Pile foundation construction surrounding stratum deformation prediction method and system

Abstract

The present application provides a pile foundation construction surrounding stratum deformation prediction method and system, belonging to the technical field of geotechnical engineering monitoring and numerical prediction, comprising: S1: determining the calculation domain range; S2: laying monitoring equipment; S3: selecting natural circulation load source; S4: inverting sample calculation small strain constraint modulus; S5: correcting the hysteresis loop and small strain constraint modulus of low-permeability fine-grained soil layer; S6: statistical uncertainty; S7: identifying low-modulus abnormal section and high-modulus section; S8: inputting parameters into the pile foundation construction surrounding deformation prediction model to obtain the prediction result; S9: if the prediction result deviates more than the threshold value, constructing a calibration objective function, updating the parameters, and outputting rolling prediction. The method can realize low-disturbance parameter acquisition; can realize continuous parameter acquisition, overcoming the shortcomings of discrete test points; can accurately identify soft interlayers; can update and rolling predict online, reducing the inversion multi-solution; can output early warning information for engineering application; and has wide applicability.

Description

Technical Field

[0001] This invention relates to the field of geotechnical engineering monitoring and numerical prediction technology, specifically to a method and system for predicting the deformation of strata surrounding pile foundation construction. Background Technology

[0002] Pile foundations are a common foundation type for bridges, high-rise buildings, ports, and rail transit projects, and are widely used in complex strata such as soft soil, saturated fine sand, and coastal alluvial plains. The construction process of pile foundations (such as borehole unloading, mud / casing wall protection, borehole cleaning, concrete pouring and backfilling for bored piles; soil displacement during the driving of driven cast-in-place piles and precast piles; and pile disturbances caused by CFG / CFA, etc.) will cause stress redistribution, pore water pressure disturbance, and structural damage to the soil around the pile. This may be accompanied by seepage, consolidation, and creep effects, resulting in cumulative ground displacement and deformation during the construction and operation periods. Typical manifestations include surface settlement troughs, lateral displacement, concentrated deep shear deformation zones, and additional deformation and damage risks to adjacent buildings, underground pipelines, and existing transportation structures. Especially in coastal soft clay and high groundwater level sites, construction disturbance and pore pressure dissipation processes are coupled with each other. The most unfavorable deformation often occurs during the superposition of the construction stage and the short-term consolidation stage, making accurate prediction and risk warning of the deformation of the strata around the pile foundation construction a key technical problem in geotechnical engineering practice.

[0003] Existing methods for predicting deformation during pile foundation construction include: empirical formulas and semi-empirical methods, analytical / semi-analytical model methods, numerical simulation methods, and construction monitoring and inversion calibration methods. However, these methods suffer from the following problems: 1. Discrete and insufficiently representative stiffness parameters; 2. Sampling disturbances and scale effects leading to modulus deviations; 3. Difficulty in reflecting the time-varying nature of construction parameters in real time; 4. Model calibration exhibits multiple solutions and instability. Therefore, a method is urgently needed to improve the prediction accuracy and early warning reliability of surface settlement, lateral displacement, and responses to adjacent structures.

[0004] Therefore, we propose a method and system for predicting the deformation of the surrounding strata during pile foundation construction. Summary of the Invention

[0005] The purpose of this invention is to provide a method and system for predicting the deformation of the strata surrounding pile foundation construction, so as to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] This invention provides a method for predicting the deformation of the strata surrounding pile foundation construction, comprising the following steps: S1: Determine the target pile foundation construction type, determine the surrounding deformation control objects based on the pile foundation construction type, and determine the calculation domain range; S2: Set up monitoring holes and install monitoring equipment inside the monitoring holes; S3: Select a natural circulating load source; S4: Construct the effective stress increment, plot the effective stress increment-strain hysteresis loop in a single period, and calculate the small strain constraint modulus by inverting the sample. S5: If a spindle-shaped hysteresis loop and phase lag appear in a low-permeability fine-grained soil layer, the hysteresis loop and small strain constraint modulus of the low-permeability fine-grained soil layer shall be corrected. S6: Using a single period as the unit, repeat the inversion sample calculation and statistically analyze the uncertainty of the small strain constraint modulus calculated in all periods. The uncertainty includes the mean, standard deviation, coefficient of variation, confidence interval of the representative value, intra-layer variability, and inter-layer mutation index. S7: Form a continuous profile along the depth of the small strain constraint modulus and divide it into segments to identify low modulus anomaly segments and high modulus segments; S8: Divide the continuous profile into layers, convert the small strain constraint modulus of each layer into the required input parameters of the pile foundation construction perimeter deformation prediction model, input the parameters into the pile foundation construction perimeter deformation prediction model, and obtain the prediction results; S9: If the prediction result deviates beyond the threshold, construct a calibration objective function, update the parameters, and output a rolling prediction.

[0008] Furthermore, step S1 specifically includes the following steps: S11: Determine the target pile foundation construction type, which includes bored piles, driven piles, precast piles, CFG / CFA piles, and pile group construction. S12: Taking the proposed construction pile location as the center, determine the surrounding deformation control objects, which include the ground surface, adjacent buildings, underground pipelines, existing tunnels, and foundation pits; S13: Determine the scope of the calculation domain: Using pile diameter D and pile length L as the scale, the planar scope covers the outer extension of the pile center ≥ (10-30)D, and the depth scope covers the depth to the pile tip ≥ (0.5-1.0)L, including weak interlayers.

[0009] Furthermore, step S2 specifically includes the following steps: S21: At least one monitoring hole shall be set around the pile location, and the distance between the monitoring hole and the center of the pile shall be 1D~10D; S22: Distributed fiber optic strain sensing cables, inclinometer tubes and settlement monitoring instruments are laid out along the depth direction in the monitoring hole, and pore pressure gauges are arranged at key depths, namely the weak interlayer, aquifer, top boundary of the impermeable layer and bottom boundary of the impermeable layer. S23: Install a mechanical anchoring sleeve or a roughening sleeve on the outside of the distributed optical fiber strain sensing cable; S24: Backfill the monitoring holes and perform maintenance.

[0010] Furthermore, step S4 specifically includes the following steps: S41: Obtaining Pore Pressure Time History Based on Pore Pressure Gauge Strain time history obtained based on distributed fiber optic strain sensing cable , for pore pressure time history The process involves removing trend terms, filtering, and extracting periodic components, and then analyzing the pore pressure time history. With strain time history Perform phase alignment; S42: Based on Terzaghi's effective stress principle, the effective stress increment is constructed under the condition of small strain and approximately constant total stress, and the formula is:

[0011] in, Indicates depth; Indicates time; Indicates the effective stress increment; Indicates the increment of pore pressure; S43: For each depth, the cycle is used as a unit. Plot the effective stress increment-strain hysteresis loop; S44: In each lap, select an initial linear segment or use robust least squares fitting of the linear segment to obtain the slope, as shown in the formula:

[0012] Will This serves as the small strain constraint modulus at that depth.

[0013] Furthermore, the specific process of step S5 includes: S51: If a spindle-shaped hysteresis loop and phase lag appear in a low-permeability fine-grained soil layer, first take the mean value of the pore pressure increment and strain time history of the low-permeability fine-grained soil layer, using the following formula:

[0014]

[0015] in, This represents the pore pressure increment after removing the mean. This represents the strain time history after removing the mean; This represents the average value of the pore pressure increment; This represents the average value of the strain time history; S52: Sampling time points within a single period Calculate cross-correlation:

[0016] in, express and Correlation functions between them; Represents the discrete-time lag step, used to describe the phase misalignment between two discrete sequences; Indicates the summation index; S53: Calculate phase lag time The formula is:

[0017]

[0018] in, Indicates depth The optimal discrete lag step at the point; , These represent the lower and upper bounds of the lag step search interval, respectively; Indicates the sampling time interval for monitoring time series data; S54: Pore pressure sequence translation correction, the corrected pore pressure increment is:

[0019] in, This represents the corrected pore pressure increment; The effective stress increment is constructed using the following formula:

[0020] in, This represents the effective stress increment after correction; Subsequently Reconstruct the homing line; S55: Calculate the area of ​​the loop; The area of ​​a hysteresis loop is defined as the integral of a closed curve:

[0021] in, Indicates the area of ​​the loop; Discretize the area of ​​the hysteresis loop and approximate it using polygon summation:

[0022] in, Indicates the summation index; Indicates depth First The corrected effective stress increment at each discrete point; Indicates depth First The corrected effective stress increment at each discrete point; Indicates the first Strain values ​​at discrete points; Indicates the first Strain values ​​at discrete points; To eliminate the influence of dimensions, the normalized area is given:

[0023] in, Represents the normalized area; Indicates the effective stress increment amplitude; Indicates the strain amplitude; S56: Calculate the ratio of unloading slope to loading slope and the relative difference; Linear regression was performed on the initial small strain linear segment of the hysteresis loop for both the loading and unloading segments, yielding the following results:

[0024] in, Indicates depth The loading slope of the hysteresis loop loading segment within the selected small strain near-linear interval, which is also the loading constraint modulus; This represents the corrected effective stress increment on the loaded segment. With strain The rate of change;

[0025] in, Indicates depth The unloading slope of the unloading segment at the loop within the same small strain near-linear interval, which is also the unloading constraint modulus; This represents the corrected effective stress increment on the unloading section. With strain The rate of change;

[0026] in, Indicates depth The ratio of the unloading slope to the loading slope;

[0027] in, Indicates depth The relative difference between the loading slope and the unloading slope at the location; S57: Set the lap area threshold Slope difference threshold

[0028] If the hysteresis is weak, that is and

[0029] The hysteresis loop is then considered approximately linear, and a corrected modulus is obtained using "linear segment regression".

[0030] in, This represents the modified small strain constraint modulus; If the hysteresis is significant, that is or

[0031] Then a viscoelastic model is used to... Fit the relationship and output .

[0032] Furthermore, the specific process of step S6 includes: S61: Select in units of a single cycle Each cycle, for each depth Repeat the linear segment fitting to obtain the modulus sample set at this depth:

[0033] in For the first Small strain constraint modulus of one cycle ; S62: Calculate the mean, standard deviation, and coefficient of variation of the small strain constraint modulus; The formula for calculating the mean is as follows:

[0034] in, Indicates depth The mean value of the small strain constraint modulus at a given point also represents the depth. Representative constraint modulus at the location; Indicates the summation index; The formula for calculating standard deviation is as follows:

[0035] in, Indicates depth The standard deviation of the small strain constraint modulus at the point; The formula for calculating the coefficient of variation is as follows:

[0036] in, Indicates depth The coefficient of variation of the small strain constraint modulus at the point; S63: Calculate the confidence interval for the representative value; when And when the sample is approximately symmetrically distributed, take Distribute the information intervals:

[0037] in, Indicates the confidence interval; Indicates the confidence level; Indicates sample size; express The critical value of the distribution; If the sample is not normally distributed or contains outliers, the bootstrap method is used to... Resampling yields quantile confidence intervals; S64: Calculate intra-layer variability and inter-layer mutation index; Divide the depth into segments according to known stratigraphic boundaries, the first... The segment depth range is The value represented within the defined layer is:

[0038] in, Indicates the first The segment's intra-layer representative value; Indicates the first The lower limit depth of the segment; Indicates the first The upper limit depth of the segment; Define the intralayer variability as:

[0039] in, Indicates the first Intralayer variability of a segment is used to describe depth fluctuations within the same coating layer; The interlayer mutation index is defined as:

[0040] in, Used to describe the intensity of interlayer mutations; Indicates the first The representative value within the segment.

[0041] Furthermore, the specific process of step S7 includes: S71: Form a continuous profile along the depth using small strain constraint modulus. Perform smoothing and calculate the rate of change:

[0042] in, Indicates the rate of change; S72: Divide the formation into segments according to the rate of change and the stratigraphic interface, and calculate the statistics for each segment; Assuming the segmentation is Section, No. The segment depth range is ,but

[0043] in, Indicates the first The segment's intra-layer representative value; Indicates the first The lower limit depth of the segment; Indicates the first The upper limit depth of the segment;

[0044] in, Indicates the first within paragraph The segmented standard deviation;

[0045] in, Indicates the first within paragraph coefficient of variation; S73: Identify low-modulus anomaly segments and high-modulus segments based on absolute threshold criteria or relative criteria.

[0046] Furthermore, the specific process of step S8 includes: S81: Based on the segmented interface from step S72, discretize the continuous profile into... Layer, number Layer depth range is ,in ; No. The thickness of the layer is:

[0047] in, Indicates the first Layer thickness; Indicates the first The lower limit depth of the layer; Indicates the first Upper limit depth of the layer; No. The representative constraint modulus of the layer is

[0048]

[0049] in, Indicates the first The layer's internal representative value; S82: Elastic parameter conversion formula:

[0050] in, Indicates the first The elastic modulus of the layer; Indicates the first The Poisson's ratio of the layer is determined by experimental or empirical methods.

[0051] in, Indicates the first Shear modulus of the layer;

[0052] in, Indicates the first The bulk modulus of the layer; The formation includes at least the parameters Layered parameter field ;

[0053] S83: Small strain constitutive parameter mapping, the formula is:

[0054] in, Indicates the first The initial elastic modulus of the layer under a small strain range;

[0055] in, Indicates the first The initial shear modulus of the layer in a small strain range; The formation includes at least the parameters Layered parameter field ;

[0056] in, Indicates the first The shear strain of a layer is determined by experimental or empirical methods. Indicates the first The cohesion of the layer is determined by experimental or empirical values. Indicates the first The internal friction angle of the layer is determined by experimental or empirical values. Indicates the first The permeability coefficient of the layer is determined by experimental or empirical methods. S84: Each layer Transformed into a hierarchical parameter field ;

[0057] S85: Perform a consistency check; The first parameter is obtained from discrete experiments or empirical parameters. The initial elastic modulus and initial shear modulus of the layer, the initial elastic modulus is denoted as . The initial shear modulus is denoted as ; The result obtained from step S83 , and , To make a comparison, define the relative deviation:

[0058] in, for and The relative deviation;

[0059] in, for and The relative deviation; Set relative deviation threshold , If the following conditions are met: or

[0060] This layer is then marked as the layer to be calibrated. The conversion parameters of this layer are not input into the deformation prediction model around the pile foundation construction, but are instead added to the set to be calibrated. ;

[0061] Otherwise, this layer is a consistent layer, and the conversion parameters of this layer are input into the deformation prediction model around the pile foundation construction. S86: The deformation prediction model around pile foundation construction adopts a numerical prediction engine and an analytical / semi-analytical fast engine; For numerical prediction engines, if an elastic model is used, the hierarchical parameter field obtained in step S82 will be... Input the numerical prediction engine. If a small-strain model is used, the layered parameter field obtained in step S83 will be used. Input the numerical prediction engine, apply the construction path, and output the prediction results, which include the surface settlement trough. Lateral displacement Location of deep shear zones and deformation of adjacent structures; For the parsing / semi-parsing fast engine, the hierarchical parameter field obtained in step S84 Input a parsing / semi-parsing fast engine, output prediction results, including the radius of influence. Settlement magnitude.

[0062] Furthermore, the specific process of step S9 includes: S91: In the same construction phase, cross-check the prediction results of the numerical prediction engine in step S85 with the prediction results of the analytical / semi-analytical fast engine, and define the normalization bias:

[0063] in, Indicates normalization bias; This represents the output of the numerical prediction engine; This represents the output of the parsing / semi-parsing fast engine prediction results; Set deviation threshold ,like Mark this layer as the layer to be calibrated and add it to the calibration set. ; S92: For the layers to be calibrated marked in steps S85 and S92, construct the calibration objective function. The objective function is:

[0064] in, The parameter vector of the layer to be calibrated; For on-site monitoring; The predicted output is for the deformation prediction model around the pile foundation construction. , As weight; Indicates a time range; By minimizing The updated parameters are obtained and fed back to the deformation prediction model around the pile foundation construction. S93: Output rolling prediction; the output results should include at least: Surface subsidence trough The range and lateral displacement The interval; The indicator set and its exceedance probability and control ratio, the indicator set includes: maximum settlement Settling trough width Maximum lateral displacement Maximum lateral depth corresponds to depth Radius of influence Deformation growth rate ; Additional deformation indices of adjacent structures and their control ratios; The identification results of low-modulus anomaly segments and high-modulus segments, and the control contribution of weak interlayers to the index set; Tiered early warning and recommended construction adjustment measures.

[0065] This invention also provides a system for predicting soil deformation around pile foundation construction, comprising: The data acquisition module is used for strain acquisition, pore pressure acquisition, settlement acquisition, and inclination measurement acquisition; The time series processing module is used to remove trend terms, filter and extract periodic components from the pore pressure time history data, and to perform phase alignment between the pore pressure time history data and the strain time history data. The stress-strain hysteresis module is used to periodically plot the effective stress increment-strain hysteresis at each depth. The modulus inversion and correction module is used for the inversion of small strain constraint modulus and the correction of low-permeability fine-grained soil hysteresis and small strain constraint modulus. The continuous profile and interlayer identification module is used to form a continuous profile along the depth of the small strain constraint modulus, segment it, identify low modulus anomaly segments and high modulus segments, and perform parameter conversion. The pile foundation construction prediction module is used to output the prediction results of the deformation of the surrounding strata during pile foundation construction; The online calibration and early warning module is used to calibrate the prediction results, update parameters, and output rolling predictions.

[0066] Compared with the prior art, the present invention has the following technical effects: 1. In-situ low disturbance: This method uses pore pressure changes caused by tidal or seasonal groundwater fluctuations as a natural circulating load source for passive excitation, eliminating the need for manual loading and avoiding the disturbance of traditional indoor test sampling and the requirement for manual loading equipment. It can achieve long-term, low-disturbance, and repeatable parameter acquisition.

[0067] 2. Parameter continuity: This method obtains a small strain constraint modulus profile that changes continuously along the depth by continuously measuring the depth of distributed strain and inverting the hysteresis slope. This overcomes the shortcomings of discrete test points and provides a more realistic "controlling stratum" input for predicting the morphology of settlement troughs, the peak position of lateral displacement and the deformation concentration zone in pile foundation construction.

[0068] 3. Identification of weak interlayers: This method can accurately identify weak interlayers, which can provide a basis for interpreting abrupt deformation changes and risks around the pile foundation.

[0069] 4. Online updates and rolling predictions reduce inversion ambiguity: This method converts the inverted small strain constraint modulus into parameters, establishes a parameter field, inputs the parameter field into the deformation prediction model around the pile foundation construction, and outputs the prediction results to achieve deformation prediction during construction. At the same time, it marks the layers to be calibrated. During the pile foundation construction, it continuously acquires pore pressure, strain, settlement, and inclination data. For the set to be calibrated, it constructs a calibration objective function, updates the parameters, and inputs them back into the deformation prediction model around the pile foundation construction to output rolling predictions. It establishes a prediction framework of "parameter field - model prediction - set to be calibrated - parameter update - rolling prediction", which reduces the ambiguity caused by inversion and improves the reliability of prediction.

[0070] 5. Output early warning information for engineering applications: Based on the prediction results, this method forms an indicator system that can directly guide construction control, such as: maximum settlement value, settlement trench width, maximum lateral displacement and its depth location, deformation control value of adjacent structures, radius of influence, deformation growth rate, etc. It can further provide risk level classification and threshold early warning, so as to realize feedforward control and process management of pile foundation construction risks.

[0071] 6. Wide applicability: This method is applicable to pile foundation construction in areas with periodic water level fluctuations, such as tidal zones, river deltas, and coastal plains. It can also be extended to non-tidal areas, such as pile foundation construction in areas with high groundwater levels or controllable water levels, by constructing quasi-periodic pore pressure fluctuations through controlled drainage or storage facilities. Attached Figure Description

[0072] Figure 1 This is a flowchart of the deformation prediction method according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the monitoring hole layout according to an embodiment of the present invention; Figure 3 This is a schematic diagram of the time history of pore pressure and strain synchronization in an embodiment of the present invention; Figure 4 This is a schematic diagram of weak interlayer identification according to an embodiment of the present invention; Figure 5 This is a schematic diagram of the system structure according to an embodiment of the present invention. Detailed Implementation

[0073] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the protection scope of the present invention.

[0074] In this article, terms such as "left," "right," "up," "down," "front," and "back" are established based on the positional relationships shown in the attached drawings. Depending on the attached drawings, the corresponding positional relationships may also change. Therefore, they should not be interpreted as an absolute limitation on the scope of protection.

[0075] Please see Figure 1 This embodiment provides a method for predicting the deformation of the strata surrounding the pile foundation during construction, including the following steps: S1: Determine the target pile foundation construction type, determine the surrounding deformation control objects based on the pile foundation construction type, and determine the calculation domain range.

[0076] Specifically, step S1 includes the following steps: S11: Determine the target pile foundation construction type, which includes bored piles, driven piles, precast piles, CFG / CFA piles, and pile group construction.

[0077] S12: Taking the proposed construction pile location as the center, determine the surrounding deformation control objects, which include the ground surface, adjacent buildings, underground pipelines, existing tunnels, and foundation pits.

[0078] S13: Determine the scope of the calculation domain: Using the pile diameter D and pile length L as the scale, the planar scope covers the outer extension of the pile center ≥ (10-30)D, the depth scope covers the depth to the pile tip ≥ (0.5-1.0)L, and includes the weak interlayer.

[0079] S2: Install monitoring holes and place monitoring equipment inside the monitoring holes, such as... Figure 2 As shown.

[0080] Specifically, step S2 includes the following steps: S21: Set at least one monitoring hole (preferably 2-6) around the pile location. The distance between the monitoring hole and the center of the pile is 1D~10D. The near field is used to identify strong disturbances, and the far field is used to identify the range of influence.

[0081] S22: Distributed fiber optic strain sensing cables, inclinometers, and settlement monitors are deployed along the depth direction within the monitoring borehole, and borehole pressure gauges are arranged at key depths, namely the weak interlayer, aquifer, top boundary of the impermeable layer, and bottom boundary of the impermeable layer. In this embodiment, the distributed fiber optic strain sensing cables adopt the UWFBG and OTDR principles or an equivalent distributed fiber optic scheme to achieve meter-level spatial resolution and micro-strain level accuracy, thereby capturing the periodic response of "several to tens of micro-strains" caused by natural loading.

[0082] S23: Install a mechanical anchoring sleeve or a roughened sleeve on the outside of the distributed optical fiber strain sensing cable to ensure "soil-cable coupling".

[0083] S24: Backfill the monitoring holes with backfill material that matches the in-situ stiffness, and set a "consolidation curing period" for curing to reduce decoupling error.

[0084] S3: Select natural circulating load sources, including tidal river channels, nearshore tides, seasonal groundwater fluctuations, reservoir scheduling, and quasi-periodic water level fluctuations caused by pumping or recharge.

[0085] S4: Construct the effective stress increment, plot the effective stress increment-strain hysteresis loop in units of a single period, and calculate the small strain constraint modulus by inverting the sample.

[0086] Specifically, step S4 includes the following steps: S41: Obtaining Pore Pressure Time History Based on Pore Pressure Gauge Strain time history obtained based on distributed fiber optic strain sensing cable , for pore pressure time history The process involves removing trend terms, filtering, and extracting periodic components, and then analyzing the pore pressure time history. (i.e., pore water pressure) and strain time history Perform phase alignment, and the aligned relationship is as follows: Figure 3 As shown. In step S2, the sampling frequency of the monitoring equipment must be able to cover the slow tidal cycle.

[0087] S42: Based on Terzaghi's effective stress principle, the effective stress increment is constructed under the condition of small strain and approximately constant total stress, and the formula is:

[0088] in, Indicates depth; Indicates time; Indicates the effective stress increment; This indicates the increment of pore pressure.

[0089] S43: For each depth, the cycle is used as a unit. Plot the effective stress increment-strain hysteresis loop.

[0090] S44: In each lap, select an initial linear segment or use robust least squares fitting of the linear segment to obtain the slope, as shown in the formula:

[0091] Will This serves as the small strain constraint modulus (i.e., the equivalent deformation modulus) at that depth.

[0092] S5: For well-drained sand layers, the hysteresis loop is usually close to linear with small hysteresis. However, for low-permeability fine-grained soil layers such as silt and clay layers, due to the time lag in pore pressure (i.e., pore water pressure) diffusion, spindle-shaped hysteresis loops and phase lag may occur in low-permeability fine-grained soil layers. If spindle-shaped hysteresis loops and phase lag occur in low-permeability fine-grained soil layers, the hysteresis loop and small strain constraint modulus of the low-permeability fine-grained soil layers should be corrected.

[0093] Specifically, the process of step S5 includes: S51: If a spindle-shaped hysteresis loop and phase lag appear in a low-permeability fine-grained soil layer, first take the mean value of the pore pressure increment and strain time history of the low-permeability fine-grained soil layer, using the following formula:

[0094]

[0095] in, This represents the pore pressure increment after removing the mean. This represents the strain time history after removing the mean; This represents the average value of the pore pressure increment; This represents the average value of the strain time history.

[0096] S52: Sampling time points within a single period Calculate cross-correlation:

[0097] in, express and Correlation functions between them; Represents the discrete-time lag step (an integer, in units of "sampling steps"), used to describe the phase misalignment between two discrete sequences; Indicates the summation index.

[0098] S53: Calculate phase lag time The formula is:

[0099]

[0100] in, For depth The optimal discrete lag step (integer) at that depth represents the number of steps the two sequences lag / lead at that depth. , These represent the lower and upper bounds of the lag step search interval, respectively. The maximum search lag step (integer) is preset to limit the search window for cross-correlation peak finding and avoid crossing excessively large non-physical mismatches; This indicates the sampling time interval for monitoring time series data, which is also the time step between two adjacent samples.

[0101] S54: Pore pressure sequence translation correction, the corrected pore pressure increment is:

[0102] in, This represents the corrected pore pressure increment.

[0103] The effective stress increment is constructed using the following formula:

[0104] in, This represents the effective stress increment after correction.

[0105] Subsequently Reconstruct the loop.

[0106] S55: Calculate the area of ​​the loop; The area of ​​a hysteresis loop is defined as the integral of a closed curve:

[0107] in, This indicates the area of ​​the loop.

[0108] Discretize the area of ​​the hysteresis loop and approximate it using polygon summation:

[0109] in, Indicates the summation index; Indicates depth First The corrected effective stress increment at each discrete point; Indicates depth First The corrected effective stress increment at each discrete point; Indicates the first Strain values ​​at discrete points; Indicates the first The strain values ​​at discrete points.

[0110] To eliminate the influence of dimensions, the normalized area is given:

[0111] in, Represents the normalized area; Indicates the effective stress increment amplitude; This indicates the strain amplitude.

[0112] S56: Calculate the ratio of unloading slope to loading slope and the relative difference; Linear regression was performed on the initial small strain linear segment of the hysteresis loop for both the loading and unloading segments, yielding the following results:

[0113] in, Indicates depth The loading slope of the hysteresis loop loading segment within the selected small strain near-linear interval, which is also the loading constraint modulus; This represents the corrected effective stress increment on the loaded segment. With strain The rate of change.

[0114]

[0115] in, Indicates depth The unloading slope of the unloading segment at the loop within the same small strain near-linear interval, which is also the unloading constraint modulus; This represents the corrected effective stress increment on the unloading section. With strain The rate of change.

[0116]

[0117] in, Indicates depth The ratio of the unloading slope to the loading slope.

[0118]

[0119] in, Indicates depth The relative difference between the loading slope and the unloading slope.

[0120] S57: Set the lap area threshold Slope difference threshold , and Used to determine the strength of hysteresis.

[0121] If the hysteresis is weak, that is and

[0122] The hysteresis loop is then considered approximately linear, and a corrected modulus is obtained using "linear segment regression".

[0123] in, This represents the modified small strain constraint modulus.

[0124] If the hysteresis is significant, that is or

[0125] Then a viscoelastic model is used to... Fit the relationship and output .

[0126] Specifically, , representing strain rate, symbol " "" indicates that under significant hysteresis conditions, a viscoelastic constitutive relation including strain and strain rate terms is used to characterize the behavior. right The rate-dependent response is used to eliminate the modulus underestimation caused by phase hysteresis / hysteresis.

[0127] S6: Repeat the inversion sample calculation on a single cycle basis, and statistically analyze the uncertainty of the small strain constraint modulus calculated in all cycles. The uncertainty includes the mean, standard deviation, coefficient of variation, confidence interval of the representative value, intra-layer variability, and inter-layer mutation index.

[0128] Specifically, the process of step S6 includes: S61: Select in units of a single cycle Each cycle, for each depth Repeat the linear segment fitting to obtain the modulus sample set at this depth:

[0129] in For the first Small strain constraint modulus of one cycle .

[0130] S62: Calculate the mean, standard deviation, and coefficient of variation of the small strain constraint modulus; The formula for calculating the mean is as follows:

[0131] in, Indicates depth The mean value of the small strain constraint modulus at a given point also represents the depth. Representative constraint modulus at the location; Indicates the summation index.

[0132] The formula for calculating standard deviation is as follows:

[0133] in, Indicates depth The standard deviation of the small strain constraint modulus at a given point is used to characterize the degree of periodic repeatability dispersion.

[0134] The formula for calculating the coefficient of variation is as follows:

[0135] in, Indicates depth The coefficient of variation of the small strain constraint modulus at a given point is used to characterize the degree of periodic repeatability dispersion.

[0136] S63: Calculate the confidence interval for the representative value (which can be set to any confidence level as needed); when And when the sample is approximately symmetrically distributed, take Distribute the information intervals:

[0137] in, Indicates the confidence interval; Indicates the confidence level; Indicates sample size; express The critical value of the distribution.

[0138] If the sample is not normally distributed or contains outliers, a bootstrap method should be used. Resampling yields quantile confidence intervals (e.g., 2.5%-97.5%).

[0139] S64: Calculate intra-layer variability and inter-layer mutation index; Divide the depth into segments according to known stratigraphic boundaries, the first... The segment depth range is The value represented within the defined layer is:

[0140] in, Indicates the first The segment's intra-layer representative value; Indicates the first The lower limit depth of the segment; Indicates the first The upper limit of segment depth.

[0141] Define the intralayer variability as:

[0142] in, Indicates the first Intralayer variability is used to describe depth fluctuations within the same coating layer.

[0143] The interlayer mutation index is defined as:

[0144] in, Used to describe the intensity of interlayer mutations; Indicates the first The representative value within the segment.

[0145] S7: Form a continuous profile along the depth using the small strain constraint modulus and segment it to identify low-modulus anomalous segments and high-modulus segments.

[0146] Specifically, the process of step S7 includes: S71: Form a continuous profile along the depth using small strain constraint modulus. Perform smoothing and calculate the rate of change:

[0147] in, Indicates the rate of change.

[0148] S72: Automatically segment the formation according to the rate of change at the stratigraphic interface and calculate the statistics for each segment.

[0149] Assuming the segmentation is Section, No. The segment depth range is ,in, .but

[0150] in, Indicates the first The intra-layer representative value of a segment characterizes the average stiffness level of that segment; Indicates the first The lower limit depth of the segment; Indicates the first The upper limit of segment depth.

[0151]

[0152] in, Indicates the first within paragraph The segmented standard deviation characterizes the undulation / non-uniformity of the profile segment.

[0153]

[0154] in, Indicates the first within paragraph The coefficient of variation (dimensionless) is used to normalize and characterize the uncertainty / dispersion of the segment. The larger the value, the more significant the difference in the content of the segment.

[0155] S73: Identify low-modulus anomalous segments (i.e., weak interlayers) based on absolute or relative threshold criteria. Figure 4 (as shown) and the high modulus segment; Representative values ​​within segmented layers across the entire depth range Calculate the baseline mean and dispersion:

[0156]

[0157] in, Indicates the baseline mean; Indicates the degree of dispersion; and This is a robust statistic.

[0158] Absolute threshold criterion discrimination Set the absolute threshold for low modulus and high modulus absolute threshold , and It can be given by CPT, pressure-meter test, empirical range or design parameters.

[0159] when At that time, it is the low modulus anomaly segment; when The first segment is the high-modulus segment; the rest are the conventional segments.

[0160] Simultaneously, stability constraints are introduced:

[0161] in, Indicates the upper limit of the coefficient of variation; That is, a segment is considered "reliable anomaly / reliable high modulus" only when the coefficient of variation within the segment does not exceed the upper limit.

[0162] When a reliable absolute threshold is lacking on-site, a relative criterion is used for discrimination.

[0163] Set significance coefficient , , when At that time, it is the low modulus anomaly segment; when When the modulus is high, it is the high modulus segment; otherwise, it is the conventional segment.

[0164] Simultaneously, an intra-segment dispersion constraint is introduced:

[0165] This can prevent "large fluctuations / non-converging segments" from being misidentified as specific strata.

[0166] Specifically, to avoid false alarms triggered by isolated thin-layer noise, the first The thickness of the segment is The formula is:

[0167] Set minimum segment thickness If a certain segment satisfies the criterion and If a segment satisfies the criterion but... If the segment is not found, the statistic is merged with the adjacent segment, the statistic is recalculated, and the segment is then re-evaluated.

[0168] S8: Divide the continuous profile into layers, convert the small strain constraint modulus of each layer into the required input parameters for the pile foundation construction perimeter deformation prediction model, input the parameters into the pile foundation construction perimeter deformation prediction model, and obtain the prediction results.

[0169] Specifically, the process of step S8 includes: S81: Based on the segmented interface from step S72, discretize the continuous profile into... Layer, number Layer depth range is ,in .

[0170] No. The thickness of the layer is:

[0171] in, Indicates the first Layer thickness; Indicates the first The lower limit depth of the layer; Indicates the first The upper limit of the layer depth.

[0172] No. The representative constraint modulus of the layer is

[0173]

[0174] in, Indicates the first The representative value within a layer.

[0175] S82: Elasticity parameter conversion When an isotropic linear elastic relationship is adopted and constrained by the modulus Compared with Poisson When inputting, please use the following formula for conversion:

[0176] in, Indicates the first The elastic modulus of the layer; Indicates the first The Poisson's ratio of a layer is determined by experiments (consolidation tests, triaxial tests, in-situ tests, etc.) or empirical values.

[0177] in, Indicates the first Shear modulus of the layer;

[0178] in, Indicates the first The bulk modulus of the layer; The formation includes at least the parameters Layered parameter field ;

[0179] S83: Small strain constitutive parameter mapping, the formula is:

[0180] in, Indicates the first The initial elastic modulus of the layer in a small strain range is used to characterize the initial elastic stiffness of the layer.

[0181]

[0182] in, Indicates the first The initial shear modulus of the layer in a small strain range.

[0183] The formation includes at least the parameters Layered parameter field ;

[0184] in, Indicates the first The shear strain of a layer is determined by experimental or empirical methods. Indicates the first The cohesion of the layer is determined by experimental or empirical values. Indicates the first The internal friction angle of the layer is determined by experimental or empirical values. Indicates the first The permeability coefficient of the layer is determined by experimental or empirical methods.

[0185] S84: Each layer Transformed into a hierarchical parameter field ;

[0186] When the layered parameter field Only need to achieve When output is required, it can be omitted. , And the conversion is done internally by the program.

[0187] S85: Perform a consistency check; The first parameter is obtained from discrete tests (such as indoor consolidation tests, triaxial tests, CPT tests, or pressuremeter tests) or empirical parameters. The initial elastic modulus and initial shear modulus of the layer, the initial elastic modulus is denoted as . The initial shear modulus is denoted as ; The result obtained from step S83 , and , To make a comparison, define the deviation index: Relative deviation:

[0188] in, for and The relative deviation.

[0189]

[0190] in, for and The relative deviation.

[0191] Set relative deviation threshold , If the following conditions are met: or

[0192] This layer is then marked as the layer to be calibrated. The conversion parameters of this layer are not input into the deformation prediction model around the pile foundation construction, but are instead added to the set to be calibrated. .

[0193]

[0194] Otherwise, this layer is a consistent layer, and the conversion parameters of this layer are input into the deformation prediction model around the pile foundation construction.

[0195] Specifically, the deviation index can also be a comprehensive deviation. The formula is:

[0196] in, , As weight, .

[0197] Set threshold ,like The layer is marked as a layer to be calibrated; otherwise, it is a consistent layer.

[0198] S86: The deformation prediction model around pile foundation construction adopts a numerical prediction engine and an analytical / semi-analytical fast engine; For numerical prediction engines, if an elastic model is used, the hierarchical parameter field obtained in step S82 will be... Input the numerical prediction engine. If a small-strain model is used, the layered parameter field obtained in step S83 will be used. Input the numerical prediction engine, apply the construction path, and output the prediction results, which include the surface settlement trough. Lateral displacement Location of deep shear zones and deformation of adjacent structures.

[0199] The construction working condition path shall include at least one of the following: hole unloading (removing soil and reducing lateral pressure), wall restraint; grouting and backfilling loading (concrete self-weight and mud replacement); pile driving soil displacement / vibration disturbance (volume strain source term or equivalent displacement boundary); construction dewatering and pore pressure dissipation (consolidation coupling).

[0200] For the parsing / semi-parsing fast engine, the hierarchical parameter field obtained in step S84 Input a parsing / semi-parsing fast engine, output prediction results, including the radius of influence. Settlement magnitude.

[0201] S9: If the prediction result deviates beyond the threshold, construct a calibration objective function, update the parameters, and output a rolling prediction.

[0202] Specifically, the process of step S9 includes: S91: In the same construction phase, cross-check the prediction results of the numerical prediction engine in step S85 with the prediction results of the analytical / semi-analytical fast engine, and define the normalization bias:

[0203] in, Indicates normalization bias; This represents the output of the numerical prediction engine; This indicates the output of the parsing / semi-parsing fast engine prediction results.

[0204] Set deviation threshold ,like Mark this layer as the layer to be calibrated and add it to the calibration set. .

[0205] S92: For the layers to be calibrated marked in steps S85 and S92, construct the calibration objective function. The objective function is:

[0206] in, The parameter vector of the layer to be calibrated; For on-site monitoring; The predicted output is for the deformation prediction model around the pile foundation construction. , As weight; Indicates a time range.

[0207] By minimizing The updated parameters are obtained and fed back to the deformation prediction model around the pile foundation construction.

[0208] S93: Output rolling prediction; the output results should include at least: Surface subsidence trough The range and lateral displacement The interval; The indicator set and its exceedance probability and control ratio, the indicator set includes: maximum settlement Settling trough width Maximum lateral displacement Maximum lateral depth corresponds to depth Radius of influence Deformation growth rate ; Additional deformation indices of adjacent structures and their control ratios; The identification results of low modulus anomaly segments (i.e., low modulus anomaly segments) and high modulus segments, and the control contribution of weak interlayers to the index set; Graded early warning (normal, attention, alert, and danger) and suggested construction adjustment measures (such as changing the construction sequence, controlling the pile driving speed, optimizing the mud slurry specific gravity, setting up vibration isolation or crowding reduction measures, etc.).

[0209] Specifically, the exceedance probability is the probability that, given the parameters and the parameter distribution after online updates, the predicted parameters for the next time window will exceed a threshold. The indicators in the following indicator set, as well as the additional deformation indicators of adjacent structures, are all based on indicators. To express it, using indicators To describe the probability of exceeding the limit and the control ratio.

[0210] Set control limits The control values ​​are determined by the design requirements.

[0211] Control ratio:

[0212] in, This indicates the numerical value of the forecast indicator; The control ratio represents the "safety margin" that the predicted value uses relative to the control value.

[0213] Probability of exceeding the limit:

[0214] in, Indicates the probability of exceeding the limit; It represents probability.

[0215] Specifically, the adjacent structures of construction piles typically include the ground surface, adjacent buildings, underground pipelines, existing tunnels, and foundation pits. The adjacent structures vary depending on the construction site, and the deformation index of the adjacent structures needs to be determined based on the actual construction conditions. The following uses an adjacent building as an example to describe the additional deformation index of the adjacent structure.

[0216] For adjacent buildings, at structural critical points Extraction of sedimentation And give:

[0217] in, This indicates the maximum settlement.

[0218]

[0219] in, Indicates differential settlement; Indicates in Settlement value at the location; Indicates in Settlement value at the location.

[0220]

[0221] in, Indicates angular deformation; Indicates the feature length.

[0222] Specifically, the predicted index values ​​are as follows when weak interlayers are present: After replacing the weak interlayer with the background layer, we get... The contribution of the weak interlayer is defined as follows:

[0223] in, Indicates the contribution of the weak interlayer; To prevent very small positive numbers with a denominator of zero; This indicates the predicted index value after the weak interlayer is equivalently replaced with the background layer.

[0224] The greater the contribution of the weak interlayer, the more the indicator is mainly controlled by the weak interlayer.

[0225] Specifically, the identification results for low-modulus anomaly segments (i.e., low-modulus anomaly segments) and high-modulus segments include the type label and key parameters for each segment: Type tags ： {Low modulus abnormal segment, normal segment, high modulus segment}; Representative modulus: ； index: 、 Depth range: Abnormal intensity score.

[0226] The formula for scoring the anomaly strength in the low modulus range is:

[0227] in, This indicates the score for the intensity of the anomaly.

[0228] The formula for scoring the abnormal strength of the high modulus range is:

[0229] Abnormal intensity score The larger the value, the more significant the abnormality.

[0230] Specifically, this method utilizes pore pressure variations caused by tidal or seasonal groundwater fluctuations as a passive excitation source of natural cyclic loads, eliminating the need for manual loading and avoiding the disturbances and equipment requirements of traditional indoor testing. This allows for long-term, low-disturbance, and repeatable parameter acquisition. Through continuous depth measurement of distributed strain and hysteresis loop slope inversion, a continuously varying small-strain constraint modulus profile along the depth is obtained, overcoming the limitations of discrete test points. This provides a more realistic "controlling stratum" input for predicting settlement trough morphology, peak lateral displacement location, and deformation concentration zones during pile foundation construction. It can accurately identify weak interlayers, providing a basis for interpreting abrupt deformation changes and risks around the pile foundation. The inverted small strain constraint modulus is parametrically converted to establish a parameter field. This parameter field is then input into a deformation prediction model around the pile foundation construction site, outputting prediction results to predict deformation during construction. Simultaneously, layers to be calibrated are marked. During pile foundation construction, pore pressure, strain, settlement, and inclinometer data are continuously acquired. For the set to be calibrated, a calibration objective function is constructed, parameters are updated, and the data is fed back into the deformation prediction model around the pile foundation construction site, outputting rolling predictions. This establishes a prediction framework of "parameter field—model prediction—set to be calibrated—parameter update—rolling prediction," reducing the ambiguity caused by inversion and improving prediction reliability. Based on the prediction results, an indicator system that can directly guide construction control is formed, such as: maximum settlement value, settlement trough width, maximum lateral displacement and its depth, deformation control values ​​of adjacent structures, radius of influence, and deformation growth rate. Further risk level classification and early warning can be provided, enabling feedforward control and process management of pile foundation construction risks. It is applicable to pile foundation construction in areas with periodic water level fluctuations, such as tidal zones, river deltas, and coastal plains. It can also be extended to non-tidal areas, such as pile foundation construction in areas with high groundwater levels or controllable water levels, by constructing quasi-periodic pore pressure fluctuations through controlled pumping or storage facilities.

[0231] Specifically, to further illustrate this prediction method, a typical engineering example will be used below to further explain the invention.

[0232] A pilot project was selected in a coastal reclamation complex in southeastern China. Drilled pile foundations (40m long) were planned to be installed on the soft soil foundation formed by the reclamation. Pile diameter 1.2 The foundation pile construction process involves weak soil, a high groundwater level, and proximity to existing deep foundation pits and municipal pipelines, necessitating high-precision settlement prediction and risk control during the construction period. The groundwater level in this area is significantly affected by tides, with an average tidal range of 1.8 meters. The conditions for carrying out the method of this invention are met.

[0233] 1. Engineering and Geological Overview The strata are distributed from top to bottom as follows: fill layer (artificially filled sand, thickness 2-4 mm) ); Saturated silty clay layer (approximately 12 mm thick) Natural moisture content 45-55%, permeability coefficient ); silty clay with silt lenses (1-2 in some areas) Low modulus interlayer); medium-dense silt layer (10-20) Underlying dense sand layer (pile tip bearing layer); groundwater level 0.5-1.5 meters below the surface. It varies periodically due to the influence of nearshore tides, with a period of approximately 12.4. .

[0234] 2. Monitoring equipment deployment and data acquisition Monitoring hole arrangement: 3 on the outer edge of the pile location 6 10 Three monitoring holes are installed at each location (one hole for each location), with a depth of 40 mm. It traverses the entire length of the pile and the soft soil layer.

[0235] Sensor deployment: Distributed fiber optic strain sensing cables are laid along the borehole depth, using UWFBG and OTDR solutions, with a spatial resolution of 1. Strain accuracy Three orifice manometers are installed in each monitoring well (located at: 10...). 20 30 The depth corresponds to the weak layer, transition layer, and bearing layer. The backfill material is a mixture of natural clay and quartz sand with similar stiffness, compacted in sections to ensure coupling. After backfilling, a 7-day curing period is required to fully restore the pore pressure field and sensor-formation coupling.

[0236] Data collection period: sampling interval of 10 minutes, continuous collection for 30 days, covering at least 60 complete tidal cycles.

[0237] 3. Data Processing and Modulus Inversion Signal processing: The principal period of the semi-diurnal tide (M2 component) is extracted from the pore pressure time history, and the trend term is removed using a bandpass filter. The strain time history and the pore pressure time history are phase-aligned, and then the effective stress increment is constructed.

[0238] Constructing hysteresis loops and inverting small strain constraint moduli: per single period, every 1 Deeply extract continuous pore pressure-strain data to construct a hysteresis loop, and invert the small strain constraint modulus from the slope of the linear segment of the hysteresis loop. For low-permeability fine-grained soil layers (such as silt and clay layers) exhibiting spindle-shaped hysteresis and phase lag, the hysteresis and small strain constraint modulus of the low-permeability fine-grained soil layers are corrected.

[0239] Identifying weak interlayers: Inversion sample calculations are repeated on a single-cycle basis. Uncertainty is statistically analyzed, and a continuous profile of the low-strain constraint modulus is formed along the depth, segmented to identify low-modulus anomaly segments and high-modulus segments. (10.5-12.0) Deep recognition identified a segment The weak silty clay interlayers are highly consistent with the field drilling results; the modulus beneath the interlayers rapidly recovers to 10. The above demonstrates a clear interface.

[0240] 4. Result Prediction (In this project example, the maximum settlement value is the main indicator; the values ​​of other indicators are not given here) Parameter conversion: Convert the small strain constraint modulus of each layer into the required input parameters of the deformation prediction model around the pile foundation construction, perform consistency checks, screen out the layers to be calibrated, and add the layers to be calibrated to the calibration set.

[0241] Model prediction: Input parameters into the numerical prediction engine and the analytical / semi-analytical fast engine Numerical prediction engine prediction output: Predicted pile perimeter 0-20 Settlement troughs are distributed within the radius, with the maximum settlement occurring at a distance of 2.5 from the pile edge. At approximately 12.8 ; the upper part of the pile body 10 The area exhibits a clear downward vertical compression trend, consistent with the top grouting load; a deep, weak interlayer (approximately 11.2 km²) has been identified. Shear strain concentration occurs at point ( ), the displacement of the bearing stratum at the pile tip is small, and the stability is good.

[0242] Parsing / Semi-parsing Fast Engine: Impact Radius Settlement magnitude.

[0243] 5. Construction period monitoring and rolling forecasting The prediction results are cross-checked to identify layers to be calibrated, which are then added to the calibration set. Settlement, pore pressure, and strain are collected simultaneously during pile construction. For the calibration set, a calibration objective function is constructed, parameters are updated, and the data is fed back to the deformation prediction model around the pile foundation construction site to output rolling predictions.

[0244] 6. Practical Application Effects and Comparative Analysis

[0245] The results show that the prediction error of the method of the present invention is significantly lower than that of the traditional empirical method; the continuous modulus profile effectively identifies and captures the controlling weak layer; tidal natural excitation can be used for long-term, multi-point, and low-cost modulus monitoring; the prediction model can be coupled with the monitoring data in real time and has the ability to be updated on a rolling basis.

[0246] like Figure 5As shown, this embodiment also provides a system for predicting the deformation of the strata surrounding the pile foundation during construction, used to implement the above-described method for predicting the deformation of the strata surrounding the pile foundation during construction, including: The data acquisition module is used for strain acquisition, pore pressure acquisition, settlement acquisition, and inclination measurement acquisition; The time series processing module is used to remove trend terms, filter and extract periodic components from the pore pressure time history data, and to perform phase alignment between the pore pressure time history data and the strain time history data. The stress-strain hysteresis module is used to periodically plot the effective stress increment-strain hysteresis at each depth. The modulus inversion and correction module is used for the inversion of small strain constraint modulus and the correction of low-permeability fine-grained soil hysteresis and small strain constraint modulus. The continuous profile and interlayer identification module is used to form a continuous profile along the depth of the small strain constraint modulus, segment it, identify low modulus anomaly segments and high modulus segments, and perform parameter conversion. The pile foundation construction prediction module is used to output the prediction results of the deformation of the surrounding strata during pile foundation construction; The online calibration and early warning module is used to calibrate the prediction results, update parameters, and output rolling predictions.

[0247] The above embodiments merely illustrate the basic principles and characteristics of the present invention, but are not limited to the above implementation schemes. It should be understood that those skilled in the art can make various changes and modifications to the present invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for predicting soil deformation around pile foundation construction, characterized in that, Includes the following steps: S1: Determine the target pile foundation construction type, determine the surrounding deformation control objects based on the pile foundation construction type, and determine the calculation domain range; S2: Set up monitoring holes and install monitoring equipment inside the monitoring holes; S3: Select a natural circulating load source; S4: Construct the effective stress increment, plot the effective stress increment-strain hysteresis loop in a single period, and calculate the small strain constraint modulus by inverting the sample. S5: If a spindle-shaped hysteresis loop and phase lag appear in a low-permeability fine-grained soil layer, the hysteresis loop and small strain constraint modulus of the low-permeability fine-grained soil layer shall be corrected. S6: Using a single period as the unit, repeat the inversion sample calculation and statistically analyze the uncertainty of the small strain constraint modulus calculated in all periods. The uncertainty includes the mean, standard deviation, coefficient of variation, confidence interval of the representative value, intra-layer variability, and inter-layer mutation index. S7: Form a continuous profile along the depth of the small strain constraint modulus and divide it into segments to identify low modulus anomaly segments and high modulus segments; S8: Divide the continuous profile into layers, convert the small strain constraint modulus of each layer into the required input parameters of the pile foundation construction perimeter deformation prediction model, input the parameters into the pile foundation construction perimeter deformation prediction model, and obtain the prediction results; S9: If the prediction result deviates beyond the threshold, construct a calibration objective function, update the parameters, and output a rolling prediction.

2. The method for predicting soil deformation around pile foundation construction according to claim 1, characterized in that, Step S1 specifically includes the following steps: S11: Determine the target pile foundation construction type, which includes bored piles, driven piles, precast piles, CFG / CFA piles, and pile group construction. S12: Taking the proposed construction pile location as the center, determine the surrounding deformation control objects, which include the ground surface, adjacent buildings, underground pipelines, existing tunnels, and foundation pits; S13: Determine the scope of the calculation domain: Using pile diameter D and pile length L as the scale, the planar scope covers the outer extension of the pile center ≥ (10-30)D, and the depth scope covers the depth to the pile tip ≥ (0.5-1.0)L, including weak interlayers.

3. The method for predicting soil deformation around pile foundation construction according to claim 2, characterized in that, Step S2 specifically includes the following steps: S21: At least one monitoring hole shall be set around the pile location, and the distance between the monitoring hole and the center of the pile shall be 1D~10D; S22: Distributed fiber optic strain sensing cables, inclinometer tubes and settlement monitoring instruments are laid out along the depth direction in the monitoring hole, and pore pressure gauges are arranged at key depths, namely the weak interlayer, aquifer, top boundary of the impermeable layer and bottom boundary of the impermeable layer. S23: Install a mechanical anchoring sleeve or a roughening sleeve on the outside of the distributed optical fiber strain sensing cable; S24: Backfill the monitoring holes and perform maintenance.

4. The method for predicting soil deformation around pile foundation construction according to claim 3, characterized in that, Step S4 specifically includes the following steps: S41: Obtaining Pore Pressure Time History Based on Pore Pressure Gauge Strain time history obtained based on distributed fiber optic strain sensing cable , for pore pressure time history The process involves removing trend terms, filtering, and extracting periodic components, and then analyzing the pore pressure time history. With strain time history Perform phase alignment; S42: Based on Terzaghi's effective stress principle, the effective stress increment is constructed under the condition of small strain and approximately constant total stress, and the formula is: in, Indicates depth; Indicates time; Indicates the effective stress increment; Indicates the increment of pore pressure; S43: For each depth, the cycle is used as a unit. Plot the effective stress increment-strain hysteresis loop; S44: In each lap, select an initial linear segment or use robust least squares fitting of the linear segment to obtain the slope, as shown in the formula: Will This serves as the small strain constraint modulus at that depth.

5. The method for predicting soil deformation around pile foundation construction according to claim 4, characterized in that, The specific process of step S5 includes: S51: If a spindle-shaped hysteresis loop and phase lag appear in a low-permeability fine-grained soil layer, first take the mean value of the pore pressure increment and strain time history of the low-permeability fine-grained soil layer, using the following formula: in, This represents the pore pressure increment after removing the mean. This represents the strain time history after removing the mean; This represents the average value of the pore pressure increment; This represents the average value of the strain time history; S52: Sampling time points within a single period Calculate cross-correlation: in, express and Correlation functions between them; Represents the discrete-time lag step, used to describe the phase misalignment between two discrete sequences; Indicates the summation index; S53: Calculate phase lag time The formula is: in, Indicates depth The optimal discrete lag step at the point; , These represent the lower and upper bounds of the lag step search interval, respectively; Indicates the sampling time interval for monitoring time series data; S54: Pore pressure sequence translation correction, the corrected pore pressure increment is: in, This represents the corrected pore pressure increment; The effective stress increment is constructed using the following formula: in, This represents the effective stress increment after correction; Subsequently Reconstruct the loop. S55: Calculate the area of ​​the loop; The area of ​​a hysteresis loop is defined as the integral of a closed curve: in, Indicates the area of ​​the loop; Discretize the area of ​​the hysteresis loop and approximate it using polygon summation: in, Indicates the summation index; Indicates depth First The corrected effective stress increment at each discrete point; Indicates depth First The corrected effective stress increment at each discrete point; Indicates the first Strain values ​​at discrete points; Indicates the first Strain values ​​at discrete points; To eliminate the influence of dimensions, the normalized area is given: in, Represents the normalized area; Indicates the effective stress increment amplitude; Indicates the strain amplitude; S56: Calculate the ratio of unloading slope to loading slope and the relative difference; Linear regression was performed on the initial small strain linear segment of the hysteresis loop for both the loading and unloading segments, yielding the following results: in, Indicates depth The loading slope of the hysteresis loop loading segment within the selected small strain near-linear interval, which is also the loading constraint modulus; This represents the corrected effective stress increment on the loaded segment. With strain The rate of change; in, Indicates depth The unloading slope of the unloading segment at the loop within the same small strain near-linear interval, which is also the unloading constraint modulus; This represents the corrected effective stress increment on the unloading section. With strain The rate of change; in, Indicates depth The ratio of the unloading slope to the loading slope; in, Indicates depth The relative difference between the loading slope and the unloading slope at the location; S57: Set the lap area threshold Slope difference threshold If the hysteresis is weak, that is and The hysteresis loop is then considered approximately linear, and a corrected modulus is obtained using "linear segment regression". in, This represents the modified small strain constraint modulus; If the hysteresis is significant, that is or Then a viscoelastic model is used to... Fit the relationship and output .

6. The method for predicting soil deformation around pile foundation construction according to claim 5, characterized in that, The specific process of step S6 includes: S61: Select in units of a single cycle Each cycle, for each depth Repeat the linear segment fitting to obtain the modulus sample set at this depth: in For the first Small strain constraint modulus of one cycle ; S62: Calculate the mean, standard deviation, and coefficient of variation of the small strain constraint modulus; The formula for calculating the mean is as follows: in, Indicates depth The mean value of the small strain constraint modulus at a given point also represents the depth. Representative constraint modulus at the location; Indicates the summation index; The formula for calculating standard deviation is as follows: in, Indicates depth The standard deviation of the small strain constraint modulus at the point; The formula for calculating the coefficient of variation is as follows: in, Indicates depth The coefficient of variation of the small strain constraint modulus at the point; S63: Calculate the confidence interval for the representative value; when And when the sample is approximately symmetrically distributed, take Distribute the information intervals: in, Indicates the confidence interval; Indicates the confidence level; Indicates sample size; express The critical value of the distribution; If the sample is not normally distributed or contains outliers, the bootstrap method is used to... Resampling yields quantile confidence intervals; S64: Calculate intra-layer variability and inter-layer mutation index; Divide the depth into segments according to known stratigraphic boundaries, the first... The segment depth range is The value represented within the defined layer is: in, Indicates the first The segment's intra-layer representative value; Indicates the first The lower limit depth of the segment; Indicates the first The upper limit depth of the segment; Define the intralayer variability as: in, Indicates the first Intralayer variability of a segment is used to describe depth fluctuations within the same coating layer; The interlayer mutation index is defined as: in, Used to describe the intensity of interlayer mutations; Indicates the first The representative value within the segment.

7. The method for predicting soil deformation around pile foundation construction according to claim 6, characterized in that, The specific process of step S7 includes: S71: Form a continuous profile along the depth using small strain constraint modulus. Perform smoothing and calculate the rate of change: in, Indicates the rate of change; S72: Divide the formation into segments according to the rate of change and the stratigraphic interface, and calculate the statistics for each segment; Assuming the segmentation is Section, No. The segment depth range is ,but in, Indicates the first The segment's intra-layer representative value; Indicates the first The lower limit depth of the segment; Indicates the first The upper limit depth of the segment; in, Indicates the first within paragraph The segmented standard deviation; in, Indicates the first within paragraph coefficient of variation; S73: Identify low-modulus anomaly segments and high-modulus segments based on absolute threshold criteria or relative criteria.

8. The method for predicting soil deformation around pile foundation construction according to claim 7, characterized in that, The specific process of step S8 includes: S81: Based on the segmented interface from step S72, discretize the continuous profile into... Layer, number Layer depth range is ,in ; No. The thickness of the layer is: in, Indicates the first Layer thickness; Indicates the first The lower limit depth of the layer; Indicates the first Upper limit depth of the layer; No. The representative constraint modulus of the layer is in, Indicates the first The layer's internal representative value; S82: Elastic parameter conversion formula: in, Indicates the first The elastic modulus of the layer; Indicates the first The Poisson's ratio of the layer is determined by experimental or empirical methods. in, Indicates the first Shear modulus of the layer; in, Indicates the first The bulk modulus of the layer; The formation includes at least the parameters Layered parameter field ; S83: Small strain constitutive parameter mapping, the formula is: in, Indicates the first The initial elastic modulus of the layer under a small strain range; in, Indicates the first The initial shear modulus of the layer in a small strain range; The formation includes at least the parameters Layered parameter field ; in, Indicates the first The shear strain of a layer is determined by experimental or empirical methods. Indicates the first The cohesion of the layer is determined by experimental or empirical values. Indicates the first The internal friction angle of the layer is determined by experimental or empirical values. Indicates the first The permeability coefficient of the layer is determined by experimental or empirical methods. S84: Each layer Transformed into a hierarchical parameter field ; S85: Perform a consistency check; The first parameter is obtained from discrete experiments or empirical parameters. The initial elastic modulus and initial shear modulus of the layer, the initial elastic modulus is denoted as . The initial shear modulus is denoted as ; The result obtained from step S83 , and , To make a comparison, define the relative deviation: in, for and The relative deviation; in, for and The relative deviation; Set relative deviation threshold , If the following conditions are met: or This layer is then marked as the layer to be calibrated. The conversion parameters of this layer are not input into the deformation prediction model around the pile foundation construction, but are instead added to the set to be calibrated. ; Otherwise, this layer is a consistent layer, and the conversion parameters of this layer are input into the deformation prediction model around the pile foundation construction. S86: The deformation prediction model around pile foundation construction adopts a numerical prediction engine and an analytical / semi-analytical fast engine; For numerical prediction engines, if an elastic model is used, the hierarchical parameter field obtained in step S82 will be... Input the numerical prediction engine. If a small-strain model is used, the layered parameter field obtained in step S83 will be used. Input the numerical prediction engine, apply the construction path, and output the prediction results, which include the surface settlement trough. Lateral displacement Location of deep shear zones and deformation of adjacent structures; For the parsing / semi-parsing fast engine, the hierarchical parameter field obtained in step S84 Input a parsing / semi-parsing fast engine, output prediction results, including the radius of influence. Settlement magnitude.

9. The method for predicting soil deformation around pile foundation construction according to claim 8, characterized in that, The specific process of step S9 includes: S91: In the same construction phase, cross-check the prediction results of the numerical prediction engine in step S85 with the prediction results of the analytical / semi-analytical fast engine, and define the normalization bias: in, Indicates normalization bias; This represents the output of the numerical prediction engine; This represents the output of the parsing / semi-parsing fast engine prediction results; Set deviation threshold ,like Mark this layer as the layer to be calibrated and add it to the calibration set. ; S92: For the layers to be calibrated marked in steps S85 and S92, construct the calibration objective function. The objective function is: in, The parameter vector of the layer to be calibrated; For on-site monitoring; The predicted output is for the deformation prediction model around the pile foundation construction. , As weight; Indicates a time range; By minimizing The updated parameters are obtained and fed back to the deformation prediction model around the pile foundation construction. S93: Output rolling prediction; the output results should include at least: Surface subsidence trough The range and lateral displacement The interval; The indicator set and its exceedance probability and control ratio, the indicator set includes: maximum settlement Settling trough width Maximum lateral displacement Maximum lateral depth corresponds to depth Radius of influence Deformation growth rate ; Additional deformation indices of adjacent structures and their control ratios; The identification results of low-modulus anomaly segments and high-modulus segments, and the control contribution of weak interlayers to the index set; Tiered early warning and recommended construction adjustment measures.

10. A system for predicting soil deformation around pile foundation construction, characterized in that, include: The data acquisition module is used for strain acquisition, pore pressure acquisition, settlement acquisition, and inclination measurement acquisition; The time series processing module is used to remove trend terms, filter and extract periodic components from the pore pressure time history data, and to perform phase alignment between the pore pressure time history data and the strain time history data. The stress-strain hysteresis module is used to periodically plot the effective stress increment-strain hysteresis at each depth. The modulus inversion and correction module is used for the inversion of small strain constraint modulus and the correction of low-permeability fine-grained soil hysteresis and small strain constraint modulus. The continuous profile and interlayer identification module is used to form a continuous profile along the depth of the small strain constraint modulus, segment it, identify low modulus anomaly segments and high modulus segments, and perform parameter conversion. The pile foundation construction prediction module is used to output the prediction results of the deformation of the surrounding strata during pile foundation construction; The online calibration and early warning module is used to calibrate the prediction results, update parameters, and output rolling predictions.

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