaneous slope at the current moment Instantaneous slope of the previous moment Then, the rate of change of the slope of the load-displacement curve at the current moment can be calculated using the load difference method, i.e. , here This represents the load value at the current moment. This represents the load value at the previous moment. The rate of change of the slope calculated in this way can reflect the rate of stiffness degradation as the load increases.

[0060] Alternatively, the time difference method can be used to calculate the rate of change of the slope of the load-displacement curve at the current moment, i.e. , here This represents the time interval between the current moment and the previous moment. The rate of change of the slope calculated in this way can reflect the rate of change of stiffness over time.

[0061] By using an adaptive sliding window as the data extraction interval to calculate the rate of change of the load-displacement curve slope, the sensitivity to stiffness changes can be maximized while ensuring the stability of the fit, which helps to achieve earlier and more reliable early warning.

[0062] Since the settlement rate reflects the creep characteristics in the time dimension and the slope change rate reflects the stiffness degradation characteristics in the load dimension, a single indicator is prone to misjudgment or omission due to data noise or stage characteristics. However, the combination of the two can more comprehensively evaluate the working status of the pile-soil system.

[0063] Therefore, in this embodiment, a stability index is calculated based on the settlement rate and the rate of change of the load-displacement curve slope. The stability index is used to quantify the stability of the pile-soil system under the current load. Its value ranges from 0 to 1, and the larger the value, the more stable the working state of the pile-soil system.

[0064] Specifically, see Figure 3 Based on the settlement rate and the rate of change of the slope of the load-displacement curve, the stability index is calculated, including the following steps: S310. Normalize the settlement rate and the rate of change of the slope of the load-displacement curve to generate a stability score.

[0065] S320. Based on the pile foundation type, the corresponding foundation allocation weight is obtained by matching through a preset weight allocation database.

[0066] S330. Based on the stability score, a stability index is generated by weighted aggregation through basic allocation weights.

[0067] Because the settlement rate and the rate of change of the slope of the load-displacement curve have different dimensions and numerical ranges, they need to be normalized first.

[0068] Settlement rate Normalization can generate a corresponding stability score. , can be represented as: in, To preset a dangerous threshold for settlement rate, such as =2.0mm / h, The stability score represents the level of danger; the higher the level of danger, the higher the stability score. The smaller.

[0069] Similarly, normalizing the rate of change R of the load-displacement curve slope can generate the corresponding stability score. , can be represented as: in, A preset danger threshold for the rate of change of slope is set, for example, a value of 0.00005 mm / kN. 2 , The stability score represents the level of danger; the higher the level of danger, the higher the stability score. The smaller.

[0070] Then, based on the pile foundation type, the corresponding foundation allocation weight is obtained by matching through a preset weight allocation database. This preset weight allocation database is a database built based on a large amount of historical static load test data and engineering experience. This database is indexed by pile foundation type and stores the corresponding foundation allocation weight.

[0071] For example, the bearing capacity of friction piles mainly depends on side friction, while settlement rate is a more sensitive indicator. The foundation allocation weight is: settlement rate weight. =0.6, slope change rate weight =0.4; The bearing capacity of end-bearing piles mainly depends on the end resistance, and the rate of change of slope better reflects the precursory characteristics of sudden failure. The foundation allocation weight is: settlement rate weight. =0.4, slope change rate weight =0.6.

[0072] Finally, based on the stability score, a stability index can be generated by weighted aggregation using the basic allocation weights. The stability index is: ,but It can be represented as: After obtaining the stability index, the stability level can be determined by comparing it with a preset stable state threshold range. The stability level is divided into stable state, light column state, abnormal state, and dangerous state. In the stable state, the load-displacement curve is linear, the settlement is stable, and normal operation is possible. In the watch state, the load-displacement curve begins to bend, and the stiffness degrades slightly, prompting a reminder to strengthen monitoring. In the abnormal state, the load-displacement curve bends significantly, and the stiffness degrades significantly, prompting a reminder to prepare to terminate loading. In the dangerous state, the load-displacement curve drops sharply or bends abruptly, prompting a reminder to immediately terminate loading.

[0073] Once the stable state level is determined, corresponding early warning information can be generated based on the stable state level, as shown in Table 1.

[0074] Table 1 During the static load test, the settlement rate under the current loading level and the rate of change of the slope of the load-displacement curve at the current moment are calculated by the real-time received load-displacement data stream, and the stability index is calculated. Using the stability index as a risk monitoring indicator, the entire process from the start of loading to the end of the test can be effectively monitored, and early warning can be given in the early stage of stiffness degradation, thus improving safety.

[0075] After the static load test, a complete load-displacement data curve can be obtained. In order to comprehensively evaluate the reliability of the test data from different dimensions, multi-dimensional quality indicators will be calculated based on the complete load-displacement data curve, combined with pile foundation design parameters and geological survey data. Among them, the multi-dimensional quality indicators include the proportion of stable time, the coefficient of variation, and the goodness of fit.

[0076] Specifically, based on complete load-displacement data curves, combined with pile foundation design parameters and geological survey data, multidimensional quality indicators are calculated, including the following steps: S510. For each load level, obtain the duration from when the load level reaches the preset stability standard to when the level ends, and use this duration as the stability duration of that level.

[0077] S520. Calculate the ratio of the sum of the stability duration of each load level to the total test duration, and use it as the stability time percentage.

[0078] S530. For each load level, extract the displacement data within the stable duration, calculate the ratio of the standard deviation to the mean of the displacement data, and use it as the dispersion coefficient for that load level. Take the average of the dispersion coefficients of all loading levels as the overall dispersion coefficient.

[0079] S540. Based on the pile foundation design parameters and geological survey data, a theoretical load-displacement model curve is constructed. The load-displacement data curve is compared with the theoretical load-displacement model curve, and the goodness of fit is calculated.

[0080] The stabilization time percentage refers to the ratio of the total duration after each load level reaches the stabilization standard to the total test duration during a static load test. It is used to evaluate the standardization of the test process. A high stabilization time percentage indicates that each load level has been given sufficient stabilization time, resulting in high data quality. Conversely, a low stabilization time percentage indicates that the loading pace is too fast, and the data may have systematic biases.

[0081] The stability standard here, according to the "Technical Specification for Testing Building Foundation Piles," is that the settlement at the top of the pile should not exceed 0.1 mm per hour, and this should occur twice consecutively. For example, after each load is applied, the settlement at the top of the pile should be measured every 30 minutes, meaning that the settlement measured three times consecutively within 1.5 hours should meet the requirements.

[0082] For each load level, the time when that load level reaches the preset stability criterion can be obtained. The duration until the end of that level The length of time between these intervals is taken as the stable duration of that level, i.e. .

[0083] Calculate the sum of the stability durations of each load level and the total test duration. The ratio of the two values ​​is used as the percentage of the steady-state time. It can be represented as: Where N is the total number of loading levels.

[0084] The coefficient of variation (COP) is the ratio of the standard deviation to the mean of displacement data over the duration of stability of each load level. It is used to measure the stability and reliability of displacement measurement data. A small COP indicates that the measurement system is stable, the sensor noise is low, and there is no external interference. Conversely, a large COP may indicate problems, such as poor sensor contact, swaying of the reference beam, temperature effects, or slow creep of the pile body.

[0085] For each load level, displacement data can be extracted during the stable duration, and the standard deviation can be calculated. with the mean The ratio of these values ​​is used as the dispersion coefficient for that load level. ,Right now The average of the discrete coefficients of all loading levels is taken as the overall discrete coefficient. , can be represented as: Goodness of fit refers to the coefficient of determination R between the measured load-displacement curve and the theoretical load-displacement model curve. 2 This is used to measure the degree of agreement between measured pile-soil behavior and design expectations. If R 2 A value close to 1 indicates that the measured curve closely matches the theoretical expectation, the pile-soil behavior conforms to the design expectation, and the data has high reliability; if R... 2 A value that is too low (e.g., <0.8) may indicate an abnormal situation, such as discrepancies between geological conditions and geological surveys, defects in the pile body, or abnormalities in the testing operation.

[0086] Among them, the theoretical load-displacement model curve (referred to as the theoretical Qs curve) is a theoretical relationship curve between the pile top load and the pile top settlement obtained by theoretical calculation or numerical simulation methods based on the pile foundation design parameters and geological survey data.

[0087] Specifically, based on the pile foundation design parameters and geological survey data, a theoretical load-displacement model curve is constructed, including the following steps: S541. Based on the pile length in the pile foundation design parameters, the pile body is discretized into multiple micro-segments along the depth direction.

[0088] S542. Based on the characteristic value of the side friction resistance of the soil layer where each micro-element is located, establish the pile side load transfer function for each micro-element.

[0089] S543. Based on the characteristic value of the end resistance of the soil layer at the pile tip, establish the load transfer function at the pile tip.

[0090] S544. Based on the static equilibrium principle of each micro-segment of the pile under axial load, the differential equation of the pile body is constructed by using the pile diameter and the elastic modulus of the pile body.

[0091] S545. Based on the pile side load transfer function and the pile end load transfer function, iteratively solve the differential equation of the pile body, calculate the pile top settlement under each level of load, and generate theoretical load-displacement model curves.

[0092] First, based on the pile length L in the pile foundation design parameters, the pile body is divided into M equal micro-segments along the depth direction, with each micro-segment having a length of... .

[0093] Then, based on the soil layer distribution in the geological survey data, the soil layer in which each micro-element is located is determined, and the characteristic value of the side friction resistance of that soil layer can be obtained. By adopting the ideal elastic-plastic model, the load transfer function of each micro-element segment can be established. The so-called ideal elastic-plastic model is a simplified mechanical model that describes the stress behavior of materials or structures. Its core feature is that before reaching the yield limit, the material exhibits perfect elasticity (stress and strain are proportional, and there is no residual deformation after unloading); after reaching the yield limit, the material exhibits perfect plasticity (stress no longer increases, but strain can continue to increase).

[0094] The pile side load transfer function describes the pile side friction. relative displacement between pile and soil The relationship between pile and soil, specifically pile-soil relative displacement, refers to the relative slippage between a point on the pile and the soil. For the first... For each infinitesimal segment, the load transfer function of the pile side can be expressed as: in, For the first Shear stiffness coefficient of the soil along the side of the pile in a micro-element segment No. The displacement required for the side friction resistance of the micro-element pile to reach its limit value is determined according to the soil type; for example, it is 5~10mm for cohesive soil and 10~15mm for sandy soil.

[0095] Similarly, using an ideal elastic-plastic model, the characteristic value of the end resistance of the soil layer at the pile tip can be used. Establish the pile end load transfer function, which describes the pile end resistance. With pile end displacement The relationship between these factors, where pile tip displacement refers to the settlement at the pile tip, and the pile tip load transfer function can be expressed as: in, This is the stiffness coefficient of the soil at the pile tip. The displacement required for the pile tip resistance to reach its ultimate value is also determined based on the type of soil layer at the pile tip; for example, 10-25 mm for cohesive soil, 15-30 mm for sandy soil, and 2-5 mm for rock. The cross-sectional area of ​​the pile tip. D is the pile diameter.

[0096] Then, based on the static equilibrium principle of each micro-segment of the pile under axial load, the pile diameter and the elastic modulus of the pile are used to determine the equilibrium. The differential equation of the pile body can be constructed. The principle of static equilibrium here refers to the fact that in the static equilibrium state, the sum of all axial forces acting on any infinitesimal segment of the pile body is zero. In engineering mechanics, static equilibrium specifically refers to the state where an object is at rest, and all forces acting on it cancel each other out, with a resultant force of zero.

[0097] The differential equation of the pile body represents the differential relationship between the axial force and the side friction resistance of the pile body, as well as the displacement relationship between the pile body displacement and the axial force of the pile body. It can be expressed as: in, Let z be the axial force of the pile at depth z. The circumference of the pile body This represents the circumferential range of contact between the pile and the soil. Specifically, for any infinitesimal segment, the decrease in axial force along the depth is equal to the frictional resistance provided by the soil along the pile within that depth range. .

[0098] It is the pile displacement at depth z. According to the elastic compression law of the pile, the rate of change of the pile displacement along the depth is equal to the ratio of the axial force of the pile at that depth to the compressive stiffness of the pile. Compressive stiffness = cross-sectional area of ​​the pile × elastic modulus of the pile.

[0099] After establishing the differential equation of the pile body, the differential equation of the pile body can be solved iteratively based on the load transfer function of the pile side and the load transfer function of the pile end, the settlement of the pile top under each level of load can be calculated, and the theoretical load-displacement model curve can be generated.

[0100] For example, using an iterative algorithm from the pile tip to the pile top, first assume a pile tip displacement. The initial value is calculated segment by segment from the pile tip to the pile top to obtain the axial force at the pile top. According to the above division of micro-segments, =0 indicates the position of the pile top, that is If the difference between the pile top load and the target load is less than the allowable error, then record the pile top displacement. , Complete the calculation of this level of load.

[0101] like If the load is less than the target load, then increase... The assumed value is repeatedly calculated segment by segment from the pile tip to the pile top. Similarly, if If the load exceeds the target load, then reduce the load. The assumed value is used to repeatedly calculate the load segment by segment from the pile tip to the pile top, until the calculated pile top load is equal to the target load (the difference is within the allowable error).

[0102] The theoretical ultimate bearing capacity is divided into N levels (consistent with the total number of loading levels in the actual test), and for each level of target load... By repeating the above iterative solution process, the corresponding pile top settlement can be obtained. , to load targets at all levels The corresponding theoretical pile top settlement By connecting the components, a theoretical load-displacement model curve can be formed.

[0103] By comparing the load-displacement data curves with the theoretical load-displacement model curves, the goodness of fit R can be calculated. 2 , can be represented as: in, This refers to the pile top settlement recorded after the j-th load level stabilizes in the actual static load test, which is the measured settlement and can be extracted from the load-displacement data curve. This represents the pile top settlement calculated using a theoretical model under the j-th level of load. This represents the average of all measured settlement values ​​used in the comparison.

[0104] At the same time, after obtaining the complete load-displacement data curve, the measured value of the vertical ultimate bearing capacity of a single pile will be determined based on the complete load-displacement data curve.

[0105] Specifically, see Figure 4 Based on the complete load-displacement data curve, the measured value of the vertical ultimate bearing capacity of a single pile is determined, including the following steps: S610. Based on the complete load-displacement data curve, extract characteristic parameters.

[0106] S620. Determine whether the total settlement before the ultimate loading exceeds the preset settlement verification threshold.

[0107] S630. If not, the maximum stable load shall be taken as the measured value of the ultimate bearing capacity.

[0108] S640. If so, obtain the last load value before termination and determine whether the load value at the curve inflection point exceeds the last load value before termination.

[0109] S650. If the load value at the inflection point of the curve exceeds the last load value before termination, then the last load value before termination shall be taken as the measured value of the ultimate bearing capacity.

[0110] S660. If the load value at the curve inflection point does not exceed the last load value before termination, then the load value at the curve inflection point shall be taken as the measured value of the ultimate bearing capacity.

[0111] First, based on the complete load-displacement data curve, characteristic parameters are extracted. These characteristic parameters include the maximum stable load, the total settlement before ultimate loading, and the load value at the curve inflection point.

[0112] Maximum stable load This refers to the load applied at the final stage of stable loading during the test.

[0113] Total settlement before ultimate loading , refers to the cumulative settlement from the start of loading to the limit state point. The limit state point is the point where the curve inflection point or the termination point occurs first. The curve inflection point is the turning point where the curve transitions from the linear segment to the nonlinear segment; the termination point is the point where the test stops due to the achievement of termination conditions, which may be the failure point or the point where the maximum loading is reached.

[0114] Curve inflection point load value This is achieved by calculating the rate of change of the slope of the load-displacement curve at each point, and taking the load value corresponding to the first time the rate of change of the slope exceeds a preset threshold as the inflection point load value. This preset threshold can be determined based on the pile foundation type, load magnitude, and data acquisition frequency, and its value ranges from 0.00002 to 0.00012 mm / kN. 2 .

[0115] Load value at the inflection point of the curve As a candidate value for ultimate bearing capacity, it determines the total settlement before ultimate loading. Does it exceed the preset settlement verification threshold? The preset settlement verification threshold here is a critical value of settlement used to distinguish the failure mode of pile foundation. It can be determined according to the pile diameter, for example, it can be taken as 5% to 10% of the pile diameter, or a fixed value of 40mm to 60mm.

[0116] like If the surface pile foundation exhibits a steep drop failure characteristic, or if the test does not reach the failure state, then the maximum stability load will be applied. As the measured value of ultimate bearing capacity, i.e., the measured value of ultimate bearing capacity .

[0117] like If the curve ends before the inflection point appears, it indicates that the pile foundation is in a slow-change failure state. There are two situations: one is that the test ends before the curve inflection point appears, which means that no obvious nonlinear deformation was observed and the bearing capacity should be calculated according to the load at the end; the other situation is that the curve has already reached the inflection point before the end point appears, and the bearing capacity should be calculated according to the load value when the curve reaches the inflection point.

[0118] Therefore, the last load value before termination will be obtained at this time. Determine the load value at the inflection point of the curve. Whether it exceeds the last load value before termination. The so-called last load value before termination refers to the load value corresponding to the level of load that was being applied or last completed when the test was terminated.

[0119] like In the first scenario above, the last load value before termination will be... As the measured value of ultimate bearing capacity, i.e., the measured value of ultimate bearing capacity .

[0120] like In the second scenario above, the load value at the inflection point of the curve will be taken as the measured value of the ultimate bearing capacity, i.e., the measured value of the ultimate bearing capacity. .

[0121] As mentioned earlier, in order to comprehensively evaluate the reliability of the test data from different dimensions, multidimensional quality indicators were obtained for the complete load-displacement data curve. Therefore, in this embodiment of the application, after determining the measured value of the ultimate bearing capacity, a confidence report for evaluating the credibility of the ultimate bearing capacity result will be generated based on the multidimensional quality indicators.

[0122] Specifically, based on multidimensional quality indicators, a confidence report assessing the reliability of the ultimate bearing capacity result is generated, including the following steps: S710. Normalize the percentage of stable time, the coefficient of variation, and the goodness of fit to obtain the corresponding quality scores.

[0123] S720. Based on the pile foundation type, the weight coefficients of each quality indicator are obtained through a preset weight allocation rule.

[0124] S730. Calculate the overall confidence score by weighting and summing each quality score with its corresponding weight coefficient.

[0125] S740. Based on the comprehensive confidence score, a confidence report containing the confidence level and the measured value of the ultimate bearing capacity is generated through a pre-set confidence level mapping table.

[0126] First, the proportion of stable time, the coefficient of variation, and the goodness of fit are normalized to obtain the corresponding quality scores. In other words, the three quality indicators with different dimensions and numerical ranges are mapped to a unified scoring range (0~100 points).

[0127] Percentage of stable time After normalization, the corresponding quality score can be obtained, which is denoted as the stability time percentage score. , can be represented as .

[0128] For the discrete coefficients After normalization, the corresponding quality score can be obtained, denoted as the coefficient of variation score. , can be represented as: The smaller the coefficient of variation, the higher the score. This is the maximum allowable value for the coefficient of variation, for example, 0.05. Data exceeding this value is considered unacceptable and will receive a score of 0.

[0129] For the goodness of fit R 2 After normalization, the corresponding quality score is obtained, which is denoted as the goodness-of-fit score. , can be represented as .

[0130] Then, based on the pile foundation type, the weight coefficients of each quality indicator are obtained through a preset weight allocation rule. The weight coefficients corresponding to the stability time percentage, dispersion coefficient, and goodness of fit are recorded as follows: , and .

[0131] The preset weighting rules here are designed to address the varying sensitivities of different pile foundation types to the three quality indicators. Corresponding weighting coefficients are pre-set. For example, for friction piles, because the frictional resistance needs to be fully utilized, the stabilization time is more important; therefore, the weighting coefficient is set to... =0.4, =0.3, =0.3; For end-bearing piles, the reliance on theoretical expectations is greater, and the goodness-of-fit ratio is more important. The weighting coefficient is set to 0.3. =0.3, =0.3, =0.4; other default settings are: =0.35, =0.3, =0.35.

[0132] The overall confidence score can be calculated by weighting and summing each quality score with its corresponding weighting coefficient. Let the overall confidence score be denoted as [value missing]. , can be represented as: Finally, based on the comprehensive confidence score, a confidence report containing the confidence level and the measured value of the ultimate bearing capacity can be generated by using a pre-set confidence level mapping table. This pre-set confidence level mapping table is used to map the comprehensive confidence score to an intuitive confidence level and the corresponding engineering meaning, as shown in Table 2.

[0133] Table 2 Based on the comprehensive confidence score, a pre-set confidence level mapping table can be used to match the corresponding confidence level and engineering meaning. The measured results of the ultimate bearing capacity are then output together with the confidence level and engineering meaning to form a confidence report. Of course, detailed scores for each quality indicator can also be added, and corresponding engineering suggestions can be generated based on the engineering meaning.

[0134] This application also provides an intelligent monitoring and evaluation system for pile foundation bearing capacity in static load tests. See [link to relevant documentation]. Figure 5 The system includes: a data acquisition module 101, a real-time monitoring and early warning module 102, a data quality assessment module 103, and a load-bearing capacity determination and report generation module 104.

[0135] The data acquisition module 101 is used to acquire pile foundation design parameters and geological survey data, and to receive load-displacement data streams generated from static load tests in real time.

[0136] The real-time monitoring and early warning module 102 is used to calculate the settlement rate under the current loading level and the rate of change of the slope of the load-displacement curve at the current moment based on the real-time received load-displacement data stream. Based on the settlement rate and the rate of change of the slope of the load-displacement curve, it calculates the stability index. Based on the stability index, it compares the results with a preset stable state threshold range to determine the stable state level and generates corresponding early warning information based on the stable state level.

[0137] The data quality assessment module 103 is used to obtain complete load-displacement data curves after the static load test, and to calculate multi-dimensional quality indicators based on the complete load-displacement data curves, combined with pile foundation design parameters and geological survey data.

[0138] The bearing capacity determination and report generation module 104 is used to determine the measured value of the vertical ultimate bearing capacity of a single pile based on the complete load-displacement data curve, and generate a confidence report to evaluate the credibility of the ultimate bearing capacity result according to multi-dimensional quality indicators.

[0139] In this embodiment of the application, the data acquisition module 101 is specifically used to acquire pile foundation design parameters and geological survey data, and to receive load-displacement data streams generated from static load tests in real time.

[0140] The real-time monitoring and early warning module 102 is used to calculate the settlement rate under the current loading level and the rate of change of the slope of the load-displacement curve at the current moment based on the load-displacement data stream received in real time by the data acquisition module 101. Based on the settlement rate and the rate of change of the slope of the load-displacement curve, the stability index is calculated. According to the stability index, the stability level is determined by comparison with the preset stable state threshold range, and the corresponding early warning information is generated according to the stability level.

[0141] The data quality assessment module 103 is specifically used to obtain complete load-displacement data curves after the static load test, and to calculate multi-dimensional quality indicators based on the complete load-displacement data curves, combined with pile foundation design parameters and geological survey data.

[0142] The bearing capacity determination and report generation module 104 is specifically used to determine the measured value of the vertical ultimate bearing capacity of a single pile based on the complete load-displacement data curve, and generate a confidence report to evaluate the credibility of the ultimate bearing capacity result based on the multi-dimensional quality indicators generated by the data quality assessment module 103.

[0143] This application also provides a computer-readable storage medium storing a computer program that can be loaded by a processor and executed by any of the above-described methods for intelligent monitoring and evaluation of pile foundation bearing capacity for static load testing.

[0144] The embodiments described in this application are preferred embodiments of this application and are not intended to limit the scope of protection of this application. Therefore, all equivalent changes made in accordance with the principles of this application should be included within the scope of protection of this application.

Claims

1. A method for intelligent monitoring and evaluation of pile foundation bearing capacity for static load testing, characterized in that, include: Acquire pile foundation design parameters and geological survey data, and receive load-displacement data streams generated from static load tests in real time; Based on the real-time received load-displacement data stream, calculate the settlement rate under the current loading level and the rate of change of the slope of the load-displacement curve at the current moment. The stability index is calculated based on the settlement rate and the rate of change of the slope of the load-displacement curve. Based on the stability index, the stability level is determined by comparing the preset stable state threshold range, and corresponding early warning information is generated according to the stability level. After the static load test, complete load-displacement data curves are obtained. Based on the complete load-displacement data curves, combined with pile foundation design parameters and geological survey data, multidimensional quality indicators are calculated. The multidimensional quality indicators include the proportion of stable time, the coefficient of variation, and the goodness of fit. Based on the complete load-displacement data curve, the measured value of the vertical ultimate bearing capacity of a single pile is determined, and a confidence report is generated to evaluate the credibility of the ultimate bearing capacity result according to multi-dimensional quality indicators.

2. The intelligent monitoring and evaluation method for pile foundation bearing capacity for static load testing according to claim 1, characterized in that, The load-displacement data stream includes timestamps, load data, and displacement data. The calculation of the settlement rate under the current loading level and the rate of change of the load-displacement curve slope at the current moment, based on the real-time received load-displacement data stream, includes: The load data and displacement data are treated as data pairs and arranged in chronological order to form a data set, which is denoted as the loading data set. Determine the current load level and extract a subset of data belonging to the current load level from the loaded data set, denoted as the effective data set; Settlement rate is calculated by linear regression based on the change of displacement data over time in the effective dataset. Based on the load increment of the current loading level and the number of data pairs in the effective data set, obtain an adaptive sliding window; Based on the loaded dataset, an adaptive sliding window is used to perform linear regression on the data pairs within the window to calculate the rate of change of the slope of the load-displacement curve.

3. The intelligent monitoring and evaluation method for pile foundation bearing capacity for static load testing according to claim 2, characterized in that, Based on the load increment of the current loading level and the number of data pairs in the valid data set, an adaptive sliding window is obtained, including: Based on the load increment of the current loading level, the width of the sliding window is obtained through a preset proportional coefficient; Centered on the current load value, determine the candidate load range according to the width of the sliding window; The number of data pairs whose load values ​​fall within the candidate load interval in the loaded dataset is counted. If the number of data pairs reaches the preset minimum fitting number, the candidate load interval is used as an adaptive sliding window. If the number of data pairs does not reach the preset minimum fitting number, then the preset target number of consecutive data pairs are selected in reverse time order with the current time as the endpoint, and the load value interval formed by the selected data pairs is used as an adaptive sliding window.

4. The intelligent monitoring and evaluation method for pile foundation bearing capacity for static load testing according to claim 1, characterized in that, The pile foundation design parameters include the pile foundation type. The stability index, calculated based on the settlement rate and the rate of change of the load-displacement curve slope, includes: The settlement rate and the rate of change of the slope of the load-displacement curve are normalized to generate a stability score. Based on the pile foundation type, the corresponding foundation allocation weight is obtained by matching through a preset weight allocation database; Based on the stability score, a stability index is generated by weighted aggregation using basic allocation weights.

5. The intelligent monitoring and evaluation method for pile foundation bearing capacity for static load testing according to claim 1, characterized in that, The multidimensional quality indicators are calculated based on complete load-displacement data curves, combined with pile foundation design parameters and geological survey data, including: For each load level, the duration from when the load level reaches the preset stability standard to the end of the load level is obtained, which is taken as the stability duration of the load level. Calculate the ratio of the sum of the stability durations of each load level to the total test duration, and use this as the percentage of stability time. For each load level, the displacement data during the stable duration is extracted, and the ratio of the standard deviation to the mean of the displacement data is calculated as the dispersion coefficient of that load level. The average of the dispersion coefficients of all loading levels is then taken as the overall dispersion coefficient. Based on the pile foundation design parameters and geological survey data, a theoretical load-displacement model curve is constructed. The load-displacement data curve is compared with the theoretical load-displacement model curve to calculate the goodness of fit.

6. The intelligent monitoring and evaluation method for pile foundation bearing capacity for static load testing according to claim 5, characterized in that, The pile foundation design parameters include pile diameter, pile length, and pile elastic modulus; the geological survey data includes characteristic values ​​of side skin friction and end resistance of each soil layer; and the theoretical load-displacement model curve constructed based on the pile foundation design parameters and geological survey data includes: Based on the pile length, the pile body is discretized into multiple micro-segments along the depth direction; Based on the characteristic value of the side friction resistance of the soil layer where each micro-element is located, the pile side load transfer function of each micro-element is established. Based on the characteristic value of the end resistance of the soil layer at the pile tip, a load transfer function at the pile tip is established. Based on the static equilibrium principle of each micro-segment of the pile under axial load, the differential equation of the pile body is constructed by using the pile diameter and the elastic modulus of the pile body. Based on the pile side load transfer function and the pile end load transfer function, the differential equation of the pile body is solved iteratively to calculate the pile top settlement under each load level and generate theoretical load-displacement model curves.

7. The intelligent monitoring and evaluation method for pile foundation bearing capacity for static load testing according to claim 1, characterized in that, The determination of the measured value of the vertical ultimate bearing capacity of a single pile based on the complete load-displacement data curve includes: Based on the complete load-displacement data curve, feature parameters are extracted, including the maximum stable load, the total settlement before ultimate loading, and the load value at the curve inflection point. Determine whether the total settlement before the ultimate loading exceeds the preset settlement verification threshold; If not, the maximum stable load shall be taken as the measured value of the ultimate bearing capacity; If so, obtain the last load value before termination and determine whether the load value at the curve inflection point exceeds the last load value before termination. If the load value at the inflection point of the curve exceeds the last load value before termination, then the last load value before termination shall be taken as the measured value of the ultimate bearing capacity. If the load value at the curve inflection point does not exceed the last load value before termination, then the load value at the curve inflection point will be taken as the measured value of the ultimate bearing capacity.

8. The intelligent monitoring and evaluation method for pile foundation bearing capacity for static load testing according to claim 1, characterized in that, The process of generating a confidence report assessing the reliability of the ultimate bearing capacity result based on multidimensional quality indicators includes: The stability time percentage, the coefficient of variation, and the goodness of fit were normalized to obtain the corresponding quality scores. Based on the pile foundation type, the weight coefficients of each quality indicator are obtained through a preset weight allocation rule; The overall confidence score is calculated by weighting and summing each quality score with its corresponding weight coefficient. Based on the comprehensive confidence score, a confidence report containing the confidence level and the measured value of the ultimate bearing capacity is generated through a pre-set confidence level mapping table.

9. An intelligent monitoring and evaluation system for pile foundation bearing capacity used in static load tests, characterized in that, include: The data acquisition module (101) is used to acquire pile foundation design parameters and geological survey data, and to receive load-displacement data streams generated from static load tests in real time; The real-time monitoring and early warning module (102) is used to calculate the settlement rate under the current loading level and the rate of change of the slope of the load-displacement curve at the current moment based on the real-time received load-displacement data stream. Based on the settlement rate and the rate of change of the slope of the load-displacement curve, it calculates the stability index. Based on the stability index, it compares the results with the preset stable state threshold range to determine the stable state level and generates corresponding early warning information based on the stable state level. The data quality assessment module (103) is used to obtain the complete load-displacement data curve after the static load test, and to calculate multi-dimensional quality indicators based on the complete load-displacement data curve, combined with the pile foundation design parameters and geological survey data. The multi-dimensional quality indicators include the proportion of stable time, the coefficient of variation, and the goodness of fit. The bearing capacity determination and report generation module (104) is used to determine the measured value of the vertical ultimate bearing capacity of a single pile based on the complete load-displacement data curve, and generate a confidence report to evaluate the credibility of the ultimate bearing capacity result according to multi-dimensional quality indicators.

10. A computer-readable storage medium storing a computer program capable of being loaded by a processor and executed as described in any one of claims 1 to 8, for a method of intelligent monitoring and evaluation of pile bearing capacity for static load testing.

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